Looking for AI jobs? Well, here are our top 5 AI jobs along with all the skills needed to land them
Rapid technological advances and the promotion of machine learning have shifted manual processes to automated ones. This has not only made the lives of humans easier but has also generated error-free results. To only associate AI with IT is baseless. You can find AI integrated into our day-to-day lives. From self-driven trains to robot waiters, from marketing chatbots to virtual consultants, all are examples of AI.
We can find AI everywhere without even knowing it. It is hard to explain how quickly it has become a part of our daily routine. AI will automatically find suitable searches, foods, and products even without you uttering a word. It is not hard to say that robots will replace humans very shortly.
The evolution of AI has increased the demand for AI experts. With the diversified AI job roles and emerging career opportunities, it won’t be difficult to find a suitable job matching your interests and goals. Here are the top 5 AI jobs picks that may come in handy along with the skills that will help you land them effortlessly.
Must-have skills for AI jobs
To land the AI job you need to train yourself and become an expert in multiple skills. These skills can only be mastered through great zeal of effort, hard work, and enthusiasm to learn them. Every job required its own set of core skills i.e. some may require data analysis, so others might demand expertise in machine learning. But even with the diverse job roles, the core skills needed for AI jobs remain constant which are,
Expertise in a programming language (especially in Python, Scala, and Java)
Hands-on knowledge of Linear Algebra and Statistics
Proficient at Signal Processing Techniques
Profound knowledge of the Neural Network Architects
They are responsible for discovering and designing self-driven AI systems that can run smoothly without human intervention. Their main task is to automate predictive models.
What do they do?
From designing ML systems, drafting ML algorithms, and selecting appropriate data sets they sand then analyzing large data along with testing and verifying ML algorithms.
Qualifications are required? Individuals with bachelor’s or doctoral degrees in computer science or mathematics along with proficiency in a modern programming language will most likely get this job. Knowledge about cloud applications, expertise in mathematics, computer science, machine learning, programming language, and related certifications are preferred,
2. Robotics Scientist
Who are they? They design and develop robots that can be used to perform the error-free day-to-day task efficiently. Their services are used in space exploration, healthcare, human identification, etc.
What do they do? They design and develop robots to solve problems that can be operated with voice commands. They operate different software and understand the methodology behind it to construct mechanical prototypes. They collaborate with other field specialists to control programming software and use them accordingly.
Qualifications required? A robotics scientist must have a bachelor’s degree in robotics/ mechanical engineering/ electrical engineering or electromechanical engineering. Individuals with expertise in mathematics, AI certifications, and knowledge about CADD will be preferred.
3. Data Scientist
Who are they? They evaluate and analyze data and extract valuable insights that assist organizations in making better decisions.
What do they do? They gather, organize and interpret a large amount of data using ML and predict analytics into much more valuable perspicuity. They use tools and data platforms like Hadoop, Spark, Hive, and programming languages especially Java, SQL, and Python to go beyond statistical analysis.
Qualification required? They must have a master’s or doctoral degree in computer sciences with hands-on knowledge of programming languages, data platforms, and cloud tools.
Who are they? They analyze data and evaluate gathered information using restrained-based examinations.
What do they do? Research scientists have expertise in different AI skills from ML, NLP, data processing and representation, and AI models which they use for solving problems and seeking modern solutions.
Qualifications required? Bachelor or doctoral degree in computer science or other related technical fields. Along with good communication, knowledge about AI, parallel computing, AI algorithms, and models is highly recommended for those who are thinking of pursuing this career opportunity.
5. Business Intelligence Developer
Who are they? They organize and generate the business interface and are responsible for maintaining it.
What do they do? They organize business data, extract insights from it, keep a close eye on market trends and assist organizations in achieving profitable results. They are also responsible for maintaining complex data in cloud base platforms.
Qualifications required? Bachelor’s degree in computer science, and other related technical fields with added AI certifications. Individuals with experience in data mining, SSRS, SSIS, and BI technologies and certifications in data science will be preferred.
A piece of advice for those who want to pursue AI as their career,” invest your time and money”. Take related short courses, acquire ML and AI certifications, and learn about what data science and BI technologies are all about and practices. With all these, you can become an AI expert having a growth-oriented career in no time.
With the surge in demand and interest in AI and machine learning, many contemporary trends are emerging in this space. As a tech professional, this blog will excite you to see what’s next in the realm of Artificial Intelligence and Machine Learning trends.
Data security and regulations
In today’s economy, data is the main commodity. To rephrase, intellectual capital is the most precious asset that businesses must safeguard. The quantity of data they manage, as well as the hazards connected with it, is only going to expand after the emergence of AI and ML. Large volumes of private information are backed up and archived by many companies nowadays, which poses a growing privacy danger. Don Evans, CEO of Crewe Foundation
The future currency is data. In other words, it’s the most priceless resource that businesses must safeguard. The amount of data they handle, and the hazards attached to it will only grow when AI and ML are brought into the mix. Today’s businesses, for instance, back up and store enormous volumes of sensitive customer data, which is expected to increase privacy risks by 2023.
Overlap of AI and IoT
There is a blurring of boundaries between AI and the Internet of Things. While each technology has merits of its own, only when they are combined can they offer novel possibilities? Smart voice assistants like Alexa and Siri only exist because AI and the Internet of Things have come together. Why, therefore, do these two technologies complement one another so well?
The Internet of Things (IoT) is the digital nervous system, while Artificial Intelligence (AI) is the decision-making brain. AI’s speed at analyzing large amounts of data for patterns and trends improves the intelligence of IoT devices. As of now, just 10% of commercial IoT initiatives make use of AI, but that number is expected to climb to 80% by2023. Josh Thill, Founder of Thrive Engine
Why then do these two technologies complement one other so well? IoT and AI can be compared to the brain and nervous system of the digital world, respectively. IoT systems have become more sophisticated thanks to AI’s capacity to quickly extract insights from data. Software developers and embeddedengineers now have another reason to include AI/ML skills in their resumes because of this development in AI and machine learning.
The growth of augmented intelligence should be a relieving trend for individuals who may still be concerned about AI stealing their jobs. It combines the greatest traits of both people and technology, offering businesses the ability to raise the productivity and effectiveness of their staff.
40% of infrastructure and operations teams in big businesses will employ AI-enhanced automation by 2023, increasing efficiency. Naturally, for best results, their staff should be knowledgeable in data science and analytics or have access to training in the newest AI and ML technologies.
Moving on from the concept of Artificial Intelligence to Augmented Intelligence, where decisions models are blended artificial and human intelligence, where AI finds, summarizes, and collates information fromacross the information landscape – for example, company’s internal data sources. This information is presented to the human operator, who can make a human decision based on that information.This trend is supported by recent breakthroughs in Natural Language Processing (NLP) and Natural Language Understanding (NLU). Kuba Misiorny, CTO of Untrite Ltd
Despite being increasingly commonplace, there are trust problems with AI. Businesses will want to utilize AI systems more frequently, and they will want to do so with greater assurance. Nobody wants to put their trust in a system they don’t fully comprehend.
As a result, in 2023 there will be a stronger push for the deployment of AI in a visible and specified manner. Businesses will work to grasp how AI models and algorithms function, but AI/ML software providers will need to make complex ML solutions easier for consumers to understand.
The importance of experts who work in the trenches of programming and algorithm development will increase as transparency becomes a hot topic in the AI world.
Composite AI is a new approach that generates deeper insights from any content and data by fusing different AI technologies. Knowledge graphs are much more symbolic, explicitly modeling domain knowledge and, when combined with the statistical approach of ML, create a compelling proposition. Composite AI expands the quality and scope of AI applications and, as a result, is more accurate, faster, transparent ,and understandable, and delivers better results to the user. Dorian Selz, CEO of Squirro
It’s a major advance in the evolution of AI and marrying content with context and intent allows organizations to get enormous value from the ever-increasing volume of enterprise data. Composite AI will be a major trend for 2023 and beyond.
Continuous focus on healthcare
There has been concern that AI will eventually replace humans in the workforce ever since the concept was first proposed in the 1950s. Throughout 2018, a deep learning algorithm was constructed that demonstrated accurate diagnosis utilizing a dataset consisting of more than 50,000 normal chest pictures and 7,000 scans that revealed active Tuberculosis. Since then, I believe that the healthcare business has mostly made use of Machine Learning (ML) and Deep Learning applications of artificial intelligence. Marie Ysais, Founder of *Ysais Digital Marketing
Learn more about the reading role of AI in healthcare:
Pathology-assisted diagnosis, intelligent imaging, medical robotics, and the analysis of patient information are just a few of the many applications of artificial intelligence in the healthcareindustry. Leading stakeholders in the healthcare industry have been presented with advancements and machine-learning models from some of the world’s largest technology companies. Next year, 2023, will be an important year to observe developments in the field of artificial intelligence.
Advanced algorithms are taking on the skills of human doctors, and while AI may increase productivity in the medical world, nothing can take the place of actual doctors. Even in robotic surgery, the whole procedure is physician-guided. AI is a good supplement to physician-led health care. The future of medicine will be high-tech with a human touch.
The low-code/No Code ML revolution accelerates creating a new breed of Citizen AI. These tools fuel mainstream ML adoption in businesses that were previously left out of the first ML wave (mostly taken advantage of by BigTech and other large institutions with even larger resources). Maya Mikhailov Founder of Savvi AI
Low-code intelligent automation platforms allow business users to build sophisticated solutions that automate tasks, orchestrate workflows, and automate decisions. They offer easy-to-use, intuitive drag-and-drop interfaces, all without the need to write a line of code. As a result, low-code intelligent automation platforms are popular with tech-savvy business users, who no longer need to rely on professional programmers to design their business solutions.
Cognitive analytics is another emerging trend that will continue to grow in popularity over the next few years. The ability for computers to analyze data in a way that humans can understand is something that has been around for a while now but is only recently becoming available in applications such as Google Analytics or Siri—and it’ll only get better from here!
Virtual assistants are another area where NLP is being used to enable more natural human-computer interaction. Virtual assistants like Amazon Alexa and Google Assistant are becoming increasingly common in homes and businesses. In 2023, we can expect to see them become even more widespreadas they evolve and improve. Idrees Shafiq-Marketing Research Analyst at Astrill
Virtual assistants are becoming increasingly popular, thanks to their convenience and ability to provide personalized assistance. In 2023, we can expect to see even more people using virtual assistants, as they become more sophisticated and can handle a wider range of tasks. Additionally, we can expect to see businesses increasingly using virtual assistants for customer service, sales, and marketing tasks.
Information security (InfoSec)
The methods and devices used by companies to safeguard information fall under the category of information security. It comprises settings for policies that are essentially designed to stop the act of stopping unlawful access to, use of, disclosure of, disruption of, modification of, an inspection of, recording of, or data destruction.
With AI models that cover a broad range of sectors, from network and security architecture to testing and auditing, AI prediction claims that it is a developing and expandingfield. To safeguard sensitive data from potential cyberattacks, information security procedures are constructed on the three fundamental goals of confidentiality, integrity, and availability, or the CIA. Daniel Foley, Founder of Daniel Foley SEO
The continued growth of the wearable market. Wearable devices, such as fitness trackers and smartwatches, are becoming more popular as they become more affordable and functional. These devices collect data that can be used by AI applications to provide insights into user behavior. Oberon, Founder, and CEO of Very Informed
It can be characterized as a combination of tools and methods with heavy reliance on artificial intelligence (AI) and machine learning to assess the performance of persons participating in the business process. In comparison to prior versions of process mining, these goes further in figuring outwhat occurs when individuals interact in different ways with various objects to produce business process events.
The methodologies and AI models vary widely, from clicks of the mouse for specific reasons to opening files, papers, web pages, and so forth. All of this necessitates various information transformation techniques. The automated procedure using AI models is intended to increase the effectiveness of commercial procedures. Salim Benadel, Director at Storm Internet
Robotic Process Automation, or RPA.
An emerging tech trend that will start becoming more popular is Robotic Process Automation or RPA. It is like AI and machine learning, and it is used for specific types of job automation. Right now, it is primarily used for things like data handling, dealing with transactions, processing/interpreting job applications, and automated email responses. It makes many businesses processes much faster and more efficient, and as time goes on, increased processes will be taken over by RPA. Maria Britton, CEO of Trade Show Labs
Robotic process automation is an application of artificial intelligence that configures a robot (software application) to interpret, communicate and analyze data. This form of artificial intelligence helps to automate partially or fully manual operations that are repetitive and rule based. Percy Grunwald, Co-Founder of Hosting Data
Most individuals say AI is good for automating normal, repetitive work. AI technologies and applications are being developed to replicate creativity, one of the most distinctive human skills. Generative AI algorithms leverage existing data (video, photos, sounds, or computer code) to create new, non-digital material.
Deepfake films and the Metaphysic act on America’s Got Talent have popularized the technology. In 2023, organizations will increasingly employ it to manufacture fake data. Synthetic audio and video data can eliminate the need to record film and speech on video. Simply write what you want the audience to see and hear, and the AI creates it. Leonidas Sfyris
With the rise of personalization in video games, new content has become increasingly important. Companies are not able to hire enough artists to constantly create new themes for all the different characters so the ability to put in a concept like a cowboy and then the art assets created for all their characters becomes a powerful tool.
Observability in practice
By delving deeply into contemporary networked systems, Applied Observability facilitates the discovery and resolution of issues more quickly and automatically. Applied observability is a method for keeping tabs on the health of a sophisticated structure by collecting and analyzing data in real time to identify and fix problems as soon as they arise.
Utilize observability for application monitoring and debugging. Telemetry data including logs, metrics, traces, and dependencies are collected by Observability. The data is then correlated in actuality to provide responders with full context for the incidents they’re called to. Automation, machine learning, and artificial intelligence (AIOps) might be used to eliminate the need for human interaction in problem-solving. Jason Wise, Chief Editor at Earthweb
Natural Language Processing
As more and more business processes are conducted through digital channels, including social media, e-commerce, customer service, and chatbots, NLP will become increasingly important for understanding user intent and producing the appropriate response.
Read more about NLP tasks and techniques in this blog:
In 2023, we can expect to see increased use of Natural Language Processing (NLP) for communication and data analysis. NLP has already seen widespread adoption in customer service chatbots, but it may also be utilized for data analysis, such as extracting information from unstructured texts oranalyzing sentiment in large sets of customer reviews. Additionally, deep learning algorithms have already shown great promise in areas such as image recognition and autonomous vehicles.
In the coming years, we can expect to see these algorithms applied to various industries such as healthcare formedical imaging analysis and finance for stock market prediction. Lastly, the integration of AI tools into various industries will continue to bring about both exciting opportunities and ethical considerations. Nicole Pav, AI Expert.
Do you know any other AI and Machine Learning trends
Share with us in comments if you know about any other trending or upcoming AI and machine learning.
What can be a better way to spend your days listening to interesting bits about trending AI and Machine learning topics? Here’s a list of the 10 best AI and ML podcasts.
1. The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Artificial intelligence and machine learning are fundamentally altering how organizations run and how individuals live. It is important to discuss the latest innovations in these fields to gain the most benefit from technology. The TWIML AI Podcast outreaches a large and significant audience of ML/AI academics, data scientists, engineers, tech-savvy business, and IT (Information Technology) leaders, as well as the best minds and gather the best concepts from the area of ML and AI.
The podcast is hosted by a renowned industry analyst, speaker, commentator, and thought leader Sam Charrington. Artificial intelligence, deep learning, natural language processing, neural networks, analytics, computer science, data science, and other technologies are discussed.
2. The AI Podcast
One individual, one interview, one account. This podcast examines the effects of AI on our world. The AI podcast creates a real-time oral history of AI that has amassed 3.4 million listens and has been hailed as one of the best AI and machine learning podcasts. They always bring you a new story and a new 25-minute interview every two weeks. Consequently, regardless of the difficulties, you are facing in marketing, mathematics, astrophysics, paleo history, or simply trying to discover an automated way to sort out your kid’s growing Lego pile, listen in and get inspired.
3. Data Skeptic
Data Skeptic launched as a podcast in 2014. Hundreds of interviews and tens of millions of downloads later, we are a widely recognized authoritative source on data science, artificial intelligence, machine learning, and related topics.
Data Skeptic runs in seasons. By speaking with active scholars and business leaders who are somehow involved in our season’s subject, we probe it.
We carefully choose each of our visitors using a system internally. Since we do not cooperate with PR firms, we are unable to reply to the daily stream of unsolicited submissions. Publishing quality research to the arxiv is the greatest approach to getting on the show. It is crawled. We will locate you.
Data Skeptic is a boutique consulting company in addition to its podcast. Kyle participates directly in each project our team undertakes. Our work primarily focuses on end-to-end machine learning, cloud infrastructure, and algorithmic design.
The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.
Podcast.ai is entirely generated by artificial intelligence. Every week, they explore a new topic in-depth, and listeners can suggest topics or even guests and hosts for future episodes. Whether you are a machine learning enthusiast, just want to hear your favorite topics covered in a new way or even just want to listen to voices from the past brought back to life, this is the podcast for you.
The podcast aims to put incremental advances into a broader context and consider the global implications of developing technology. AI is about to change your world, so pay attention.
5. The Talking Machines
Talking machines is a podcast hosted by Katherine Gorman and Neil Lawrence. The objective of this show is to bring you clear conversations with experts in the field of machine learning, insightful discussions of industry news, and useful answers to your questions. Machine learning is changing the questions we can ask of the world around us, here we explore how to ask the best questions and what to do with the answers.
6. Linear Digressions
If you are interested in learning about unusual applications of machine learning and data science. In each episode of linear digressions, your hosts explore machine learning and data science through interesting apps. Ben Jaffe and Katie Malone host the show, they assure themselves to produce the most exciting additions in the industry such as AI-driven medical assistants, open policing data, causal trees, the grammar of graphics and a lot more.
7. Practical AI: Machine Learning, Data Science
Making artificial intelligence practical, productive, and accessible to everyone. Practical AI is a show in which technology professionals, businesspeople, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, GANs (Generative adversarial networks), MLOps (machine learning operations) (machine learning operations), AIOps, and more).
The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you!
8. Data Stories
Enrico Bertini and Moritz Stefaner discuss the latest developments in data analytics, visualization, and related topics. The data stories podcast consists of regular new episodes on a range of discussion topics related to data visualization. It shares the importance of data stories in different fields including statistics, finance, medicine, computer science, and a lot more to name. The podcast’s hosts Enrico and Moritz invite industry leaders, experienced professionals, and instructors in data visualization to share the stories and the importance of representation of data visuals into appealing charts and graphs.
9. The Artificial Intelligence Podcast
The Artificial intelligence podcast is hosted by Dr. Tony Hoang. This podcast talks about the latest innovations in the artificial intelligence and machine learning industry. The recent episode of the podcast discusses text-to-image generator, Robot dog, soft robotics, voice bot options, and a lot more.
10. Learning Machines 101
Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. The objective of this podcast series is to inform students and instructors about the advanced technologies introduced by AI and the following:
How do these devices work?
Where do they come from?
How can we make them even smarter?
And how can we make them even more human-like?
Have we missed any of your favorite podcasts?
Do not forget to share in comments the names of your most favorite AI and ML podcasts. Read this amazing blog if you want to know about Data Science podcasts.
Most people have heard the terms “data science” and “AI” at least once in their lives. Indeed, both of these are extremely important in the modern world as they are technologies that help us run quite a few of our industries.
But even though data science and Artificial Intelligence are somewhat related to one another, they are still very different. There are things they have in common which is why they are often used together, but it is crucial to understand their differences as well.
What is Data Science?
As the name suggests, data science is a field that involves studying and processing data in big quantities using a variety of technologies and techniques to detect patterns, make conclusions about the data, and help in the decision-making process. Essentially, it is an intersection of statistics and computer science largely used in business and different industries.
The standard data science lifecycle includes capturing data and then maintaining, processing, and analyzing it before finally communicating conclusions about it through reporting. This makes data science extremely important for analysis, prediction, decision-making, problem-solving, and many other purposes.
What is Artificial Intelligence?
Artificial Intelligence is the field that involves the simulation of human intelligence and the processes within it by machines and computer systems. Today, it is used in a wide variety of industries and allows our society to function as it currently does by using different AI-based technologies.
Some of the most common examples in action include machine learning, speech recognition, and search engine algorithms. While AI technologies are rapidly developing, there is still a lot of room for their growth and improvement. For instance, there is no powerful enough content generation tool that can write texts that are as good as those written by humans. Therefore, it is always preferred to hire an experienced writer to maintain the quality of work.
What is Machine Learning?
As mentioned above, machine learning is a type of AI-based technology that uses data to “learn” and improve specific tasks that a machine or system is programmed to perform. Though machine learning is seen as a part of the greater field of AI, its use of data puts it firmly at the intersection of data science and AI.
Similarities between Data Science and AI
By far the most important point of connection between data science and Artificial Intelligence is data. Without data, neither of the two fields would exist and the technologies within them would not be used so widely in all kinds of industries. In many cases, data scientists and AI specialists work together to create new technologies or improve old ones and find better ways to handle data.
As explained earlier, there is a lot of room for improvement when it comes to AI technologies. The same can be somewhat said about data science. That’s one of the reasons businesses still hire professionals to accomplish certain tasks like custom writing requirements, design requirements, and other administrative work.
Differences between Data Science and AI
There are quite a few differences between both. These include:
Purpose – It aims to analyze data to make conclusions, predictions, and decisions. Artificial Intelligence aims to enable computers and programs to perform complex processes in a similar way to how humans do.
Scope – This includes a variety of data-related operations such as data mining, cleansing, reporting, etc. It primarily focuses on machine learning, but there are other technologies involved too such as robotics, neural networks, etc.
Application – Both are used in almost every aspect of our lives, but while data science is predominantly present in business, marketing, and advertising, AI is used in automation, transport, manufacturing, and healthcare.
Examples of Data Science and Artificial Intelligence in use
To give you an even better idea of what data science and Artificial Intelligence are used for, here are some of the most interesting examples of their application in practice:
Analytics – Analyze customers to better understand the target audience and offer the kind of product or service that the audience is looking for.
Monitoring – Monitor the social media activity of specific types of users and analyze their behavior.
Recommendation – Recommend products and services to customers based on their customer profiles, buying behavior, etc.
Forecasting – Predict the weather based on a variety of factors and then use these predictions for better decision-making in the agricultural sector.
Communication – Provide high-quality customer service and support with the help of chatbots.
Automation – Automate processes in all kinds of industries from retail and manufacturing to email marketing and pop-up on-site optimization.
Diagnosing – Identify and predict diseases, give correct diagnoses, and personalize healthcare recommendations.
Transportation – Use self-driving cars to get where you need to go. Use self-navigating maps to travel.
Assistance – Get assistance from smart voice assistants that can schedule appointments, search for information online, make calls, play music, and more.
Filtering – Identify spam emails and automatically get them filtered into the spam folder.
Cleaning – Get your home cleaned by a smart vacuum cleaner that moves around on its own and cleans the floor for you.
Editing – Check texts for plagiarism and proofread and edit them by detecting grammatical, spelling, punctuation, and other linguistic mistakes.
It is not always easy to tell which of these examples is about data science and which one is about Artificial Intelligence because many of these applications use both of them. This way, it becomes even clearer just how much overlap there is between these two fields and the technologies that come from them.
What is your choice?
At the end of the day, data science and AI remain some of the most important technologies in our society and will likely help us invent more things and progress further. As a regular citizen, understanding the similarities and differences between the two will help you better understand how data science and Artificial Intelligence are used in almost all spheres of our lives.
In this blog, we will discuss how Artificial Intelligence and computer vision are contributing to improving road safety for people.
Each year, about 1.35 million people are killed in crashes on the world’s roads, and as many as 50 million others are seriously injured, according to the World Health Organization. With the increase in population and access to motor vehicles over the years, rising traffic and its harsh effects on the streets can be vividly observed with the growing number of fatalities.
We call this suffering traffic “accidents” — but, in reality, they can be prevented. Governments all over the world are resolving to reduce them with the help of artificial intelligence and computer vision.
Humans make mistakes, as it is in their nature to do so, but when small mistakes can lead to huge losses in the form of traffic accidents, necessary changes are to be made in the design of the system.
A technology deep-dive into this problem will show how a lack of technological innovations has failed to lower this trend over the past 20 years. However, with the adoption of the ‘Vision Zero’ program by governments worldwide, we may finally see a shift in this unfortunate trend.
Role of Artificial Intelligence for improving road traffic
AI can improve road traffic by reducing human error, speeding up the process of detection and response to accidents, as well as improving safety. With the advancement of computer vision, the quality of data and predictions made with video analytics has increased ten-folds.
Artificial Intelligence is already leveraging the power of vision analytics in scenarios like identifying mobile phone usage by the driver on highways and recognize human errors much faster. But what lies ahead to be used in our everyday life? Will progress be fast enough to tackle the complexities self-driving cars bring with them?
In recent studies, it’s been inferred through data that subtle distractions on a busy road are correlated to the traffic accidents there. Experts believe that in order to minimize the risk of an accident, the system must be planned with the help of architects, engineers, transport authorities, city planners and AI.
With the help of AI, it becomes easier to identify the problems at hand, however they will not solve them on their own. Designing the streets in a way that can eliminate certain factors of accidents could be the essential step to overcome the situation at hand.
AI also has a potential to help increase efficiency during peak hours by optimizing traffic flow. Road traffic management has undergone a fundamental shift because of the quick development of artificial intelligence (AI). With increasing accuracy, AI is now able to predict and manage the movement of people, vehicles, and goods at various locations along the transportation network.
As we make advancements into the field, simple AI programs along with machine learning and data science, are enabling better service for citizens than ever before while also reducing accidents by streamlining traffic at intersections and enhancing safety during times when roads are closed due to construction or other events.
Deep learning impact on improved infrastructure for road safety
Deep learning system’s capacity for processing, analyzing, and making quick decisions from enormous amounts of data has also facilitated the development of efficient mass transit systems like ride-sharing services. With the advent of cloud-edge devices, the process of gathering and analyzing data has become much more efficient.
Increase in the number of different sources of data collection has led to an increase of not only quality but quantity of variety of data as well. These systems leverage the data from real-time edge devices and can tackle them effectively by retrofitting existing camera infrastructure for road safety.
Join our upcoming webinar
In our upcoming webinar on 29th November, we will summarize the challenges in the industry and how AI plays its part in making a safe environment by solutions catering to avoiding human errors.
Applications leveraging AI powered search are on the rise. My colleague, Sam Partee, recently introduced vector similarity search (VSS) in Redis and how it can be applied to common use cases. As he puts it:
“Users have come to expect that nearly every application and website provide some type of search functionality. With effective search becoming ever-increasingly relevant (pun intended), finding new methods and architectures to improve search results is critical for architects and developers. “
For example, in eCommerce, allowing shoppers to browse product inventory with a visual similarity component brings online shopping one step closer to mirroring an in-person experience. This is highlighted in our Fashion Product Finder demo which demonstrates Redis VSS for visual search over a diverse product catalog.
However, this is only the tip of the iceberg. Here, we will pick up right where Sam left off with another common use case for vector similarity: Document Search.
Lastly, we will share about an exciting upcoming hackathon co-hosted by Redis, MLOps Community, and Saturn Cloud from October 24 – November 4 that you can join in the coming weeks!
The use case
Whether we realize it or not, we take advantage of document search and processing capabilities in everyday life. We see its impact while searching for a long-lost text message in our phone, automatically filtering spam from our email inbox, and performing basic Google searches.
Businesses use it for information retrieval (e.g. insurance claims, legal documents, financial records), and even generating content-based recommendations (e.g. articles, tweets, posts).
Beyond lexical search
Traditional search, i.e. lexical search, emphasizes the intersection of common keywords between docs. However, a search query and document may be very similar to one another in meaning and not share any of the same keywords (or vice versa). For example, in the sentences below, all readers should be able to parse that they are communicating the same thing. But – only two words overlap.
“The weather looks dark and stormy outside.” <> “The sky is threatening thunder and lightning.”
Another example…with pure lexical search, “USA” and “United States” would not trigger a match though these are interchangeable terms.
This is where lexical search breaks down on its own.
Search has evolved from simply finding documents to providing answers. Advances in NLP and large language models (GPT-3, BERT, etc) have made it incredibly easy to overcome this lexical gap AND expose semantic properties of text. Sentence embeddings form a condensed vector-like representation of unstructured data that encodes “meaning”.
These embeddings allow us to compute similarity metrics (e.g. cosine similarity, euclidean distance, and inner product) to find similar documents, i.e. neural (or vector) search. Neural search respects word order and understands the broader context beyond the explicit terms used.
Immediately this opens up a host of powerful use cases
In a production software environment, document search must take advantage of a low-latency database that persists all docs and manages a search index that can enable nearest neighbors vector similarity operations between documents.
RediSearch was introduced as a module to extend this functionality over a Redis cluster that is likely already handling web request caching or online ML feature serving (for low-latency model inference).
Below we will highlight the core components of a typical production workflow.
In this phase, documents must be gathered, embedded, and stored in the vector database. This process happens up front before any client tries to search and will also consistently run in the background on document updates, deletions, and insertions.
Up front, this might be iteratively done in batches from some data warehouse. Also, it’s common to leverage streaming data structures (e.g. Kafka, Kinesis, or Redis Streams) to orchestrate the pipeline in real time.
Scalable document processing services might take advantage of a high-throughput inference server like NVIDIA’s Triton. Triton enables teams to deploy, run, and scale trained AI models from any standard backend on GPU (or CPU) hardware.
Depending on the source, volume, and variety of data, a number of pre-processing steps will also need to be included in the pipeline (including embedding models to create vectors from text).
After a client enters a query along with some optional filters (e.g. year, category), the query text is converted into an embedding projected into the same vector space as the pre-processed documents. This allows for discovery of the most relevant documents from the entire corpus.
With the right vector database solution, these searches could be performed over hundreds of millions of documents in 100ms or less.
We are pretty certain that the names of these two icons don’t show up verbatim in the paper abstracts… Because of the semantic properties encoded in the sentence embeddings, this application is able to associate “Jay Z” and “Beyonce” with topics like Music, Celebrities, and Spotify.
Scaling embedding workflows
That was the happy path. Realistically, most production-grade document retrieval systems rely on hundreds of millions or even billions of docs. It’s the price to pay for a system that can actually solve real-world problems over unstructured data.
Beyond scaling the embedded workflows, you’ll also need to have a database with enough horsepower to build the search index in a timely fashion.
In 2022, giving out free computers is the best way to make friends with anybody. Thankfully, our friends at Saturn Cloud have partnered with us to share access to GPU hardware.
They have a solid free tier that gives us access to an NVIDIA T4 with the ability to upgrade for a fee. Recently, Google Colab also announced a new pricing structure, a “Pay As You Go” format, that allows users to have flexibility in exhausting their compute quota over time.
These are both great options when running workloads on your CPU bound laptop or instance won’t cut it.
What’s even better is that Hugging Face Transformers can take advantage of GPU acceleration out-of-the-box. This can speed up ad-hoc embedding workflows quite a bit. However, for production use cases with massive amounts of data, a single GPU may not cut it.
Multi-GPU with Dask and cuDF
What if data will not fit into RAM of a single GPU instance, and you need the boost? There are many ways a data engineer might address this issue, but here I will focus on one particular approach leveraging Dask and cuDF.
The RAPIDS team at NVIDIA is dedicated to building open-source tools for executing data science and analytics on GPUs. All of the Python libraries have a comfortable feel to them, empowering engineers to take advantage of powerful hardware under the surface.
Scaling out workloads on multiple GPUs w/ RAPIDS tooling involves leveraging multi-node Dask clusters and cuDF data frames. Most Pythonista’s are familiar with the popular Pandas data frame library. cuDF, built on Apache Arrow, provides an interface very similar to Pandas, running on a GPU, all without having to know the ins and outs of CUDA development.
In the above workflow, a cuDF data frame of arXiv papers was loaded and partitions were created across a 3 node Dask cluster (with each worker node as an NVIDIA T4). In parallel, a user-defined function was applied to each data frame partition that processed and embedded the text using a Sentence Transformer model.
This approach provided linear scalability with the number of nodes in the Dask cluster. With 3 worker nodes, the total runtime decreased by a factor of 3.
Even with multi-GPU acceleration, data is mapped to and from machines. It’s heavily dependent on RAM, especially after the large embedding vectors have been created.
A few variations to consider:
Load and process iterative batches of documents from a source database.
Programmatically load partitions of data from a source database to several Dask workers for parallel execution.
Perform streaming updates from the Dask workers directly to the vector database rather than loading embeddings back to single GPU RAM.
Call to action – it’s YOUR turn!
Inspired by the initial work on the arXiv search demo, Redis is officially launching a Vector Search Engineering Lab (Hackathon) co-sponsored by MLOps Community and Saturn Cloud. Read more about it here.
This is the future. Vector search & document retrieval is now more accessible than ever before thanks to open-source tools like Redis, RAPIDS, Hugging Face, Pytorch, Kaggle, and more! Take the opportunity to get ahead of the curve and join in on the action. We’ve made it super simple to get started and acquire (or sharpen) an emerging set of skills.
In the end, you will get to showcase what you’ve built and win $$ prizes.
The hackathon will run from October 24 – November 4 and include folks across the globe, professionals and students alike. Register your team (up to 4 people) today! You don’t want to miss it.
The use of AI in culture raises interesting ethical reflections termed as AI ethics nowadays.
In 2016, a Rembrandt painting, “The Next Rembrandt”, was designed by a computer and created by a 3D printer, 351 years after the painter’s death.
The achievement of this artistic prowess becomes possible when 346 Rembrandt paintings were together analyzed. The keen analysis of paintings pixel by pixel resulted in an upscale of deep learning algorithms to create a unique database.
Every detail of Rembrandt’s artistic identity could then be captured and set the foundation for an algorithm capable of creating an unprecedented masterpiece. To bring the painting to life, a 3D printer recreated the texture of brushstrokes and layers of paint on the canvas for a breath-taking result that could trick any art expert.
The ethical dilemma arose when it came to crediting the author of the painting. Who could it be?
We cannot overlook the transformations brought by intelligent machine systems in today’s world for the better. To name a few, artificial intelligence contributed to optimizing planning, detecting fraud, composing art, conducting research, and providing translations.
Undoubtedly, it all contributed to the more efficient and consequently richer world of today. Leading global tech companies emphasize adopting boundless landscape of artificial intelligence and step ahead of the competitive market.
Amidst the boom of overwhelming technological revolutions, we cannot undermine the new frontier for ethics and risk assessment.
Regardless of the risks AI offers, there are many real-world problems that are begging to be solved by data scientists. Check out this informative session by Raja Iqbal (Founder and lead instructor at Data Science Dojo) on AI For Social Good
Some of the key ethical issues in AI you must learn about are:
1. Privacy & surveillance – Is your sensitive information secured?
Access to personal identifiable information must only be accessible for the authorized users only. The other key aspects of privacy to consider in artificial intelligence are information privacy, privacy as an aspect of personhood, control over information about oneself, and the right to secrecy.
Business today is going digital. We are associated with the digital sphere. Most digital data available online connects to a single Internet. There is increasingly more sensor technology in use that generates data about non-digital aspects of our lives. AI not only contributes to data collection but also drives possibilities for data analysis.
Much of the most privacy-sensitive data analysis today–such as search algorithms, recommendation engines, and AdTech networks–are driven by machine learning and decisions by algorithms. However, as artificial intelligence evolves, it defines ways to intrude privacy interests of users.
For instance, facial recognition introduces privacy issues with the increased use of digital photographs. Machine recognition of faces has progressed rapidly from fuzzy images to rapid recognition of individual humans.
2. Manipulation of behavior – How does the internet know our preferences?
Usage of internet and online activities keep us engaged every day. We do not realize that our data is constantly collected, and information is tracked. Our personal data is used to manipulate our behavior online and offline as well.
If you are thinking about exactly when businesses make use of the information gathered and how they manipulate us, then marketers and advertisers are the best examples. To sell the right product to the right customer, it is significant to know the behavior of your customer.
Their interests, past purchase history, location, and other key demographics. Therefore, advertisers retrieve the personal information of potential customers that is available online.
Social media has become the hub of manipulating user behaviors by marketers to maximize profits. AI with its advanced social media algorithms identifies vulnerabilities in human behavior and influences our decision-making process.
Artificial intelligence integrates such algorithms with digital media that exploit human biases detected by AI algorithms. It implies personalized addictive strategies for consumption of (online) goods or benefits from the vulnerable state of individuals to promote products and services that match well with their temporary emotions.
3. Opacity of AI systems – Complexed AI processes
Danaher stated, “we are creating decision-making processes that constrain and limit opportunities for human participation”
Artificial Intelligence supports automated decision-making, thus neglecting the free will of personnel to speak of their choice. AI processes work in a way that no one knows how the output is generated. Therefore, the decision will remain opaque even for the experts
Machine learning captures existing patterns in the data with the help of these techniques. And then label these patterns in such a way that it gets useful for the decision the system makes, while the programmer does not really know which patterns in the data the system has used.
4. Human-robot interaction – Are robots more capable than us?
As AI is now widely used to manipulate human behavior, it is also actively driving robots. It can get problematic if their processes or appearance involve deception or threatening human dignity
The key ethical issue here is, “Should robots be programmed to deceive us?” If we answer this question with a yes, then the next question to ask is “what should be the limits of deception?” If we say that robots can deceive us if it does not seriously harm us, then the robot might lie about its abilities or pretend to have more knowledge than it has.
If we believe that robots should not be programmed to deceive humans, then the next ethical question becomes “should robots be programmed to lie at all?” The answer would depend on what kind of information they are giving and whether humans are able to provide an alternative source.
Robots are now being deployed in the workplace to do jobs that are dangerous, difficult, or dirty. The automation of jobs is inevitable in the future, and it can be seen as a benefit to society or a problem that needs to be solved. The problem arises when we start talking about human robot interaction and how robots should behave around humans in the workplace.
5. Autonomous systems – AI gaining self-sufficiency
An autonomous system can be defined as a self-governing or self-acting entity that operates without external control. It can also be defined as a system that can make its own decisions based on its programming and environment.
The next step in understanding the ethical implications of AI is to analyze how it affects society, humans, and our economy. This will allow us to predict the future of AI and what kind of impact it will have on society if left unchecked.
In societies where AI is rapidly replacing humans can get harmed or suffer in the longer run. For instance, thinking of AI writers as a replacement for human copywriters when it is just designed to bring efficiency to a writer’s job, provide assistance, and help in getting rid of writer’s block while generating content ideas at scale.
Secondly, autonomous vehicles are the most relevant examples for a heated debate topic of ethical issues in AI. It is not yet clear what the future of autonomous vehicles will be. The main ethical concern around autonomous cars is that they could cause accidents and fatalities.
Some people believe that because these cars are programmed to be safe, they should be given priority on the road. Others think that these vehicles should have the same rules as human drivers.
6. Machine ethics – Can we infuse good behavior in machines?
Before we get into the ethical issues associated with machines, we need to know that machine ethics is not about humans using machines. But it is solely related to the machines operating independently as subjects.
The topic of machine ethics is a broad and complex one that includes a few areas of inquiry. It touches on the nature of what it means for something to be intelligent, the capacity for artificial intelligence to perform tasks that would otherwise require human intelligence, the moral status of artificially intelligent agents, and more.
The field is still in its infancy, but it has already shown promise in helping us understand how we should deal with certain moral dilemmas.
In the past few years, there has been a lot of research on how to make AI more ethical. But how can we define ethics for machines?
AI programmed machines with rules for good behavior and to avoid making bad decisions based on the principles. It is not difficult to imagine that in the future, we will be able to tell if an AI has ethical values by observing its behavior and its decision-making process.
Three laws of robotics by Isaac for machine ethics are:
First Law—A robot may not injure a human being or, through inaction, allow a human being to come to harm.
Second Law—A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
Third Law—A robot must protect its own existence if such protection does not conflict with the First or Second Laws.
Artificial Moral Agents
The development of artificial moral agents (AMA) is a hot topic in the AI space. The AMA has been designed to be a moral agent that can make moral decisions and act according to these decisions. As such, it has the potential to have significant impacts on human lives.
The development of AMA is not without ethical issues. The first issue is that AMAs (Artificial Moral Agents) will have to be programmed with some form of morality system which could be based on human values or principles from other sources.
This means that there are many possibilities for diverse types of AMAs and several types of morality systems, which may lead to disagreements about what an AMA should do in each situation. Secondly, we need to consider how and when these AMAs should be used as they could cause significant harm if they are not used properly
Closing on AI ethics
Over the years, we went from, “AI is impossible” (Dreyfus 1972) and “AI is just automation” (Lighthill 1973) to “AI will solve all problems” (Kurzweil 1999) and “AI may kill us all” (Bostrom 2014).
Several questions arise with the increasing dependency on AI and robotics. Before we rely on these systems further, we must have clarity about what the systems themselves should do, and what risks they have in the long term.
Let us know in the comments if you also think it also challenges the human view of humanity as the intelligent and dominant species on Earth.
From managing your cash flow to making lending decisions for you, here is a list of 15 fintech startups using Artificial Intelligence to enhance your experience.
1. Affirm is changing the way people buy stuff
Affirm is a consumer application that grants loans for purchases at various retailers. The startup makes use of multiple machine learning algorithms for credit underwriting and happens to be the exclusive buy now, pay later partner for Amazon.
Max Levchin, the co-founder of PayPal, along with Nathan Gettings, Jeffrey Kaditz, and Alex Rampell introduced Affirm in 2012 to the world.
Affirm also partnered with Walmart in 2019, allowing customers to access the app in-store and on Walmart’s website.
3. SparkCognition is building smarter, safer, and sustainable solutions for the future
SparkCognition focuses on creating AI-powered cyber-physical software for the safety, security, and reliability of IT, OT, and the IoT. The startup builds artificial intelligence solutions for applications in energy, oil and gas, manufacturing, finance, aerospace, defense, and security.
The startup’s work in the financial sector enables businesses to improve analytical accuracy, minimize risks, accelerate reaction time to fluctuating market conditions, and sustain a competitive advantage.
Previously, SparkCognition enabled a fintech startup to use a machine learning model to detect fraud with 90% accuracy, saving the company over $450K each year.
6. Vise AI is the financial advisor of the future.
An AI-driven asset management platform, Vise AI is built and designed specifically as a financial advisory platform.
The startup builds hyper-personalized portfolios and automates portfolio management. Moreover, they aim to enable financial advisory across businesses so they can focus on developing their clients and growing their businesses.
8. Clinc is revolutionizing conversational AI, one bank at a time.
Clinc develops intelligent personal financial assistants. The platform enables personal and instant answers to any common or complex questions.
Inspired by conversational AI, Clinc focuses on revolutionizing conversational AI at some of the biggest banks in the world. The startup utilizes NLP which understands how people talk, powering exceptional customer experiences that build loyalty and generate ROI.
To learn more about Conversational AI, click here.
9. Sentieo is centralizing financial research tools into a single platform
Sentieo is an AI-powered financial research startup that develops and distributes a range of systems across the financial world.
Sentieo is a financial intelligence platform that aims to centralize multiple financial research tools into a single innovative Ai-powered platform. Sentieo helps analysts save time but also discover alpha-driving insights.
10. CognitiveScale is industrializing scalable Enterprise AI
Pioneering the concept of ‘AI engineering,’ CognitiveScale aims to industrialize scalable Enterprise AI development and deployment.
The startup makes use of its award-winning Cortex AI Platform to empower businesses. The startup helps implement trusted decision intelligence into business processes and applications for better customer experience and operational efficiency.
11. Kyndi is building the world’s first Explainable AI platform
AI company Kyndi is trying to build the world’s first Explainable AI platform for governments and commercial institutions.
The startup hopes to transform business processes by offering auditable AI solutions across various platforms. It is built on the simple policy that higher-performing teams can produce trusted results and better business outcomes.
12. NumerAI is bridging the gap between the stock market and Data Scientists
Another startup transforming the financial sector is NumerAI. The startup aims to transform and regularize financial data into machine learning problems for a global network of Data Scientists.
Given the inefficiency of the stock market concerning developments in machine learning and artificial intelligence, the startup recognized that only a fraction of the world’s Data Scientists have access to its data and create solutions to combat that.
14. Trade Ideas’ virtual research analyst helps with smarter trading
Trade Ideas built a virtual research analyst that can sift through multiple aspects of business and finances, including technical, fundamental, social, and much more. The virtual assistant sifts through thousands of trades every day to help find the highest probability.
The startup makes use of thousands of data centers and makes them play with different trading scenarios every single day.
15. Datrics is democratizing self-service financial analytics using data science
Fintech startup, Datrics helps democratize self-service analytics as well as machine learning solutions by providing an easy-to-use drag-and-drop interface.
Datrics provides a no-code platform that can easily generate analytics and data science. The startup makes use of data-driven decision-making that allows enterprises to make better use of their financial service analytics.
Healthcare is a necessity for human life, yet many do not have access to it. Here are 10 startups that are using AI to change healthcare.
Healthcare is a necessity that is inaccessible to many across the world. Despite rapid developments and improvements in medical research, healthcare systems have become increasingly unaffordable.
However, multiple startups and tech companies have been trying their best to integrate AI and machine learning for improvements in this sector.
As the population of the planet increases along with life expectancy due to advancements in agriculture, science, medicine, and more, the demand for functioning healthcare systems also rises.
According to McKinsey & Co., by the year 2050, in Europe and North America, 1 in 4 people will be over the age of 65 Source). Healthcare systems by that time will have to manage numerous patients with complex needs.
Here is a list of a few Artificial Intelligence (AI) startups that are trying their best to revolutionize the healthcare industry as we know it today and help their fellow human beings:
1. Owkin aims to find the right drug for every patient.
Originating in Paris, France, Owkin was launched in 2016 and develops a federated learning AI platform, that helps pharmaceutical companies discover new drugs, enhance the drug development process, and identify the best drug for the ‘right patient.’ Pretty cool, right?
Owkin makes use of different machine learning models to test AI models on distributed data.
The startup also aims to empower researchers across hospitals, educational institutes, and pharmaceutical companies to understand why drug efficacy varies from patient to patient.
3. From the mid-Atlantic health system to an enterprise-wide AI workforce, Olive AI is improving operational healthcare efficiency.
Founded in 2012, Olive AI is the only known AI as a Service (AIaaS) built for the healthcare sector. The premier AI startup utilizes the power of cloud computing by implementing Amazon Web Services (AWS) and automating systems that accelerate time to care.
With more than 200 enterprise customers such as health systems, insurance companies, and a growing number of healthcare companies. Olive AI assists healthcare workers with time-consuming tasks like prior authorizations and patient verifications.
Want to learn more about AI as a Service? Click here.
4. Insitro provides better medicines for patients with the overlap of biology and machine learning.
The perfect cross between biology and machine learning, Insitro aims to support pharmaceutical research and development, and improve healthcare services. Founded in 2018, Insitro promotes Machine Learning-Based Drug Discovery for which it has raised a substantial amount of funding over the years.
According to a recent Forbes ranking of the top 50 AI businesses, the HealthTech startup is ranked at 35 for having the most promising AI-based medication development process.
5. Caption Health makes early disease detection easier.
Founded in 2013, Caption Health has since been a top provider of medical artificial intelligence. The startup is responsible for the early identification of illnesses.
Caption Health was the first to provide the FDA-approved AI imaging and guiding software for cardiac ultrasonography. The startup has helped remove numerous barriers to treatment and enabled a wide range of people to perform heart scans of diagnostic quality.
6. InformAI is trying to transform the way healthcare is delivered and improve patient outcomes.
Founded in 2017, InformAI expedites medical diagnosis while increasing the productivity of medical professionals.
Focusing on AI and deep learning, as well as business analytics solutions for hospitals and medical companies, InformAI was built for AI-enabled medical image classification, healthcare operations, patient outcome predictors, and much more.
InformAI not only has top-tier medical professionals at its disposal, but also has 10 times more access to proprietary medical datasets, as well as numerous AI toolsets for data augmentation, model optimization, and 3D neural networks.
The startup’s incredible work can be further explored here.
7. Recursion is decoding biology to improve lives across the globe.
A biotechnology startup, Recursion was founded in 2013 and focuses on multiple disciplines, ranging from biology, chemistry, automation, and data science, to even engineering.
Recursion focuses on creating one of the largest and fastest-growing proprietary biological and chemical datasets in the world.
8. Remedy Health provides information and insights for better navigation of the healthcare industry.
As AI advances, so does the technology that powers it. Another marvelous startup known as Remedy Health is allowing people to conduct phone screening interviews with clinically skilled professionals to help identify hidden chronic conditions.
The startup makes use of virtual consultations, allowing low-cost, non-physician employees to proactively screen patients.
Founded in 2013, Sensely is an avatar and chatbot-based platform that aids insurance plan members and patients.
The startup provides virtual assistance solutions to different enterprises including insurance and pharmaceutical companies, as well as hospitals to help them converse better with their members.
Sensely’s business ideology can further be explored here.
10. Oncora Medical provides a one-stop solution for oncologists.
Another digital health company, founded in 2014, Oncora Medical focuses on creating a crossover between data and machine learning for radiation oncology.
The main aim of the startup was to create a centralized platform for better collection and application of real-world data that can in some way help patients.
Other details on Oncora Medical can be found here.
With the international AI in the healthcare market expected to reach over USD 36B by the year 2025, it is only accurate to expect that this market and specific niche will continue to grow even further.
If you would like to learn more about Artificial Intelligence, click here.
Was there any AI-based healthcare startup that we missed? Let us know in the comments below. For similar listicles, click here.
This blog discusses the applications of AI in healthcare. We will learn about some businesses and startups that are using AI to revolutionize the healthcare industry. This advancement in AI has helped in fighting against Covid19.
COVID-19 was first recognized on December 30, 2019, by BlueDot. It did so nine days before the World Health Organization released its alert for coronavirus. How did BlueDot do it? BlueDot used the power of AI and data science to predict and track infectious diseases. It identified an emerging risk of unusual pneumonia happening around a market in Wuhan.
The role of data science and AI in the Healthcare industry is not limited to that. Now, it has become possible to learn the causes of whatever symptoms you are experiencing, such as cough, fever, and body pain, without visiting a doctor and self-treating it at home. Platforms like Ada Health and Sensely can diagnose the symptoms you report.
The Healthcare industry generates 30% of 1.145 trillion MB of data generated every day. This enormous amount of data is the driving force for revolutionizing the industry and bringing convenience to people’s lives.
Applications of Data Science in Healthcare:
1. Prediction and spread of diseases
Predictive analysis, using historical data to find patterns and predict future outcomes, can find the correlation between symptoms, patients’ habits, and diseases to derive meaningful predictions from the data. Here are some examples of how predictive analytics plays a role in improving the quality of life and medical condition of the patients:
Magic Box, built by the UNICEF office of innovation, uses real-time data from public sources and private sector partners to generate actionable insights. It provides health workers with disease spread predictions and countermeasures. During the early stage of COVID-19, Magic box correctly predicted which the African States are most likely to see imported cases using airline data. This prediction proved beneficial in planning and strategizing quarantine, travel restrictions, and enforcing social distancing.
Another use of analytics in healthcare is AIME. It is an AI platform that helps health professionals in tackling mosquito-borne diseases like dengue. AIME uses data like health center notification of dengue, population density, and water accumulation spots to predict outbreaks in advance with an accuracy of 80%. It aids health professionals in Malaysia, Brazil, and the Philippines. The Penang district of Malaysia saw a cost reduction of USD 500,000 by using AIME.
BlueDot is an intelligent platform that warns about the spread of infectious diseases. In 2014, it identified the Ebola outbreak risk in West Africa accurately. It also predicted the spread of the Zika virus in Florida six months before the official reports.
Sensely uses data from trusted sources like Mayo Clinic and NHS to diagnose the disease. The patient enters symptoms through a chatbot used for diagnosis. Sensely launched a series of customized COVID-19 screening and education tools with enterprises around the world which played their role in supplying trusted advice urgently.
According to a survey carried out in January 2020, 85 percent of the respondents working in smart hospitals reported being satisfied with their work compared to 80 percent of the respondents from digital hospitals. Similarly, 74 percent of the respondents from smart hospitals would recommend the medical profession to others, while only 66 percent of the respondents from digital hospitals recommend it.
Staff retention has been a challenge but is now becoming an enormous challenge, especially post-pandemic. For instance, after six months of the COVID-19 outbreak, almost a quarter of care staff quit their job in Flanders & Belgium. The care staff felt exhausted, experienced sleep deprivation, and could not relax properly. A Smart healthcare system can solve these issues.
Smart healthcare systems can help optimize operations and provide prompt service to patients. It forecasts the patient load at a particular time and plans resources to improve patient care. It can optimize clinic staff scheduling and supply, which reduces the waiting time and overall experience.
Getting data from partners and other third-party sources can be beneficial too. Data from various sources can help in process management, real-time monitoring, and operational efficiency. It leads to overall clinic performance optimization. We can perform deep analytics of this data to make predictions for the next 24 hours, which helps the staff focus on delivering care.
3. Data science for medical imaging
According to the World Health Organization (WHO), radiology services are not accessible to two-thirds of the world population. Patients must wait for weeks and travel distances for simple ultrasound scans. One of the foremost uses of data science in the healthcare industry is medical imaging. Data Science is now used to inspect images from X-rays, MRIs, and CT scan to find irregularities. Traditionally, radiologists did this task manually, but it was difficult for them to find microscopic deformities. The patient’s treatment depends highly on insights gained from these images.
Data science can help radiologists with image segmentation to identify different anatomical regions. Applying some image processing techniques like noise reduction & removal, edge detection, image recognition, image enhancement, and reconstruction can also help with inspecting images and gaining insights.
One example of a platform that uses data science for medical imaging is Medo. It provides a fully automated platform that enables quick and accurate imaging evaluations. Medo transforms scans taken from different angles into a 3D model. They compare this 3D model against a database of millions of other scans using machine learning to produce a recommended diagnosis in real-time. Platforms like Medo make radiology services more accessible to the population worldwide.
4. Drug discovery with data science
Traditionally, it took decades to discover a new drug, but the time has now been reduced to less than a year using data science. Drug discovery is a complex task. Pharmaceutical industries rely heavily on data science to develop better drugs. Researchers need to identify the causative agent and understand its characteristics which may require millions of test cases to understand. This is a huge problem for pharmaceutical companies because it can take decades to perform these tests. Data science solved this problem and can perform this task in a month or even a few weeks.
For example, the causative agent for COVID-19 is the SARS-CoV-2 virus. For discovering an effective drug for COVID-19, deep learning is used to identify and design a molecule that binds to SARS-CoV-2 to inhibit its function by using extracted data from scientific literature through NLP (Natural Language Processing).
5. Monitoring patients’ health
The human body generates two terabytes of data daily. Humans are trying to collect most of this data using smart home devices and wearables. The data these devices collect includes heart rate, blood sugar, and even brain activity. Data can revolutionize the healthcare industry if known how to use it.
Every 36 seconds, a person dies from cardiovascular disease in the United States. Data science can identify common conditions and predict disorders by identifying the slightest change in the health indicators. Timely alert of changes in health indicators can save thousands of lives. Personal health coaches are designed to help to gain deep insights into the patient’s health and alert if the health indicator reaches a dangerous level.
Companies like Corti can detect cardiac arrest in 48 seconds through phone calls. This solution uses real-time natural language processing to listen to emergency calls and look out for several verbal and non-verbal patterns of communication. It is trained on a dataset of emergency calls and acts as a personal assistant of the call responder. It helps the responder ask relevant questions, provide insights, and predict if the caller is suffering from cardiac arrest. Corti finds cardiac arrest more accurately and faster than humans.
6. Virtual assistants in healthcare
The WHO estimated that by 2030, the world will need an extra 18 million health workers worldwide. Using virtual assistant platforms can fulfill this need. According to a survey by Nuance, 92% of clinicians believe virtual assistant capabilities would reduce the burden on the care team and patient experience.
Patients can enter their symptoms as input to the platform and ask questions. The platform would tell you about your medical condition using the data of symptoms and causes. It is possible because of the predictive modeling of disease. These platforms can also assist patients in many other ways, like reminding them to take medication on time.
An example of such a platform is Ada Health, an AI-enabled symptom checker. A person enters symptoms through a chatbot, and Ada uses all available data from patients, past medical history, EHR, and other sources to predict a potential health issue. Over 11 million people (about twice the population of Arizona) use this platform.
Other examples of health chatbots are Babylon Health, Sensely, and Florence.
In this blog, we discussed the applications of AI in healthcare. We learned about some businesses and startups that are using AI to revolutionize the healthcare industry. This advancement in AI has helped in fighting against Covid19. To learn more about data science enroll in our Data Science Bootcamp, a remote instructor-led Bootcamp where you will learn data science through a series of lectures and hands-on exercises. Next, we will be creating a prognosis prediction system in python. You can follow along with my next blog post here.
Recommender systems are one of the most popular algorithms in data science today. Learn how to build a simple movie recommender system.
Recommender systems possess immense capability in various sectors ranging from entertainment to e-commerce. Recommender Systems have proven to be instrumental in pushing up company revenues and customer satisfaction with their implementation. Therefore, it is essential for machine learning enthusiasts to get a grasp on it and get familiar with related concepts.
As the amount of available information increases, new problems arise as people are finding it hard to select the items they actually want to see or use. This is where the recommender system comes in. They help us make decisions by learning our preferences or by learning the preferences of similar users.
They are used by almost every major company in some form or the other. Netflix uses it to suggest movies to customers, YouTube uses it to decide which video to play next on autoplay, and Facebook uses it to recommend pages to like and people to follow.
This way recommender systems have helped organizations retain customers by providing tailored suggestions specific to the customer’s needs. According to a study by McKinsey, 35 percent of what consumers purchase on Amazon and 75 percent of what they watch on Netflix come from product recommendations based on such algorithms.
Recommender systems can be classified under 2 major categories: Collaborative Systems and Conent-Based Systems.
Collaborative systems provide suggestions based on what other similar users liked in the past. By recording the preferences of users, a collaborative system would cluster similar users and provide recommendation based on the activity of users within the same group.
Content-Based systems provide recommendation based on what the user liked in the past. This can be in the form of movie ratings, likes and clicks. All the recorded activity allows these algorithms to provide suggestions on products if they possess similar features to the products liked by the user in the past.
A hands-on practice, in R, on recommender systems will boost your skills in data science by a great extent. We’ll first practice using the MovieLens 100K Dataset which contains 100,000 movie ratings from around 1000 users on 1700 movies. This exercise will allow you to recommend movies to a particular user based on the movies the user has already rated. We’ll be using the recommender lab package which contains a number of popular recommendation algorithms.
After completing the first exercise, you’ll have to use recommender lab to recommend music to the customers. We use the last.fm dataset that has 92,800 artist listening records from 1892 users. We are going to recommend artists to a user that the user is highly likely to listen.
Install and import required libraries
The recommenderlab frees us from the hassle of importing the MovieLens 100K dataset. It provides a simple function below that fetches the MovieLens dataset for us in a format that will be compatible with the recommender model. The format of MovieLense is an object of class “realRatingMatrix” which is a special type of matrix containing ratings. The data will be in form of a sparse matrix with the movie names in the columns and User IDs in the rows. The interaction of User ID and a particular movie will provide us the rating given by that particular user from a scale of 1-5.
As you will see in the output after running the code below, the MovieLense matrix will consists of 943 users (rows) and 1664 movies (columns) with overall 99392 ratings given.
By running the code below, we will visualize a small part of the dataset for our understanding. The code will only display the first 10 rows and 10 columns of our dataset. You can notice that the scores given by the users are integers ranging from 1-5. You’ll also note that most of the values are missing (marked as ‘NA’) indicating that the user hasn’t watched or rated that movie.
With the code below, we’ll visualize the MovieLens data matrix of the first 100 rows and 100 columns in the form of a heatmap. Run this code to visualize the movie ratings with respect to combination of respective rows and columns.
We will now train our model using recommenderlab‘s Recommender function below. The function learns a recommender model from the given data. In this case our data is the MovieLens data. In the parameters, we are going to specify one of the several algorithms offered by recommenderlab for learning. Here we’ll choose UBCF – User based Collaborative-Filtering. Collaborative filtering uses given rating data by many users for many items as the basis for predicting missing ratings and/or for creating a top-N recommendation list for a given user, called the active user.
train <- MovieLense
our_model <- Recommender(train, method = "UBCF")
our_model #storing our model in our_model variable
We will now move ahead and create predictions. From our interaction matrix which is in our dataset MovieLens, we will predict the score for the movies the user hasn’t rated using our recommender model and list the top scoring movies that our model scored. We will use recommenderlab’s predict function that creates recommendations using a recommender model, our_model in this case, and data about new users.
We will be predicting for a specified user. Below, we have specified a user with ID 115. We have also set n = 10 as our parameter to limit the response to the top 10 ratings given by our model. These will be the movies our model will recommend to the specified user based on his previous ratings.
User = 115
pre <- predict(our_model, MovieLense[User], n = 10)
List already liked
In the code below we will list the movies the user has already rated and display the score he gave.
In the code below, we will display the predictions created in our pre variable. We will display it in form of a list.
Using the recommenderlab library we just created a movie recommender system based on the collaborative filtering algorithm. We have successfully recommended 10 movies that the user is likely to prefer. The recommenderlab library could be used to create recommendations using other datasets apart from the MovieLens dataset. The purpose of the exercise above was to provide you a glimpse of how these models function.
Practice with lastFM dataset
For more practice with recommender systems, we will now recommend artists to our users. We will use the LastFM dataset. This dataset contains social networking, tagging, and music artist listening information from a set of 2K users from Last.fm online music system. It contains almost 92,800 artist listening records from 1892 users.
We will again use the recommenderlab library to create our recommendation model. Since this dataset cannot be fetched using any recommenderlab function as we did for the MovieLens dataset, we will manually fetch the dataset and practice converting it to the realRatingMatrix which is the format that our model will input for modeling.
Below we’ll import 2 files, the user_artists.dat file and artists.dat into the user_artist_data and artist_data variables respectively. The user_artists.dat file is a tab separated file that contains the artists listened by each user. It also provides a listening count for each [user, artist] pair marked as attribute weight. The artists.dat file contains information about music artists listened and tagged by the users. It is a tab separated file that contains the artist id, its name, URL and picture URL. It is available on this link to the zip file.
Following the steps as we did with our Movie Recommender system, we’ll view the first few rows of our dataset by using the head method.
We’ll use the head method to view the first 10 rows of the artist dataset below. Think which columns will be useful for our purpose as we’ll be using collaborative filtering method for designing our model.
In the code below, we will use the acast method to convert our user_artist dataset into an interaction matrix. This will be later converted to a matrix and then to realRatingMatrix. The realRatingMatrix is the format which will be taken by recommenderlab‘s Recommender function. It is a matrix containing ratings, typically 1-5 stars, etc. We will store in it our rrm_data variable. After running the code, you’ll notice that the output provides us the dimensions and class of our variable rrm_data.
Let’s visualize the user_artist data matrix of the first 100 rows and 100 columns in form of a heatmap. Write a single line code with rrm_data variable to visualize the movie ratings with respect to combination of respective rows and columns using the image function.
Using a similar procedure as we used to build our model for movie recommender system, write a code that builds our Recommender method of the recommenderlab library using the “UBCF” algorithm. Store the model in a variable named artist_model.
We’ll use the predict function to create a prediction for UserID 114 and store the prediction in variable artist_pre. Also note that we need the top 12 predictions for listed. The function below will list our prediction using the as method.
train <- rrm_data
artist_model <- Recommender(train, method = "UBCF")
User = 114
artist_pre <- predict(artist_model, rrm_data[User], n = 10)
Learn how to create a bird recognition app using Custom Vision AI and Power BI for application to track the effect of climate change on bird populations.
Imagine a world without birds: the ecosystem would fall apart, bug populations would skyrocket, erosion would be catastrophic, crops would be torn down by insects, and so many other damages. Did you know that 1,200 species are facing extinction over the next century, and many more are suffering from severe habitat loss? (source).
Birds are fascinating and beautiful creatures who keep the ecosystem organized and balanced. They have emergent properties that help them react spontaneously in many situations, which are unique to other organisms.
Here are some fun facts: Parasitic jaegers ( a type of bird species) obtain food by stealing it directly from the beaks of other birds. The Bassian Thrush finds its food using the most unique way possible: they have adapted their foraging methods to depend on creating a large amount of gas to surprise earworms and trigger them to start moving (so the birds can find and eat it).
Due to the intriguing behaviors of birds, I got inspired and lifted to create an app that could identify any bird which you are captivated by in real-time. I also built this app to raise awareness of the heart-breaking reality that most birds face around the world.
Global trends of bird species survival chart
I first researched bird populations and their global trends from the data that contains the information of the past 24 years. I then analyzed this data set and created interactive visuals using Power BI.
This chart displays the Red List Index (RLI) of species survival from 1988 to 2012. RLI values range from 1 (no species at risk of extinction in the near term) down to 0 (all species are extinct).
As you click on the Power BI Line Chart you will notice that since 1988, bird species have faced a steadily increasing risk of extinction in every major region of the world (change being more rapid in certain regions). 1 in 8 currently known bird species in the world are at the threshold of extinction. The main reasons are degradation/loss of habitat (due to deforestation, sea-level rise, more frequent wildfires, droughts, flooding, loss of snow and ice, and more), bird trafficking, pollution, and global warming. As figured, most of these are a result of us humans.
Due to industrialization, more than 542,390,438 birds have lost their lives. Climate change is causing the natural food chain to fall apart. Birds starve with lesser food (therefore must fly longer distances), choke on human-made pollutants, and end up becoming weaker. Change is necessary, and with change comes compassion. This web app can help to build an understanding and empathy toward birds.
Let’s look at the Power BI reports and the web app:
Power BI report: Bird attributes / Bird Recognition
As you can see in this report, along with recognizing a specific bird in real-time, interactive visualizations from Power BI display the unique attributes and information about each bird and its status in the wild. The fun facts on the visualization about each bird will linger in your mind for days.
AI web app – To create a bird recognition app
In this webapp, I used cognitive services to upload the images (of the 85 bird species), tagged them, trained the model, and evaluated the results. With Microsoft Custom Vision AI, I could
train the model to recognize 85 bird species. You can upload an image from your file explorer, and it will then predict the species name of the bird and the accuracy tied to that tag.
The Custom Vision Service uses machine learning to classify the images I uploaded. The only thing I was required to do was specify the correct tag for each image. You can also tag thousands of images at a time. The AI algorithm is immensely powerful as it gives us great accuracy and once the model is trained, we can use the same model to classify new images according to the needs of our app.
Choose a bird image from your PC
Upload a bird image URL
Take a picture of a bird in real-time (only works on the phone app as described later in the blog)
Once you upload an image, it will call the Custom Vision Prediction API (which was already trained by Custom Vision, powered by Microsoft) to get the species of the bird.
I also created a phone application, called ‘AI for Birds’, that you can use with camera integration for taking pictures of birds in real-time. After using the built-in camera to take a picture, the name of the bird species will be identified and shown. As of now, I added 85 bird species into the AI model, however that number will increase.
The journey of building my own custom model, training it, and deploying it has been noteworthy. Here is the link to my other blog for how to build your own AI custom model. You can also follow along with these steps and use it as a tutorial: Instructions for how to create Power BI reports and publish them to the web will also be provided in the other blog.
The grim statistics are not just sad news for bird populations. They are sad news for the planet because the health of bird species is a key- measure for the state of ecosystems and biodiversity on planet earth in general.
I believe in: Exploring- Learning- Teaching- Sharing. There are several thousands of other bird species that are critical to biodiversity on planet earth.
Consider looking at my app and supporting organizations that work to fight the constant threats of habitat destruction and global warming today.
Our Earth is full of unique birds which took millions of years to evolve into the striking bird species we see today. We do not want to destroy organisms which took millions of years to evolve in just a couple of decades.
Self-driving car ethics require proper study, training, and attention to detail. We must understand the ethical concerns of autonomous technology to minimize risk.
New technology, new problems
When it comes to autonomous technology of any kind, the first thing that often comes to our minds is our safety, our well-being, and our survival. What are the self-driving car ethics? It’s not ridiculous for us to have these concerns. First, we should ask the hard questions-
Who is responsible should a death result from an edge case accident?
What is an acceptable level of autonomy and what isn’t?
How does this technology come to a decision?
Second, with driverless cars – a prime example of autonomous technology – starting to be deployed on public roads across the world, we must seek answers to these questions sooner rather than later. The ethical dilemmas we face with driverless cars now will be similar to the ethical dilemmas we will face later on. Facing these issues head-on now could help us get a head start on the many ethical issues we will need to face as technology becomes ever-more high-tech.
Confronting the self-driving ethical issues
Currently, MIT researchers are confronting the ethical dilemmas of driverless cars by playing out hypothetical scenarios. In time, when it comes to autonomous cars making decisions on the safety of their passengers and people the car contacts on the street, how do they choose between the lesser of two evils? Then, the viewer must judge which decision they would make if placed in a particularly intense scenario. Eventually, this data is then compared with others and made publicly available.
In the meantime, researchers are gathering many people’s views on what is considered acceptable and not acceptable behavior of an autonomous car. So, what leads to the impossible choice of sacrificing one life over another’s? As alarming as it is, this research could be used to help data scientists and engineers.
This will help them gain a better understanding of what actions might be taken should a far-fetched accident occur. Of course, avoiding the far-fetched accident in the first place is a bigger priority. Furthermore, the research is a step toward facing the issue head-on rather than believing that engineering alone is going to solve the problem.
Meanwhile, some proposed ideas for minimizing the risk of self-driving car accidents include limiting the speed of autonomous cars beyond that speed limit. This is in certain densely populated areas and has a designated right of way for these cars.
More sophisticated mechanisms for this include using machine learning to continuously assess the risk of an accident and predict the probability of an accident occurring so that action can be taken preemptively to avoid such a situation. The Center for Autonomous Research at Stanford (a name suspiciously chosen for its acronym CARS, it seems) is looking into these ideas for “ethical programming.”
Next, transparency in the design of driverless cars and how algorithms come to a decision needs to be looked at. Then, we will work through the ethical dilemmas of driverless cars and other autonomous technology. This includes consumers of these cars, and the general public, who have a right to contribute to the algorithms and models that come to a decision.
Factors to consider
A child, for example, might have a stronger weight than a full-grown adult when it comes to a car deciding who gets priority in safety and survival. A pregnant woman, for example, might be given priority over a single man. Humans are the ones who will need to decide what kinds of weights are placed on what kinds of people, and research like MIT’s simulations of hypothetical scenarios is one way of letting the public openly engage in the design and development of these vehicles.
Where do we go from here?
In conclusion, as data scientists, we hold great responsibility when building models that directly impact people’s lives. The algorithms, smarts, rules, and logic that we create are not too far off from a doctor working in an emergency who has to make critical decisions in a short amount of time.
Lastly, understanding the ethical concerns of autonomous technology, implementing ways to minimize risk, and then programming the hard decisions is by no means a trivial task. For this reason, self-driving car ethics require proper study, training, and attention to detail.
Raja Iqbal, Chief Data Scientist and CEO of Data Science Dojo, held a community talk on AI for Social Good. Let’s look at some key takeaways.
This discussion took place on January 30th in Austin, Texas. Below, you will find the event abstract and my key takeaways from the talk.I’ve also included the video at the bottom of the page.
“It’s not hard to see machine learning and artificial intelligence in nearly every app we use – from any website we visit, to any mobile device we carry, to any goods or services we use. Where there are commercial applications, data scientists are all over it. What we don’t typically see, however, is how AI could be used for social good to tackle real-world issues such as poverty, social and environmental sustainability, access to healthcare and basic needs, and more.
What if we pulled together a group of data scientists working on cutting-edge commercial apps and used their minds to solve some of the world’s most difficult social challenges? How much of a difference could one data scientist make let alone many?
In this discussion, Raja Iqbal, Chief Data Scientist and CEO of Data Science Dojo, will walk you through the different social applications of AI and how many real-world problems are begging to be solved by data scientists. You will see how some organizations have made a start on tackling some of the biggest problems to date, the kinds of data and approaches they used, and the benefit these applications have had on thousands of people’s lives. You’ll learn where there’s untapped opportunity in using AI to make impactful change, sparking ideas for your next big project.”
1. We all have a social responsibility to build models that don’t hurt society or people
2. Data scientists don’t always work with commercial applications
Criminal Justice – Can we build a model that predicts if a person will commit a crime in the future?
Education – Machine Learning is being used to predict student churn at universities to identify potential dropouts and intervene before it happens.
Personalized Care – Better diagnosis with personalized health care plans
3. You don’t always realize if you’re creating more harm than good.
“You always ask yourself whether you could do something, but you never asked yourself whether you should do something.”
4. We are still figuring out how to protect society from all the data being gathered by corporations.
5. There is not a better time for data analysis than today. APIs and SKs are easy to use. IT services and data storage are significantly cheaper than 20 years ago, and costs keep decreasing.
6. Laws/Ethics are still being considered for AI and data use. Individuals, researchers, and lawmakers are still trying to work out the kinks. Here are a few situations with legal and ethical dilemmas to consider:
Granting parole using predictive models
Availability of data implying consent
Self-driving car incidents
7. In each stage of data processing there are possible issues that arise. Everyone has inherent bias in their thinking process which effects the objectivity of data.
8. Modeler’s Hippocratic Oath
I will remember that I didn’t make the world and it doesn’t satisfy my equations.
Though I will use models boldly to estimate value, I will not be overly impressed by mathematics.
I will never sacrifice reality for elegance without explaining why I have done so.
I will not give the people who use my model false comfort about accuracy. Instead, I will make explicit its assumptions and oversights.
I understand that my work may have an enormous impact on society and the economy, many of them beyond my comprehension.
I will aim to show how my analysis makes life better or more efficient.
US-AI vs China-AI – What does the race for AI mean for data science worldwide? Why is it getting a lot of attention these days?
Although it may still be recovering from the effects of the government shutdown, data science has received a lot of positive attention from the United States Government. Two major recent milestones include the OPEN Government Data Act, which passed in January as part of the Foundations for Evidence-Based Policymaking Act, and the American AI Initiative, which was signed as an executive order on February 11th.
The future of data science and AI
The first thing to consider is why and more specifically the US administration has passed these recent measures. Although it’s not mentioned in either of the documents, any political correspondent who has been following these topics could easily explain that they are intended to stake a claim against China.
China has stated its intention to become the world leader in data science and AI by 2030. And with far more government access, data sets (a benefit of China being a surveillance state), and an estimated $15 billion in machine learning, they seem to be well on their way. In contrast, the US has only $1.1 billion budgeted annually for machine learning.
So rather than compete with the Chinese government directly, the US appears to have taken the approach of convincing the rest of the world to follow their lead, and not China’s. They especially want to direct this message to the top data science companies and researchers in the world (especially Google) to keep their interest in American projects.
So, what do these measures do?
On the surface, both the OPEN Government Data Act and the American AI Initiative strongly encourage government agencies to amp up their data science efforts. The former is somewhat self-explanatory in name, as it requires agencies to publish more machine-readable publicly available data and requires more use of this data in improved decision making. It imposes a few minimal standards for this and also establishes the position of Chief Data Officers at federal agencies. The latter is somewhat similar in that it orders government agencies to re-evaluate and designate more of their existing time and budgets towards AI use and development, also for better decision making.
Critics are quick to point out that the American AI Initiative does not allocate more resources for its intended purpose, nor does either measure directly impose incentives or penalties. This is not much of a surprise given the general trend of cuts to science funding under the Trump administration. Thus, the likelihood that government agencies will follow through with what these laws ‘require’ has been given skeptical estimations.
However, this is where it becomes important to remember the overall strategy of the current US administration. Both documents include copious amounts of values and standards that the US wants to uphold when it comes to data, machine learning, and artificial intelligence. These may be the key aspects that can hold up against China, having a government that receives a hefty share of international criticism for its use of surveillance and censorship. (Again, this has been a major sticking point for companies like Google.)
These are some of the major priorities brought forth in both measures: Make federal resources, especially data and algorithms, available to all data scientists and researchers; Prepare the workforce for technology changes like AI and optimization; Work internationally towards AI goals while maintaining American values; and finally, Create regulatory standards, to protect security and civil liberties in the use of data science.
So there you have it. Both countries are undeniably powerhouses for data science. China may have the numbers in its favor, but the US would like the world to know that they have an American spirit.
Not working for both? – US-AI vs China-AI
In short, the phrase “a rising tide lifts all ships” seems to fit here. While the US and China compete for data science dominance at the government level, everyone else can stand atop this growing body of innovations and make their own.
The thing data scientists can get excited about in the short term is the release of a lot of new data from US federal sources or the re-release of such data in machine-readable formats. The emphasis is on the public part – meaning that anyone, not just US federal employees or even citizens, can use this data. To briefly explain for those less experienced in the realm of machine learning and AI, having as much data to work with as possible helps scientists to train and test programs for more accurate predictions.
A lot of what made the government shutdown a dark period for data scientists suggests the possibility of a golden age shortly.
Explore three real-life examples to see what types of AI are transforming the education industry and how.
This article is neither a philosophical essay on the role of Artificial Intelligence in the contemporary world nor a horror description that Artificial Intelligence will soon replace us all. Here, we analyze real-life examples from the education industry to see the different types of artificial intelligence in action and evaluate the effect of adopting Artificial Intelligence in education.
Jill Watson – A virtual teaching assistant (AI)
While delivering a massive open online course, the Georgia Institute of Technology found it challenging to provide high-quality learning assistance to the course students. With about 500 students enrolled, a teaching assistant wasn’t able to answer the heaps of messages that the students sent. And without personalized assistance, many students soon lost the feeling of involvement and dropped out of the course. To provide personal attention at scale and prevent students from dropping out, Georgia Tech decided to introduce a virtual teaching assistant.
Jill Watson (that’s the assistant’s name) is a chatbot intended to reply to a variety of predictable questions (for example, about the formatting of the assignments and the possibility to resubmit the assignments). Jill was trained on a comprehensive database consisting of the student’s questions about the course, introduction emails, and the corresponding answers that the teaching staff had provided.
Initially, the relevance of Jill’s answers was checked by a human. Soon, Jill started to automatically reply to the students’ introductions and repeated questions without any backup. When Jill receives a message, ‘she’ maps it to the relevant question-answer pair from the training database and retrieves an associated answer.
AI type used: Being a chatbot, Jill represents interactive AI – ‘she’ automates communication without compromising on interactivity.
Third space learning using AI- An online learning platform
While giving one-to-one math lessons to 3,500 pupils weekly, Third Space Learning was looking to improve the learners’ engagement and identify best practices in teaching. To achieve that, they have applied Artificial Intelligence to analyze the recorded lessons and identify the patterns in the teachers’ and pupils’ behavior. For example, it can identify if a pupil is showing signs that correspond to the ‘losing interest’ pattern.
In the future, Third Space Learning plans to provide its tutors with real-time AI-powered feedback during each lesson. For example, if a tutor talks too fast, Artificial intelligence will advise them to slow down.
Third Space Learning’s AI (with both its current and future functionality) looks exactly like analytic AI, which is focused on revealing patterns in data and producing recommendations based on the findings.
Duolingo – A language-learning platform
Among the three use cases that we are considering, Duolingo appears to be an absolute champion in terms of the number of challenges solved with its help.
For example, when many users felt so discouraged from being offered too simple learning materials that they dropped out of the course immediately, Duolingo introduced an AI-powered placement test. Being computer-adaptive, the test adjusts the questions to the previously given answers, generating a simpler question if a user made a mistake and a more complex question if the user answered correctly. The complexity of the words and the grammar used also influence the test configuration.
Besides, Duolingo uses Artificial Intelligenceto optimize and personalize lessons. For that, they have developed a ‘half-life regression model’, which analyzes the error patterns that millions of language learners make while practicing newly learned words, to predict how soon a user will forget a word. The model also takes into account words’ complexity.
These insights allow identifying the right time when a user should practice the word. Duolingo says that they have seen a 12% boost in user engagement after putting the model in production.
With the same purpose of boosting user engagement, Duolingo tried bots to help learners practice the language. Available 24/7, the bots readily communicated with the users, as well as shared their feedback on a better version of the user’s answer.
Besides, the bots contained a ‘Help me reply’ button for those who experienced difficulties with finding the right word or applying the right grammar rule. Though currently unavailable, the bots will reappear (at least the official message from Duolingo’s help center leaves no doubt about this).
Artificial Intelligence type used:Analytic AI (the placement test and the prediction model), interactive (bots).
Two Sentences – Long findings
The examples we considered show that it positively affects the education industry, allowing its adopters to solve such challenges as bringing personal attention at scale, improving students’ performance and engagement, identifying teaching best practices, and reducing teachers’ workload. And as we see, to solve these challenges, the industry players resort to analytic and interactive AI.
In the first part of this introductory series to chatbots, we talk about what this revolutionary technology is and why it has suddenly become so popular.
It took less than 24 hours of interaction with humans for an innocent, self-learning AI chatbot to turn into a chaotic, racist Nazi.
In March 2016, Microsoft unveiled Tay; a twitter-based, friendly, self-learning chatbot modeled to behave like a teenage girl. The AI chatbot was supposed to be an experiment in “conversational understanding”, as described by Microsoft. The bot was designed to learn from interacting with people online through casual conversation, slowly developing its personality.
What Microsoft didn’t consider, however, was the effect of negative inputs on Tay’s learning. Tay started off by declaring “humans are super cool” and that it was “a nice person”. Unfortunately, the conversations didn’t stay casual for too long.
In less than 24 hours Tay was tweeting racist, sexist and extremely inflammatory remarks after learning from all sorts of misogynistic, racist garbage tweeted at it by internet trolls.
This entire experiment, despite becoming a proper PR disaster for Microsoft, proved to be an excellent study into the inherently negative human bias and its effect on self-learning Artificial Intelligence.
So, what are Chatbots?
A chatbot is a specialized software that allows conversational interaction between a computer and a human. Modern chatbots are versatile enough to carry out complete conversations with their human users and even carry out tasks given during conversations.
Having become mainstream because of personal assistants from the likes of Google, Amazon, and Apple, chatbots have become a vital part of our everyday lives whether we realize it or not.
This is mainly because people have all but moved on to chat as the primary mode of communication. Couple that with the huge number of conversational platforms (Skype, WhatsApp, Slack, Kik, etc.) available, and the environment makes complete sense to use AI and the cloud to connect with people.
At the other end of the support chain, businesses love chatbots because they’re available 24×7, have near-immediate response times and are very easy to scale without the huge human resource bill that normally comes with having a decent customer support operations team.
Outside of business environments, smart virtual assistants dominate almost every aspect of modern life. We depend on these smart assistants for everything; from controlling our smart homes to helping us manage our day-to-day tasks. They have, slowly, become a vital part of our lives and their usefulness will only increase as they keep becoming smarter.
Types of Chatbots
Chatbots can be broadly classified into two different types:
The very first bots to see the light of day, rule-based chatbots relied on pattern-matching methodologies to ‘guess’ appropriate responses from an existing database. These bots started with the release of ELIZA in 1966 and continued till around 2001 with the release of SmarterChild developed by ActiveBuddy.
The simplest rule-based chatbots have one-to-one tables of inputs and their responses. These bots are extremely limited and can only respond to queries if they are an exact match with the inputs defined in their database. This means the conversation can only follow a number of predefined flows. In a lot of cases, the chatbot doesn’t even allow users to type in queries, relying, instead on, preset inputs that the bot understands.
This doesn’t necessarily limit their use though. Rule-based Chatbots are widely used in modern businesses for customer support tasks. A Customer Support Chatbot has an extremely limited job description. A customer support chatbot for a bank, for example, would need to answer some operational queries about the bank (timings, branch locations) and complete some basic tasks (authenticate users, block stolen credit cards, activate new credit cards, register complaints).
In almost all of these cases, the conversation would follow a pattern. The flow of conversation, once defined, would stay mostly the same for a majority of the users. The small number of customers who need more specialized support could be forwarded to a human agent.
A lot of modern customer-facing chatbots are AI-based Chatbots that use Retrieval based Models (which we’ll be discussing below). They are primarily rule-based but employ some form of Artificial Intelligence (AI) to help them understand the flow of human conversations.
These are a relatively newer class of chatbots, having come out after the proliferation of artificial intelligence in recent years. These bots (like Microsoft’s Tay) learn by being trained on conversational datasets, instead of having hard-coded rules like their rule-based kin.
AI-based chatbots are based on complex machine learning models that enable them to self-learn. These types of chatbots can be broadly classified into two main types depending on the types of models they use.
1. Retrieval-based models
As their name suggests, Chatbots using retrieval-based models are provided with a database of answers and are trained to retrieve the most relevant answer based on the input question. These bots already have a provided list of responses and are trained to rank each response based on the input/question. They cannot generate their own answers but with an extensive database of answers and proper training, they can be very productive and useful.
Usually easier to develop and customize, retrieval-based chatbots are mostly used in customer support and feedback applications where the conversation is limited to a topic (either a product, a service, or an entity).
2. Generative models
Generative models, unlike Retrieval based models, can generate their own responses by analyzing the input word by word to understand the query. These models are more ‘human’ during their interactions but at the same time also more prone to errors as they need to build sentence responses themselves.
Chatbots based on Generative Models are quite complex to build and are usually overkill for customer-facing applications. They are mostly used in applications where conversations are expected to be general/not limited to a specific topic. Take Google Assistant as an example. The Assistant is an always listening chatbot that can answer questions, tell jokes and carry out very ‘human’ conversations. The one thing it can’t do. Provide customer support for Google products.
Modern Virtual Assistants are a very good example of AI-based Chatbots
History of chatbots
Modern chatbots: Where are they used?
The use of chatbots has been growing exponentially in the Customer Services industry. The chatbot market is projected to grow from $2.6 billion in 2019 to $9.4 billion by 2024. This really isn’t surprising when you look at the immense benefits chatbots bring to businesses. According to a study by IBM, chatbots can reduce customer services cost by up to 30%. Couple that with customers being open to interacting with bots for support and purchases, it’s a win-win scenario for both parties involved.
Despite not being a substitute for healthcare professionals, chatbots are gaining popularity in the healthcare industry. They are mostly used as self-care assistants, helping patients manage their medications and help them track and monitor their fitness.
Financial chatbots usually come bundled with apps from leading banks. Once linked to your bank account, they can extend the functionality of the app by providing you with a conversational (text or voice) interface to your bank. Besides these, there are quite a few financial assistant chatbots available. These are able to track your expenses, budget your resources and help you manage your finances. Charlie is a very good example of a financial assistant. The chatbot is designed to help you budget your expenses and track your finances so that you end up saving more.
Chatbots have become a very popular way of interacting with the modern smart home. These bots are, at their core, complex. They don’t need to have contextual awareness but need to be trained properly to be able to extract an actionable command from an input statement. This is not always an easy task as the chatbot is required to understand the flow of natural language. Modern virtual assistants (such as the Google Assistant, Alexa, Siri) handle these tasks quite well and have become the de facto standard to provide a voice or text-based interface to a smart home.
Tools for building intelligent chatbots
Building a chatbot as powerful as the virtual assistants from Google and Amazon is an almost impossible task. These companies have been able to achieve this feat after spending years and billions of dollars in research, something that not everyone with a use for a chatbot can afford.
Luckily, almost every player in the tech market (including Google and Amazon) allows businesses to buy their technology platforms to design customized chatbots for their own use. These platforms have pre-trained language models and easy-to-use interfaces that make it extremely easy for new users to set up and deploy customized chatbots in no time. If that wasn’t good enough, almost all of these platforms allow businesses to push their custom chatbot apps to the Google Assistant or Amazon Alexa and have them instantly be available to millions of new users.
Now that we’re familiar with the basics of chatbots, we’ll be going into more detail about how to build them. In the second blog of the series, we’ll be talking about how to create a simple Rule-based chatbot in Python. Stay tuned!
Learn about the different types of AI as a Service, including examples from the top three leading cloud providers – Azure, AWS, and GCP.
Artificial Intelligence as a Service (AIaaS) is an AI offering that you can use to incorporate AI functionality without in-house expertise. It enables organizations and teams to benefit from AI capabilities with less risk and investment than would otherwise be required.
Types of AI as a service
Multiple types of AIaaS are currently available. The most common types include:
Cognitive computing APIs—APIs enable developers to incorporate AI services into applications with API calls. Popular services include natural language processing (NLP), knowledge mapping, computer vision, intelligent searching, and translation.
Machine learning (ML) frameworks—frameworks enable developers to quickly develop ML models without big data. This allows organizations to build custom models appropriate for smaller amounts of data.
Fully-managed ML services—fully-managed services can provide pre-built models, custom templates, and code-free interfaces. These services increase the accessibility of ML capabilities to non-technology organizations and enterprises that don’t want to invest in the in-house development of tools.
Bots and digital assistance—including chatbots, digital assistants, and automated email services. These tools are popular for customer service and marketing and are currently the most popular type of AIaaS.
Why AI as a Service can be transformational for Artificial Intelligence projects
In addition to being a sign of how far AI has advanced in recent years, AIaaS has several wider implications for AI projects and technologies. A few exciting ways that AIaaS can help transform AI are covered below:
Robust AI development requires a complex system of integrations and support. If teams are only able to use AI development tools on a small range of platforms, advancements take longer to achieve because fewer organizations are working on compatible technologies. However, when vendors offer AIaaS, they help development teams overcome these challenges and speed advances.
Several significant AIaaS vendors have already encouraged growth. For example, AWS in partnership with NVIDIA provides access to GPUs used for AI as a Service. Or, Siemens and SAS, have partnered to include AI-based analytics in Siemens’ Industrial Internet of things (IIoT) software. As these vendors implement AI technologies, they help standardize the environmental support of AI.
AI as a Service eliminates much of the expertise and resources that are needed to develop and perform AI computations. This elimination can decrease the overall cost and increase the accessibility of AI for smaller organizations. This increased accessibility can drive innovation since teams that were previously prevented from using advanced AI tools can now compete with larger organizations.
Additionally, when small organizations are better equipped to incorporate AI capabilities, it is more likely to be adopted in previously lacking industries. This opens markets for AI that were previously inaccessible or unappealing and can drive the development of new offerings.
The natural cost curve of technologies decreases as resources become more widely available and demand increases. As demand increases for AIaaS, vendors can reliably invest to scale up their operations, driving down the cost for consumers. Additionally, as demand increases, hardware and software vendors will compete to produce those resources at a more competitive cost, benefiting AIaaS vendors and traditional AI developers alike.
AI as a Service Platforms
Currently, all three major cloud providers offer some form of AIaaS services:
Azure provides AI capabilities in three different offerings—AI Services, AI Tools and Frameworks, and AI Infrastructure. Microsoft also recently announced that it is going to make the Azure Internet of Things Edge Runtime public. This enables developers to modify and customize applications for edge computing.
AI Services include:
Cognitive Services—enables users without machine learning expertise to add AI to chatbots and web applications It allows you to easily create high-value services, such as chatbots with the ability to provide personalized content. Services include functionality for decision making, language and speech processing, vision processing, and web search improvements.
Cognitive Search—adds Cognitive Services capabilities to Azure Search to enable more efficient asset exploration. This includes auto-complete, geospatial search, and optical character recognition (OCR).
Azure Machine Learning (AML)—supports custom AI development, including the training and deployment of models. AML helps make ML development accessible to all levels of expertise. It enables you to create custom AI to meet your organizational or project needs.
AI Tools & Frameworks include Visual Studio tools, Azure Notebooks, virtual machines optimized for data science, various Azuremigration tools, and the AI Toolkit for Azure IoT Edge.
Amazon offers AI capabilities focused on AWS services and its consumer devices, including Alexa. These capabilities overlap significantly since many of AWS’ cloud services are built on the resources used for its consumer devices.
AWS’ primary services include:
Amazon Lex—a service that enables you to perform speech recognition, convert speech to text, and apply natural language processing to content analysis. It uses the same algorithm currently used in Alexa devices.
Amazon Polly—a service that enables you to convert text to speech. It uses deep learning capabilities to deliver natural-sounding speech and real-time, interactive “conversation”.
Amazon Rekognition—a computer vision API that you can use to add image analysis, object detection, and facial recognition to your applications. This service uses the algorithm employed by Amazon to analyze Prime Photos.
Google has made serious efforts to market Google Cloud as an AI-first option, even rebranding its research division as “Google AI”. They have also invested in acquiring a significant number of AI start-ups, including DeepMind and Onward. All of this is reflected in their various offerings, including:
AI Hub—a repository of plug-and-play components that you can use to experiment with and incorporate AI into your projects. These components can help you train models, perform data analyses, or leverage AI in services and applications.
AI building blocks—APIs that you can incorporate into application code to add a range of AI capabilities, including computer vision, NLP, and text-to-speech. It also includes functions for working with structured data and training ML models.
AI Platform—a development environment that you can use to quickly and easily deploy AI projects. Includes a managed notebooks service, VMs and containers pre-configured for deep learning, and an automated data labeling service.
Cloud computing vendors and third-party service providers continue to extend capabilities into more realms, including AI and machine learning. Today, there are cognitive computing APIs that enable developers to leverage ready-made capabilities like NLP and computer vision. If you are into building your own models, you can use machine learning frameworks to fast-track development.
There are also bots and digital assistants that you can use to automate various services. Some services require configuration, but others are fully managed and come with a variety of licensing. Be sure to check the shared responsibility model offered by your provider, to ensure that you are fully compliant with regulatory requirements.
In this blog, let’s go behind the scenes and take a look at the journey of how this was created.
What is Azure custom vision?
Azure Custom Vision is an image recognition AI service part of Azure Cognitive Services that enables you to build, deploy, and improve your own image identifiers. An image identifier applies labels (which represent classes or objects) to images, according to their visual characteristics. It allows you to specify the labels and train custom models to detect them.
What does Azure custom vision do?
The Custom Vision service uses a Machine Learning algorithm to analyze images. You can submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of the submission. Then, the algorithm trains to this data and calculates its accuracy by testing itself on those same images.
Once the algorithm is trained, you can run a test, retrain, and eventually use it in your image recognition app to classify new images. You can also export the model itself for offline use.
How does it work?
Upload images – Bring your own labelled images or use Custom Vision to quickly add tags to any unlabeled images.
Train the model – Use your labelled images to teach Custom Vision the concepts you care about.
Evaluate the result – Use simple REST API calls to quickly tag images with your new custom computer vision model.
The Custom Vision Service uses machine learning to classify the images you upload. The only thing that is required to do is specify the correct tag for each image. You can also tag thousands of images at a time. The AI algorithm is immensely powerful and once the model is trained, you can use the same model to classify new images according to the needs of the app.
Prerequisites to create bird recognition app
Here are the prerequisites:
An account with Custom Vision AI; you can either use the free subscription or use your Azure account.
2. I added as many relevant images as possible and tagged them correctly.
3. I trained my model with 4590 images of 85 different species of birds.
4. Model evaluation using ‘Quick Test’
I calibrated the precision to be higher than 90%. The Precision value increases as you upload and train with more and more images.
When I trained the model with the new data, a new iteration was created. The accuracy and precision improved over time as I increased the training data set to 1200 images of 85 different species. (We should keep an eye on the precision value during various iterations.) I tested my model during this process using ‘Quick Test’ and deployed it.
Using the Model with the Prediction API
The Custom Vision AI worked as expected. Then I needed the required keys to create an application using Custom Vision AI.
So, I clicked on the “Gear Icon” (settings) and saved my project ID and prediction key. After that, I got the prediction URL from the Performance tab.
How to Experience the Custom Vision API in Power Apps, Mobile Application, & the Website
1. Power Apps:
The Custom Vision API can be linked to the Power Apps by the “Custom Vision” connector. By providing a few details to the custom vision connector such as “Prediction Key” as well as “Site URL”, you can seamlessly use Custom Vision API in your Power App.
2. Mobile Application (Android and iOS):
In the Flutter Application, we called the Custom Vision API by using HTTP requests as well as Dio Packages. For Power BI Reports part of the mobile app, we embedded the Power BI report iframes into the flutter app by using WebView.
The Custom Vision API is connected to the website via Ajax & HTML tags. On the website, we published the Power BI Report through the HTML iframe. The generated Power BI Embedded iframe is effortlessly compatible with all the browsers.
The possibilities of Cognitive Services and Machine Learning are limitless!
If you have not tried the AI for Birds Mobile app yet, there is no better time! Both (Android & iOS) apps are available to download.
To download this app, please search “AI for Birds” in the Google Play Store, or the Apple’s App Store.
How to Improve your Classifier?
Let’s talk about the ways to improve the quality of your Custom Vision Service Classifier. The quality of your classifier depends on the amount, quality, and variety of the labelled data that you provide and how balanced the overall dataset is.
A good classifier has a balanced training dataset that represents the submitted classifier. The process of building such a classifier is iterative and it’s common to implement a few rounds of training to reach expected results.
The following is a general pattern to help you build a more accurate classifier:
Add more images and balance data, then retrain it.
Add Images with varying background, lighting, object size, camera angle, and style; retrain.
Use the new Image(s) to test the prediction.
Modify existing training data according to predicted results.
Power BI is a business analytics service by Microsoft. It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end-users to create their reports and dashboards.
Power BI is a business suite that includes several technologies that work together to deliver outstanding data visualizations and business intelligence solutions.
You can use the Power BI Desktop tool to import data from various data sources such as files, Azure source, online services, DirectQuery, or gateway sources. You can use this tool to clean and transform the imported data.
Once the data is transformed and formatted, it is ready for creating visualizations in a report. A report is a collection of visualizations like graphs, charts, tables, filters, and slicers.
Post-installation, I opened Power BI desktop and then clicked “Get Data” > “Text/CSV”.
Next, I selected the CSV file by browsing the required folder and then clicked “Load”.
From the visualizations pane, I selected a visual for my report. Then, from the Fields pane, I chose the required column(s) for that visual.
Then, I created a report with the collection of different visuals and slicers by adding the specific columns from the table. You can also modify the visuals, and apply filters to discover more in-depth insights.
The Process of Publishing the Power BI Report
In Power BI Desktop, I chose to Publish the report on the Home tab. However, you can also go to File > Publish > Publish to Power BI.
2. I signed into my Power BI account.
3. Then I chose the destination from the list (you can also choose “My workspace”) and clicked on the Select button.
4. Once the publishing was complete, I received a link to my report. I selected the link to open my report using Power BI service.
2. After opening the required report from the workspace, I navigated to the “Share” dropdown > “Embed report” > “Publish to web” to create the Embed URL and the iframe.
3. Then I clicked “Create Embed Code”.
4. After generating the Embed URL, I selected the required iframe size and copied the generated iframe, so I can use the iframe in my website.
This way, using Microsoft Power BI, I was able to create a highly interactive & customizable report of various bird species from the original data set.
What is Power Apps?
Power Apps is a suite of apps, services, connectors, and data platform that provides a rapid application development environment to build custom apps of your needs. Apps built using Power Apps provide rich business logic and workflow capabilities to transform your manual business processes to digital, automated processes.
Power Apps also provides an extensible platform that lets pro-developers: programmatically interact with data and metadata, apply business logic, create custom connectors, and integrate with external data.
Using Power Apps, you can create three types of apps: canvas, model-driven, and portal.
Power Apps Studio is the app designer used for building canvas apps. The app designer makes creating apps feel more like building a slide deck in Microsoft PowerPoint. More information: Generate an app from data.
App designer for model-driven apps lets you define the sitemap and add components to build a model-driven app.
Power Apps portals Studio is a WYSIWYG (what you see is what you get) design tool to add and configure webpages, components, forms, and lists.
2. I clicked on the Create > Canvas app from blank.
3. After specifying my app name as “AI for Birds” > I selected “Phone” to be the Power Apps Format > and clicked “Create”.
4. I checked “Don’t show me this again” from the pop up > Skip.
5. From the dropdown menu, I selected my Country as “United States” > Get Started.
6. From the blank canvas, I added some new screens and UI elements with proper screen navigations.
Steps to Connect Custom Vision with Power Apps
Power App uses Custom Vision API to detect Bird species with the help of the images. I connected Custom Vision API with Power Apps.
Here are the steps I followed:
1. First, I clicked the File menu.
2. Then I clicked on Collections on the left navigation bar.
3. To establish the connection, I clicked on a New connection option from the top navigation bar.
4. On the new connections list screen, I clicked the “+”icon & put my prediction key and site URL.
5. Once the connection got established between the Custom Vision and the Power Apps, I was able to implement the same onto the Power Apps.
(Note: The prediction key and the site URL are accessible from the Custom Vision AI website, wherein I created an image classifier.)
Implementing the Custom Vision into Power Apps:
After connecting the Custom Vision to Power Apps, here are the steps that I followed:
In the image container (in my case, it was “UploadedImage2“), I created a Collection that stores the results of custom vision prediction.
To store results in the gallery, the following syntax was used:
On click Syntax: ClearCollect (<Name of your Collection to store the predicted results>, CustomVision.ClassifyImageV2(“<Your Project ID>”, “<YourProject name which can be obtained from the Custom Vision website>”, <Your Image Container>).predictions);
Publishing My Power App:
· To publish the Power App, I clicked on File > Save > Publish.
Sign up with your Microsoft Office 365 account in Power Apps.
Click Create > “Canvas app from blank”.
5. After specifying the app name > Select “Phone” to be the format > Create.
6. After Clicking Create, it opens the Power App Studio in a new tab. It shows the steps to start building an app from a blank canvas. Just click Skip.
7. Click on File > Open >Browse (Browse File). Browse the extracted file in Power Apps Studio and upload it.
8. After adding the extracted file, click “Don’t Save” and now you are ready to use “Power App Studio”.
9. To use the Prebuilt custom Vision on Power Apps click “Ask for access”. An email window will open where you can ask the developer of the Custom Vision to grant access for a particular tenant. (Note: There might be a cost associated with the Custom Vision service.)
10. Once the access is granted from the developer of the app, you can use the Custom Vision API on your Power Apps.
11. After modifying the App, you can save/publish it and view it on your phone.
How to download Power Apps on your Mobile Devices (Android/iOS):
The Power Apps application is available through the Apple App Store and the Google Play Store.
Artificial Intelligence (AI) has added ease to the job of content creators and webmasters. It has wonder us by introducing different inventions for work. Here you will learn how it is helping webmasters and content creators!
Technology has worked wonders for us. From using the earliest generation of computers with the capability of basic calculation to the era of digitization, where everything is digital, the world has changed quite swiftly. How did this happen?
The obvious answer would be “advancement in technology.” However, when you dig deep, the answer “advancement in technology” won’t be substantial. Another question may arise, “how advanced technology made possible and how has it changed the entire landscape?”. The answer to this particular question is the development of advanced algorithms that are capable of solving bigger problems.
These advanced algorithms are developed on the basis of Artificial Intelligence. Although, advanced technologies are often pronounced together and, in some situations, work in tandem with each other.
However, we will keep our focus only on Artificial Intelligence in this writing. You will find several definitions of Artificial Intelligence, but a simple definition of AI will be the ability of machines to work on their own without any input from mankind. This technology has revolutionized the landscape of technology and made the jobs of many people easier.
Content creators and webmasters around the world are also among those people. This writing is mainly focused on the topic of how it is helping content creators and webmasters to make their jobs easier. We put together a massive amount of details to help you understand the said topic.
1. Focused content
Content creators and webmasters around the world want to serve their audience with the type of content they want. The worldwide audience also tends to appreciate the type of content that is capable of answering their questions and resolving their confusion.
This is where AI-backed tools can help webmasters and content creators to get ideas about the content their audience needs. For instance, AI-backed tools will come up with high-ranking queries and keywords searched on google regarding a specific niche or topic, and content creators can articulate content accordingly. Webmasters will also publish the content on their website after getting ideas about the choice of their audience.
2. Easy and quick plagiarism check with AI
The topmost concern of any content creator or webmaster will be the articulation of plagiarism-free content. Just a couple of decades earlier, it was quite problematic and laborious for content creators and webmasters to spot plagiarism in a given content. They had to dig a massive amount of content for this purpose.
This entire task of content validation took a huge amount of effort and time; besides, it was tiresome as well. However, it is not a difficult task these days. Whether you are a webmaster or a content creator, you can simply check plagiarism by pasting the content or its URL on an online plagiarism detector. Once you paste the content, you will get the plagiarism report in a matter of seconds.
It is because of this technology, that this laborious task became so easy and quick. The algorithms of the plagiarism checkers are based on this technology. This technology works on its own to understand the meaning of content given by the user and then find similar content, even if it is in a different language.
Not only that but the AI-backed algorithm of such a tool can also check patch plagiarism (the practice of changing a few words in a phrase). This whole process of finding plagiarism is easy because it enables webmasters and content creators to mold or rephrase content to avoid penalties imposed by search engines.
3. AI reduced the effort of paraphrasing
As mentioned earlier, an effective option to remove plagiarism from content is rephrasing or rewriting. However, in the fast-paced business environment, content creators and webmasters don’t have substantial time to rewrite or rephrase the plagiarized content.
Now a question like “what is the easiest method of paraphrasing plagiarized content?” may strike your mind. The answer to this question will be using a capable paraphrase tool. Advanced rewriting tools these days make it quite easier for everyone to remove plagiarism from their content.
These tools make use of AI-backed algorithms. These AI-backed algorithms first understand the meaning of the whole writing. Once the task of understanding the content is done, the tool rewrites the entire content by changing words where needed to remove plagiarism from it.
The best thing about this entire process is it happens in a quick time. If you try to do it yourself, it will take plenty of time and effort as well. Using an AI-backed paraphrasing tool will allow you to rewrite an article, business copy, blog, or anything else in a few minutes.
4. Searching copied images is far easier with
Another headache for webmasters and content creators is the use of their images by other sources. Not a long time ago, finding images or visuals created by you that are being used by other sources without your consent was difficult. You had to enter relevant queries and various other kinds of methods to find out the culprit.
However, it is quite easier these days, and credit obviously goes to AI. You may ask, “how?”. Well! We have an answer to this question. There are advanced image search methods that make use of machine learning and artificial intelligence to help you find similar images.
Suppose you are a webmaster or a content creator looking for the stolen images published from your end. All you have to do is search by image one by one, and you will get to see similar image results in a matter of seconds.
If you discover that certain sources are utilizing photographs that are your intellectual property without your permission, you can ask them to remove them, give you a backlink, or face the repercussions of copyright laws. This image search solution has made things a lot easier for content creators and webmasters and worried about copied and stolen images. No worries, because AI is here to assist you!
Artificial intelligence has certainly made a lot of things easier for us. If we focus our lens on the jobs of content creators and webmasters, it is helping them as well. From the creation of content to detecting plagiarism and paraphrasing it to remove plagiarism, it has shown to be quite beneficial to webmasters and content providers. It can also search for stolen or copied images using it. All these factors have made a huge impact on the web content creation industry. We hope it will help them in a number of other ways in the coming days because technology is seeing advancements rather swiftly.