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Despite major layoffs in 2022, there are many optimistic fintech trends to look out for in 2023. Every crisis bespells new opportunities. In this blog, let’s see what the future holds for fintech trends in 2023. (more…)
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 job 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, effort, hard work, and enthusiasm to learn them.
Every job requires its own set of core skills, i.e. some may require data analysis, while 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:
Read blog about AI and Machine learning trends for 2023
Who are they?
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 analyze 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 languages, and related certifications are preferred.
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 it 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.
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 like 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.
Master these data science tools to grow your career as Data Scientist
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.
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 on cloud-based 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 with a growth-oriented career in no time.
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.
Throughout history, we’ve chased the extraordinary. Today, the spotlight is on AI—a game-changer, redefining human potential, augmenting our capabilities, and fueling creativity. Curious about AI and how it is reshaping the world? You’re right where you need to be.
The Future of Data and AI podcast hosted by the CEO and Chief Data Scientist at Data Science Dojo, dives deep into the trends and developments in AI and technology, weaving together the past, present, and future. It explores the profound impact of AI on society, through the lens of the most brilliant and inspiring minds in the industry.
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.
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.
Here are 6 Books to Help you Learn Data Science
DataFramed is a weekly podcast exploring how artificial intelligence and data are changing the world around us. On this show, we invite data & AI leaders at the forefront of the data revolution to share their insights and experiences into how they lead the charge in this era of AI.
Whether you’re a beginner looking to gain insights into a career in data & AI, a practitioner needing to stay up-to-date on the latest tools and trends, or a leader looking to transform how your organization uses data & AI, there’s something here for everyone.
Data Skeptic launched as a podcast in 2014. Hundreds of interviews and tens of millions of downloads later, it is a widely recognized authoritative source on data science, artificial intelligence, machine learning, and related topics.
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.
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.
Data Skeptic is a boutique consulting company in addition to its podcast. Kyle participates directly in each project the team undertakes. Our work primarily focuses on end-to-end machine learning, cloud infrastructure, and algorithmic design.
Pro-tip: Enroll in the Large Language Models Bootcamp today to get ahead in the world of Generative AI
Tune in to Last Week in AI for your weekly dose of insightful summaries and discussions on the latest advancements in AI, deep learning, robotics, and beyond. Whether you’re an enthusiast, researcher, or simply curious about the cutting-edge developments shaping our technological landscape, this podcast offers insights on the most intriguing topics and breakthroughs from the world of artificial intelligence.
Discover The Everyday AI podcast, your go-to for daily insights on leveraging AI in your career. Hosted by Jordan Wilson, a seasoned martech expert, this podcast offers practical tips on integrating AI and machine learning into your daily routine.
Stay updated on the latest AI news from tech giants like Microsoft, Google, Facebook, and Adobe, as well as trends on social media platforms such as Snapchat, TikTok, and Instagram. From software applications to innovative tools like ChatGPT and Runway ML, The Everyday AI has you covered.
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:
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!
The Artificial Intelligence podcast talks about the latest innovations in the artificial intelligence and machine learning industry. The recent episode of the podcast discusses text-to-image generators, Robot dogs, soft robotics, voice bot options, and a lot more.
Do not forget to share in the comments the names of your 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.
In this blog, we will explore the answers to data science vs AI vs machine learning, hoping to find the right demand for the advancing digital world.
As the name suggests, data science is a field that involves studying and processing large quantities of data using a variety of technologies and techniques to detect patterns, make conclusions about the data, and aid 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.
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.
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.
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, 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.
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.
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:
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.
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.
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 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.
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.
References:
Written by Aadam Nadeem
The use of AI in culture raises interesting ethical reflections termed 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 became 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?
Curious about how generative AI is reshaping the creative industry and what it means for artists and creators? Watch this podcast now!
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 a 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, many real-world problems 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:
Access to personally identifiable information must only be accessible to 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.
The use of the internet and online activities keeps 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.
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
AI systems use machine learning techniques in neural networks to retrieve patterns from a given dataset. With or without “correct” solutions provided, i.e., supervised, semi-supervised or unsupervised.
Read this blog to learn more about AI powered document search
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.
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 can 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.
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, assist, 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.
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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.
Read this blog to learn about Big Data Ethics
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 that 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
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.
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:
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:
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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 would 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 jobs 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.
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.
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 has 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).
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 health indicators. A 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.
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 implementation, 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.
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Learn how to use Chatterbot, the Python library, to build and train AI-based chatbots.
Chatbots have become extremely popular in recent years and their use in the industry has skyrocketed. The chatbot market is projected to grow from $2.6 billion in 2019 to $9.4 billion by 2024. This doesn’t come as a surprise 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%.
In the third blog of A Beginners Guide to Chatbots, we’ll be taking you through how to build a simple AI-based chatbot with Chatterbot; a Python library for building chatbots.
Chatterbot is a python-based library that makes it easy to build AI-based chatbots. The library uses machine learning to learn from conversation datasets and generate responses to user inputs. The library allows developers to train their chatbot instances with pre-provided language datasets as well as build their datasets.
A newly initialized Chatterbot instance starts with no knowledge of how to communicate. To allow it to properly respond to user inputs, the instance needs to be trained to understand how conversations flow. Since conversational chatbot Python relies on machine learning at its backend, it can very easily be taught conversations by providing it with datasets of conversations.
Chatterbot’s training process works by loading example conversations from provided datasets into its database. The bot uses the information to build a knowledge graph of known input statements and their probable responses. This graph is constantly improved and upgraded as the chatbot is used.
Chatterbot knowledge graph (Source: Chatterbot Knowledgebase)
The Chatterbot Corpus is an open-source user-built project that contains conversational datasets on a variety of topics in 22 languages. These datasets are perfect for training a chatbot on the nuances of languages – such as all the different ways a user could greet the bot. This means that developers can jump right to training the chatbot on their customer data without having to spend time teaching common greetings.
Chatterbot has built-in functions to download and use datasets from the Chatterbot Corpus for initial training.
Conversational chatbot Python uses Logic Adapters to determine the logic for how a response to a given input statement is selected.
A typical logic adapter designed to return a response to an input statement will use two main steps to do this. The first step involves searching the database for a known statement that matches or closely matches the input statement. Once a match is selected, the second step involves selecting a known response to the selected match. Frequently, there will be several existing statements that are responses to the known match. In such situations, the Logic Adapter will select a response randomly. If more than one Logic Adapter is used, the response with the highest cumulative confidence score from all Logic Adapters will be selected.
Chatterbot stores its knowledge graph and user conversation data in an SQLite database. Developers can interface with this database using Chatterbot’s Storage Adapters.
Storage Adapters allow developers to change the default database from SQLite to MongoDB or any other database supported by the SQLAlchemy ORM. Developers can also use these Adapters to add, remove, search, and modify user statements and responses in the Knowledge Graph as well as create, modify and query other databases that Chatterbot might use.
In this tutorial, we will be using the Chatterbot Python library to build an AI-based Chatbot.
We will be following the steps below to build our chatbot
The first thing we’ll need to do is import the modules we’ll be using. The ChatBot module contains the fundamental Chatbot class that will be used to instantiate our chatbot object. The ListTrainer module allows us to train our chatbot on a custom list of statements that we will define. The ChatterBotCorpusTrainer module contains code to download and train our chatbot on datasets part of the ChatterBot Corpus Project.
#Importing modules
from chatterbot import ChatBot
from chatterbot.trainers import ListTrainer
from chatterbot.trainers import ChatterBotCorpusTrainerA chatbot instance can be created by creating a Chatbot object. The Chatbot object needs to have the name of the chatbot and must reference any logic or storage adapters you might want to use.
In the case you don’t want your chatbot to learn from user inputs after it has been trained, you can set the read-only parameter to True.
BankBot = ChatBot(name = 'BankBot',
read_only = False,
logic_adapters = ["chatterbot.logic.BestMatch"],
storage_adapter = "chatterbot.storage.SQLStorageAdapter")Training your chatbot agent on data from the Chatterbot-Corpus project is relatively simple. To do that, you need to instantiate a ChatterBotCorpusTrainer object and call the train() method. The ChatterBotCorpusTrainer takes in the name of your ChatBot object as an argument. The train() method takes in the name of the dataset you want to use for training as an argument.
Detailed information about ChatterBot-Corpus Datasets is available on the project’s Github repository.
corpus_trainer = ChatterBotCorpusTrainer(BankBot)
corpus_trainer.train("chatterbot.corpus.English")You can also train ChatterBot on custom conversations. This can be done by using the module’s ListTrainer class.
In this case, you will need to pass in a list of statements where the order of each statement is based on its placement in a given conversation. Each statement in the list is a possible response to its predecessor in the list.
The training can be undertaken by instantiating a ListTrainer object and calling the train() method. It is important to note that the train() method must be individually called for each list to be used.
greet_conversation = [
"Hello",
"Hi there!",
"How are you doing?",
"I'm doing great.",
"That is good to hear",
"Thank you.",
"You're welcome."
]
open_timings_conversation = [
"What time does the Bank open?",
"The Bank opens at 9AM",
]
close_timings_conversation = [
"What time does the Bank close?",
"The Bank closes at 5PM",
]
#Initializing Trainer Object
trainer = ListTrainer(BankBot)
#Training BankBot
trainer.train(greet_conversation)
trainer.train(open_timings_conversation)
trainer.train(close_timings_conversation)Once the chatbot has been trained, it can be used by calling Chatterbot’s get response() method. The method takes a user string as an input and returns a response string.
while (True):
user_input = input()
if (user_input == 'quit'):
break
response = BankBot.get_response(user_input)
print (response)This blog was hands-on to building a simple AI-based chatbot in Python. The functionality of this bot can easily be increased by adding more training examples. You could, for example, add more lists of custom responses related to your application.
As we saw, building an AI-based chatbot is easy compared to building and maintaining a Rule-based Chatbot. Despite this ease, chatbots such as this are very prone to mistakes and usually give robotic responses because of a lack of good training data.
A better way of building robust AI-based Chatbots is to use Conversational AI Tools offered by companies like Google and Amazon. These tools are based on complex machine learning models with AI that has been trained on millions of datasets. This makes them extremely intelligent and, in most cases, are almost indistinguishable from human operators.
In the next blog to learn data science, we’ll be looking at how to create a Dialog Flow Chatbot using Google’s Conversational AI Platform.
Want to upgrade your Python abilities? Check out Data Science Dojo’s Introduction to Python for Data Science.
Written by Usman Shahid
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 that 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.
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.
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.
In this web app, 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.
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 to 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 them 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 that took millions of years to evolve in just a couple of decades.
Sources:
Written by Saumya Soni
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
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:
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
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.
Learning is an important aspect of life. It is crucial to develop the education industry with advanced technological tools to facilitate all stakeholders. Let’s take a look at three real-life examples that assist students and teachers within the education industry.
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.
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, a step towards revamping the education industry.
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 of resubmitting 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.
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 plans for the education industry, 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. It aims to create a progressive education industry with empowered teachers.
Among the three use cases that we are considering within the education industry, 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 Intelligence to 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).
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 my first blog, ‘Bird Recognition App using Microsoft Custom Vision AI and Power BI’, we looked at the intriguing behaviors and attributes of birds using Power BI. This inspired me to create an ‘AI for birds’ web app’ using Azure Custom Vision along with a phone app using Power Apps and an iPhone / Android platform that could identify a bird in real-time. I created this app to raise awareness of the heart-breaking reality which most birds face around the world.
In this blog, let’s go behind the scenes and take a look at the journey of how this was created.
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.
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.
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.
Here are the prerequisites:
I first visited https://customvision.ai/, then I logged in with the Azure credentials.
1. I created a new project.
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.
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.
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.
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.
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:
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.
Next, you can publish the reports created in Power BI desktop to Power BI Service or Power BI Report Server.
Here are the Prerequisites:
I installed Power BI Desktop from the Windows Store. You can also download it from this URL: https://powerbi.microsoft.com/en-us/desktop/
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.
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.
1. To generate the Embed URL and iframe, I signed into the Power BI service (https://www.powerbi.com/).
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.
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.
To create an app, you start with make.powerapps.com.
· A Microsoft 365 Business Premium Account.
1. I signed in to Power Apps.
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.
Power App uses Custom Vision API to detect Bird species with the help of the images. I connected Custom Vision API with Power Apps.
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 was established between the Custom Vision and the Power Apps, I was able to implement the same into 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.)
After connecting the Custom Vision to Power Apps, here are the steps that I followed:
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);
· To publish the Power App, I clicked on File > Save > Publish.
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 to a particular tenant. (Note: There might be a cost associated with the Custom Vision service.)
10. Once 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.
The Power Apps application is available through the Apple App Store and the Google Play Store.
In this blog, we have seen how easy it is to create power Apps and use it with Custom vision API.
I hope that this blog helps you see how to use custom vision API, Power BI and Power Apps to create a real world application like ‘aiforbirds’.
Using this app, you can easily find the answer to the question, “What type of bird is that?”
Explore bird statuses and trends with maps, species information, and some fun facts. Go to: http://aiforbirds.com/ for the webapp and “AI for Birds” in the App store for the phone app.
Thank you for your time. Good luck!
Artificial intelligence and machine learning are part of our everyday lives. These data science movies are my favorite.
Advanced artificial intelligence (AI) systems, humanoid robots, and machine learning are not just in science fiction movies anymore. We come across this technological advancement in our everyday life. Today our cellphones, cars, TV sets, and even household appliances are using machine learning to improve themselves.
As we advance towards faster connectivity and the possibility of making the Internet of Things (IoT) more common, the idea of machines taking over and controlling humans might sound funny, but there are some challenges that need attention, including ethical and moral dimensions of machines thinking and acting like humans.
Here we are going to talk about some amazing movies that bring to life these moral and ethical aspects of machine learning, artificial intelligence, and the power of data science. These data science movies are a must-watch for any enthusiast willing to learn data science.
This classic film by Stanley Kubrick addresses the most interesting possibilities that exist within the field of Artificial Intelligence. Scientists, like always, are misled by their pride when they develop a highly advanced 9000 series of computers.
This AI system is programmed into a series of memory banks giving it the ability to solve complex problems and think like humans. What humans don’t comprehend is that this superior and helpful technology has the ability to turn against them and signal the destruction of mankind.
The movie is based on the Discovery One space mission to the planet Jupiter. Most aspects of this mission are controlled by H.A.L the advanced AI program. H.A.L is portrayed as a humanistic control system with an actual voice and ability to communicate with the crew.
Initially, H.A.L seems to be a friendly advanced computer system, making sure the crew is safe and sound. But as we advance into the storyline, we realize that there is a glitch in this system, and what H.A.L is trying to do is fail the mission and kill the entire human crew.
As the lead character, Dave tries to dismantle H.A.L we hear the horrifying words “I’m Sorry Dave.” This phrase has become iconic as it serves as a warning against allowing computers to take control of everything.
Christopher Nolan’s cinematic success won an Oscar for Best Visual Effects and grossed over $677 million worldwide. The film is centered around astronauts’ journey to the far reaches of our galaxy to find a suitable planet for life as Earth is slowly dying.
The lead character played by Oscar winner Matthew McConaughey, an astronaut and spaceship pilot, along with mission commander Brand and science specialists are heading towards a newly discovered wormhole.
The mission takes the astronauts on a spectacular interstellar journey through time and space, but at the same time, they miss out on their own life back home light years away. On board the spaceship, Endurance is a pair of quadrilateral robots called TARS and CASE. They surprisingly resemble the monoliths from 2001: A Space Odyssey.
TARS is one of the crew members of Mission Endurance. TARS’ personality is witty, sarcastic, and humorous, traits programmed into him to make him a suitable companion for its human crew on this decades-long journey.
CASE’s mission is the maintenance and operations of the Endurance in the absence of human crew members. CASE’s personality is quiet and reserved as opposed to TARS. TARS and CASE are true embodiments of the progress that human beings have made in AI technology, thus promising us great adventures in the future.
Based on the real-life story of Alan Turing, A.K.A. the father of modern computer science, The Imitation Game is centered around Turing and his team of code-breakers at top secret British Government Code and Cipher School. They’re determined to decipher the Nazi German military code called “Enigma”.
Enigma is a key part of the Nazi military strategy to safely transmit important information to its units. To crack this Enigma, Turing created a primitive computer system that would consider permutations at a faster rate than any human.
This achievement helped Allied forces ensure victory over Nazi German in the second world war. The movie not only portrays the impressive life of Alan Turning but also describes the important process of creating the first ever machine of its kind giving birth to the field of cryptography and cyber security.
The cult classic, Terminator, starring Arnold Schwarzenegger as a cyborg assassin from the future is the perfect combination of action, sci-fi technology, and personification of machine learning.
The humanistic cyborg was created by Cyberdyne Systems and is known as T-800 model 101. Designed specifically for infiltration and combat and is sent on a mission to kill Sarah Connor before she gives birth to John Connor, who would become the ultimate savior for humanity after the robotic uprising.
In this classic, we get to see advanced artificial intelligence in the works and how it has considered humanity the biggest threat to the world. Bent upon total destruction of the human race, only freedom fighters led by John Connor stand in their way. Therefore, sending The Terminator back in time to alter their future is the top priority.
The sequel to the 1982 original Blade Runner has impressive visuals capturing the audience’s attention throughout the film. The story is about bio-engineered humans known as “Replicants” After the uprising of 2022 they are being hunted down by LAPD Blade Runner.
Blade Runner is an officer who hunts and retires (kills) rogue replicants. Ryan Gosling stars as “K” hunting down replicants who are considered a threat to the world. Every decision he makes is based on analysis.
The films explore the relationships and emotions of artificially intelligent beings and raise moral questions regarding the freedom to live and the life of self-aware technology.
Will Smith stars as Chicago policeman Del Spooner in the year 2035. He is highly suspicious of the AI technology, data science, and robots are being used as household helpers. One of these mass-produced robots (cueing in the data science / AI angle), named Sonny, goes rogue and is held responsible for the death of its owner.
Its owner falls from a window on the 15th floor. Del investigates this murder and discovers a larger threat to humanity by Artificial Intelligence. As the investigation continues, there are multiple murder attempts on Del but he manages to barely escape with his life.
The police detective continues to unravel mysterious threats from AI technology and tries to stop the mass uprising.
Minority Report and Data Science? That is correct! It is a 2002 action thriller directed by Steven Spielberg and starring Tom Cruise. The most common use of data science is using current data to infer new information, but here data are being used to predict crime predispositions.
A group of humans gifted with psychic abilities (PreCogs) provide the Washington police force with information about crimes before they are committed. Using visual data and other information by PreCogs, it is up to the PreCrime police unit to use data to explore the finer details of a crime in order to prevent it.
However, things take a turn for the worse when one day PreCogs predict John Anderson one of their own, is going to commit murder. To prove his innocence, he goes on a mission to find the “Minority Report” which is the prediction of the PreCog Agatha that might tell a different story and prove John’s innocence.
Her (2013) is a Spike Jones science fiction film starring Joaquin Phoenix as Theodore Twombly, a lonely and depressed writer. He is going through a divorce at the time, and to make things easier, purchases an advanced operating system with an A.I. virtual assistant designed to adapt and evolve.
The virtual assistant names itself Samantha. Theodore is amazed at the operating system’s ability to emotionally connect with him. Samantha uses its highly advanced intelligence system to help with every one of Theodore’s needs, but now he’s facing an inner conflict of being in love with a machine.
The story is centered around a 26-year-old programmer, Caleb, who wins a competition to spend a week at a private mountain retreat belonging to the CEO of Blue Book, a search engine company. Soon afterward Caleb realizes he’s participating in an experiment to interact with the world’s first real artificially intelligent robot.
In this British science fiction, AI does not want world domination but simply wants the same civil rights as humans.
The Machine is an Indie-British film centered around two artificial intelligence engineers who come together to create the first-ever, self-aware artificial intelligence machines. These machines are created for the Ministry of Defense.
The Government intends to create a lethal soldier for war. The cyborg told its designer, “I’m a part of the new world and you’re part of the old.” this chilling statement gives you an idea of what is to come next.
Transcendence is a story about a brilliant researcher in the field of Artificial Intelligence, Dr. Will Caster, played by Johnny Depp. He’s working on a project to create a conscious machine that combines the collective intelligence of everything along with the full range of human emotions.
Dr. Caster has gained fame due to his ambitious project and controversial experiments. He’s also become a target for anti-technology extremists who are willing to do anything to stop him.
However, Dr. Caster becomes more determined to accomplish his ambitious goals and achieve the ultimate power. His wife Evelyn and best friend Max are concerned with Will’s unstoppable appetite for knowledge which is evolving into a terrifying quest for power.
A.I. Artificial Intelligence is a science fiction drama directed by Steven Spielberg. The story takes us to the not-so-distant future where ocean waters are rising due to global warming and most coastal cities are flooded. Humans move to the interior of the continents and keep advancing their technology.
One of the newest creations is realistic robots known as “Mechas”. Mechas are humanoid robots, very complex but lack emotions. This changes when David, a prototype Mecha child capable of experiencing love, is developed. He is given to Henry and his wife Monica, whose son contracted a rare disease and has been placed in cryostasis.
David is providing all the love and support for his new family, but things get complicated when Monica’s real son returns home after a cure is discovered. The film explores every possible emotional interaction humans could have with an emotionally capable A.I. technology.
Billy Beane, played by Brad Pitt, and his assistant, Peter Brand (Jonah Hill), are faced with the challenge of building a winning team for the Major League Baseball’s Oakland Athletics’ 2002 season with a limited budget.
To overcome this challenge Billy uses Brand’s computer-generated statistical analysis to analyze and score players’ potential and assemble a highly competitive team. Using historical data and predictive modeling they manage to create a playoff-bound MLB team with a limited budget.
The 2011 American drama film written and directed by J.C. Chandor is based on the events of the 2007-08 global financial crises. The story takes place over a 24-hour period at a large Wall Street investment bank.
One of the junior risk analysts discovers a major flaw in the risk models which has led their firm to invest in the wrong things, winding up on the brink of financial disaster. A seemingly simple error is in fact affecting millions of lives. This is not only limited to the financial world.
An economic crisis like this caused by flawed behavior between humans and machines can have trickle-down effects on ordinary people. Technology doesn’t exist in a bubble, it affects everyone around it and spreads exponentially. Margin Call explores the impact of technology and data science on our lives.
Ben Campbell, a mathematics student at MIT, is accepted at the prestigious Harvard Medical School but he’s unable to afford the $300,000 tuition. One of his professors at MIT, Micky Rosa (Kevin Spacey), asks him to join his blackjack team consisting of five other fellow students.
Ben accepts the offer to win enough cash to pay his Harvard tuition. They fly to Las Vegas over the weekend to win millions of dollars using numbers, codes, and hand signals. This movie gives insights into Newton’s method and Fibonacci numbers from the perspective of six brilliant students and their professors.
Thanks for reading we hope you will enjoy our recommendations on data science-based movies. Also, check out the 18 Best Data Science Podcasts.
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Written by Muhammad Bilal Awan
