artificial intelligence

AI in healthcare has improved patient care

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.

Introduction:

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 African states were 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 the Mayo Clinic and the 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 a role in supplying trusted advice urgently.

Want to learn more about predictive analytics? Join our Data Science Bootcamp today.

2. Optimizing clinic performance

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.

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 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).

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 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.

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 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.

Conclusion:

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.

Want to create data science applications with python? checkout our Python for Data Science training.

Artificial Intelligence

Usman Shahid

| June 10

Easily build AI-based chatbots in Python

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.

Read Part 2

Introduction to chatterbot

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.

Training chatterbot

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)

Chatterbot corpus

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.

Chatterbot logic adapters

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.

logic adapters in chatbot — *Working process of logic adapters- How logic adapters work (Source: Chatterbot Knowledgebase)*

Chatterbot storage adapters

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.

Building an AI-based chatbot

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

Importing Dependencies
Instantiating a ChatBot Instance
Training on Chatbot-Corpus Data
Training on Custom Data
Building a front end

Importing dependencies

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 ChatterBotCorpusTrainer

Instantiating chatbots instance

A 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 on chatterbot-corpus data

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")

Training on custom list data

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)

Building a front end

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)

Conclusion

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.

Artificial Intelligence

Saumya Soni

| December 15

Create bird recognition app using Microsoft Custom Vision AI and Power BI

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.

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 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.

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.

Bird recognition using AI — Measure the effect of climate change on birds

Phone application

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.

Conclusion

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:

Artificial Intelligence

Nathan Piccini

| February 20

AI for social good meetup – Key takeaways from the community talk

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.

Event abstract

“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
Detecting disease
Military strikes
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.

Highlights of AI for social good

Artificial Intelligence

Irene Mikhailouskaya

| October 14

AI empowering the education industry – 3 examples you must know about

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.

Examples from an AI-powered education industry

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.

AI-powered examples in education industry — Three examples of AI empowering the education industry

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.

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.

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 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.

Duolingo – A language-learning platform

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).

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.

Artificial Intelligence

Saumya Soni

| December 18

Using custom vision AI and power BI to build a bird recognition app

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.

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.

Azure Custom Vision Work Flow. Source: (https://www.customvision.ai/)

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.
A database of images for training the model.
Enough data to get started.

The Journey of Creating my Custom Vision AI Model

I first visited https://customvision.ai/, then I logged in with the Azure credentials.

Using custom vision AI and power BI to build a bird recognition app | Data Science Dojo — Custom Vision AI Website

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.

text, graphs — Evaluating the model using ‘Quick Test’

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.

Custom Vision AI, Prediction API — Custom Vision AI and the Prediction API

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.

3. Website:

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:

First-round training.
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.

References

Power BI

What is Power BI and what does it do?

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.

Pre-requisites

Here are the Prerequisites:

Power BI Desktop App.
Power BI Pro Account.

The Journey of Creating the Power BI Reports

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.

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.

publishing power BI — 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.

How did I generate the Embed URL and the iframe?

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”.

Embed Public Website — Embed in a Public Website

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.

Embed Report Power BI — Embedding Report Power BI 3

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

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.

To create an app, you start with make.powerapps.com.

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.

Prerequisites for Power Apps Development

· A Microsoft 365 Business Premium Account.

My Power Apps Development Process (Canvas App)

1. I signed in to Power Apps.

power apps interface — Power Apps Interface

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.

skip power apps — ‘Welcome to Power Apps Studio’ interface

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.

Connecting Custom Vision with Power Apps

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.)

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.

How to Consume Power Apps?

Desktop:

The ‘AI for Birds’ Power Apps can be downloaded from this link – AI For Birds Power Apps.
Download the zip file and extract it, open the Power Apps Studio – https://make.powerapps.com/
Sign up with your Microsoft Office 365 account in Power Apps.
Click Create > “Canvas app from blank”.

Creating App Power Apps — Create App Power Apps

5. After specifying the app name > Select “Phone” to be the format > Create.

specify name power apps 1 — Specify Name Power Apps

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.

Power Apps Studio Interface — Welcome to Power Apps Studio

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.

Custom Vision API on Power Apps — Using 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.

Download the Power Apps from here. (For Android | For iOS)
Sign in with your credentials.
Use the App on your mobile phone.

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!

Sources:

Artificial Intelligence

Muhammad Bilal Awan

| April 8

15 spectacular AI, ML, and data science movies | Entertainment and Data

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.

List of data science movies

2001: A Space Odyssey (1968)

A Space Odyssey movie poster-data-science-movie — 2001: A Space Odyssey Movie Poster

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 A.I 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.

Interstellar (2014)

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.

The Imitation Game (2014)

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 Terminator (1984)

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.

Blade Runner 2049 (2017)

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.

I, Robot (2004)

Will Smith stars as Chicago policeman Del Spooner in the year 2035. He is highly suspicious of the A.I 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 15^th 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 the A.I technology and tries to stop the mass uprising.

Minority Report (2002)

Minority Report Movie poster — Minority Report Movie Poster

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 worst 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)

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.

Ex-Machina (2014)

Ex Machina movie poster — Ex-Machina Movie Poster

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, A.I do not want world domination but simply want the same civil rights as humans.

The Machine (2013)

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’s intention is 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 the idea of what is to come next.

Transcendence (2014)

Transcendence movie poster — Transcendence Movie Poster

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 is 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 (2001)

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.

Moneyball (2011)

Money Ball movie poster — Money Ball Movie Poster

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.

Margin Call (2011)

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 at 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.

21 (2008)

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 professor.

Thanks for reading we hope you will enjoy our recommendations on data science-based movies. Also, check out the 18 Best Data Science Podcasts.

Want to learn more about AI, Machine Learning, and Data Science? Check out Data Science Dojo’s online Data Science Bootcamp program!

Data Science

Bootcamps

LLM - Online Courses

Reviews

Consulting

Community

artificial intelligence

AI in healthcare has improved patient care

Introduction:

1. Prediction and spread of diseases

2. Optimizing clinic performance

3. Data science for medical imaging

4. Drug discovery with data science

5. Monitoring patients’ health

6. Virtual assistants in healthcare

Conclusion:

Easily build AI-based chatbots in Python

Introduction to chatterbot

Training chatterbot

Chatterbot corpus

Chatterbot logic adapters

Chatterbot storage adapters

Building an AI-based chatbot

Importing dependencies

Instantiating chatbots instance

Training on chatterbot-corpus data

Training on custom list data

Building a front end

Conclusion

Create bird recognition app using Microsoft Custom Vision AI and Power BI

Global trends of bird species survival chart

Power BI report: Bird attributes / Bird Recognition

AI web app – To create a bird recognition app

Phone application

Conclusion

AI for social good meetup – Key takeaways from the community talk

Event abstract

Highlights of AI for social good

AI empowering the education industry – 3 examples you must know about

Examples from an AI-powered education industry

Jill Watson – A virtual teaching assistant (AI)

Third space learning using AI- An online learning platform

Duolingo – A language-learning platform

Two Sentences – Long findings

Using custom vision AI and power BI to build a bird recognition app

What is Azure custom vision?

What does Azure custom vision do?

How does it work?

Prerequisites to create bird recognition app

The Journey of Creating my Custom Vision AI Model

Using the Model with the Prediction API

How to Experience the Custom Vision API in Power Apps, Mobile Application, & the Website

1. Power Apps:

2. Mobile Application (Android and iOS):

3. Website:

How to Improve your Classifier?

References

Power BI

What is Power BI and what does it do?

Pre-requisites

The Journey of Creating the Power BI Reports

The Process of Publishing the Power BI Report

How did I generate the Embed URL and the iframe?

POWER APPS

What is Power Apps?

Prerequisites for Power Apps Development

My Power Apps Development Process (Canvas App)

Steps to Connect Custom Vision with Power Apps

Here are the steps I followed:

Implementing the Custom Vision into Power Apps:

Publishing My Power App:

How to Consume Power Apps?

Desktop:

How to download Power Apps on your Mobile Devices (Android/iOS):

Sources:

15 spectacular AI, ML, and data science movies | Entertainment and Data

List of data science movies

2001: A Space Odyssey (1968)

Interstellar (2014)

The Imitation Game (2014)

The Terminator (1984)