What is Machine Learning(ML), Artificial Intelligence(AI) & Deep Learning(DL)? And what are it’s Uses cases?
What is Machine Learning(ML)?
Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect.
Machine Learning and Artificial Intelligence Timeline:
Machine Learning Methods :
Supervised machine learning
Supervised machine learning trains itself on a labeled data set. That is, the data is labeled with information that the machine learning model is being built to determine and that may even be classified in ways the model is supposed to classify data. For example, a computer vision model designed to identify purebred German Shepherd dogs might be trained on a data set of various labeled dog images.
Supervised machine learning requires less training data than other machine learning methods and makes training easier because the results of the model can be compared to actual labeled results. But, properly labeled data is expensive to prepare, and there’s the danger of overfitting, or creating a model so closely tied and biased to the training data that it doesn’t handle variations in new data accurately.
Unsupervised machine learning
Unsupervised machine learning ingests unlabeled data — lots and lots of it — and uses algorithms to extract meaningful features needed to label, sort, and classify the data in real-time, without human intervention. Unsupervised learning is less about automating decisions and predictions, and more about identifying patterns and relationships in data that humans would miss. Take spam detection, for example — people generate more email than a team of data scientists could ever hope to label or classify in their lifetimes. An unsupervised learning algorithm can analyze huge volumes of emails and uncover the features and patterns that indicate spam (and keep getting better at flagging spam over time).
Semi-supervised learning offers a happy medium between supervised and unsupervised learning. During training, it uses a smaller labeled data set to guide classification and feature extraction from a larger, unlabeled data set. Semi-supervised learning can solve the problem of having not enough labeled data (or not being able to afford to label enough data) to train a supervised learning algorithm.
Reinforcement machine learning
Reinforcement machine learning is a behavioral machine learning model that is similar to supervised learning, but the algorithm isn’t trained using sample data. This model learns as it goes by using trial and error. A sequence of successful outcomes will be reinforced to develop the best recommendation or policy for a given problem.
Deep learning is a subset of machine learning (all deep learning is machine learning, but not all machine learning is deep learning). Deep learning algorithms define an artificial neural network that is designed to learn the way the human brain learns. Deep learning models require large amounts of data that pass through multiple layers of calculations, applying weights and biases in each successive layer to continually adjust and improve the outcomes.
How Machine Learning Works?
So how do machines learn?
The answer is, from data. In today’s world, we create huge volumes of data as we go about our everyday lives. From the text messages, emails, and social media posts we send to the photographs and videos we take on our phones, we generate massive amounts of information. More data still is created by millions of sensors in our homes, cars, cities, public transport infrastructure, and factories.
Data scientists can use all of that data to train machine learning models that can make predictions and inferences based on the relationships they find in the data.
For example, suppose an environmental conservation organization wants volunteers to identify and catalog different species of wildflower using a phone app. The following animation shows how machine learning can be used to enable this scenario.
- A team of botanists and data scientists collects samples of wildflowers.
- The team labels the samples with the correct species.
- The labeled data is processed using an algorithm that finds relationships between the features of the samples and the labeled species.
- The results of the algorithm are encapsulated in a model.
- When new samples are found by volunteers, the model can identify the correct species label.
Real-world Machine Learning Use Cases :
As noted at the outset, machine learning is everywhere. Here are just a few examples of machine learning you might encounter every day:
- Digital assistants: Apple Siri, Amazon Alexa, Google Assistant, and other digital assistants are powered by natural language processing (NLP), a machine learning application that enables computers to process text and voice data and ‘understand’ human language the way people do. Natural language processing also drives voice-driven applications like GPS and speech recognition (speech-to-text) software.
- Recommendations: Deep learning models drive ‘people also liked’ and ‘just for you’ recommendations offered by Amazon, Netflix, Spotify, and other retail, entertainment, travel, job search, and news services.
- Contextual online advertising: Machine learning and deep learning models can evaluate the content of a web page — not only the topic, but nuances like the author’s opinion or attitude — and serve up advertisements tailored to the visitor’s interests.
- Chatbots: Chatbots can use a combination of pattern recognition, natural language processing, and deep neural networks to interpret input text and provide suitable responses.
- Fraud detection: Machine learning regression and classification models have replaced rules-based fraud detection systems, which have a high number of false positives when flagging stolen credit card use and are rarely successful at detecting criminal use of stolen or compromised financial data.
- Cybersecurity: Machine learning can extract intelligence from incident reports, alerts, blog posts, and more to identify potential threats, advise security analysts, and accelerate response.
- Medical image analysis: The types and volume of digital medical imaging data have exploded, leading to more available information for supporting diagnoses but also more opportunity for human error in reading the data. Convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other deep learning models have proven increasingly successful at extracting features and information from medical images to help support accurate diagnoses.
- Self-driving cars: Self-driving cars require a machine learning tour de force — they must continuously identify objects in the environment around the car, predict how they will change or move, and guide the car around the objects as well as toward the driver’s destination. Virtually every form of machine learning and deep learning algorithm mentioned above plays some role in enabling a self-driving automobile.
What is Artificial Intelligence(AI)?
In computer science, the term Artificial Intelligence (AI) refers to any human-like intelligence exhibited by a computer, robot, or other machine. In popular usage, artificial intelligence refers to the ability of a computer or machine to mimic the capabilities of the human mind — learning from examples and experience, recognizing objects, understanding and responding to language, making decisions, solving problems — and combining these and other capabilities to perform functions a human might perform, such as greeting a hotel guest or driving a car.
After decades of being relegated to science fiction, today, AI is part of our everyday lives. The surge in AI development is made possible by the sudden availability of large amounts of data and the corresponding development and wide availability of computer systems that can process all that data faster and more accurately than humans can. AI is completing our words as we type them, providing driving directions when we ask, vacuuming our floors, and recommending what we should buy or binge-watch next. And it’s driving applications — such as medical image analysis — that help skilled professionals do important work faster and with greater success.
Artificial intelligence, Machine learning and Deep learning
The easiest way to understand the relationship between artificial intelligence (AI), machine learning, and deep learning is as follows:
- Think of Artificial Intelligence as the entire universe of computing technology that exhibits anything remotely resembling human intelligence. AI systems can include anything from an expert system — a problem-solving application that makes decisions based on complex rules or if/then logic — to something like the equivalent of the fictional Pixar character Wall-E, a computer that develops the intelligence, free will, and emotions of a human being.
- Machine learning is a subset of AI application that learns by itself. It actually reprograms itself, as it digests more data, to perform the specific task it’s designed to perform with increasingly greater accuracy.
- Deep learning is a subset of machine learning application that teaches itself to perform a specific task with increasingly greater accuracy, without human intervention.
Types of Artificial Intelligence — Weak AI vs. Strong AI
Weak AI — also called Narrow AI or Artificial Narrow Intelligence (ANI) — is AI trained and focused to perform specific tasks. Weak AI drives most of the AI that surrounds us today. ‘Narrow’ is a more accurate descriptor for this AI, because it is anything but weak; it enables some very impressive applications, including Apple’s Siri and Amazon’s Alexa, the IBM Watson computer that vanquished human competitors on Jeopardy, and self-driving cars.
Strong AI, also called Artificial General Intelligence (AGI), is AI that more fully replicates the autonomy of the human brain — AI that can solve many types or classes of problems and even choose the problems it wants to solve without human intervention. Strong AI is still entirely theoretical, with no practical examples in use today. But that doesn’t mean AI researchers aren’t also exploring (warily) artificial super intelligence (ASI), which is artificial intelligence superior to human intelligence or ability. An example of ASI might be HAL, the superhuman (and eventually rogue) computer assistant in 2001: A Space Odyssey.
Artificial Intelligence Applications
As noted earlier, artificial intelligence is everywhere today, but some of it has been around for longer than you think. Here are just a few of the most common examples:
- Speech recognition: Also called speech to text (STT), speech recognition is AI technology that recognizes spoken words and converts them to digitized text. Speech recognition is the capability that drives computer dictation software, TV voice remotes, voice-enabled text messaging and GPS, and voice-driven phone answering menus.
- Natural language processing (NLP): NLP enables a software application, computer, or machine to understand, interpret, and generate human text. NLP is the AI behind digital assistants (such as the aforementioned Siri and Alexa), chatbots, and other text-based virtual assistance. Some NLP uses sentiment analysis to detect the mood, attitude, or other subjective qualities in language.
- Image recognition (computer vision or machine vision): AI technology that can identify and classify objects, people, writing, and even actions within still or moving images. Typically driven by deep neural networks, image recognition is used for fingerprint ID systems, mobile check deposit apps, video and medical image analysis, self-driving cars, and much more.
- Real-time recommendations: Retail and entertainment web sites use neural networks to recommend additional purchases or media likely to appeal to a customer based on the customer’s past activity, the past activity of other customers, and myriad other factors, including time of day and the weather. Research has found that online recommendations can increase sales anywhere from 5% to 30%.
- Virus and spam prevention: Once driven by rule-based expert systems, today’s virus and spam detection software employs deep neural networks that can learn to detect new types of virus and spam as quickly as cybercriminals can dream them up.
- Automated stock trading: Designed to optimize stock portfolios, AI-driven high-frequency trading platforms make thousands or even millions of trades per day without human intervention.
- Ride-share services: Uber, Lyft, and other ride-share services use artificial intelligence to match up passengers with drivers to minimize wait times and detours, provide reliable ETAs, and even eliminate the need for surge pricing during high-traffic periods.
- Household robots: iRobot’s Roomba vacuum uses artificial intelligence to determine the size of a room, identify and avoid obstacles, and learn the most efficient route for vacuuming a floor. Similar technology drives robotic lawn mowers and pool cleaners.
- Autopilot technology: This has been flying commercial and military aircraft for decades. Today, autopilot uses a combination of sensors, GPS technology, image recognition, collision avoidance technology, robotics, and natural language processing to guide an aircraft safely through the skies and update the human pilots as needed. Depending on who you ask, today’s commercial pilots spend as little as three and a half minutes manually piloting a flight.
👉🏻 Here is my list of the top 10 AI companies that have the power and resources to shape our connected future. These are the big players in artificial intelligence. 😬😯😯
Use Case of AI in Microsoft
Microsoft is involved in Artificial Intelligence on both the consumer and business sides. Cortana, Microsoft’s AI digital assistant, is in direct competition with Alexa, Siri, and Google Assistant. Artificial Intelligence features are a large part of the company’s Azure Cloud service, which provides chatbots and machine learning services to some of the biggest names in the business. Microsoft also purchased five AI companies in 2018 alone.
⭕ How Office 365 and Azure spot hackers trying to break into accounts, how Cortana can recognize what you’re saying, how Kinect can detect the position of your fingers or the joints of your skeleton from an infrared image. It’s also why the keyboard on Windows Phone is so accurate: Data derived from thousands of people correcting mistakes on their phones enables the software to guess which letter you’re going to type next and make that key (invisibly) bigger.
⭕ Machine learning technique makes it easier to touch the right menu on a Windows tablet with your finger and helps OneNote figure out your handwriting. Launch an app in Windows 8, and three-quarters of the time it opens almost instantly, thanks to machine learning that tells the system which apps to preload into memory because you’re going to need them.
⭕ Machine learning takes enormous amounts of data — whether it’s a server log, a stream of information from sensors or a huge collection of images, videos, or audio recordings — and merges it into a system that’s better at handling complex situations than any algorithm. The idea has been around for 50 years, but as more and more data becomes available, machine learning has become increasingly useful, going from academic research to powering breakthroughs like usable voice recognition.
“I honestly can’t think of any recent product development that Microsoft has been involved in that hasn’t involved machine learning. Everything we do now is influenced, one way or another, by machine learning.”, says Microsoft’s director of research, Peter Lee.
Take the recent Microsoft Band, the flagship device for Microsoft’s new Health platform. “We wanted to get the blood flow sensor to provide accurate readings even under extreme athletic duress like rowing,” Lee explains (the vice president who approved the project is an avid rower). “It’s a very low-cost sensor; just to interpret the reading from the sensor, we’ve found machine learning is the only practical approach to doing that.”
⭕ Voice recognition used to mean training your computer to learn your voice, or sticking to a few simple commands; now it means you can buy a new phone and start talking to it — and Windows 10 will bring that to your PC.
⭕ Image recognition has gone from spotting when there’s a face in a photograph to coping with everything from text to traffic signals. The ImageNet benchmark tests identifying photos of a thousand objects, like recognizing not only pictures of 150 different dogs but also their breeds
A team of Microsoft researchers in the Beijing lab announced that their deep learning system was the first to beat untrained humans on the benchmark (narrowly beating Google to the achievement).
That’s all thanks to deep learning. It’s one of the fastest-moving areas in AI today; the pioneers of deep learning work at Google, at Facebook, at Baidu — and at Microsoft.
♦️ Microsoft’s big machine learning future 🙌🏻
CEO Satya Nadella called out machine learning — and the big data that powers it — as a key development in his memo to Microsoft last July. “Billions of sensors, screens, and devices — in conference rooms, living rooms, cities, cars, phones, PCs — are forming a vast network and streams of data that simply disappear into the background of our lives. This computing power will digitize nearly everything around us and will derive insights from all of the data being generated by interactions among people and between people and machines. We are moving from a world where computing power was scarce to a place where it now is almost limitless, and where the true scarce commodity is increasingly human attention.”
Whether or not Microsoft makes more fundamental breakthroughs in AI, what it learns about using machine learning will carry on showing up in all the products you use — including ones you build yourself.
The way big companies defend their computer systems against attacks — hackers trying to penetrate their networks, malware that intrudes their email systems and web browsers, and more. “Every one of those things today are most efficiently detected in real-time and automatically, using machine learning algorithms”
How Does Google Uses AI and ML?
In modern times, Google is everywhere!!! So much so that you are most probably reading this article using Google Search. And while Machine Learning has long been a part of Google, now it seems that ML is everywhere! From Google Search to Google Photos to even Google Translate, everything uses Machine Learning.
And these are only the more common items! In fact, Google and its parent company Alphabet are heavily invested in Machine Learning Research in almost all imaginable fields like Ethical Principles, Quantum Computing, Healthcare, Robotics, Perception, etc. Sundar Pichai, the CEO of Google commented that “Machine learning is a core, transformative way by which we’re rethinking how we’re doing everything. We are thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. And we’re in early days, but you will see us apply machine learning in all these areas.”
So it is obvious that Google eventually plans on fully integrating Machine Learning in all its operations. But that futuristic world is still a little far away! For now, let’s see some of the ways in which Google currently uses Machine Learning so that we can understand the full scope of its applications in the future.
1. Google Translate
Want to translate a text from English to Hindi but don’t know Hindi? Well, Google Translate is the tool for you! While it’s not exactly 100% accurate, it is still a great tool to convert text, images, or even real-time video from one language to another. And in case you wonder how it translates more or less accurately, well Google Translate uses Machine Learning of course!
It uses Statistical machine translation (SMT) which is a fancy way of saying that it analyses millions of documents that are already translated from one language to another (English to Hindi in this case) and then looks for the common patterns and basic vocabulary of the language. After that, it picks the most accurate translation possible based on educated guesses that mostly turn out to be correct. For Example: Let’s see how Google Translate translates “Machine Learning is cool” into Hindi!!!
2. Google Photos
In case you are a millennial, I am sure you are a selfie addict! And of course, you use Google Photos a lot if you are an Android user as well. And it’s no shock that you do! Google Photos allows you to back up all your photos in a single location even if they were shot from multiple devices and it also offers lots of other cool effects using Machine Learning.
For Example, Google Photos also automatically creates albums of photos taken during a specific period without any input from you. And that’s not all, it can also select the “best photos”. And in case you haven’t sorted all your pictures into albums, you can also search for them by typing in names. Suppose you want to find a picture with your dog, type in “Dog” and you will get all the dog pictures! This is done using Image Recognition, wherein Deep Learning is used to sort millions of images on the internet in order to classify them more accurately. So using Deep Learning, the images that are classified as “Dog” in your Google Photos are displayed.
Suppose you want to know who is the CEO of Google? And then you want to know who is his wife? But how do you search this on Google? You cannot exactly write the name of Sundar Pichai or his wife since you don’t know it! In this case, you can simply search “CEO of google wife” on Google and you will get the required results. This is achieved using RankBrain in Google Search.
RankBrain is basically a deep neural network that is helpful in providing the required search results. It is one of the factors in the Google Search algorithm that determines which search pages are displayed. In case there are any unique words or phrases on Google Search (like “CEO of google wife” in our case!) then RankBrain makes intelligent guesses to find which search results fit the situation and filter them accordingly. In fact, RankBrain is currently so important that Google says it is its third most important page ranking factor for the results of a search query.
4. Google Assistant
Want a little help in organizing your calendar? Want to know the best Italian restaurants near your home? Want to book movie tickets on the go? Well, never fear!!! Google Assistant is here to make your life easier! It is basically a personal assistant that is enabled using a combination of Google Knowledge Graph, Image Recognition, and Natural Language Processing.
The Google Assistant is envisioned as a chatbot by Google which can be connected to your phones, TVs, speakers, etc. with the ability to actually have a conversation with you. Here the Google Knowledge Graph provides information gathered from various sources while Natural Language Processing allows the Google Assistant to interact with you and formulate its answers according to your questions.
We all know that humans dream? Well, what if computers dream as well?!! This is the premise of Google DeepDream that used convolutional neural networks to find random patterns in various images and amplifies them in different ways. These images can be tweaked in any possible manner using the input data and various parameters so that the results obtained can be funny, weird or even trippy!!!
There are multiple layers in the neural networks in DeepDream wherein each layer extracts more and more high-level features from the input image until the final output is produced by the end layer. To demonstrate this, we have an image from Google DeepDream that is a weird hybrid of a woman and lots of gears. All in all, it’s very difficult to just explain the complicated effects of DeepDream so its best that you just try it yourself by uploading any image you want and then just watching the show!
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