Neural Networks: Why do we care and what are they?

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Neural Networks, among similarly high tech sci-fi sounding terms are used more and more commonly in the articles around the Internet.

In this article I am attempting to:

• Give a few examples why would we care about this technology at all.
• Demystify the terminologies like Neural Networks, Artificial Intelligence, Machine Learning and Deep Learning.
• Classify them with simple terms, where they belong and how do they relate to each other.

Let's have a quick overview about the current state of the technology:

Amazon Go
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Last Monday, Amazon opened Amazon Go, a convenience store at Seattle. Their selling point focuses on cashier-less and cashier line-less experience, to greatly speed up the whole shopping process. You enter the store by launching their app and scanning the displayed QR code at the gate. When you walk out from the store, all the bought items will be charged to your Amazon account after a few moments.

The magic of this technology is in the store. They've installed hundreds of cameras at the celling, so they can track and process every item's position, whenever you pick them up or put them back. Behind this technology is a heavy processing power and a machine learning algorithm that can track and understand what happens at the store at any moment.

Amazon used similar machine learning technologies to suggest relevant product for potential customers, based on their previous buying or browsing behaviors. This approach made Amazon the number 1 e-commerce retailer in the world.

Twitter
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Project Veritas, an undercover journalist activist group presented to the public that Twitter is perhaps using machine learning algorithms that can suppress articles, stories, tweets with certain political views and promote ones that are different kind of political views. On similar idea, Facebook announced that it will battle the so called "fake news" stories and will suppress them from our feed, preventing them from spreading around.

YouTube
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YouTube is using its own machine learning technology implementation, called Content ID, to scan the content of every user's uploaded videos and find the ones that are breaking their Terms of Services and Copyright laws. By the way, Google is using machine learning for almost all of their services. For search results, speech recognition, translation, maps, etc., with great success.

Self-Driving Cars
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Self driving cars is another emerging market for Artificial Intelligence, large number of companies are pushing out their own version of self-driving algorithms, so they can save time and money for many people and companies around the world. Tesla, BMW, Volvo, GM, Ford, Nissan, Toyota, Google, even Apple is working on their solutions and most of them aims to be street ready around 2020-2021.

Targeting ads using ultrasound + microphone
Targeting ads generally is a huge field nowadays and every ad company is trying to introduce more and more creative approaches to get ahead of the competition. One less known idea lays around the fact that the installed application can access most of the mobile phone hardware, so theoretically they can easily listen to microphone input signals. Retail stores can emit ultrasound signals from certain products and if that signal gets picked up by the app (for instance the person spend more than a few seconds in from of a certain item), it can automatically report to ad companies that the user was interested about the product, so a little extra push, in a form of carefully targeted ad may cause the person decide to buy it.

Blizzard
Blizzard announced that they may ban Overwatch players for "toxic comments" on social media, like YouTube, Facebook and similar places. Gathering and processing data this size, also making the required connections between them certainly needs their own machine learning strategies and processing power.

Facebook
Facebook patented a technology that allows to track dust or fingerprints smudges on camera lenses, this way the image recognition algorithms can recognize if any of the presented pictures are made with the same camera or not. They claimed that they never put this patented technology in use, but nevertheless it’s a great idea, with many different application possibilities from development perspective.

Boston Dynamic
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Boston Dynamic is one of the leaders in robotics by building one of the most advanced ones on earth. They are using efficient machine learning technologies to teach their robots for doing certain tasks and overcoming certain problems.

Ok… Artificial Intelligence, Machine Learning, and Neural Networks ... what do they exactly mean and how do these terms relate to each other?

We learned that these technologies are popping out almost everywhere and becoming more and more relevant to our normal days in every aspects that we do, aiding or controlling our lives in one way or another. Reading all these “buzzwords” in technical articles around the Internet, you probably noticed that many of these terms are used interchangeably, or without any explanatory context. So let’s demystify their meaning and let’s properly categorize them for future references.

First of all, let’s clear their meaning:

Artificial Intelligence, or AI has the broadest meaning of all the three mentioned.

It usually attempts to mimic "cognitive" functions in humans and other beings, for example learning, adapting, judgment, evaluation, logic, problem solving.

Generally speaking, an AI usually does:

• Learn - by observing, sensing, or any ways that it can gather data.
• Process - by logic, adapting, evaluating, or judging the data.
• Apply - by solving the given problem.
   

AI can be as simple for instance, as the ghosts in Pacman. Each have their own objectives and each tries to accomplish them by observing the player's behavior and position, so they can process that data and react upon it.

AI can be a chess player that tries to outsmart a human player.

AI can also be a search engine that gives you more relevant results to any of your search terms than any human could ever do, given the amount of constantly changing data and human behavior around the whole Internet.

Machine Learning or ML, has again many implementation and a fairly broad meaning.

Usually we can generalize the ideas behind it by stating: Machine Learning is subset of Computer Science, and its objective is to create systems that are programmed and controlled by the processed data, rather than specifically instructed by human programmers. In other words, Machine Learning algorithms are attempting to program themselves, rather than relying on human programmers to do so.

Neural Networks, or more accurately referred as Artificial Neural Networks, are a subset of Computer Science, and their objective is to create systems that resembles natural neural networks, like our human brains for instance, so they can produce similar cognitive capabilities. Again, there are many implementation of this idea, but generally it’s based on the model of artificial neurons spread across at least three, or more layers.

We will get into the details of the "how exactly" in the next article.

Neural network are great approach to identify non-linear patterns (for linear patterns, classical computing is better). Patterns where there is no clear one-to-one relation between the output and the input values. Neural networks are also excellent for approximations.

We also hear a lot about Deep Learning and that is just one, more complex implementation on the idea of Neural Networks involving much more layers. All that can create much greater level of abstraction than we normally would use for simpler tasks. Think of the complexity required for image recognition, search engines, translations.

We learned now the general meanings behind these few terms, but how do they relate to each other then?

Artificial Intelligence has been around quite some time now, and some implementations of Machine Learning is used to create much more efficient Artificial Intelligences that just wasn't a possibility before. Following this combining idea, Machine Learning is using the technologies of Neural Networks to implement its learning algorithms.

So as we can see, all of these technologies can function and work by themselves, but also they can be combined with each other to create more efficient solutions to certain problems. Most of the times however the latter is the case nowadays. All of the mentioned three technologies are combined and used together, as the currently most efficient and effective solution to the given problems: our currently most advanced versions of Artificial Intelligences are created with a Machine Learning algorithms that are using Neural Networks as their learning and data processing mechanism.
 
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In summary:

• We were given a few examples why would we care about this technology at all?
• We demystified the terminologies like Neural Networks, Artificial Intelligence, Machine Learning and Deep Learning.
• We classified them with simple terms, explained where they belong, and how do they relate to each other.

In next articles I will explain in simplified steps how Neural Networks work, and will provide a programming example that any of the readers could implement and try out themselves as well. Furthermore, I will talk about the relations and differences between Artificial Neural Networks and Natural Neural Networks (our human brain, for example). I will talk about the concept of consciousness, as a natural question that tipically follows these ideas.