• After listening to Eric Schmidt’s (Executive Chairman @ Google) speech at the TUM (Technical University of Munich) speaker series, where he spoke majorly about Artificial Intelligence (AI); how it begun, its impact on the present-day life and what to expect from AI in the future. I decided to put together this article to clarify the misconceptions between Artificial intelligence and Machine learning.

    Artificial Intelligence (AI) and Machine Learning (ML) are two interesting hot technology terms right now, and often seem to be used interchangeably. They are not quite the same thing, albeit a bit intertwined but the perception that they are, can lead to some confusion.

    Both terms are often heard when the topic is Big Data Analytics, and are now widespread thanks to the wonderful technologies creatively portrayed on television; sci-fi movies, tv series, documentaries, etc. and the machines that are gradually seeping their way into our lives, e.g. Voice-powered digital assistants (Google Now, Siri and Alexa), Self-driving cars, Navigation systems; Internet search engines and more.


    So, what then is Artificial Intelligence and what is Machine Learning?

    Artificial Intelligence is an area of computer science that emphasizes the creation of intelligent machines that are able to work and react like humans, as well as carrying out tasks in a way that we would consider “smart”.

    Whereas;

    Machine Language is a technology within the sphere of Artificial Intelligence based around the idea that we should just be able to give machines access to data and let them learn for themselves.  We can also say it is the science of getting computers to act without being explicitly programmed.

    Machine Language is a subset of Artificial Intelligence, i.e. All Machine Language counts as Artificial Intelligence but not all Artificial Intelligence counts as Machine Language.
    The pioneering technology within Machine Language mimics (to a very rudimentary level) the pattern recognition abilities of the human brain by processing thousands or even millions of data points.


    Now, let’s talk more AI


     The computer concept of AI has many facets. They range from Neural networks, expert systems, some languages (e.g. LISP and PROLOG), Planning systems (goal based reasoning), to real subfields such as Natural Language processing, Expert systems, Rule-based systems, Blackboard architectures, Image processing/recognition, Cybernetics(robotics), control systems (fuzzy logic) and, relations (semantic nets). Command systems, data/sensor fusion, Bayesian statistics, Discourse production, View-points and focus of attentions, speech understanding (e.g. Hearsay), and more.

    AI has thus become a broad field, involving many disciplines ranging from robotics to machine learning and deep learning.

    Talking of AI subsets; Artificial Intelligence is divided into three (3) main groups.

    1. Natural Language Processing (NLP) – The ability of machines to understand and interpret human language the way it is written or spoken. The objective of NLP is to make machines communicate effectively and as intelligent as human beings in an understanding language i.e. (English, German, French, Chinese, etc.).
    2. Knowledge Representation and Automated Reasoning– This is a field of AI that is dedicated to representing information about the world in a form that a computer system can utilize to solve complex tasks. Basically, this field allows the computer to store complex information, breakdown this information, and then use it to answer queries and infer new facts from existing data.
    3. Machine Learning -  Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning (supported by other facets of AI) has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without even knowing it.

     The Rise of Machine Learning:

    Two important breakthroughs led to the emergence of Machine Learning as the vehicle which is driving Artificial Intelligence development forward with the speed it currently has.

    One of these was the realization – credited to Arthur Samuel in 1959 – that rather than teaching computers everything they need to know about the world and how to carry out tasks, it might be possible to teach them to learn for themselves.

    The second, more recently, was the emergence of the internet, and the huge increase in the amount of digital information being generated, stored, and made available for analysis.

    Once these innovations were in place, engineers realized that rather than teaching computers and machines how to do everything, it would be far more efficient to code them to think like human beings, and then plug them into the internet to give them access to all of the information in the world.


    A Case of Branding?

    Artificial Intelligence – and in particular today Machine Learning certainly has a lot to offer. With its promise of automating mundane tasks as well as offering creative insight, industries in every sector from banking to healthcare and manufacturing are reaping the benefits. So, it’s important to bear in mind that AI and ML are something else … they are products which are being sold – consistently, and lucratively.

    Machine Learning has certainly been seized as an opportunity by marketers. After AI has been around for so long, it’s possible that it started to be seen as something that’s in some way “old hat” even before its potential has ever truly been achieved. There have been a few false starts along the road to the “AI revolution”, and the term Machine Learning certainly gives marketers something new, shiny and, importantly, firmly grounded in the here-and-now, to offer.

    The fact that we will eventually develop human-like AI has often been treated as something of an inevitability by technologists. Certainly, today we are closer than ever and we are moving towards that goal with increasing speed. Much of the exciting progress that we have seen in recent years is thanks to the fundamental changes in how we envisage Artificial Intelligence working, which have been brought about by Machine Learning. One thing is obvious, Machine Learning is a big driving force behind successes in Artificial Intelligence, though, we still have to give recognitions to other subsets of AI which are just as important or in some certain cases are the backbone for an AI application. Because AI in most cases heavily depends on ML, and both are often used together to develop complex and intelligent applications, it is quite understandable why folks around confuse them as being the same when in reality they are not. I hope this piece has helped a few people understand the distinction between AI and ML.  In my next publications, I intend to dig deeper into another hot buzzword – Deep Learning.

    Stay tuned…


    By: Ogunjale Moyosore

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