Artificial Intelligence Vs Machine Learning — Understanding the Differences
Unfortunately, a lot of tech companies are deceiving customers by proclaiming to have true AI capabilities or the use of AI on their technologies, whilst not being clear about their products’ limits. Let’s try and demystify AI for you on this article.
In the recently published The State of AI 2019 Divergence Report by MMC Ventures & Barclays UK Ventures, highlights the issue of misuse from companies claiming to use Artificial Intelligence on their products and services. According to the Verge, 40% of European start-ups that claimed to use AI don’t actually use the technology and this number is far greater in Sri Lanka and the South Asian region. Last year, TechTalks, stated such misuse by companies claiming to use Machine Learning and advanced Artificial Intelligence to gather and examine thousands of users’ data to enhance user experience in their products and services.
Unfortunately, there’s much confusion surrounding Artificial Intelligence and Machine Learning — be it within organisations (from the strategic leaders to the staff), Governments and its officials, the public and the media. Often people refer to AI and Machine Learning as synonyms and use them interchangeably, some use them as separate, parallel technologies (telling the users that AI technology is being used to enhance customer experience, instead using AI as an excuse to collect data for personal gain), whilst most others are taking advantage of the trend to create hype and excitement, as to increase sales and revenue by advertising these technological buzzwords surrounding us today (for example, “AI”, “Machine Learning”, “Computer Vision”, “Deep Learning”, and “Neural Networks” etc.).
A few months ago, I came across a Head of IT, from a reputed Insurance firm — a fully owned subsidiary of a large conglomerate in Sri Lanka — who had been convinced that a simulated chat interface, (mimicking a chatbot, with none of the true capabilities of an AI chatbots, and coded entirely in Java Script) is a chatbot. He has in turn informed his CEO and management team that the organisation’s website now contains an AI chatbot.
If a Head of IT is so gullible and doesn’t know how AI works and what an actual chatbot (that is AI and Machine Learning powered, with Natural Language Processing and Natural Language Understanding) is, how can a non-technical person know for sure that they are being sold the real deal?
More recently, a tech company in the Sri Lanka, released a video on LinkedIn claiming to have created an AI-powered feedback generator on happiness using human emotions. However, this solution was just scanning a QR code and sending an emoji. That is not AI — Real AI would use facial cues to recognise emotion through Facial Recognition and Computer Vision — you can try it out on Vevro’s website — type cool features.
(I will not name these companies to avoid shaming them. As for the tech company I believe some of their solutions are pretty cool, even if they’re marketing themselves in a very deceptive way for some of their products and capabilities).
So today, I’ll (try to) discern the differences between Artificial Intelligence and Machine Learning to help you recognise fact from fiction where AI is concerned.
We Know Machine Learning
We’ll start with Machine Learning, which is the easier part of the AI vs ML equation. Machine Learning is a subset of Artificial Intelligence, just one of the many ways you can perform AI. Machine Learning relies on defining behavioural rules by examining and comparing large data sets to find common patterns. This is an approach that is especially efficient for solving classification problems.
For instance, if you provide a Machine Learning program with your choice of movies and videos along with their corresponding genres, it will be able to automate and assist (depending on the Machine Learning model of the programme) in generating a recommender system as to suggest what to watch next. Real life experience of this is already given by streaming platforms such as Netflix and YouTube.
Similar algorithms and programmes can be used in a variety of other day to day activities too. For example, if you provide a Machine Learning program with a lot of x-ray images and their corresponding symptoms, it will be able to assist (or possibly automate) the analysis of x-ray images in the future. The Machine Learning application will compare all those different images and find what are the common patterns found in images that have been labelled with similar symptoms. And when you provide it with new images it will compare its contents with the patterns it has gleaned and tell you how likely the images contain any of the symptoms it has studied before.
This type of Machine Learning is called “supervised learning,” where an algorithm trains on human-labelled data. Unsupervised learning, another type of ML, relies on giving the algorithm unlabelled data and letting it find patterns by itself. For instance, you provide an ML algorithm with a constant stream of network traffic and let it learn by itself what is the baseline, normal network activity and what are the outlier and possibly malicious behaviour happening on the network.
Reinforcement learning, the third popular type of Machine Learning algorithm, relies on providing an ML algorithm with a set of rules and constraints and let it learn by itself how to best achieve its goals. Reinforcement learning usually involves a sort of reward, such as scoring points in a game or reducing electricity consumption in a facility. The ML algorithm tries its best to maximize its rewards within the constraints provided. Reinforcement learning is famous in teaching AI algorithms to play different games such as Go, Poker, Chess, StarCraft and Dota.
Machine Learning is fascinating, especially it’s more advanced subsets such as Deep Learning and neural networks. But it’s not magic, even if we sometimes have problem discerning its inner workings. At its heart, ML is the study of data to classify information or to predict future trends. In fact, while many like to compare Deep Learning and neural networks to the way the human brain works, there are huge differences between the two.
Bottom line: We know what Machine Learning is. It’s a subset of Artificial Intelligence. We also know what it can and can’t do.
Everybody is yet to find out the exact capabilities of AI
Anybody who is claiming to know all about AI is lying. The term “Artificial Intelligence” is very broad in scope. According to Andrew Moore, Dean of Computer Science at Carnegie Mellon University, “Artificial Intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence.”
This is one of the best ways to define AI in a single sentence, but it still shows how broad and vague the field is. For instance, “until recently” is something that changes with time. Several decades ago, a pocket calculator would be considered AI, because calculation was something that only the human brain could perform. Today, the calculator is one of the dumbest applications we find on every computer, phone and even wearables like watches (not necessarily on the smart ones either).
As Zachary Lipton, the editor of Approximately Correct explains, the term AI “is aspirational, a moving target based on those capabilities that humans possess but which machines do not.”
AI also encompasses a lot of technologies that we know. Machine Learning is just one of them. Earlier works of AI used other methods such as good old-fashioned AI (GOFAI), which is the same if-then rules that we use in other applications. Other methods include A*, fuzzy logic, expert systems and a lot more. Deep Blue, the AI that defeated the world’s chess champion Gary Kasparov in 1997, used a method called tree search algorithms to evaluate millions of moves at every turn.
A lot of the references made to AI pertain to general AI, or human-level intelligence. That is the kind of technology you see in sci-fi movies such as Matrix, Mission Impossible or 2001: A Space Odyssey. But we still don’t know how to create Artificial Intelligence that is on par with the human mind. That being said, Deep Learning, the most advanced type of AI, can rival the mind of a human child, and some aspects of an adult mind. It is perfect for narrow tasks, not general, abstract decisions, which isn’t a bad thing at all.
AI, as we know it today is symbolized by Siri and Alexa, by the freakishly precise movie and video recommendation systems that power Netflix and YouTube, by the algorithms hedge funds use to make micro-trades that rake in millions of dollars every year. These technologies are becoming increasingly important in our daily lives. In fact, they are the augmented intelligence technologies that enhance our abilities and making us more productive.
Bottom line: Unlike Machine Learning, AI is a moving target, and its definition changes as its related technologies become more advanced. What is an isn’t AI can easily be contested, unlike Machine Learning which, is very clear-cut in its definition. Maybe in a few decades, today’s cutting-edge AI technologies will be considered as dumb and dull as calculators are to us right now and our great grandchildren or maybe even our grandchildren, might be fascinated about the era where the cars had to be driven by humans.
So, if we go back to the examples mentioned at the beginning of the article, what does “Machine Learning and advanced AI” actually mean? After all, aren’t Machine Learning and Deep Learning the most advanced AI technologies currently available? And what does “AI-powered predictive analytics” mean? Doesn’t predictive analytics use Machine Learning, which is a branch of AI anyway?
Why do tech companies like to use AI and ML interchangeably?
Since the term “artificial intelligence” was coined by John McCarthy a Maths Professor at Dartmouth in 1955, the industry has gone through many ups and downs. In the early decades, there was a lot of hype surrounding the industry, and many scientists promised that human-level AI was just around the corner. But undelivered promises caused a general disenchantment with the industry and led to the “AI winter”, a period where funding and interest in the field subsided considerably.
Afterwards, companies tried to dissociate themselves with the term AI, which had become synonymous with unsubstantiated hype, and used other terms to refer to their work. For instance, IBM described Deep Blue as a supercomputer and explicitly stated that it did not use artificial intelligence, whilst technically it did.
During this period, other terms such as Big Data, Predictive Analytics and Machine Learning started gaining traction and popularity. In 2012, Machine Learning, Deep Learning and Neural Networks made great strides and started being used in an increasing number of fields. Companies suddenly started to use the terms Machine Learning and Deep Learning to market their products.
Deep Learning started to perform tasks that were impossible to do with rule-based programming. Fields such as speech and face recognition, image classification and Natural Language Processing (the ability of a computer program to understand human language as it is spoken), which were at very crude stages, suddenly took great leaps.
And that is perhaps why we’re seeing a shift back to AI. For those who had been used to the limits of old-fashioned software, the effects of deep learning almost seemed magic, especially since some of the fields that neural networks and deep learning are entering were considered off limits for computers. Machine learning and deep learning engineers are even being employed at non-profits, which speaks to how hot the field is.
Add to that the misguided description of Neural Networks, which, claim that the structure mimics the working of the human brain, and you suddenly have the feeling that we’re moving toward Artificial General Intelligence again. Many scientists including Nick Bostrom and Elon Musk, started warning against an apocalyptic near-future, where super intelligent computers drive humans into slavery and extinction. Fears of technological unemployment resurfaced.
However, all these elements have helped reignite the excitement and hype surrounding Artificial Intelligence. Therefore, sales departments find it more profitable to use the vague term AI, which has a lot of baggage and exudes a mystic aura, instead of being more specific about what kind of technologies they employ. This helps them oversell or re-market the capabilities of their products without being clear about their limits.
Meanwhile, the “advanced Artificial Intelligence” that these companies claim to use is usually a variant of Machine Learning or some other known technology.
Unfortunately, this is something that tech publications often report without deep scrutiny, and they often accompany AI articles with images of crystal balls, and other magical representations. This will help those companies generate hype around their offerings. But down the road, as they fail to meet the expectations, they and their consumers are forced to hire more humans to make up for the shortcomings of their so-called AI. In the end, they end up causing mistrust in the field and not-to-mention set very low-price points from which, companies who has the real capabilities suffer for the sake of short-lived gains by these pretenders.
And there it is folks. Hopefully, I have helped you gain more understanding into AI, Machine Learning and the difference between the two.
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