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A few weeks ago, a friend and colleague (Alex G.) asked me this question. He was aware of recent breakthroughs in the world of Machine Learning, esp. where it came to computer vision. Popular media picked up these relatively recent successes and brought them to the center of attention and then started projecting a future where machines are going to render humans obsolete. So he asked me: “Other than the recent breakthroughs in computer vision, what are the other success stories for machine learning?”

At first I was quite surprised by the question. I have been using ML techniques for several years now and was thoroughly convinced of their usefulness and feasibility, I thought: surely, everyone must see it the same way! But as I started formulating my answer, I was struck by how hard I found it to come up with a satisfying answer. I though it must be one of these brain-freeze moments where you can’t seem to capture the simplest of words or ideas, but at the moment, my own answer sounded unconvincing to my own ears. Being a generous man, he didn’t call me out on my terrible answer, but went on to ask: “… and what is it that makes a particular problem more or less amenable to ML techniques?”

So what is machine learning good for? What problems has it solved for us? In what fields has it proven itself worth the investment and the hype? And should we expect it to succeed in the future at solving more problems in other domains, or is this just naive optimism? Where are we in the hype cycle of “Deep Learning”? Are we heading towards a new AI winter?

I’ll try to make the case that machine learning has already paid off, and that optimism in its future is well warranted (even if it is currently somewhat overhyped).

Spam or ham?

One of the early successes of machine learning was in the domain of text “understanding”, or more precisely, document classification. The simple task of filtering out spam email, without which email would not really be viable and the world as we know today would not exist. Most of the techniques used for document classification don’t actually rely on any kind of language understanding, but rely instead on the occurrence of various words and phrases.

Are we feeling lucky?

Robust and adaptive spam filters allowed us to take email back from the spammers, but an arguably more important success was the use of Machine Learning in search engines. Early search engine ranking systems relied on hand-crafted formulas, but very quickly machine learning and statistical models became the most important building blocks of search engines, from the filtering of spammy pages, to spell-correcting user queries and augmenting them with extra keywords, to the actual ranking of web results, to extracting relevant parts of page contents for snippets. Without machine learning, Google would not be the search engine we know today, and reaching information about anything would be at least twice as hard. It’s hard to overstate how big an impact this had on our modern world.

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Recommendations based on previous behavior and relatedness as a way for exploration of contents.

Both the previous cases had mostly to do with text documents. Understanding language was still beyond reach, but using statistical characteristics of language and documents proved very effective in a number of tasks. The first notable non-text success to my mind was collaborative filtering, also known as recommender systems, notably pioneered by Amazon and Netflix. The idea that web services can use information about some users to infer what other users may be interested in had some very lucrative applications and provided a way to deal with heterogenous objects (movies, songs, items sold in an online store, travel destinations) potentially without needing a good way to represent them. Statistics about which users liked or bought a particular object replaced statistics about words it contained or genres it belonged to. What would our modern world be if online stores were not as effective as they are today? Where would the music industry be if streaming service as we know them today did not exist?

The damn ads!

We all hate ads, but there had to be a way for companies like Google to make money. And make money they do! Once the internet became more efficient than old-fashioned media at targeting specific user groups, say, based on interest in some specific type of content, it started to really pay of. And that was just the beginning, with Machine Learning based ad targeting and selection systems, and platforms that, like Facebook, Google, and Amazon that had information-rich user profiles, ad targeting became more and more efficient. In fact, in most major internet companies, you’ll find a significant portion of the Machine Learning talent syphoned into the teams building the ad targeting and optimization platforms. Which can be seen as unfortunate, but ads are less annoying when they are actually relevant, so let’s hope for better, privacy-preserving ad-targeting systems that enable a better balance of content to ads and make the web a better place for everyone.

A tad too much?

Now you see me …

More recently and maybe more famously, computers became good at seeing things and “understanding” them. Some applications of turning written numbers or words into digital form have been around for decades and quite useful in their own right, but with recent advances is Machine Learning algorithms, computers started becoming good at detecting objects in images. Detecting faces in an image and where they are, detecting smiles and frowns, detecting cats and dogs and cars, detecting several objects from a single image and characterizing the spatial relationships between them. These algorithms had immediate applications for social networks and camera software, but the more momentous consequence was enabling advances in autonomous vehicles. Autonomous vehicles require more than just seeing and understanding images, and is not yet quite a success. But it’s only a matter of time, and the impact it would have on our world is going to be massive, even by conservative estimates. The effects on transportation is going to be significant, but the effect on jobs and employment is going to be more disruptive and more dangerous. It’s hard to estimate how quickly our societies are going to cope with such a big and quick change.

In a parallel vein, similar algorithms and techniques were used to make computers hear the spoken word and convert speech into readable, digitized text. Converting speech to text is not quite as good as actually understanding the content of the text though.

General AI is still out of our reach. There are maybe three to five major steps that AI needs to make before general AI becomes within reach. The first such step or building block is language understanding. By language understanding I mean the ability to read a paragraph of text and build a representation of its meaning in an actionable way that is comparable to how humans understand language. It is interesting to note that we have been relatively more successful in understanding image than understanding natural language. I attribute that to the fact that images as a representation usually have more redundancy, while language is usually constructed with just enough redundancy to make it discernable to an intelligent human.

Another step or building block is long-term strategic planning. We have a form of that for playing games like chess and go. But achieving the same for general situations where the rules of the game are more ambiguous is still out of reach. Recent breakthroughs have reinforcement learning algorithms play video games without knowing the rules a priori , but just by looking at the screen’s visual information like humans learn to play such games. True, these games are usually tactical and short-term, but I can’t help but feel that we’re getting close to achieving this one.

Mass coordination and hive intelligence is not something we often think about when we think about AI. But the potential for machines coordinating on a much grater scale than humans is one of machines’ few edges on humans, and may lead to fundamentally different ways of solving problems than traditional human intelligence would.

One of the final steps is perhaps developing a unified system of rewards and incentives. Or even a system of laws and bounds. This aspect has been in the realm of science fiction for decades and may be the aspect we should be focusing on the most. It’s not a requirement of AI as much as a requirement for avoiding that apocalyptic future where machines take over the world.


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