Data Mining: Text Mining, Visualization and Social Media

AI's not AI

There has been a lot of commentary recently on issues relating to an experimental chat bot that Microsoft has (or had) launched named (after, perhaps, a river in Scotland) Tay. After a brief existence online, the bot was removed due to behaviours perceived as offensive which it was persuaded to engage in. Peter Lee of MSR has this to say about it. While there is much to learn from what transpired, the thing that irks me the most is the continued use of the term Artificial Intelligence to describe these systems - Lee actually calls it an 'artificial intelligence application'. Experimenting with these interactive agents is, no doubt, a useful activity that will teach us much about how humans will interact with actual AI entities in the future, but calling a chat bot of this nature an artificial intelligence application is like calling the icing on a cake, a cake. Communicating with humans is essential to artificial intelligence; communicating as a peer in human language with not much else going on 'upstairs' is not, however, a demonstration of artificial intelligence.

Related articles

How the Tech Media Keeps Artificial Intelligence at a Distance

The Economist gets in on the AI Fluff

AI, Artificial Birds and Aeroplanes

The Economist gets in on the AI Fluff

The Economist leads with an editorial and an article on The Dawn of Artificial Intelligence.

The editorial starts of with:

“THE development of full artificial intelligence could spell the end of the human race,” Stephen Hawking warns. Elon Musk fears that the development of artificial intelligence, or AI, may be the biggest existential threat humanity faces. Bill Gates urges people to beware of it.

Dread that the abominations people create will become their masters, or their executioners, is hardly new. But voiced by a renowned cosmologist, a Silicon Valley entrepreneur and the founder of Microsoft—hardly Luddites—and set against the vast investment in AI by big firms like Google and Microsoft, such fears have taken on new weight. With supercomputers in every pocket and robots looking down on every battlefield, just dismissing them as science fiction seems like self-deception. The question is how to worry wisely.

To my knowledge, while the three titans mentioned here of undeniable intellect, it is not clear why two of them are relevant at all. I consider myself reasonably smart, but should I be quoted on my opinion of current treatments of toxoplasmosis? The argument "a smart person says X, therefore we should attend" is known as the appeal to false authority (i.e. a fallacious argument). Such arguments introduce another argumentative crime: the exclusion of actual authority. Eric Horvitz comes to mind:

The head of Microsoft’s main research lab has dismissed fears that artificial intelligence could pose a threat to the survival of the human race.

Eric Horvitz believed that humans would not “lose control of certain kinds of intelligences”, adding: “In the end we’ll be able to get incredible benefits from machine intelligence in all realms of life, from science to education to economics to daily life.”

The article then stumbles along confusing advances in machine perception and machine learning with intelligence. Predictably, the Kasparov vs Deep Blue achievement is trundled out:

Yet AI is already powerful enough to make a dramatic difference to human life. It can already enhance human endeavour by complementing what people can do. Think of chess, which computers now play better than any person.

Suggesting that any computer can now beat all humans. Deep Blue was a singular machine which was dismantled after the match. And how does chess playing make a dramatic difference to human life?

A well informed article - and one that is actually interesting to read - should lead with a concrete set of definitions regarding the components of artificial intelligence (perception, reasoning, etc.) and discuss the discrete advances in those areas. Such an article should discuss the challenges of bringing those things together in anything other than the most rudimentary ways they are combined currently. An interesting article on AI would discuss the economics that drive investments and how they dictate what areas are worked on and which aren't.

In all these articles, there is a vague notion of inevitability, as if the machines themselves were selectively investing in research areas, or as if we could stand back and do nothing and this scary version of AI would emerge. It's as if the article were suggesting that landing on the moon was inevitable. The fact that humans did this and then never returned indicates that it was an incredibly intentional endeavor.

 

AI, Artificial Birds and Aeroplanes

The Turing Test for artificial intelligence is a reasonably well understood idea: if, through a written form of communication, a machine can convince a human that it too is a human, then it passes the test. The elegance of this approach (which I believe is its primary attraction) is that it avoids any troublesome definition of intelligence and appeals to an innate ability in humans to detect entities which are not 'one of us'.

This form of AI is the one that is generally presented in entertainment (films, novels, etc.).

However, to an engineer, there are some problems with this as the accepted popular idea of artificial intelligence.

I believe that software engineering can be evaluated in a simple measure of productivity. We either create things that make the impossible possible - going from 0 to 1, or we create things that amplify some value, generally a human's ability to do something, - going from X to nX. In other words, we enable a new thing, or we multiple our ability to do something.

Turing AI, while clearly an interesting intellectual concept, is like building an artificial bird instead of building an aeroplane:

• A Turing AI can converse in natural language, but humans can't speak charts or holograms as a means to explain something.
• A Turing AI can read a book and appear to understand it, but it can't read a thousand research articles on cancer and find a connection between discoveries that results in a breakthrough.
• A Turing AI doesn't have to even be particularly intelligence (though it probably ought to at least appear self aware, reflective, etc) and so would be potentially like making a very poor hire for your team.

I believe that if we consider opportunities for applying 'AI methods' to the vast corpus of data (both in natural language and in various structured forms) on the web, we will realize that there is an economic motivation (i.e. build value for users that can build a user base) that will require all the generally accepted facets of an AI (reasoning, perception, communication with humans, theory of mind, etc.) but will be nothing like a Turing AI.

Rather than think of search engines - the fundamental agents that mediate the web corpus - as mechanisms to help humans find the 'right' document - I believe it is time to change our intentions to:optimize the value of the data on the internet for all mankind.

When we achieve this, we will have built an AI, but it won't be a Turing AI and it may not even pass the Turing Test.

How the Tech Media Keeps Artificial Intelligence at a Distance

In sympathy with yesterday's post about AI as presented in films, consider this recent article from the Wall Street Journal: Artificial Intelligence Experts are in High Demand. A list of mostly machine learning experts is produced as evidence for the topic of the article. There is an unfortunate trend being presented to the public in this space in which the term 'artificial intelligence' is being used to draw readers with stories of real technical achievements in the space of machine learning and machine perception (recognizing a cat in a image is not an act of artificial intelligence), movies are being produced that romanticize a form of unobtainable AI, and the two are being tied together with stories of impending doom (Musk, Hawking).

All this is done with little or no investment in helping us establish what we really mean - and need - in an artificial intelligence.

If artificial intelligence experts were in high demand, then linguistics, philosophers, sociologists, etc. should be very happy - not just ML peeps.

How Hollywood Keeps Artificial Intelligence at a Distance

When something doesn't exist (like artificial intelligence) it's easy to think that there is some missing piece of magic required to bring it in to existence. There has been a growing interest in movie depictions of AI of late, and these all seem to require some sort of non-linear step to realize this technology.

• Ex Machina (which I really enjoyed) required a new sort of hard/software in the form of a jelly like substance.
• Chappie (which I also liked, though I generally prefer cheese and ham combined in a sandwich) required 'terabytes of coding' and a good amount of luck to produce its AI.
• Age of Ultron (a film about one liners and explosions) required a magic jewel from Loki's staff no less to create its AI.
• Transcendence (Kurzweil summarized) gives up on AI and simply loads a human brain into the ether.

The message in all of these movies is - the reason we don't have AI is that we haven't taken some non-linear step.

Artificial Intelligence and Economics

There are lots of articles online of the form - humanity has built some amazing things, so why haven't we produced an artificial intelligence? These articles (here's an example) often include some discussion of a task that even a very young human can perform - e.g. look at an image and describe what objects are in it (though personally I don't believe this is an indication of intelligence).

I believe that one of the key reasons that we can produce algorithms that can design super efficient jet engines, but not systems with the rudimentary common sense reasoning capabilities of a 3 year old is the economic context that drives innovation. The airline industry has an annual revenue of 700 billion dollars. No one has (yet) articulated the revenue potential of a technology that can look at a photograph and tell you what is in it.

The most important (and possibly last) step in the development of artificial intelligence is to create the economic engine that will require and motivate it.

Did Web Search kill Artificial Intelligence?

The most commonly referenced definition of Artificial Intelligence (AI) is probably the Turing Test which avoids the tricky questions like 'what is intelligence' or 'what does it mean to think' and replaces them simply by the test of recognizing an anonymous agent as a fellow human (and, therefore, intelligent).

A key aspect of this is, of course, that the interrogator is communicating with an equal in terms of the mode, pace and structure of dialogue (it is possible that a computer could succeed at this deception but reveal itself by being implausibly smarter than a human, but that is a question we can enjoy a little later).

As we have since learned, creating AIs is extremely hard and requires a very large portion of the smart people coming out of a broad spectrum of disciplines including software engineering, robotics, psychology, cognitive science and linguistics.

Another enabler is industrial context which brings the pragmatics of real world problems, and the scale and funding that is often not available to academics and if so doesn't benefit from stark focus required to make progress that is provided by industry.

However, we currently have the following:

• Search engines that don't understand language and which attempt to mediate between people (searches by people and documents by people),
• The best and the brightest coming to work for document oriented web companies.

I can't help but wonder where the AI project would be today if web search (as it is currently envisioned) hadn't gobbled up so much bandwidth.

News Narratives

Google has launched an interesting new take on consuming news content. The basic idea behind Living Stories is to take a corpus of news stories and determine the graph that represents the narrative made up of events, culminating in the current episode as presented by the latest report (or reports). In Google’s interface, this graph is actually a line – effectively a time line of associated stories.

Here’s an example:

Articles published in a news paper (format) are only ever the latest episode in an ongoing saga. Journalists may, to the extent that space allows, summaries the story to date, but that isn’t a substitute for getting an in depth presentation of the major (and minor) events that have lead up to this point in the narrative.

The idea underlying this system is one which I’ve discussed a few times on this blog and which I have published research on. See ‘From Episodes to Sagas: Understanding the News by Identifying Temporally Related Story Sequences, Balasubramanyan, Lin, Cohen, Hurst & Smith, 2009’ and ‘Narratives: A Visualization to Track Narrative Events as they Develop, Fisher, Hoff, Robertson & Hurst, 2008.’

Given today’s journalistic and technological rush towards real-time everything, systems like this can help bring back context and expose readers to the bigger picture. Likely Google’s system uses no automation right now, but the potential is there and could ultimately be folded in to the overall news experience.

Tell me something I don’t know!

There were a couple of papers that I really liked at www. One was Mapping the worlds photos by David Crandall et al, another was SOFIE: a self organizing framework for information extraction by Fabian Suchanek et al. Both of these papers (while operating in different domains) used features from orthogonal spaces (images, tags and geo coding in the first case and entity, relations, text patterns and logic in the second) to automatically mine new facts from a data set.

However, after the initial impression of how great these things are (and they are great) one realises that the facts that they have surfaced are already known. In the first case, which excels at discovering landmarks and images of landmarks, this is a well known knowledge set – landmarks, by there definition, are discovered facts. In the second case, the fact used in the example (which we shouldn’t, of course, judge the entire system by) was also well known.

What one would want to see in these papers and certainly in their presentations, is the long tail of facts. The head of the knowledge-sphere is well known by definition, rather than discovering it, we should assume it. The long tail will have weaker signals and it is there that we really care about the power of these systems.

Other papers in this and related areas from the conference include:

• Exploiting web search to generate synonyms from entities by Surajit Chaudhuri et al
• Measuring the similarity between explicit semantic relations on the web by Danushka Bollegala et al

AAAI Opens Access to Digital Library

I've just heard that AAAI (the parent organization of ICWSM and many other outstanding conferences) has completed the opening of access to online versions of all papers. This is a great step to make and I believe that all will benefit from it (authors, conferences and AAAI).

Some key conferences:

• ICWSM 2008
• AAAI 1980 - the first AAAI conferece!
• KDD 1996 - the first KDD conference while still at AAAI

Congratulations to everyone at AAAI!