Turing’s Test & The Stock Market
A Non-standard Introduction to Sentiment Analysis in 3 Parts
Part 1 – CAPTCHA to Gotcha:
A Brief History of Artificial Intelligence
Alan Turing was a prominent British mathematician and one of the most inspiring pioneers of modern computer science. In 1950, at the age of 38, he published his seminal paper Computing Machinery and Intelligence, which till this day remains probably the single most influential paper in the field of Artificial Intelligence (AI).
Since Digital Trowel’s core technology is based on machine learning, a modern offshoot of AI, it would be conducive (and nice!) to get back to the basics, and learn a bit about the history that continues to shape both the science itself and the challenges we face at DT.
Big words and complications aside, Turing begins his paper with the simple yet perplexing question: “Can machines think?” Nevertheless, realizing that “thinking” is a highly ambiguous term, Turing immediately proposed an alternative question that would be free of obscurities and eschew obfuscations. Instead of dealing with machines’ capacity for thinking, he focused on their capacity to emulate human thought. In simplified terms the question he suggested was:
Could machines be made to simulate human thought well enough so as to fool a person into believing they were actually human?
This question is the essence of what has come to be called the Turing Test. It proceeds as follows: a human judge engages in a natural language conversation with one human and one machine, each of which tries to appear human. All participants are placed in isolated locations. If the judge cannot reliably tell the machine from the human, the machine is said to have passed the test. In order to test the machine’s intelligence rather than its ability to render words into audio, the conversation is limited to a text-only channel such as a computer keyboard and screen.
At the time of its publication, many people viewed the prospect of machines ever reaching the level of human computational power an impossibility, but in his paper, Turing, armed with his visionary intuition and razor-sharp mathematical analysis, set out to invalidate contemporary objections, ending with a speculation of his own, that one day machines would indeed emulate human thought, thereby passing the Turing Test!
Inspired by the challenge, Digital Trowel’s groundbreaking technology has taken several huge steps forward in proving that Turing was right. The technology we’ve developed allows computers to extract not only the facts communicated by the text, but also the underlying sentiment or, if you will, the attitude associated with the message conveyed. In simple words, we’re enabling computers to understand the full meaning not only of the text, but of the subtext – just as a human would. But hold your horses! Before we continue, let’s try to explain why the problem is so difficult, so we can more fully appreciate the profundity of Digital Trowel’s achievement and its extraordinary implications.
Well for one thing, it’s now sixty years later and the question presented by Turing has yet to be settled. In fact, it is far from being resolved. Machines have beaten world chess champions, navigated spacecrafts millions of miles away and even been used to prove mathematical theorems whose intricacy is impenetrable to human beings for their sheer magnitude of computational complexity, yet as of now no computer has been shown to pass the test.
It may be argued that the challenge machines face is simply a matter of raw computing power. It is currently estimated by some experts that the human brain can perform some 38,000 trillion operations per second (that’s 3.8×1016 operations!) and hold over 3,500 terabytes of memory. In comparison, the world’s most powerful supercomputers (e.g. IBM’s BlueGene) have computational capacity of less than a “mere” 100 trillion operations per second (only 1014) and less than 10 terabytes of storage. However, if this indeed is the case, and the capacity to “think” lies in raw computation power alone, then according to some versions of Moore’s Law (which predicts the rate at which computation performance evolves with time) machines will ultimately obtain the required criterion by circa 2018. But we may have to wait a bit longer for an answer: recently, futurist Raymond Kurzweil, revised his earlier prediction that Turing test-capable computers would be manufactured by 2020, deferring the predicted date to 2029 (I can’t help but wonder if this prediction has anything to do with the fact that asteroid (35396) 1997 XF11 is anticipated to make a close approach to Earth late in 2028 
).
But how does this all have to do with Digital Trowel’s business? That is, unless we have secret plans in store for purchasing stocks in Scientific American… (which we don’t!). Well to answer that consider first what is sometimes called a Reverse Turing Test. Imagine a modification of the Turing Test wherein the role of the judge has been switched between machines and humans. Now it’s the computer who has to determine whether it is “conversing” with a human or another machine. In fact to some of you this may just ring a (quite annoying) bell. Take a look at the images below:
Ever wonder why every once in a while you’re prompted to try to decipher the jumbled up letters in images such as these? Well, put simply, it’s because you’re taking part in a test that’s not meant for you – you’re serving as a participant in a Reversed Turing Test administered and judged by the security computers of the website with which you are attempting to engage. Humans have (or rather should have!) no problem deciphering the text in the above images, which incidentally are called CAPTCHAs (for Completely Automated Public Turing test to tell Computers and Humans Apart). However, the random distortions in a CAPTCHA make it nearly impossible for computers to decipher the letters. As a result, automated security programs can use these images and the respective responses to make certain it is a human attempting to engage with the website and not some malicious script.
. We are, however, in the business of using computers to achieve something no less illusive: deciphering the sentiment that lies hidden inside text.
Gleaning not only the formal meaning but also the sentiment associated with a text passage is crucial for any machine that hopes to “pass the Turing Test”. Albeit so, this is not part of our technological agenda nor is it a component of our business plan. In fact our aspirations are much more practical. We aim to use the highly sophisticated technologies powered by our cutting edge machine-learning and linguistic algorithms to analyze millions of lines of text, thus creating valuable business information that will help our customers make decisions in real-time. In short:
Extracting and discerning the underlying sentiment allows us to transform otherwise inert texts into vibrant business opportunities.
But again we’re jumping ahead of ourselves. Now that we’ve laid the foundations for understanding what AI is all about, we’re ready to take a tour down the path of linguistic algorithm theory, focusing of course on the art of sentiment analysis. Or as we like to call it at DT, Synergistic Sentiment Analysis, a term that is used for reasons that will become apparent in due course.
We’ve told the story, set the stage, laid the bait – are you hooked..?
