The most sensational A.I. news ever!

News sites are constantly oozing bold overstatements about artificial intelligence. Most scientists describe their research accurately enough in their papers, but journalism always tries to cut a slice of the Terminator movies’ popularity in order to make the science appeal to the general public. Unfortunately such calls upon the imagination tend to border on misinformation. Here is a selection of the most sensationalised news stories that made waves in recent history:

2014: Robot becomes indecisive after implementing the 3 laws of robotics

“A robot may not injure a human being or, through inaction, allow a human being to come to harm.”

So reads the “first law of robotics” from Asimov’s science-fiction novels. Someone set up an experiment with three small wheeled robots, two of them representing humans, and a third one was provided with behavioural rules based on the above:  The robot was programmed to avoid colliding with (“injuring”) the “humans”, except to intercept them if it saw one heading towards a square designated as unsafe. When two “humans” were introduced simultaneously, the robot took so long hesitating which one to “save” that it failed to save either.
This fired up the usual flood of discussions about ethics and how to improve upon Asimov’s “laws” (Newsflash: Nobody uses them), but programmers were quick to point out that this was just poor programming: The simple “if-then” rules did not allow the robot to take more than one target into account at a time, so it just mindlessly jittered back and forth between the two. It could not make a decision because it had no decision processes.
factual source

2014: A supercomputer has passed the Turing Test for the first time
The organiser’s boast of a “supercomputer” having passed this “milestone” intelligence test was blatantly false, but all the papers ran the story without question. In reality it concerned an ordinary chatbot with keyword-triggered responses on an ordinary computer. Although this chatbot did pass “a” version of a Turing Test by deflecting questions like a zany teenager, there has never been agreement on the rules of “the” Turing Test (because there is no such thing)*.
The passing of this supposed test of intelligence was particularly insignificant because the judges were only given 5 minutes to interrogate both the chatbot and a human volunteer at the same time. This allowed for only 5 to 10 questions and so barely probed beyond the “Hello, how are you?” stage. The scientific backlash that followed cast the Turing Test into discredit and led to a number of new tests, such as the Winograd Schema Challenge*.
factual source

NAO robots. See, hear, speak.

2015: First robot passes self-awareness test
Inspired by an ancient philosophical puzzle, three NAO robots were each given an imaginary “dumbing pill” (a tap on the head) that muted two of them, except the third robot was given a “placebo pill” that did nothing. Each robot was then asked to assess which “pill” it got, which none of them knew. But when the one robot that could still speak heard itself say “I don’t know”, it performed its analysis a second time and said “Sorry, I know now! I was able to prove that I was not given a dumbing pill”.
As cute as that was, this wasn’t a “test”. Every step of the procedure was pre-programmed specifically and exclusively for this scenario of pills and sound. The programmers had laid out the exact inference to execute and which outcome to conclude if a robot were to hear sound at the time that its output function activated. As that inference might as well apply to any other object, the only connection with the robot’s “self” was the detour of audio output to audio input, and that’s a bit of a technicality. Most people’s definitions of “self-aware” include retaining a model of oneself and the capacity of reflection upon that model, and these robots had nothing of the sort.
factual source (paper)

2015: Robot attacks and kills factory worker
No laughing matter, a robotic arm at a Volkswagen car construction factory crushed a man when it swivelled, after which he died of his injuries. While Twitter was set aflare with warnings of a robot uprising, the robot arm had of course not done this on purpose. The man was a technician, who was installing the arm while standing inside the safety cage rather than outside it.
This ordinary industrial accident only gained popular media coverage because it was initially reported by a co-worker whose name closely resembled that of the leading lady from the Terminator movies, Sarah Connor.
factual source

2017: Facebook shuts down AI experiment after robots invent their own language
Most articles put it as if the AI had become smart beyond human comprehension and its creators had pulled the plug in a panic, similar to the plot of the Terminator movies.
The reality was a different story. Facebook had trained two chatbot programs to barter and negotiate over a number of items using English phrases. When they hooked the chatbots up to one another, their use of words gradually deteriorated to a shorthand where they just repeated the most effective keywords, because their programming did not include any rewards for correct use of English.

A: balls have zero to me to me to me to me to me to me to me to me to me
B: you i everything else . . . . . . . . . . . .
A: balls have a ball to me to me to me to me to me to me to me
B: i i can i i i everything else . . . . . . . . . . . .
A: balls have a ball to me to me to me to me to me to
B: i . . . . . . . . . . . . . . . . . . .

This is a common flaw according to other machine learning practitioners. Since this gibberish was not useful for what they were trying to achieve, the researchers simply stopped the programs, and changed the reward parameters in their next versions.
The real reason that this got media attention was that Elon Musk and Facebook’s CEO had recently been in the news with strongly opposing views on whether AI was a threat to humanity. As such, it would have made an ironic story if Facebook’s own AI had gone out of control.
factual source

2017: Sophia robot granted citizenship
This story was true, but at the same time meaningless. A lifelike humanoid robot called Sophia, a creation of Hanson Robotics, was granted citizenship by Saudi Arabia at a tech conference in Riyadh. This raised all sorts of issues about human/robot rights, and some people took Sophia’s on-stage acceptance speech to be a genuine indication of her capabilities, feelings and opinions.
The truth is of course that Sophia was just an animatronic machine that only said what her makers had written for her to say, in an interview that was scripted. Sophia’s conversational subsystem, Cogbot, actually uses AIML, a chatbot scripting language that is popular for its simplicity.
Why then would the robot be granted citizenship? Well, the crown prince of Saudi is giving the country a modernisation makeover, and this announcement served as a PR signal to international investors attending the conference. As for the consequences of granting a robot citizenship, I expect there will be none at all. After all, they can just place it next to another statue and it’ll never make claim to its rights.
factual source

The end is always nigh
These stories are just the highlights. The Turing Test organiser went on to claim that programs could pass the test by invoking the fifth amendment, the NAO robot programmers went on to suggest their robots had learned to disobey orders, and Hanson’s robots have made headlines multiple times for threatening to overthrow mankind. Not a day passes without some angsty story about AI making the rounds.
Regrettably these publicity stunts can have real and harmful consequences. Whenever AI became overhyped in the past, the entire field imploded as the high expectations of investors could not be met. And when the public and governments start buying into fearmongering by famous public figures, it draws attention away from real problems to imaginary ones. Most researchers are just working on practical applications and are none too happy about their work being so misrepresented.

That is why I decided to develop a nonsense filter, which you’ll find in the next article*.


The Winograd Schema Challenge

The Winograd Schema Challenge, a $25000 contest sponsored by the aptly named company Nuance Communications, has been put forth as a better test of intelligence than Turing Tests*. Although the scientific paper tiptoes around its claims, the organisers describe the contest as requiring “common sense reasoning”. This introductory article explains the test’s strengths and weaknesses in that regard.

Example of a Winograd Schema

I used a tissue to clean the key, and then I put it in the drawer.
I used a tissue to clean the key, and then I put it in the trash.

A Winograd Schema is a sentence with an ambiguous pronoun (“it”), that, depending on one variable word (“trash/drawer”), refers to either the first or the second noun of the sentence (“tissue/key”). The Challenge is to program a computer to figure out which of the two is being referred to, when this isn’t apparent from the syntax. So what did I put in the trash? The tissue or the key? To a computer who has never cleaned anything, it could be either. A little common sense would sure come in handy, and the contest organisers suggest that this takes intelligent reasoning.
common sense computers

Common sense, not Google sense

The hare beat the tortoise because it was faster.
The hare beat the tortoise because it was too slow.

Contrary to this example, good Winograd Schemas are supposed to be non-Googleable. In this case Googling “fast hare” would return 20x more search results than “fast tortoise”, so the hare is 20x more likely to be the one who “was faster”. Although statistical probability is useful, this would make the contest won simply by the company with the largest set of statistics. It takes no reasoning to count how many times word A happens to coincide with word B in a large volume of text. Therefore this example would preferably be written with neutral nouns like “John beat Jack”, subjects of who we have no predetermined knowledge, but can still figure out which was faster.

Having said that, some example schemas involving “crop dusters” and “bassinets” still suggest that a broad range of knowledge will be required. Although one could consult online dictionaries and databases, the contest will have restrictions on internet access to rule out remote control. So failure can also be due to insufficient knowledge rather than a lack of intelligence, but I suppose that is part of the problem to solve.

Early indications

If a bed doesn’t fit in a room because it’s too big, what is too big?
If Alex lent money to Joe because they were broke, who needed the money?

With the above two questions the 2015 Loebner Prize Turing Test gave a tiny glimpse of Winograd Schemas in practice, and the answers suggest that chatbots – the majority of participants – are not cut out to handle them. Only 2 of 15 programs even answered what was asked. One was my personal A.I. Arckon*, the other was Lisa. Chatbot systems are of course designed for chat, not logic puzzles, and typically rely on their creators to anticipate the exact words that a question will contain. The problem there is that the understanding of Winograd Schemas isn’t found in which words are used, but in the implicit relations between them. Or so we presume.

The mermaid swam toward Sue and waved her tail. (Googleable)
The mermaid swam toward Sue and made her gasp. (More than a single change)

A more noteworthy experiment was done by the University of Texas, tested on Winograd Schemas composed by students. To solve the schemas they used a mixed bag of methods based on human logic, such as memorising sequences of events (i.e. verb A -> verb B), common knowledge, sentiment analysis and the aforementioned Googling. All of this data was cleverly extracted from text by A.I. software, or retrieved from online databases. However, many of the schemas did not accord with the official guidelines, and though they usefully solved 73% in total, only 65% was solved without the use of Google.

According to the same paper, the industry standard “Stanford Coreference Resolver” only correctly solved 55% of the same Winograd Schemas. The Stanford Resolver restricts the possible answers by syntax, gender(“he/she”) and amount(“it/they”), but does not examine them by knowledge or reasoning. The reason is that this level of ambiguity is rare. In my experience with the same methods however, it is still a considerable problem that causes 1/10th of text-extracted knowledge to be mistaken, with the pronoun “it” being the worst offender. So it appears (see what I mean?) that any addition of common sense would already advance the state of the art.

How to hack Winograd Schemas
Guesswork: Since the answers are a simple choice of two nouns, a machine could of course randomly guess its way to a score of 50% or more. So I did the math: With 60 schemas to solve, pure guesswork has a 5% chance to score over 60%, and a 0.5% chance to score over 65%. With the odds growing exponentially unlikely, this is not a winning tactic.
That said, the participating A.I. still have to make a guess or default choice at those schemas that they fail to solve otherwise. If an A.I. can solve 30% of the schemas and guesses half of the rest right, its total score amounts to 65%, equaling Texas’ score. It wouldn’t be until it can solve around 80% of all schemas genuinely that it could reach the winning 90% score by guessing the final stretch. That’s a steep slope.

Reverse psychology: Since Winograd Schemas are deliberately made to not match Google search results, it seems that one can apply reverse psychology and deliberately choose the opposite. While I did notice such a tendency in Winograd Schemas composed by professors, others have noticed that Winograd Schemas composed by students simply did match Google search results. So the success of using reverse psychology heavily depends on the cleverness of the composers. A countermeasure would be to use only neutral names for answers, but this may also cut off some areas of genuine reasoning. Alternatively one could include an equal amount of schemas that match and mismatch Google search results, so that neither method is reliable.

Pairing: One dirty trick that could double one’s success lies in the fact that Winograd Schemas come in pairs, where the answer to the second version is always the alternate noun. So if the A.I. can solve the first version but not the second, it suffices to choose the remaining alternate answer. Vice versa when it can solve the second version but not the first. This rather undermines the reason for having pairs: To ascertain that the first answer wasn’t just a lucky guess. Although this hack only increases the success of guesswork by a few percent, it can certainly be used to make a weak contestant into a strong contender undeservedly.

I call these hacks because not only are they against intent, they are also entirely useless in real life application. No serious researcher should use them or they will end up with an inept product.

How you can’t hack Winograd Schemas
No nonsense: The judgement of the answers is clear and objective. There is only one correct answer to each schema. The A.I. are not allowed to dodge the question, make further inquiries or give interpretable answers: It’s either answer A or B.

No humans: Erratic human performance of the judges and control subjects does not influence the results. The schemas and answers have been carefully predetermined, and schemas with debatable answers do not make the cut.

No invisible goal: While the Turing Test is strictly a win or lose game with the goalposts at fields unknown, the WSC can reward gradual increase of the number of schemas answered correctly. Partial progress in one area of common sense like spatial reasoning can already show improved results, and some areas are already proving feasible. This encourages and rewards short-term efforts.
I must admit that the organisers could still decide to move the goalposts out of reach every year by omitting particular areas of common sense once solved, e.g. all schemas to do with spatial reasoning. I think this is even likely to happen, but at the same time I expect the solutions to cover such a broad range that it will become hard to still find new problems after 6 contests.

Mostly, the WSC trims off a lot of subjective variables from the Turing Test, making for a controlled test with clear results.

The Winograd Schema Challenge beats the Turing Test
From personal experience, Turing Tests that I have participated in* have at best forced me to polish my A.I.’s output to sound less robotic. That is because in Turing Tests, appearance is a first priority if one doesn’t want to be outed immediately at the first question, regardless how intelligent the answer is. Since keeping up appearances is an enormous task already, one barely gets around to programming intelligence. I’ve had to develop spell correction algorithms, gibberish detection, calculator functions, letter-counting games and a fictional background story before encountering the first intelligent question in a Turing Test. It stalls progress with unintelligent aspects and is discouragingly unrewarding.

Solving Winograd Schemas on the other hand forced me to program common sense axioms, which can do more than just figure out what our pronouns refer to. Indirect objects and locations commonly suffer from even worse ambiguity that can be solved by the same means, and common sense can be used to distinguish figurative speech and improve problem-solving. But I’ll leave that as a story for next time.
We should be careful to draw conclusions from yet another behavioural test, but whatever the Winograd Schema Challenge is supposed to prove, it offers a practical test of understanding language with a focus on common sense. As this has always been a major obstacle for computers, the resulting solutions are bound to be useful regardless how “intelligent” they may be found.

Read more in my report on the first Winograd Schema Challenge held in 2016.