- Terminology (A to D)
- Bayes Theorem
- Big Data
- Chatbot: A Beginner’s Guide
- Computational Thinking
- Computer Vision
- Confusion Matrix
- Convolutional Neural Networks
- Data Fabric
- Data Science
- Decision Tree
- Deep Learning
- Deep Reinforcement Learning
- Digital Twin
- Dimensionality Reduction
- Terminology (E to K)
- Terminology (L to Q)
- Terminology (R to Z)
Artificial General Intelligence
What is the Turing Test and Why Does it Matter?
Table Of Contents
If you’ve been around Artificial Intelligence (AI) you have undoubtedly heard of ‘The Turing Test‘. This was a test first proposed by Alan Turing in 1950, the test was designed to be the ultimate experiment on whether or not an AI has achieved human level intelligence. Conceptually, if the AI is able to pass the test, it has achieved intelligence that is equivalent to, or indistinguishable from that of a human.
We will explore who Alan Turing is, what the test is, why it matters, and why the definition of the test may need to evolve.
Who is Alan Turing?
Turing is an eccentric British Mathematician who is recognized for his futurist ground breaking ideas.
In 1935, at the age of 22 his work on probability theory won him a Fellowship of King’s College, University of Cambridge. His abstract mathematical ideas served to push him in a completely different direction in a field that was yet to be invented.
In 1936, Turing published a paper that is now recognized as the foundation of computer science. This is where he invented the concept of a ‘Universal Machine’ that could decode and perform any set of instructions.
In 1939, Turing was recruited by the British government’s code-breaking department. At the time Germany was using what is called an ‘enigma machine‘ to encipher all its military and naval signals. Turing rapidly developed a new machine (the ‘Bombe’) which was capable of breaking Enigma messages on an industrial scale. This development has been deemed as instrumental in assisting in pushing back the aggression’s of Nazi Germany.
In 1946, Turing returned to working on his revolutionary idea published in 1936 to develop an electronic computer, capable of running various types of computations. He produced a detailed design for what was was called the Automatic Computing Engine (ACE.)
In 1950, Turing published his seminal work asking if a “Machine Can Think?“. This paper completely transformed both computer science and AI.
In 1952, after being reported to the police by a young man, Turing was convicted of gross indecency due to his homosexual activities. Due to this his security clearance for the government was revoked, and his career was destroyed. In order to punish him he was chemically castrated.
With his life shattered he was later discovered in his home by his cleaner on 8 June, 1954. He had died from cyanide poisoning the day before. A partly eaten apple lay next to his body. The coroner’s verdict was suicide.
Fortunately, his legacy continues to live on.
What is the Turing Test?
In 1950, Alan Turing published a seminal paper titled “Computing Machinery and Intelligence” in Mind magazine. In this detailed paper the question “Can Machines Think?” was proposed. The paper suggested abandoning the quest to define if a machine can think, to instead test the machine with the ‘imitation game’. This simple game is played with three people:
- a man (A)
- a woman (B),
- and an interrogator (C) who may be of either sex.
The concept of the game is that the interrogator stays in a room that is separate from both the man (A) and the woman (B), the goal is for the interrogator to identify who the man is, and who the woman is. In this instance the goal of the man (A) is to deceive the interrogator, meanwhile the woman (B) can attempt to help the interrogator (C). To make this fair, no verbal cues can be used, instead only typewritten questions and answers are sent back and forth. The question then becomes: How does the interrogator know who to trust?
The interrogator only knows them by the labels X and Y, and at the end of the game he simply states either ‘X is A and Y is B’ or ‘X is B and Y is A’.
The question then becomes, if we remove the man (A) or the woman (B), and replace that person with an intelligent machine, can the machine use its AI system to trick the interrogator (C) into believing that it’s a man or a woman? This is in essence the nature of the Turing Test.
In other words if you were to communicate with an AI system unknowingly, and you assumed that the ‘entity’ on the other end was a human, could the AI deceive you indefinitely?
Why the Turing Test Matters
In Alan Turing’s paper he alluded to the fact that he believed that the Turing Test could eventually be beat. He states: “by the year 2000 I believe that in about fifty years’ time it will be possible to programme computers, with a storage capacity of about 109, to make them play the imitation game so well that an average interrogator will not have more than 70 per cent, chance of making the right identification after five minutes of questioning.”
When looking at the Turing Test through a modern lens it seems very possible that an AI system could trick a human for five minutes. How often have humans interacted with support chatbots not knowing if the chatbot is a human or a bot?
There have been many reports of the Turing Test being passed. In 2014, a chatbot program named Eugene Goostman, which simulates a 13-year-old Ukrainian boy, is said to have passed the Turing test at an event organised by the University of Reading. The chatbot apparently convinced 33% of the judges at the Royal Society in London that it was human. Nonetheless critics were fast to point out the inadequacies of the test, the fact that so many judges were not convinced, the duration of the test (only 5 minutes), as well as the lack of forthcoming evidence for this achievement.
In 2018, a Google Duplex reservation system with the assistance of Google Assistant, made a phone call to a hair salon to schedule an appointment for a haircut. In this case, the AI system did not introduce itself as AI, and during the phone call pretended to be human while speaking to a salon’s receptionist. After a short exchange, a haircut was successfully scheduled and both parties hung up.
Nonetheless, it an age of Natural Language Processing (NLP), with its subfields of Natural-language understanding (NLU) and natural-language interpretation (NLI), the question needs to be asked, if a machine is asking and answering questions without fully understanding the context behind what it says is the machine truly intelligent?
After all, if you review the technology behind Watson, a computer system capable of answering questions posed in natural language, developed by IBM to defeat Jeopardy champions, it becomes apparent that Watson was able to beat the world champions by downloading a large chunk of the world’s knowledge via the internet, without actually understanding the context behind this language. There were 200 million pages of information, from a variety of sources including Wikipedia. There was a restriction in place that Watson could not access the internet while playing a game but this is simply a minor restriction for an AI that can simply access all of human knowledge before the game begins.
Similar to a search engine, keywords and reference points were made. If an AI can achieve this level of comprehension, then we should consider that based on today’s advancing technology, deceiving a human for 5 or 10 minutes is simply not setting the bar high enough.
Should the Turing Test Evolve?
The Turing Test has done a remarkable job of standing the test of time. Nonetheless, AI has evolved dramatically since 1950. Every time AI achieves a feat of which we claimed only humans were capable of we set the bar higher. It will only be a matter of time until AI is able to consistently pass the Turing Test as we understand it.
When reviewing the history of AI, the ultimate barometer of whether or not AI can achieve human level intelligence is almost always based on if it can defeat humans at various games. In 1949, Claude Shannon published his thoughts on the topic of how a computer might be made to play chess as this was considered the ultimate summit of human intelligence.
It wasn’t until February 10, 1996, after a grueling three hour match that world chess champion Garry Kasparov lost the first game of a six-game match against Deep Blue, an IBM computer capable of evaluating 200 million moves per second. It wasn’t long until Chess was no longer considered the pinnacle of human intelligence. Chess was then replaced with the game of Go, a game which originated in China over 3000 years ago. The bar for AI achieving human level intelligence was moved up.
Fast forward to October 2015, AlphaGo played its first match against the reigning three-time European Champion, Mr Fan Hui. AlphaGo won the first ever game against a Go professional with a score of 5-0. Go is considered to be the most sophisticated game in the world with its 10360 possible moves. All of a sudden the bar was moved up again.
Eventually the argument was that an AI had to be able to defeat teams of players at MMORPG (massively multiplayer online role-playing games). OpenAI quickly rose to the challenge by using deep reinforcement learning.
It is due to this consistent moving of the proverbial bar that we should reconsider a new modern definition of the Turing Test. The current test may rely too much on deception, and the technology that is in a chatbot. Potentially, with the evolution of robotics we may require that for an AI to truly achieve human level intelligence, the AI will need to interact and “live” in our actual world, versus a game environment or a simulated environment with its defined rules.
If instead of deceiving us, a robot can can interact with us like any other human, by having conversations, proposing ideas and solutions, maybe only then will the Turing Test be passed. The ultimate version of the Turing Test may be when an AI approaches a human, and attempts to convince us that it is self-aware.
At this point, we will also have achieved Artificial General Intelligence (AGI). It would then be inevitable than the AI/robot would rapidly surpass us in intelligence.
Antoine Tardif is a Futurist who is passionate about the future of AI and robotics. He is the CEO of BlockVentures.com, and has invested in over 50 AI & blockchain projects. He is the Co-Founder of Securities.io a news website focusing on digital assets, digital securities and investing. He is a founding partner of unite.AI & a member of the Forbes Technology Council.
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