Practical Challenges in AI Development
As developments in Artificial Intelligence advances we are constantly asked how can we build a truly strong AI? Even better, how do we test such candidates? These are some of the challenges that researches and scientist are stumbling upon trying to build AI. Since technology is advancing as such a rapid pace, there exist a phenomenon called the ‘AI Effect’. The AI Effect is when a superior algorithm that solves a complicated task and is viewed by some as AI. However, this is short lived because as technology progresses the algorithm is eventually deemed as just clever computer science. If we were to come up with a true AI, it would have to be immune to the ‘AI Effect’. The AI must be able to ‘self-update’ and stay at the forefront of what we consider to be AI at any given time period. Questions arise as to how we effectively test an AI candidate to show that it indeed is susceptible to the AI Effect? In order to answer that question, we must first explore two well-known tests, the Chinese Room and the Turning Test.
First, let’s start with the Chinese Room, where someone is placed inside a room and given a set of instructions for translating the English text into Chinese. Despite not knowing Chinese, the person inside the room is able to translate any English phrase into Chinese. To a person outside the room it would appear to speak Chinese. This is used to demonstrate that fact that a computer could simulate ‘mental states’ and appear to be behaving in an intelligent manner but would not have achieve actual sentience. However, the main flaw is that this experiment assumes that the people outside the room can reliably tell the difference between a simulated translation and actual translation which is something that comes up in the Turning Test which we will discuss next.
As many of you are familiar, in 1950 Alan Turing came up with the ‘Turning Test’ which tests whether someone can not tell whether or not they are interacting with a human or AI via conversation alone. If the person cannot tell them apart then the AI candidate has passed the test is deemed AI. However, there are two main failures with the Turning Test as a test of intelligence. The first is that the test for intelligence is based on a single skill. Today, there are algorithms that have been specifically written to pass the test but the systems running these algorithms are not intelligent. The algorithms which are written to pass the Turing Test are no different than a chess program that can only perform one predetermined task. The second failure is the assumption that there is a distinction between the simulation language and the ‘real’ language. The distinction between simulation and reality can only be made if the intelligent agent can accurately differentiate between the two. If the intelligent agent cannot distinguish between the two, then from the perspective of that agent there is no difference between simulation and reality. What argument can we make that the human wasn’t simulating the machine? Does it matter who simulates who? If multiple people were given the test believed the machine passed the Turning Test, how would we argue that it didn’t?
A true test of AI shouldn’t be based on one task alone rather broken down into smaller subset of tests. A key challenge in developing a general test for a strong AI is that the AI may not only constrained to humanoid robot as we see in the movie ex-Machina rather it must be also applied to an algorithm on a computer the size of cellphone. These subset of tests should include linguistics as well as tests for self-awareness because without these capabilities we cannot deem ‘something’ as strong AI. Strong AI should be capable of establishing connections between different sets of data which are not previously programmed into the system. Where it has abilities to begin and continue to learn, creating and building a knowledge base. Developing proper methods of testing strong AI are still being research but is without a doubt a challenge.
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