The first in a series of problem solving exercises aimed at determining what the next steps for AI might be.
An introduction to the process of developing comprehensive strategies for enterprise data manangement and exploitation.
What things actually promote or discourage innovation? We'll examine a few in this post...
Digitial Transformation is a hot topic in IT and big money maker for consultants - but what does it really mean?.
Sunday, December 4, 2016
Saturday, December 3, 2016
Wednesday, December 3, 2014
Tuesday, September 23, 2014
|Alan Turing - BTW: a movie about him will be hitting theaters soon...|
- First off, the answers are screwy and it's clear that much of what the computer heard it misinterpreted.
- Then they presented the AI as if it were an adolescent from war-torn Ukraine.
- And they also used the lowest possible threshold to gauge success - this threshold which represented a part of Turing's paper on the subject - suggested that success be declared if on average at least 30% of humans judging the AI would be fooled into thinking it was a human. So, the AI named Eugene, scored a 33% - but that is only because judges lowered the bar thinking he was a semi-illiterate teen.
|In the movie Her, this guy falls in love with his operating system (and it didn't come from the Apple store!)|
The Technovation AI Test -
AI Test Prerequisites / Expectations
- The Test is not meant to assess acquired knowledge per se, it is meant to assess cognitive ability. In other words, it is not about preparation or repetition of learned information, but is concerned with potential and / or application of any particular knowledge set.
- The Test does not have to occur in one sitting, but take place over any duration (within reason).
- The Test isn't merely concerned with correct answers or maturity in a point of time, but can also assess the ability to grow over time based upon responses to various aspects of the test (or other stimuli encountered within the time-frame of the test).
- The Test is not merely a linguistic exercise - the machine must not merely demonstrate the ability to communicate like a human, it must also demonstrate it can learn.
- Foremost above all else though, the machine must demonstrate the one trait most closely associated human intelligence (as opposed to raw computing power) - it must demonstrate intuition. In this context, Intuition represents shorthand problem-solving (which we will discuss in much more depth in a future post).
- On last aspect of the test that must be included is a review of the code to ensure that "conversational snippets" are not allowed to be prep-programmed. This implies that the majority of dialog is generated 'real time' by the machine. Now, that would not prevent the machine from reviewing logs of previously generated dialog (in some database), but that review could not lead to verbatim quoting - rather must paraphrase or other restate previous points.
The AI Test
In a series of panel interviews, the AI must convince the judges or reviewers that it should be hired to perform a complex human role. The type of job and foundational knowledge can cover any number of topics but must be sufficiently complex to avoid "lowering the bar." (so, any job that requires a degree). Also, the interview style must be open (similar to essay tests in written assessments) - the answers must not just be correct, they must demonstrate value added insight from the intelligence conveying them. And the answers may be entirely subjective... (even better as long as the machine can rationalize them)
This test necessarily implies a very high threshold - perhaps in excess of a 90% rating for a very complex set of conversations. Why raise the bar this high? Simple - this is the one way we can force the development of a system that can both learn and apply that knowledge to problem solving and do it on the fly. To have human like intelligence, machines must have the ability to understand nuances of human communication and psychology - thus it must not only be able to interact, it must be able to convince us as well.Now that we have a more concrete target to aim for - how do we get there. In our next post, we'll delve into Learning - what works and what doesn't and how human and machine intelligence differ today.