AI in the Enterprise: How can we make analytics and stats sound less scary? Let's call it AI! Welcome back to the inaugural Register Debate in which we pitch our writers against each other on contentious topics in IT and enterprise tech, and you – the reader – decide the winning side. The format is simple: a motion was proposed, for and against arguments were published on Monday, another round of arguments today, and a …
When I was at university in the early 90's studying this subject as part of an engineering degree all the lecturers insisted that under no circumstances should anything be called "Artifical Intelligence" ever. The phrase itself was castigated as an oxymoron and anyone who believed it existed a complete moron.
The course title was actually "Machine Vision and Neural Networks" and while I don't consider myself an expert, I have kept a weather eye on developments over the years. And from what I have seen all that has happened is the computers have got faster and the data sets bigger. In the intervening 30ish years there has been no advancement in the art whatsoever, as far as I can tell.
I agree 100% Billy. I think that in the intervening years "marketing" has replaced "intelligence", after some discussion it was thought back then that AI would sound better than AM, so Artificial Marketing is now called AI and now we are living with AG - Artificial Government, is this progress?
Statistics is about understanding the relationship between input variables. Artificial Intelligence (be it evolutionary computing, neural networks, learning classifier systems or a mix thereof) are about matching input to output. For example: a neural network cannot tell you what the relationship is between your height and weight but it can guess your BMI.
It's a different thing. Machine learning algorithms aren't that new but it's not statistics.
"How IBM Watson Overpromised and Underdelivered on AI Health Care" [title]
"After its triumph on Jeopardy!, IBM’s AI seemed poised to revolutionize medicine. Doctors are still waiting" [subtitle]
By Eliza Strickland
02 Apr 2019 | 15:00 GMT
https://spectrum.ieee.org/biomedical/diagnostics/how-ibm-watson-overpromised-and-underdelivered-on-ai-health-care
The author, supposedly arguing for the motion that AI is 'dumb algorithms', refers to 'the processing pipelines of human perception - algorithms many millions of years old'. Computers are conscious in the same way that an abacus is. Both are inanimate objects. Whatever human perception is, it's biological and mushy - totally unlike algorithms, 0s and 1s.
AI is texts put in an understandable to the computer format, that is in structured and related by sense patterns. These texts are the algorithm and process, they contain logic, define actions to achieve the result and feedback. Therefore, it is absurd to say "where you draw the lines between process and algorithm" — there is no distinction between them in the texts.
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