Following The Trail To AI

Many AI start-up stories I recall can trace their history through well known phases: the initial hype and formation phase sometimes leveraging a thesis, book or even a simple Power Point presentation based on a new heuristic in hopes of motivating Angel investors. If funded this usually leads to a 4 P’s of the Marketing Mix roll out plan driving towards the launch phase long before the product has been developed/tested.

Often the real programming work then begins and, especially in the case of AI, problems can lead to a kind of combinatorial explosion of issues unless there is incredible focus on the task at hand. An unfortunate consequence can be pressure to produce a tightly bounded, domain specific solution so as to make good on promised dates and give investors a warm and fuzzy feeling that they’ve picked a good pony. Product schedules, which all business naturally use as a guidepost, is a critical yeardstick to measure successful execution but sometimes problematic when the solution, like real AI, requires so much different thinking, creativity and has proven so elusive for so many decades for so many. I expect someone creatively designing and developing off in the quiet contemplative corner of their garage, without the pressure of paying back investors before the next quarter, has as good of a chance as anyone these days given the ubiquity of low cost primary and secondary storage and incredibly fast microcontrollers that cost less than a cheeseburger.

The initial roll out of many AI products burns brightly at first and is further fanned with accolades by the tech press; but, as so often has happened in the past, fails to deliver a real-time system commensurate with the things humans do so easily. For good reason this fact has jaded many today who in years past might otherwise have bet their tenure/jobs and/or pocketbooks on any one of the endless sequence of new AI paradigms.

Nowadays if you even anecdotally mention an AI project you’re working on you’re likely to get the glazed, ‘can we move onto something real expression’ or stony silence from competent developers and tech investors alike. There is often a long, perquisite recounting, starting with why deterministic rules can’t cut it to the over fitting of neural networks, in order to explain why all the obvious AI solutions of the past haven’t produced much beyond the glue logic of the best Loebner prize winners. The task is a bit easier when talking to someone with the magical 10,000 requisite hours of expert qualifying software and firmware development experience. I had passed that mark several times over in my career and was still confident simple if then else statements would be all that’s needed to produce HAL before stumbling upon tech classics like “Parallel Distributed Processing” or those for all audiences like “Computer Power and Human Reason” by the great Joseph Weizenbaum.

That said, I still expect the simple control flow mechanisms inherent in machine instructions of the last 70 years actually will be all that’s required to usher in breakthroughs in AI someday, but likely as a Turing machine emulation of the increasingly well understood scaffolding of the neocortex, with its many dissimilar ensembles of neuron layers, hosted by some enormous cluster of CPUs; capable of exceeding the signaling speed of billions of axons firing in parallel and organizing information around the temporal nature of memory . Given all the AI hyperbole of the past, or simply some of the bizarre implications and complexity of the subject, I can understand why many choose to loudly sigh when the subject is broached. But it’s also hard for developers like me to not talk about something we’re so excited about after we’ve realized the set of problems that will be solvable after this one problem is solved will have as transformative of an impact on society as PCs or the Internet.

I am happy to report that I’ve made it to this point in my career/life and I’m still very enthused about what AI has to offer and am in constant awe of how exceedingly difficult it is to mimic the even simplest biological actions – I find no reason to avoid the topic, no matter how untenable it may seem no matter how fragmented its starts and stops of the past. To me it’s as if the problems of before have simply underscored how best to make progress now. In some sense its right there in front of us all now: folks at conferences seemingly scream the solutions through their papers and presentations on theories of machines encapsulating massively parallel, self organizing algorithms – this is clearly what all neuron hosting systems are empirically built from. We might best be served by the example of the Wright Brothers deciding the best way forward was to waste a few afternoons watching birds in flight.

Its obvious now the road to AI that embodies characteristics of the neocortex will involve a Himalayan effort and some of the road ahead appears to me to be about as opposite of a direction over the first attempted passes as if one had decided instead to walk around the entire mountain range. I for one look forward to the journey, cheer along those I can see are ahead of me or those just now starting and I expect there are many like me simply enjoying their own journey everyday. I hope to see someone finish before my time on the trail is over.

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