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As Artificial Intelligence becomes more commonplace through hype and reality, it may be useful to characterize the plethora of methodologies and technologies that are part of the thoroughly confusing medley. Conventional AI appears to be largely driven by the left brain as engineers, data scientists, and technologists flock to the dream, ably assisted by capital, seeking returns somewhere. Generally speaking, that is a prescription for disaster as technology, data and mathematics do not typically solve any problem of importance to enterprises. Granted, game playing is interesting and faking human voices and interactions equally compelling but none of these are going to change anything in the lives of ordinary people. And, they add little value to the economy or even companies.
The search giant recently proclaimed that AI gets more aggressive as they get better. This observation is not substantially different from the twitter girl created by another giant, that turned nasty. What these companies seem to be missing is that building AI bottoms up from historical data will simply reflect existing information content. More generally, these AI agents should reflect society and such observations add no value to the emerging arena, except talking points. And as the hardware company found out down South recently, transforming an organization requires a bit more than a "pizza-sized box," albeit it has solved most of the world's problems already. It is about economics, stupid!. And that requires the silent right brain. AI has enormous potential but only if they are developed with a right brain dominance. It is a tough task as the normally shy right brain prefers to work from the background and simply muffles out the noise created by the left hemisphere. The old-fashioned concept of, "seeing the big picture," is still very important before diving into the details. Education systems tend to churn out left only brains in great numbers and this is problematic for the emerging regime. Scientists, whether real or of the data kind, cannot solve the problems facing humanity, let alone companies. As the singularity enthusiasts revise the date of arrival of the discontinuity, it is important to remember that civilizations did not advance by tactics in the past and it is unlikely in the future. Read more: http://www.scientificsense.com/2018/10/right-brained-artificial-intelligence.html#ixzz5TTzMYc6K Under Creative Commons License: Attribution
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A recent publication (1) that describes a Brain-machine interface (BMI) to control a robotic arm simultaneously with human arms open up interesting possibilities for maximizing brain utilization. By a quirk of nature, humans have been endowed with an organ that far surpasses their routine needs to live and die. With simple objective functions, humans have substantially sub-optimized this endowment. But now, there may be mechanical means to keep the organ interested.
There has been a lot in the literature about the inability of humans to multitask. However, it is possible that multitasking improves with practice just like anything else. The quantum computer they carry, albeit being an energy hog, requires little to maintain from an infrastructure point of view. And the calorie requirement to keep it going is very small in the grand scheme of things. Hence, maximizing the use of the brain is an important consideration for every human and humanity in general. Brain utilization shows an upward trend as people network across the world, surpassing the constraints offered by race, religion, and ignorance. This electronic extension of the brain has been unambiguously good for humanity but it feels like there is still a lot in the tank for every individual. If she can multiply limbs by mechanical multitasking it is likely that such an activity will grow neurons upstairs with unpredictable beneficial effects in the long run. Extending the brain - mechanically and electronically - is dominant for humans. That will allow them to get over all the tactical problems currently plaguing humanity. (1) BMI control of the third arm for multitasking: http://robotics.sciencemag.org/content/3/20/eaat1228 Artificial Intelligence, the contemporary darling of technologists and investors, has been largely focused on trivial consumer-oriented applications and robotics/automation, thus far. Constrained by conventional computing, AI has been bottled up in hype and confusing name calling. What the AI enthusiasts do not seem to understand is that AI was never meant to be a technology that fakes what a human being appears to do externally but rather it was supposed to replicate her thought processes internally. As the search giant demonstrates how its technology could fool a restaurant reservation system or play games, as the world's largest shipper of trinkets demonstrates how they could send you things faster and the purveyors of autonomous vehicles demonstrate how they could move people and goods without the need for humans at the driving wheel, they need to understand one important thing: these technologies are not using AI, they are using smarter automation. They do not replicate human thought processes. They either fake what a human appears to do or simply automate mundane tasks. We have been doing this for over half a century and as everybody knows, every technology gets better over time. So, before claiming victory in the AI land, these companies may need to think deeply about if their nascent technologies could actually do something good.
However, there is a silver lining on the horizon that could move AI to real applications (1) including predicting and controlling the environment, designing materials for novel applications and improving the health and happiness of humans and animals. AI has been tantalizingly "close" since the advent of computers. Imagination and media propelled it further than what it could ever deliver. As with previous technology waves, many companies attempt(ed) to reduce this problem to its apparently deterministic components. This engineering view of AI is likely misguided as real problems are driven fundamentally by dynamically connected uncertainties. These problems in domains such as the environment, materials, and healthcare require not only computing resources beyond what is currently available but also approaches further from statistical and mathematical "precision." Less sexy areas of AI such as enhancing business decisions have attracted less interest, thus far. Feeble attempts at "transforming," a large healthcare clinic using a "pizza-sized," box of technology that apparently solved all the world's problems already, seem to have failed. Organizations chasing technology to solve problems using AI may need to spend time understanding what they are trying to tackle first, before diving head first into "data lakes" and "algorithms." Real solutions exist at the intersection of domain knowledge, technology, and mathematics. All of these are available in the public domain but the combination of this unique expertise does not. Humans, always excitable by triviality and technology, may need better skills to succeed in the emerging regime, driven by free and fake information and the transformation of this noise into better decisions. Those who do this first may hold the keys to redefining AI and the future of humanity. It is unlikely to be the companies you know and love because they are focused on the status-quo and next quarter's earnings. (1) http://science.sciencemag.org/content/361/6400/342 Read more: http://www.scientificsense.com/2018/07/redefining-artificial-intelligence.html#ixzz5MnJadvot Under Creative Commons License: Attribution Recent news of celebrities exercising a put option on life, apparently sub-optimally, has to be understood in a larger context. If the individual's expectation of aggregate utility from the point of decision to the expiry of the put is negative and deterministic, the exercise is optimal from her viewpoint. In a free society, the individual's optimum decision has to be upheld over society's desires.
However, if the individual's expectations are wrong or they are motivated by incorrect assumptions, then both the individual and society lose simultaneously. The solution to this rising epidemic is not tactical intervention but rather a strategic analysis of decision processes that lead individuals to a point of no turning back. One important area of further research is the struggle between the left and right brain processes. If the left brain dominates this negotiation, a linear and logical assimilation of known information, leading to a set of assumptions and subsequently an estimation of aggregate remaining utility, it may provide a deterministic or low volatility answer. In this case, the individual may incorrectly perceive an optimum exercise horizon. It appears that one solution is to re-engage the right brain more forcibly into the conversation. As humanity move faster toward a technology-led society where predictability is increasing, it is possible that left brain dominance will continue to increase. The accelerated growth in suicide rates in developed countries is a symptom of this phenomenon. The solution is the nourishment of the right brain and the imposition of possible penalties for left brain dominance. An objective function that is primarily driven by materialism and ego, will continue to favor the left brain. The goal for societies, then, is to nourish complete individuals and not efficient humanoids. To do this, one has to get away from conventional and precise metrics and embrace uncertainty more fundamentally. Humans, lining up on a conveyor belt to nowhere that appears to go ever faster, may want to look over the horizon and into the heavens, for inspiration. Read more: http://www.scientificsense.com/2018/06/the-naked-put.html#ixzz5HyCHiOMh Under Creative Commons License: Attribution A recent article (1) that demonstrates how neural networks could be used to approximate light scattering by nanoparticles is an interesting new direction. We appear to be approaching a regime in which prescriptive analytical solutions and conventional simulation become inferior to deep learning. This is exciting but it also presents a huge downside for the advancement of abstract knowledge. Models that show robust outcomes are welcome but a generation of new scientists, prone to taking to the machine to prove hypotheses, by feeding them small samples of historical data, could dampen theoretical advancement not only in Physics but also in other areas.
This struggle between empiricism and rationalism has been with humans from inception. Did they survive by predicting where the lion is likely to be by using historical data of previous (bad) outcomes or did they rationalize by abstracting the expected behavior of the animal? Did they predict when an animal is likely to attack by using historical data on the timing of previous attacks or did they understand the animal's incentives and available alternatives? Did they migrate incrementally by using predictions, originating from previous short excursions, or did they go boldly where no woman had ever gone? Were our ancestors empiricists or rationalists? It is difficult to ascertain one way or the other. It appears that empiricism has been a hidden attribute in our psyche for long. Till the advent of computers, rationalism appears to have dominated but since then, empiricism has been on a steep rise. In Physics, they now collect and stream data to find "new particles," without even asking why such observations are important. In medicine, they "high throughput screen" looking for the needle in the haystack, without a clear understanding of the mechanism of action. In economics, they regress data to find insights without asking whether they are insights at all. There is likely no stopping the trend. As computers get more powerful, empiricism will become ever more dominant. If this is a natural outcome of evolution, then, advanced societies elsewhere (if they do exist) would be asymptomatically approaching pure empiricism for knowledge generation. That could be there Achille's heal as it also means that their knowledge is dependent on the past. A planet full of robots, with no ability to abstract but with an infinite capacity to learn from the past, could be highly inefficient. Would humans retain inefficient qualities of being a human? It seems unlikely. (1) http://advances.sciencemag.org/content/4/6/eaar4206.full Read more: http://www.scientificsense.com/2018/06/deeper-learning.html#ixzz5HJUCR99I Under Creative Commons License: Attribution They have been riding high. The abandoned and somewhat less sexy field of Statistics has taken the business world by storm. Bottling old wine in new bottles certainly helped and now both venture capitalists and operating companies may be heading for a hangover. Engineers and statisticians have always wanted to be scientists and now they are crowned as such. There is a .ai company formed every 15 minutes by graduates of prestigious universities and there are capitalists with sacks of money willing to entertain them. As we have seen before, this movie will likely end in tears for many.
Data is certainly a good thing and applying "science," to it could also be good. But those who assert their "scientific credentials," based on regressions and neural nets should be aware that the slide rules they are using have been available for nearly half a century. Mathematics does not fade but asserting old ideas have suddenly sprung to life certainly shows the maturity and age of the emerging "scientists." Consulting firms have always been creative and some of the most famous ones, who could hardly spell "data science," just a few years ago are now pretending to be experts at it. Conferences are plenty where the scientists meet their seekers and the vendors portray their wares almost like the bartering that was routine a few centuries ago. They flow tensors, cognitive networks and even hardware in a Pizza size box, that apparently has solved all the world's problems, already. Stop hiring "data scientists." They are ordinary human beings with bias and they could do your companies a lot of damage. Read more: http://www.scientificsense.com/2018/02/stop-hiring-data-scientists.html#ixzz5Gqmdf4G4 Under Creative Commons License: Attribution |
AuthorGill Eapen is the author of Scientific Sense, a global blog that attracts readers from over 150 countries Archives
October 2018
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