Perhaps, like me, you were a fan of Robert Heinlein’s awesome works of science fiction. During my Heinlein hey-day, I was compelled to read everything I could find written by him. Heinlein was one of many binge-reading authors who spoke to me on some level. Others, including Hemingway, Hermann Hesse, Robert Silverberg, Carlos Casteneda, Tom Robbins, and Orson Scott Card, all helped to serve as guides for my perceptive constructs. A philosophy of perception, a world view, an appreciation for my own perspective and the limitations and gifts of same, were all formed in some portion by binge-reading these authors.

Heinlein lent a litany of colors to my perceptive palette, including the idea of a terribly long life. Tom Robbins touched on it too in Jitterbug Perfume, but Heinlein devoted a multi-novel theme to that idea. Why not live for hundreds of years? To what extent do our own self-limiting beliefs allow aging and ultimately death to take us, when it is not necessarily inevitable? What if, by belief, and by taking action based on belief, we can live well beyond what is considered normal, maintaining youthful vigor and stamina well in multi-century lifespans? This concept has been one I have considered for many years.

Lazarus Long was Heinlein’s character who exemplified the idea across many of his novels. Born in 1912, Long managed to live over two thousand years with the aid of periodic rejuvenation techniques. The specifics of the rejuvenation techniques are not explored in detail, except for a couple of hints: 1) They are painful. Pain is an aging goad. So something must be done to alleviate the pain without pain-killing drugs, which have too many side effects. 2) Pain is something we remember. So the elimination of pain was a simple process of administering a different sort of drug, a powerful one he called Lethe, which interfered with memory formation for a period of time. Essentially, Lethe (which was named for the mythical river of forgetfulness) prevented short term memories from forming….and since pain is something we remember, there is no pain. The ‘now’ of pain was forgotten as quickly as it formed, hence, no pain. The otherwise painful specifics of the rejuvenation process could then continue.

Having experienced and acutely remembered some moments of severe pain in my own life, and having witnessed the pain inherent in so many medical processes, I think there is something that rings true of Heinlein’s analysis. The river of forgetfulness might be a stretch….but then again, perhaps that too will one day become a medicinal choice with far fewer side effects than current pain-relief alternatives may offer. Though it may be a bit ironic that the rejuvenation process, which helps to restore and augment our memory, requires memory loss in order to be truly effective.

So why not stay vital? Why not live for hundreds of years as a vital human being?

A bevy of recent announcements in this age of exponential innovation point to the real possibility of dramatic life extension. Just this past week, published studies for immune system repair and general cellular health restoration were published.

How about a fashionable exoskeleton to restore strength and balance to those wobbly legs and aching joints? Or a wearable collagen suit to restore youthful skin and glow? Our bodies, inside and out, are soon going to be able to enjoy the fruits of rejuvenation that kept Lazarus Long going.

And what about our brains? Aren’t we all slowly spiraling into the abyss of an inevitable age-related dementia? Maybe not. Between over-the-counter nootropicsm, cognitive therapies, and even AI-enhanced cognitive prostheses, not only might we enjoy the multi-century vitality of life, we might even enhance our innate capabilities beyond what any of us may have realized to date. In my previous post I offered the idea of telepathy realized by tractable technologies. What not this, and other super powers?

There is a new rejuvenation coming — one which will allow some percentage of the vast number of our aging population to remain vital, youthful, and productive. The disruptions coming from such demographic booster shots will be at least as acute as any we have seen to date from this yet nascent Network Age. And like other innovations I’ve optimistically covered over the past few years, sign me up! I’m just getting started!

Before reading, note that the topic of solipsism is discussed somewhat in earnest herein. A bit of rambling should therefore be expected.

I suffer from aspirational polymathism. It is a disease that has plagued me since my earliest memories. As a child I was not content to study just one or two subjects. I craved constant stimulation from a wide array of new subjects, new ideas, new challenges, and asipred to master them all. Perhaps had we had more of the prescriptive remedies so popular today to address such childish thinking, I might have been diagnosed as hyperactive or ADD, been given my control meds such as to dissuade such childish ambitions. Then perhaps I would have had less interest in such a wide and crippling array of fields of study. And I might have learned to appreciate golf. But those were less sophisticated times. Alas, I accept my handicap and do not allow it to disturb my otherwise placid nature. In fact, I am fortunate to have found an avocation that sometimes even lauds this general scholar disease.

One of the attributes of aspirational polymathism is a constant need for the stimulation from new and deeper subjects to study. And it can’t be just one at a time. At any given time, I don’t have one book I read on the Kindle app on my iPad, but generally three or four open and active on my queue, processing in parallel. I read, switch off to another, read more, and so on. Plus I have NetFlix or Amazon Prime or HBO running in the background, sometimes with simultaneous video streams from one course or another, sometimes working on code or email or PowerPoint, all at the same time. It can be maddening, but also rewarding. Sometimes I fool myself into believing that I actually get more accomplished by giving full vent to my disease. I fool myself into thinking I learn more and faster when I indulge my manic mode.

I confess that I do also enjoy an over-the-counter nootropic stack of my own concoction, sort of a Limitless fantasy I have, pursued for the past couple of years, and frankly it does seem to help my focus considerably. But aspirational polymathism does come with its own set of drawbacks. One of which is a tendency to jump very quickly to consider myriad down stream probable outcomes, with bifurcating branches of complex scenarios, most of which are way out of main stream thinking, as it were. My initial take on Google’s first TPU announcement, for example, was one of those moments. The more recent emergence of TPU2 and all it implies is another. But first a little more background.

So I have been reading SuperIntelligence and at the same time reading a text on Quantum Mechanics, and at the same time digesting Alan Turing’s 1950 paper for an AI course I was auditing online. Although I had read about Turing’s paper many times and read reviews and critiques, I had never actually read the original.

Turing’s paper is probably the cornerstone of all Artificial Ingelligence R&D today, if not also the backbone of Theoretical Computer Science. I remember something Scott Aaronson wrote about Turing’s paper — how 70% of AI today can be traced back to that work. But it’s not the Turing Machine, nor the Church-Turing Thesis, nor the Imitation Game itself which struck me as especially germane from Turing’s paper. All those innovations I have read about, considered, appreciated, and more-or-less learned over the past few decades. No, it wasn’t all that from Turning’s seminal paper. It was something very different. It was statements he made about solipsism. The fact that he used the term 3 times in the paper is probably not all the surprising given the nature of the question: Can Machines Think? But When presenting his counter-arguments to those who, at the time, would out-of-hand dismiss the very question, Turing outlines a series of arguments to refute the detractors. He categorized objections based on perspective, for example, theological objections, or mathematical objections. One in particular, from the perspective of consciouslness, caught my fancy.

Can machines think? First we need to stipulate what we mean by ‘think,’ which is not as straight-forward as one might….think. So what does that mean? In denying the validity of the Imitation Game (i.e.: the Turing Test) the objector in question, Sir Geoffrey Jefferson, a British neurologist and pioneering neurosurgeon, expressed the view that writing a sonnet or composing a concerto, based on thoughts and emotions felt, and was the basis for judgement; and a machine was incapable of such feeling, and therefore could not think. Per Turing:

“According to the most extreme form of this view the only way by which one could be sure that a machine thinks is to be the machine and to feel oneself thinking. One could then describe these feelings to the world, but of course no one would be justified in taking any notice. Likewise according to this view the only way to know that a man thinks is to be that particular man. It is in fact the solipsist point of view. It may be the most logical view to hold but it makes communication of ideas difficult.” (emphasis mine)

With humor and brilliance Turing refuted Jefferson’s argument; taken to the logical conclusion, Jefferson’s view necessarily leads to hard core solipsism. It may be the most logical view indeed. But, as Turing observed, the motivation to communicate just about anything is a bit hampered when sentience is not presumed to exist in the ‘other.’ Therefore, let us stipulate that other human beings, despite frequent evidence to the contrary, do actually think. Further, let us accept the evidence of thinking to be communication itself. I believe Wittgenstein would agree. Without communication in some manner, evidence cannot be gathered.

I believe the Philosopher Wittgenstein’s influence on Turing is in evidence here. Turing did attend lectures by Ludwig Wittgenstein at Cabridge in 1939. Per notes from students attending those lectures (another book I read in tandem), Turing and Wittgenstein enjoyed many robust exchanges. Given the Philosopher’s views on solipsism and the limits of understanding, it follows that Turing was influenced in some ways by the assertion that the limits of language means the limits of my world.

If thinking is manifested by the process of communication, indicating will and purpose, then we might agree that human life at all levels implies some modicum of thought. We ought not conclude otherwise. So to Turing’s question: can machines think? The question necessarily follows: can machines communicate? The two, per Wittgenstein, are linked.

So now let’s consider TPU2 and all it implies. In the ASIC v. GPU battle, even with awesome new GPU options, ASIC, at least for TensorFlow, will always win. Google’s TPU is TensorFlow burned into a chip. That’s great. Cool stuff. So where can I buy one? Where might a get a rack of TPUs to power my next AI startup? Answer: you can’t. You have to rent them from the Google Cloud. Maybe that’s not a big deal. But maybe it is.

As the market for deep learning grows, if it grows at levels predicted, the differentiation provided by the TPU will likely suffice to give Google an edge akin to that which it already enjoys in search. No need ot create and sell hardware — just rent out intelligence and take for Google mass user processing and data to sweeten the deal. I’m not sure when the “Don’t be evil” line gets crossed, but something tells me we may be getting close.

By the same token, I’m a big fan of TensorFlow. It’s awesome. This brings up the Tribble metaphor.

You remember Tribbles from StarTrek of course. Cool, happiness-inducing, soft and sweet little pets. Everybody wanted one. One arrived on the Enterprise and the entire crew was enchanted by the sweet Tribble. In relatively short order, the little thing made a baby Tribble. Now more of the crew could pet one and love on one. Then the two Tribbles gave rise to four….and so on. You get the moral of the story, yes? Exponential growth, even of really cool stuff, might have extremely deleterious unintended consequences. Our TPU enchantment may give rise to some really sweet and productivity-increasing applications — stuff we can’t yet even imagine in this early chapter of the innovation-galore Network Age. But beware the Tribbles. They may be hiding in our closets or under our beds.

Closing this entry, it was Wittgenstein who said, “Nothing is so difficult as not deceiving oneself.” The same is true of the aggregate of ourselves. I am quite sure I deceive myself with my aspirational polymathism, and I am not much more than borderline trainable and merely opinionated. And I am also quite sure, besides me, other people actually think. If there’s hope for humanity, it’s in software, solipsists notwithstanding. Can machines think? Perhaps we will know soon enough. But beware the Tribbles. They too come with progress.

With a wink at Jane Austen, the world seems to have grown rather enamored with Artificial Intelligence recently. That’s not to say that AI hasn’t been a pop-culture archetype since I can remember. In terms of actual software and solutions, however, the ebb and flow of AI is something like a Jane Austen novel played out over the past 50 years. And per Ms. Austen, “If things are going untowardly one month, they are sure to mend the next.” Though recently, things seem to have jumped up a notch and a discernible phase shift has occurred. Probably due to a Moore’s Law tipping point coupled with unicorn hunting packs of investors, the widespread commercialization of good old fashioned AI (GOFAI), turbocharged with GPUs and ASICs, is now very actually real. But a neural network is not always the best nor only choice.

Take, for example, a recent blog entry on OpenAI, the Unsupervised Sentiment Neuron. The neuron in question, though, is not your standard neural network variety, but something far simpler. Detecting sentiment from Amazon reviews (unsupervised) can be reduced to a single ‘neuron’ which, from a linear regression model designed to predict the next single character in a sequence, and in doing so the model learned an interpretable feature, and that simply predicting the next character in Amazon reviews resulted in discovering the concept of sentiment. Per the researchers:

“The sentiment neuron within our model can classify reviews as negative or positive, even though the model is trained only to predict the next character in the text.”

Classifying sentiment accurately from short bursts of text using a next-character prediction engine is pretty awesome. The thing is, the sentiment use case was never a target, never pursued, and never predicted with the next-character work. It was one of those sweet side effects that often happens when we pursue the unknown. Like the microwave oven, the technology for which was discovered while researching radar gear. Or penicillin, quite accidentally discovered due to a sink full of dirty dishes. Metaphors notwithstanding, the emergence of an unsupervised classification model from the training and execution of a supervised learning machine model is very cool. And beyond the cool factor, new research into linguistics and information theory is clearly implied.

The epic fail of prediction systems the world witnessed on Election Day in the USA in 2016 may have, perhaps, discredited data science to some degree, or at least given us pause when it comes to machine learning, prediction engines, and the stock we place in such innovations. But old school methods, like the next-character predictor, may yet hold surprises for us. I am a huge fan of software and especially AI systems. But our sentimentality for more mature pursuits like NLP may yet hold surprises for us.

Do you know the Edge? It’s one of those web sites I read periodically…a place to seek out new ideas, emerging memes, and interesting discussions. Founded in 1995, they got traction a couple of years later, so this year (2017) they are celebrated their 20th anniversary. The Edge is one of those sites that has survived entirely due to well-considered, intelligent, quality content. Their motto: To arrive at the edge of the world’s knowledge, seek out the most complex and sophisticated minds, put them in a room together, and have them ask each other the questions they are asking themselves.

So I occasionally read postings on the Edge. They ask an annual question of a small group of intellectuals each year and provide the essays in a loosely coupled collection of ideas. Last year, for example, their question was, What do consider the most interesting recent [scientific] news? What makes it important? The responses, as you might imagine, were wide and varied. Have a look here at the table of contents for 2016. Each short essay makes for excellent reading. The collection for 2017 is, as one might expect, are also quite salient.

The Edge question for 2017: What scientific term or concept ought to be more widely known?

Two essays from the 2017 collection I now juxtapose here. The two, by coincidence, hail from two men whose work I have cited before: Stuart A. Kauffman and Scott Aaronson. Both are apparently acolytes of a humanist view of reality, akin to Richard Dawkins, in outlook if not tone. From what I’ve read by Kauffman, whom I do admire, he does leave a little wiggle room for a modicum of awe. But Aaronson appears to exhibit far less humility. They are both highly accomplished men in their fields, and have a right to be unabashed in their views. But, as my father used to say, we all put our pants on one leg at a time. It is from that leveling and loving thought this entry finds inspiration.

As you might guess, the terms suggested by Aaronson and Kauffman map to elements of the title of this blog: State and Non-Ergodic. These are the terms the two gentlemen in question assert should be of greater concern to the scientific community.

Let’s take them separately before comparing. First the concept of state, which comes from Aaronson, a quantum computing expert. In computer science we understand and appreciate finite-state machines — essentially a mathematical model of computation, with emphasis on finite. From Aaronson’s essay, state also containes a system’s hidden reality. Per the essay, state “…determines its [the system’s] behavior beneath surface appearances. But in another sense, there is nothing hidden about a state — for any part of the state that never mattered for observations could be sliced off with Occam’s Razor, to yield a similar and better description.”

Despite his semantic objectionsSemantic objections aside, Aaronson is arguing is not arguing for a soft deterministic model of the universe, or at least but rather a strongly predictable computational model. All we’re missing is an understanding of the hidden variables, to the extent we even need them. This is especially interesting given the fact that Aaronson clearly has a world-class understanding of quantum physics. His reliance on reference to and dismissal of Bohmian mechanics is notable. But the point is, from Aaronson’s perspective, state is actual, or at least computationally highly probabalistic, it as real as real can get, and it even transcends the otherwise indeterminate nature of quantum physics proffered by other interpretations. Per Aaronson, all that what is needed is a fuller understanding of the ‘hidden variables’ of reality. That appears to be Scott Aaronson’s view of the state of objective reality in our discussion.

By contrast, Kauffman (with far fewer words) suggests the term non-ergodic should be the hashtag science trending meme in 2017. To better get our heads around non-ergodic, we first need to understand something of the ergodic hypothesis, which states that “…over long periods of time, the time spent by a system in some region of the phase space of microstates with the same energy is proportional to the volume of this region, i.e., that all accessible microstates are equiprobable over a long period of time.” (Emphasis mine)

So if that is ergodic, then non-ergodic must mean that all accessible microstates are not equiprobable over a longer period of time.

Which also suggests that if not all states are equiprobable, then what determines the missing states? Why are not all states likely? Ergodicity is often assumed in the statistical analysis of computational physics. Yet Kauffman argues the non-ergodic deserves significantly more attention….why?

Kauffman is a biologist, an expert in quantum physics, and probably a bit of a philosopher, though I’m not certain he would agree with all those. He has long been a proponent of the view that the infinite adjacent possible is perhaps entailed by something beyond Darwin’s blind fitness rubric, at least insofar as biology is concerned.

Though an infinite number of combinations of DNA are possible, per Kauffman, only a tiny subset of those have been or will be explored in the entire life span of the known universe. Why? Morphological constraints? Something else? We simply don’t know. But a revolution in thinking implied by Kauffman may very well be the key to unlocking the next door of human understanding. Hence, non-ergodic properties of evolutionary biology ought to be main stream thinking fodder. Because we don’t know why the biosphere, and by implication the universe that contains it, is as it is.

So, on the one hand we have Aaronson, with the view that the Mind of God can be discerned in all cases by simply determining the essential hidden variables (via a highly probabilistic computational model). And on the other, Kauffman, who sees confounding patterns, but implies this too may simply require better understanding of the hidden variables of nature to yield a non-ergodic model — which too would rival the Mind of God in ambition.

See how I introduced the concept of “the Mind of God” there? I thought that was clever of me. By this I do not necessarily mean the book by that name, though I am confident Paul Davies did a fine job of framing the argument for a creative deity as root cause as may be possible from a scientific perspective. But rather something simpler — the Mind of God as the designer of (human) consciousness itself, which is something we really don’t understand from a scientific perspective.

Per Aaronson, the quantum slit experiment would does not harbor randomness at all. And maybe he is correct. See this article on Bohmian pilot wave theory. But if the universe is essentially a predictive computational engine with no room for quantum uncertainty, then it must also follow that the human brain, which is comprised entirely of the stuff of the universe, must also be similarly entailed.

I turn to my friend Dr. Kauffman, smiling wryly on the corner of State and Non-Ergodic. I think he was recalling Dean Radin’s talk in Tuscon in 2016. Yeah….human consciousness. There’s the rub. Regardless of what pilot wave theorists may think, there is something special, measurable, and repeatable about the observer. And statistically, we can prove it.

So let’s agree to meet at the corner of State and Non-Ergodic. We can decide which way to turn after we’re there.

Happy 2017!

UPDATE: Per a comment from Scott Aaronson, he was not and is not advocating a Bohmian interpretation of Quantum Mechanics. He explicitly states, “…a state might only determine the probabilities of various observations, not the observations themselves.” He goes on to further state quite clearly that he personally “…regard[s] the indeterminism of quantum measurement outcomes as a settled fact, to pretty much the extent anything in physics is a settled fact.” I am appreciative of the clarification. Hence, the updates to my original post with much gratitude. Please adjust digestion of above blog entry accordingly.

Though I have loved math since childhood, I am not a mathematician. But I love studying math. And sometimes I find myself exploring numbers just for the fun of it, especially when I’ve been using RStudio or Octave for a project.

Finding Euler’s number is not improbable nor terribly difficult. The number is everywhere. There is more than one youtube video demonstrating the emergence of using various techniques. Euler’s number is one of those really beautifully transcendent properties of mathematics that puzzles and mystifies. And I know of nothing closer to inherent truth in this world than the beauty of mathematics.

But I wasn’t thinking about when I found it where I did. I was thinking about something else. I was pondering the relationship between an infinite series of integers and how they might relate to rational numbers. The question that occurred to me was simple — is there is limit to the following:

Note that I wasn’t considering because dividing by zero is not a good idea. And since I’m not using the values from the infinite series as an exponent, zero becomes problematic. So for the sake of cleanliness I stated the problem as listed above and wondered if a limit would emerge.

As a software developer, my approach to exploring the problem was naturally to write some code. The R code for this little project can be downloaded from github if you’re interested. As gets larger, the fraction gets smaller. Clearly the higher the value we assign to the return value will become increasingly smaller when compared to earlier iterations. But is there a limit?

As I played with the code, I realized that even though the function would always return increasingly smaller values as grew larger, it would nevertheless continue to grow and grow, even as approached infinity and approached zero.

But then my thoughts turned to the ever increasing number of fractions needed to get to the next highest integer value. For example, when , the value returned from the function is 1. So to get the return value to the next highest full integer value, in this case 2, the function must add:

Which returns the value 2.08333. For the next highest value, the function must add:

Which yields 3.019877. And so on. The number of fractions required to get to the next highest whole integer value increases.

Okay. Then it dawned on me that there might be a pattern to the increase in the number of fractions. So I tried a few experiments. What I discovered is emerges from a simple ratio created by the number of fractions required to attain the next highest integer value in the series.

So, for example, the total number of iterations required to attain 2.xxx is 4. The total number of iterations to attain 3.xxx is 11. Divide 11 by 4 and the result is 2.75. To attain the next highest integer part, 4.xxx, requires 31 iterations of the function. Take the current value for iterations and divide by the previous value, and the result is 2.818182. Continuing, to attain the next highest integer part, 5.xxx, requires 83 iterations of the function. Take the current value for iterations and divide by the previous value, and the result is 2.6774194.

If we continue, eventually the number of iterations of the functions at the current integer value divided by the number of iterations from the previous integer point appears to hover around plus or minus a very very small fraction.

integer portion	value of j	prev j	j / prevj	value of i	prev i	i / previ	i / j
2	3	1	3	4	1	4	1.333333333
3	7	3	2.333333333	11	4	2.75	1.571428571
4	20	7	2.857142857	31	11	2.818181818	1.55
5	52	20	2.6	83	31	2.677419355	1.596153846
6	144	52	2.769230769	227	83	2.734939759	1.576388889
7	389	144	2.701388889	616	227	2.713656388	1.583547558
8	1058	389	2.719794344	1674	616	2.717532468	1.582230624
9	2876	1058	2.718336484	4550	1674	2.718040621	1.582058414
10	7817	2876	2.718011127	12367	4550	2.718021978	1.582064731
11	21250	7817	2.718434182	33617	12367	2.718282526	1.581976471
12	57763	21250	2.718258824	91380	33617	2.718267543	1.581981545
13	157017	57763	2.71829718	248397	91380	2.718286277	1.5819752
14	426817	157017	2.718285281	675214	248397	2.718285648	1.581975413
15	1160207	426817	2.718277388	1835421	675214	2.718280427	1.581977182
16	3153770	1160207	2.718282169	4989191	1835421	2.718281528	1.581976809
17	8572836	3153770	2.718281929	13562027	4989191	2.718281782	1.581976723
18	23303385	8572836	2.718281908	36865412	13562027	2.718281862	1.581976696
19	63345169	23303385	2.718281872	100210581	36865412	2.718281868	1.581976693
20	172190019	63345169	2.718281784	272400600	100210581	2.718281815	1.581976711
21	468061001	172190019	2.718281836	740461601	272400600	2.718281828	1.581976707
22	1272321714	468061001	2.718281829	2012783315	740461601	2.718281829	1.581976707

One common method to derive is:

But with the methodology I discovered using RStudio, no exponential function is required. It is a simple ratio. Using the initial approach I used while experimenting:

And count the number of iterations of the function as the value of increases. Round of the fractional part of the returned value, leaving only the whole integer part. When the integer part exceeds the previous integer part, divide the current count by the previous count and the result appears to get increasingly close to .

The current iteration count (i) divided by the previous iteration count (previ) also holds true for the difference between i and previ as well. When the function tracks a difference value, j, which results from i – previ, and prevj = j – prevj, then that difference between iterations (j / prevj) also appears to hover around after a number of iterations.

I created a github repo for the code here.

Another interesting thing which emerged is the consistent and somewhat puzzling number that emerges when we divide i by j or previ by prevj. The number that emerges appears to be close to 1.581977. I can’t find that number in any math literature, but it does appear to be interesting. If we use as the hypotenuse of a right triangle and as one of the sides, the missing side would be 1.574976…which is close but not that close. I was hoping a simple relationship like that might emerge. But I don’t think it’s that simple.

The emergence of from a simple ratio of iterations over a simple sequence of 1 divided by consecutive integers, however, does appear to be an actual phenomenon. While it is likely others have found this magic before me, I have not found the work. Then again, as I stated at the outset, I am not a mathematician. I just like math.

I attended a live broadcast of a recent Google I/O Conference here in Utah — akin to a Super Bowl Party for geeks — and I came to a minor epiphany. My hope in going was to learn a bit about Google’s Cloud offerings in order to better compare Google’s Cloud choices with AWS. But not much in that vein was covered, at least not at the sessions televised to the Xactware meeting rooms in Lehi. Evidently the crowd gathered there had more of an Android developer penchant, and there’s nothing wrong with that. But my choices for sessions of interest were in the minority. And with limited rooms to assemble, most of the sessions I had hoped hear were not live in Lehi. Luckily, most of it was captured by our friends at Google and posted within hours of the conference.

One thing, however, that did grab my attention at one of the live sessions and has had me ruminating for a couple of weeks since is the mention of their TPU in one of the keynotes: The Tensor Processing Unit (TPU).

The TPU is a TensorFlow machine learning stack burned into a chip. Faster than a bank of CPUs, more powerful than a bevy of GPUs, able to leap over a collection of FPGAs, the TPU ASICS (Application Specific Integrated Circuits) promises to accomplish exactly what Google says it will in terms of performance increases: an order of magnitude better Machine Learning performances, for a lot less energy costs.

In a recent discussion with my friend Wil Bown, AI/VP developer extraordinaire, he mentioned the huge performance boost BitCoin mining efforts have achieved from burning the essential code into ASICS, boosting performance well beyond FPGAs, which had previously trumped GPU banks, which themselves only a year or so ago were the cutting edge for coin miners.

So ASICS. Yeah, that does makes sense. In some instances, as layers of software infrastructure stratify, burning the full application stack to a chip will yield that final incremental competitive advantage. In fact, we could assert in general terms that entire industries have been built on that principle. For example, before there was Cisco, there was software, running on vanilla UNIX systems which functioned as switches and routers. Most often the higher costs of ASICS production limit their adoption to military applications, or aerospace, or manufacturing processes, though sometimes a more common use case emerges — like Cisco — that can utterly change everything.

But to learn that Google assembled a raised floor environment consisting of racks TPU laden servers, which was a critical factor in their Go Champion win earlier this year, gave me pause. TensorFlow became open source endeavor just a few months ago. In my mind, open source is the beginning of the journey, and certainly not the end. To therefore commit the application as such in silico seems quite premature if not remarkably misguided. It’s too early.

Machine Pareidolia

Pareidolia is is a psychological phenomenon involving a stimulus (an image or a sound) wherein the human mind perceives a familiar pattern of something where none actually exists. The face on Mars, or the Messiah in a piece of toast, for example. We humans see all sorts of patterns in the random distributions around us. Every named constellation is a pareidoliac construct. We seem to be hard wired to see faces — in clouds, trees, rocks, potatoes…it’s rather common. But we’re also aware that the faces we see in clouds are not really faces. They’re clouds. Machines, on the other hand, might not be tuned as well.

Consider the school bus and the ham sandwich.

Work by a group of researchers at the University of Wyoming expose what may be an inherent and dangerous vulnerability in state of the art neural networks: Deep Neural Networks are easily fooled. The image depicted here from their study using a well-trained DNN concluded with a high (99%) confidence that the layered stack of colors was indeed a school bus. And it’s not just one image. The school bus is but one example of a bevy of images, specifically designed to fool the DNN. To my human eye, the image is more easily identified as a sandwich — cheese on dark bread, perhaps. A school bus? Probably not. Maybe that’s just me — but I am confident, given the choice, most humans would pick the sandwich over the school bus to best explain the image here.

Clearly we cannot place deep confidence in Deep Neural Network implementations like TensorFlow just yet. But the fact is, we already have: Google has already baked it in and raised the floor, as it were. Therein lies the temerity.

The hacking possibilities are legion. If I know I can fool the image processing systems with cleverly crafted images, any security system relying on a DNN component is vulnerable. Any digital visual processing function — from facial recognition to autonomous vehicle piloting — can therefore be rather easily compromised. Any visual processing application can be hacked.

A Different Kind of Turing Test

In addition to my misgivings about the Google announcement, another question comes to mind as I ponder the TPU and the adjacent possible it may now entail: Will DNN systems, trained independently from different data sets, draw similar pareidoliac conclusions?

In other words, just as two people might see a face in the same picture of a cloud, might two systems see the same school bus in the image of a ham sandwich? If so, is that perhaps another test for ‘intelligence’ albeit not human? Might a different kind of Turing Test then emerge to measure the consciousness of our digital offspring?

The Temerity

I applaud bold advancements in technology. I admire competitive juices. But I’m just a little concerned we may now be at a very critical juncture in spacetime; a time in which the rate of innovation finally and fully exceeds the ability of the fitscape to adequately test it.

When the rate of innovation exceeds the ability of the fitness landscape to adequately test the innovation for fitness, bad stuff will result — that is my fear. The temerity of the TPU may be a step too soon, or too far, or both.