The Retiring Mind, Part III: Autonomy

February 26, 2016February 26, 2016 Mark DavisLeave a comment

Robert Gordon’s book on the end of industrial revolutions recently came out. I’ve been arguing for a while that the coming robot apocalypse might be Industrial Revolution IV. But the Dismal Science continues to point out uncomfortable facts in opposition to my suggestion.

So I had to test the beginning of the end (or the beginning of the beginning?) when my Tesla P90D with autosteer, summon mode, automatic parking, and ludicrous mode arrived to take the place of my three-year-old P85:… Read the rest

The Goldilocks Complexity Zone

February 10, 2016February 10, 2016 Mark Davis1 Comment

Since my time in the early 90s at Santa Fe Institute, I’ve been fascinated by the informational physics of complex systems. What are the requirements of an abstract system that is capable of complex behavior? How do our intuitions about complex behavior or form match up with mathematical approaches to describing complexity? For instance, we might consider a snowflake complex, but it is also regular in it’s structure, driven by an interaction between crystal growth and the surrounding air. The classic examples of coastlines and fractal self-symmetry also seem complex but are not capable of complex behavior.

So what is a good way of thinking about complexity? There is actually a good range of ideas about how to characterize complexity. Seth Lloyd rounds up many of them, here. The intuition that drives many of them is that complexity seems to be associated with distributions of relationships and objects that are somehow juxtapositioned between a single state and a uniformly random set of states. Complex things, be they living organisms or computers running algorithms, should exist in a Goldilocks zone when each part is examined and those parts are somehow summed up to a single measure.

We can easily construct a complexity measure that captures some of these intuitions. Let’s look at three strings of characters:

x = aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa

y = menlqphsfyjubaoitwzrvcgxdkbwohqyxplerz

z = the fox met the hare and the fox saw the hare

Now we would likely all agree that y and z are more complex than x, and I suspect most would agree that y looks like gibberish compared with z. Of course, y could be a sequence of weirdly coded measurements or something, or encrypted such that the message appears random.… Read the rest

Entanglement and Information

January 10, 2016February 14, 2016 Mark DavisLeave a comment

shannons-formula-small Research can flow into interesting little eddies that cohere into larger circulations that become transformative phase shifts. That happened to me this morning between a morning drive in the Northern California hills and departing for lunch at one of our favorite restaurants in Danville.

The topic I’ve been working on since my retirement is whether there are preferential representations for optimal automated inference methods. We have this grab-bag of machine learning techniques that use differing data structures but that all implement some variation on fitting functions to data exemplars; at the most general they all look like some kind of gradient descent on an error surface. Getting the right mix of parameters, nodes, etc. falls to some kind of statistical regularization or bottlenecking for the algorithms. Or maybe you perform a grid search in the hyperparameter space, narrowing down the right mix. Or you can throw up your hands and try to evolve your way to a solution, suspecting that there may be local optima that are distracting the algorithms from global success.

Yet, algorithmic information theory (AIT) gives us, via Solomonoff, a framework for balancing parameterization of an inference algorithm against the error rate on the training set. But, first, it’s all uncomputable and, second, the AIT framework just uses strings of binary as the coded Turing machines, so I would have to flip 2^N bits and test each representation to get anywhere with the theory. Yet, I and many others have had incremental success at using variations on this framework, whether via Minimum Description Length (MDL) principles, it’s first cousin Minimum Message Length (MML), and other statistical regularization approaches that are somewhat proxies for these techniques.… Read the rest

A Soliloquy for Volcanoes and Nearest Neighbors

December 18, 2015 Mark DavisLeave a comment

A German kid caught me talking to myself yesterday. It was my fault, really. I was trying to break a hypnotic trance-like repetition of exactly what I was going to say to the tramper’s hut warden about two hours away. OK, more specifically, I had left the Waihohonu camp site in Tongariro National Park at 7:30AM and was planning to walk out that day. To put this into perspective, it’s 28.8 km (17.9 miles) with elevation changes of around 900m, including a ridiculous final assault above red crater at something like 60 degrees along a stinking volcanic ridge line. And, to make things extra lovely, there was hail, then snow, then torrential downpours punctuated by hail again—a lovely tramp in the New Zealand summer—all in a full pack.

But anyway, enough bragging about my questionable judgement. I was driven by thoughts of a hot shower and the duck l’orange at Chateau Tongariro while my hands numbed to unfeeling arresting myself with trekking poles down through muddy canyons. I was talking to myself. I was trying to stop repeating to myself why I didn’t want my campsite for the night that I had reserved. This is the opposite of glorious runner’s high. This is when all the extra blood from one’s brain is obsessed with either making leg muscles go or watching how the feet will fall. I also had the hood of my rain fly up over my little Marmot ball cap. I was in full regalia, too, with the shifting rub of my Gortex rain pants a constant presence throughout the day. I didn’t notice him easing up on me as I carried on about one-shot learning as some kind of trance-breaking ritual.… Read the rest

The IQ of Machines

September 13, 2015September 13, 2015 Mark Davis1 Comment

Perhaps idiosyncratic to some is my focus in the previous post on the theoretical background to machine learning that derives predominantly from algorithmic information theory and, in particular, Solomonoff’s theory of induction. I do note that there are other theories that can be brought to bear, including Vapnik’s Structural Risk Minimization and Valiant’s PAC-learning theory. Moreover, perceptrons and vector quantization methods and so forth derive from completely separate principals that can then be cast into more fundamental problems in informational geometry and physics.

Artificial General Intelligence (AGI) is then perhaps the hard problem on the horizon that I disclaim as having had significant progress in the past twenty years of so. That is not to say that I am not an enthusiastic student of the topic and field, just that I don’t see risk levels from intelligent AIs rising to what we should consider a real threat. This topic of how to grade threats deserves deeper treatment, of course, and is at the heart of everything from so-called “nanny state” interventions in food and product safety to how to construct policy around global warming. Luckily–and unlike both those topics–killer AIs don’t threaten us at all quite yet.

But what about simply characterizing what AGIs might look like and how we can even tell when they arise? Mildly interesting is Simon Legg and Joel Veness’ idea of an Artificial Intelligence Quotient or AIQ that they expand on in An Approximation of the Universal Intelligence Measure. This measure is derived from, voilà, exactly the kind of algorithmic information theory (AIT) and compression arguments that I lead with in the slide deck. Is this the only theory around for AGI? Pretty much, but different perspectives tend to lead to slightly different focuses.… Read the rest

Machine Learning and the Coming Robot Apocalypse

August 20, 2015August 16, 2020 Mark DavisLeave a comment

Slides from a talk I gave today on current advances in machine learning are available in PDF, below. The agenda is pretty straightforward: starting with some theory about overfitting based on algorithmic information theory, we proceed on through a taxonomy of ML types (not exhaustive), then dip into ensemble learning and deep learning approaches. An analysis of the difficulty and types of performance we get from various algorithms and problems is presented. We end with a discussion of whether we should be frightened about the progress we see around us.

Note: click on the gray square if you don’t see the embedded PDF…browsers vary.… Read the rest

Reykjavik, Recurrent Neural Networks, and the 4th of July

July 4, 2015July 4, 2015 Mark DavisLeave a comment

Sitting at JFK reading Scholarpedia’s comprehensive entry on Recurrent Neural Networks, waiting for my flight to Iceland with my kid while the sun drops just to the north of Freedom Tower…priceless.… Read the rest

Intelligence Augmentation and a Frictionless Economy

June 6, 2015 Mark DavisLeave a comment

The ever-present Tom Davenport weighs in in the Harvard Business Review on the topic of artificial intelligence (AI) and its impact on knowledge workers of the future. The theme is intelligence augmentation (IA) where knowledge workers improve their productivity and create new business opportunities using technology. And those new opportunities don’t displace others, per se, but introduce new efficiencies. This was also captured in the New York Times in a round-up of the role of talent and service marketplaces that reduce the costs of acquiring skills and services, creating more efficient and disintermediating sources of friction in economic interactions.

I’ve noticed the proliferation of services for connecting home improvement contractors to customers lately, and have benefited from them in several renovation/construction projects I have ongoing. Meanwhile, Amazon Prime has absorbed an increasingly large portion of our shopping, even cutting out Whole Foods runs, with often next day deliveries. Between pricing transparency and removing barriers (delivery costs, long delays, searching for reliable contractors), the economic impacts might be large enough to be considered a revolution, though perhaps a consumer revolution rather than a worker productivity one.

Here’s the concluding paragraph from an IEEE article I just wrote that will appear in the San Francisco Chronicle in the near future:

One of the most interesting risks also carries with it the potential for enhanced reward. Don’t they always? That is, some economists see economic productivity largely stabilizing if not stagnating. Industrial revolutions driven by steam engines, electrification, telephony, and even connected computing led to radical reshaping our economy in the past and leaps in the productivity of workers, but there is no clear candidate for those kinds of changes in the near future.

… Read the rest

Evolutionary Optimization and Environmental Coupling

February 7, 2015 Mark DavisLeave a comment

Carl Schulman and Nick Bostrom argue about anthropic principles in “How Hard is Artificial Intelligence? Evolutionary Arguments and Selection Effects” (Journal of Consciousness Studies, 2012, 19:7-8), focusing on specific models for how the assumption of human-level intelligence should be easy to automate are built upon a foundation of assumptions of what easy means because of observational bias (we assume we are intelligent, so the observation of intelligence seems likely).

Yet the analysis of this presumption is blocked by a prior consideration: given that we are intelligent, we should be able to achieve artificial, simulated intelligence. If this is not, in fact, true, then the utility of determining whether the assumption of our own intelligence being highly probable is warranted becomes irrelevant because we may not be able to demonstrate that artificial intelligence is achievable anyway. About this, the authors are dismissive concerning any requirement for simulating the environment that is a prerequisite for organismal and species optimization against that environment:

In the limiting case, if complete microphysical accuracy were insisted upon, the computational requirements would balloon to utterly infeasible proportions. However, such extreme pessimism seems unlikely to be well founded; it seems unlikely that the best environment for evolving intelligence is one that mimics nature as closely as possible. It is, on the contrary, plausible that it would be more efficient to use an artificial selection environment, one quite unlike that of our ancestors, an environment specifically designed to promote adaptations that increase the type of intelligence we are seeking to evolve (say, abstract reasoning and general problem-solving skills as opposed to maximally fast instinctual reactions or a highly optimized visual system).

Why is this “unlikely”? The argument is that there are classes of mental function that can be compartmentalized away from the broader, known evolutionary provocateurs.… Read the rest