Neutered Inventiveness

October 11, 2015October 11, 2015 Mark DavisLeave a comment

I just received an award from my employer for getting more than five patents through the patent committee this year. Since I’m a member of the committee, it was easy enough. Just kidding: I was not, of course, allowed to vote on my own patents. The award I received leaves a bit to be desired, however. First, I have to say that it is a well-crafted glass block about 4″ x 3″ and has the kind of heft to it that would make it invaluable as a weapon in a game of Clue. That being said, I give you Exhibits 1 and 2:

Exhibit 1 is a cell-phone snap through the glass surface of my award at Leonardo da Vinci’s famous Vitruvian Man, so named because it was a tribute to the architect Vitruvius—or so Wikipedia tells me. Exhibit 2 is an image of the original sketch by da Vinci, also borrowed from Wikipedia.

And now, with only minimal scrutiny, my dear reader can see the fundamental problem in the borrowing and translation of old Vitruvius. While Vitruvius was deeply enamored of a sense of symmetry to the human body, and da Vinci took that sense of wonder as a basis for drawing his figure, we can rightly believe that the presence of all anatomical parts of the man was regarded as essential for the accurate portrayal of man’s elaborate architecture.

My inventions now seem somehow neutered and my sense of wonder castrated by this lesser man, no matter what the intent of the good people in charge of the production of the award. I reflect on their motivations in light of recent arguments concerning the proper role of the humanities in our modern lives.… Read the rest

Magic in the Age of Unicorns

October 8, 2015October 9, 2015 Mark Davis1 Comment

Ah, Sili Valley, my favorite place in the world but also a place (or maybe a state of mind) that has the odd quality of being increasingly revered for abstractions that bear only cursory similarities to reality. Isn’t that always the way of things? Here’s The Guardian analyzing startup culture. The picture in the article is especially amusing to me since my first startup (freshly spun out of XeroX PARC) was housed on Jay street just across 101 from Intel’s Santa Clara campus (just to the right in the picture). In the evening, as traffic jammed up on the freeway, I watched a hawk hunt in the cloverleaf interchange of the Great American Parkway/101 intersection. It was both picturesque and unrelenting in its cruelty. And then, many years later, I would pitch in the executive center of the tall building alongside Revolution Analytics (now gone to Microsoft).

Everything changes so fast, then changes again. If it is a bubble, it is a more beautiful bubble than before, where it isn’t enough to just stand up a website, but there must be unusual change and disruption. Even the unicorns must pop those bubbles.

I will note that I am returning to the startup world in a few weeks. Startup next will, I promise, change everything!… 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

Active Deep Learning

December 28, 2014December 29, 2014 Mark DavisLeave a comment

Deep Learning methods that use auto-associative neural networks to pre-train (with bottlenecking methods to ensure generalization) have recently been shown to perform as well and even better than human beings at certain tasks like image categorization. But what is missing from the proposed methods? There seem to be a range of challenges that revolve around temporal novelty and sequential activation/classification problems like those that occur in natural language understanding. The most recent achievements are more oriented around relatively static data presentations.

Jürgen Schmidhuber revisits the history of connectionist research (dating to the 1800s!) in his October 2014 technical report, Deep Learning in Neural Networks: An Overview. This is one comprehensive effort at documenting the history of this reinvigorated area of AI research. What is old is new again, enhanced by achievements in computing that allow for larger and larger scale simulation.

The conclusions section has an interesting suggestion: what is missing so far is the sensorimotor activity loop that allows for the active interrogation of the data source. Human vision roams over images while DL systems ingest the entire scene. And the real neural systems have energy constraints that lead to suppression of neural function away from the active neural clusters.

… Read the rest

Inequality and Big Data Revolutions

October 25, 2014October 26, 2014 Mark DavisLeave a comment

I had some interesting new talking points in my Rock Stars of Big Data talk this week. On the same day, MIT Technology Review published Technology and Inequality by David Rotman that surveys the link between a growing wealth divide and technological change. Part of my motivating argument for Big Data is that intelligent systems are likely the next industrial revolution via Paul Krugman of Nobel Prize and New York Times fame. Krugman builds on Robert Gordon’s analysis of past industrial revolutions that reached some dire conclusions about slowing economic growth in America. The consequences of intelligent systems on everyday life will have enormous impact and will disrupt everything from low-wage workers through to knowledge workers. And how does Big Data lead to that disruption?

Krugman’s optimism was built on the presumption that the brittleness of intelligent systems so far can be overcome by more and more data. There are some examples where we are seeing incremental improvements due to data volumes. For instance, having larger sample corpora to use for modeling spoken language enhances automatic speech recognition. Google Translate builds on work that I had the privilege to be involved with in the 1990s that used “parallel texts” (essentially line-by-line translations) to build automatic translation systems based on phrasal lookup. The more examples of how things are translated, the better the system gets. But what else improves with Big Data? Maybe instrumenting many cars and crowdsourcing driving behaviors through city streets would provide the best data-driven approach to self-driving cars. Maybe instrumenting individuals will help us overcome some of things we do effortlessly that are strangely difficult to automate like folding towels and understanding complex visual scenes.

But regardless of the methods, the consequences need to be considered.… Read the rest