Category: AI

The IQ of Machines

Mark Davis1 Comment

standard-dudePerhaps 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

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Daliesque creepy dogsSlides 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

Intelligence Augmentation and a Frictionless Economy

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Speed SkatingThe 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

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Red QueensCarl 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

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BrainDeep 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

The Deep Computing Lessons of Apollo

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Apollo 11With the arrival of the Apollo 11 mission’s 45th anniversary, and occasional planning and dreaming about a manned mission to Mars, the role of information technology comes again into focus. The next great mission will include a phalanx of computing resources, sensors, radars, hyper spectral cameras, laser rangefinders, and information fusion visualization and analysis tools to knit together everything needed for the astronauts to succeed. Some of these capabilities will be autonomous, predictive, and knowledgable.

But it all began with the Apollo Guidance Computer or AGC, the rather sophisticated for-its-time computer that ran the trigonometric and vector calculations for the original moonshot. The AGC was startlingly simple in many ways, made up exclusively of NOR gates to implement Arithmetic Logic Unit-like functionality, shifts, and register opcodes combined with core memory (tiny ferromagnetic loops) in both RAM and ROM forms (the latter hand-woven by graduate students).

Using NOR gates to create the entire logic of the central processing unit is guided by a few simple principles. A NOR gate combines both NOT and OR functionality together and has the following logical functionality:

[table id=1 /]

The NOT-OR logic can be read as “if INPUT1 or INPUT2 is set to 1, then the OUTPUT should be 1, but then take the logical inversion (NOT) of that”. And, amazingly, circuits built from NORs can create any Boolean logic. NOT A is just NOR(A,A), which you can see from the following table:

[table id=2 /]

AND and OR can similarly be constructed by layering NORs together. For Apollo, the use of just a single type of integrated circuit that packaged NORs into chips improved reliability.

This level of simplicity has another important theoretical result that bears on the transition from simple guidance systems to potentially intelligent technologies for future Mars missions: a single layer of Boolean functions can only compute simple things.… Read the rest

Inching Towards Shannon’s Oblivion

Mark Davis3 Comments

SkynetFollowing Bill Joy’s concerns over the future world of nanotechnology, biological engineering, and robotics in 2000’s Why the Future Doesn’t Need Us, it has become fashionable to worry over “existential threats” to humanity. Nuclear power and weapons used to be dreadful enough, and clearly remain in the top five, but these rapidly developing technologies, asteroids, and global climate change have joined Oppenheimer’s misquoted “destroyer of all things” in portending our doom. Here’s Max Tegmark, Stephen Hawking, and others in Huffington Post warning again about artificial intelligence:

One can imagine such technology outsmarting financial markets, out-inventing human researchers, out-manipulating human leaders, and developing weapons we cannot even understand. Whereas the short-term impact of AI depends on who controls it, the long-term impact depends on whether it can be controlled at all.

I almost always begin my public talks on Big Data and intelligent systems with a presentation on industrial revolutions that progresses through Robert Gordon’s phases and then highlights Paul Krugman’s argument that Big Data and the intelligent systems improvements we are seeing potentially represent a next industrial revolution. I am usually less enthusiastic about the timeline than nonspecialists, but after giving a talk at PASS Business Analytics Friday in San Jose, I stuck around to listen in on a highly technical talk concerning statistical regularization and deep learning and I found myself enthused about the topic once again. Deep learning is using artificial neural networks to classify information, but is distinct from traditional ANNs in that the systems are pre-trained using auto-encoders to have a general knowledge about the data domain. To be clear, though, most of the problems that have been tackled are “subsymbolic” for image recognition and speech problems.… Read the rest

Computing the Madness of People

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Bubble playing cardThe best paper I’ve read so far this year has to be Pseudo-Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-of-sample Performance by David Bailey, Jonathan Borwein, Marcos López de Prado, and Qiji Jim Zhu. The title should ring alarm bells with anyone who has ever puzzled over the disclaimers made by mutual funds or investment strategists that “past performance is not a guarantee of future performance.” No, but we have nothing but that past performance to judge the fund or firm on; we could just pick based on vague investment “philosophies” like the heroizing profiles in Kiplingers seem to promote or trust that all the arbitraging has squeezed the markets into perfect equilibria and therefore just use index funds.

The paper’s core tenets extend well beyond financial charlatanism, however. They point out that the same problem arises in drug discovery where main effects of novel compounds may be due to pure randomness in the sample population in a way that is masked by the sample selection procedure. The history of mental illness research has similar failures, with the head of NIMH remarking that clinical trials and the DSM for treating psychiatric symptoms is too often “shooting in the dark.”

The core suggestion of the paper is remarkably simple, however: use held-out data to validate models. Remarkably simple but apparently rarely done in quantitative financial analysis. The researchers show how simple random walks can look like a seasonal price pattern, and how by sending binary signals about market performance to clients (market will rise/market will fall) investment advisors can create a subpopulation that thinks they are geniuses as other clients walk away due to losses. These rise to the level of charlatanism but the problem of overfitting is just one of pseudo-mathematics where insufficient care is used in managing the data.… Read the rest