The Lamisphere

Today’s keynote at the Singularity Summit was Peter Norvig, Director of Research from Google. His talk was titled The History and Future of Technological Change, and he couched his presentation as an analysis of “how to evaluate technical change”.

This is the first time I have heard Norvig speak, and I found his talk to be extremely pragmatic. His trek through the art of predicting the future, to demonstrations of narrow AI to his list of AGI prerequisites pointed toward a technologist with a perspective firmly grounded in continuous improvement, averse to making high-risk, long-shot bets. If Norvig speaks from a place of authority on Google product direction, it seems to me that we should expect continued evolutionary innovation from GOOG, but they will leave the breakthrough innovation of AGI to others. This is an important observation for the investment community that has put Google on pedestal related to the continued release of major breakthroughs.

Norvig began his talk discussing how the predictions he was used to making are about incremental advancements in technology. A 1% improvement here, a 2% improvement there. He pointed out that predictions about AGI are 100% “or greater” improvement ruminations. He pointed out the dichotomy between other prognosticators. “We will all be dead in 100 years” vs. “We will live to be 1000 years old”. “AGI can’t happen for another 100 years” vs. “within the next 10 years”.

From there, Norvig took a detour through other concepts of “Artificial General”. He postulated about “Artificial General Space Exploration”, “Artificial General Materials Science”, and “Artificial General Culture” – equating these concepts to the emergence of AGI.

Here Norvig was at his most pragmatic. He sees continuous innovation in these areas bringing about a more advanced capability, but certainly no “rapture”, no “big bang”. He commented that “the Singularity is a period, not a point”. He sees a date in the future when we look back at the progress and say – wow that was a big change.

In preparation for this presentation Norvig used Google Scholar to query papers presenting breakthroughs in AI. His keywords were “AI” and “unlike previous”. From 1968 – present, Norvig can’t tell the difference in breakthrough claims, with claims of novelty repeating in the data set. This indicates to him that we are not on the verge of discovering something major in AI.

To bring about an AGI, Norvig offered his list of prerequisites:

• Probabilistic First-Order Logic
• Hierarchical Representation and Problem Solving
• Learning over the data from above
• With lots of data
• Online
• Efficiently

I think the recursive thinking nature of Norvig’s AGI underpins his continuous improvement philosophy, and also presents a very Googlian view of success. Let an algorithm loose on lots of data, and eventually it might get there.

Rodney Brooks asked Norvig a question during the Q&A session:

Brooks – Any emergent property of Google materializing within the massive systems that has been a surprise?

Norvig’s best answer was that he was surprised at how Game theoretic Google’s role in the internet is. Initially, he thought Google would be an observer of the internet – just serving up search results. Now Google is co-evolving with the web.

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J. Storrs Hall is a noted scientist and author. He is chief scientist at Nanorex and has published extensively on the subject. His most recent book is titled Beyond AI: Creating the Conscience of the Machine (2007).

Hall spoke at The Singularity Summit this morning on the topic of revising Asimov’s Three Laws of Robotics. As a refresher, Asimov’s laws follow:

• A robot may not injure a human being or, through inaction, allow a human being to come to harm.
• A robot must obey orders given to it by human beings except where such orders would conflict with the First Law.
• A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

With Asimov, the 3 laws were “hardwired into the circuitry.” He envisioned the laws being codified in the circuitry. Alas, according to Hall, the Robotic AGIs (Artificial General Intelligence) of the future will be software and wetware. And “Asimov’s robots didn’t Improve Themselves. Our AIs, we hope, Will.”

So, Hall posed the question, “how can you imagine writing a law that is to govern in an environment you can’t predict. Like Hammurabi writing laws that predict the Enron scandal.” Our new “laws” have to be much more abstract and flexible – more like a conscience. According to Hall, we’ve done this for ages – it’s called raising children.

To punctuate his perspective, Hall predicted “by 2050 – most corporations will be run by their management information systems. Their first law will be ‘make a profit’.”

Hall’s New Laws of Robotics:

• Law #1 – A Robot shall understand as much as possible.

Hall referenced Socrates – “there is no good but knowledge, and no evil but ignorance” as a basis for morality across cultures. The same should apply to AGI.

o Law 1a – in particular a robot shall understand mimetic evolution.

Mimetic evolution is the reflective or representative of actuality or reality of human experience (derived from Aristotle’s concept of mimesis or imitation). This is important because evolution is where morals come from.

• Law #2 – A robot shall be Open Source.

We live in a world largely run by artificial organizations that have no conscious – Corporations and Governments. But corporations are required by law to have an “open-source motivational system” – Auditing – because Money is their Emotion. Transparency to robot motives and capabilities will be critical with an AGI.

• Law #3 - A robot shall be Economically Sentient

Our economic environment is the necessary outcome of evolution. We must train our AGIs to understand and appreciate the power of economics so that they will drive toward optimal decisions.

• Law #4: A robot shall be “Trustworthy, Loyal, Helpful, Friendly, Courteous, Kind, Obedient, Cheerful, Thrifty, Brave, Clean, and Reverent” and shall do a good turn daily.

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Kwabena Boahen is part of a small but growing community of scientists and engineers using a process they call ‘neuromorphing’ to build complicated electronic circuits meant to model the behavior of neural circuits. Their work takes advantage of anatomical diagrams of different parts of the brain generated through years of painstaking animal studies by neuroscientists around the world. The hope is that hardwired models of the brain will yield insights difficult to glean through existing experimental techniques.

Technology Review: Silicon Brains

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Wired magazine reports on another example of tapping into the prefrontal cortex to monitor subconscious recognition of potential threats that have not bubbled up to the conscious mind.

DARPA, as usual, has some very interesting projects ongoing with your tax dollars
Pentagon to Merge Next-Gen Binoculars With Soldiers’ Brains -

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The creation of insects whose flesh grows around computer parts — known from science fiction as cyborgs — has been described as one of the most ambitious robotics projects ever conceived by the Defense Advanced Research Projects Agency (DARPA), the research and development arm of the U.S. Department of Defense.

FOXNews.com - Scientist: Military Working on Cyborg Spy Moths - Technology News | News On Technology

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Kurzweil AI pointed out a fascinating paper on IBM’s progress toward simulating a mouse brain. Done on the Gene / L supercomputer, the simulation covers the firing of 8 million neurons with 6300 synapses per neuron!
You can download the research paper here:

http://www.modha.org/papers/rj10404.pdf

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Some fear that the advent of ‘human black boxes’ combined with the extension of medical, financial and other digital records will lead to loss of privacy and a dramatic expansion of the nanny state.

Telegraph | News | Computers ‘could store entire life by 2026′

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How much of what you know is common sense? You can burn yourself if you touch a hot light bulb. A cold shower can wake you up. A mountain is bigger than a mole-hill. Is 50% of knowledge common sense? 90%? 99.99%? According to Push Singh at MIT, several attempts to benchmark the scope of common sense put it in the order of hundreds of millions of rules.

Hard to believe that you could keep several hundred of anything in your brain, let alone several hundred million. Yet, there they are, right behind your eyes. The rules that you live by. So what will it take for a computer to learn those rules? Sure, itÂ?s the stuff of science fiction [think HAL or C-3PO], but it is also the stuff of science fact for a couple of below the radar software companies and research projects.

Cycorp, based in Austin, Texas is a 20-year-old research project turned start up turned government funded technology vendor. Cycorp was started as a research project in 1984 by Doug Lenat, then a Stanford professor. Lenat moved to Austin and his project took up residence at MCC. In 1994, Lenat spun out of MCC and formed Cycorp as a for-profit venture. CycÂ?s original goal was to Â?codify in machine readable formatÂ? the rules that make up common sense. We will give Cyc a report card at the end of this article

Rules, rules, rules

Cyc has three main technology concepts that merit understanding. The first is the knowledge base of rules Â? or assertions - that have been entered into Cyc. The rules that Cyc knows about have generally been hand entered, and are grouped around specific key words. Several hundred thousand keywords each have 10 plus assertions entered about them. For example, the key word dog might have assertions such as dogs are mammals. Dogs can be pets. Dogs are color-blind etc. Assertions are furbundleddeled into related concepts called microtheories.

Twenty years, and a reported $60 million in government and venture backing later, Cyc appears to be making some progress. According to company press releases, Cyc has assembled a knowledge base of over 3 million rules of thumb. While this is only of sliver of human common sense, what is important, is that this knowledge has been successfully pointed at solving business problems.

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