Tag Archives: MIT

Highlights in Smart Artificial Intelligence: Investing; Automated Warehousing and its Discontents; MIT & Noam Chomsky

On March 24, 2014, WSJ reported that Elon Musk (Tesla), Mark Zuckerberg (Facebook), and Ashton Kutcher (That ’70s Show / Dude, Where’s My Car?), along with a bevy of other investors, made a joint $400 million investment in Vicarious FPC, an artificial intelligence company.  WSJ states:

The funding round, the second major infusion of capital for the company in two years, is the latest sign of life in artificial intelligence. Last month, GoogleGOOG -4.58% acquired another AI company called Deep Mind for $400 million. Vicarious has an ambitious goal: Replicating the neocortex, the part of the brain that sees, controls the body, understands language and does math. Translate the neocortex into computer code and “you have a computer that thinks like a person,” says Vicarious co-founder Scott Phoenix. “Except it doesn’t have to eat or sleep.”

It may be decades before companies like Vicarious can create computers with human-like intelligence. But web outfits like Google, YahooYHOO -4.18%, Facebook and others have more immediate uses for artificial intelligence.

You can read the rest of the article for yourself, but it is somewhat speculative as these things are really hush hush (corporate trade secrets and all — the minute you patent/copyright is the minute when people start stealing and IP becomes an international paper tiger).

However, Facebook recently released a research paper on facial recognition, the sourcing of which is presumably DeepFace, or Facebook’s software research project.

In any case, as the article alludes to, it will be decades before there will be viable commercial options for this technology, which may also be referred to as deep learning or neurobusiness (refer to the Inaugural Post, it is still in the innovation trigger on the Gartner Hype Cycle). So, speculating here, but this funding is probably mostly for R&D, as well as possible commercial products.

Gartner’s definition of neurobusinessNeurobusiness is the capability of applying neuroscience insights to improve outcomes in customer and other business decision situations.

CaptureSource: MIT Technology Review


A couple years ago, Amazon made some very large investments in IT and smart infrastructure, in order to lead to cost reductions in operations/logistics expenses.

First, in 2009 Amazon acquired Zappos, an online retailer. But details on the deal are kind of fuzzy.  NYT reports that it went down for $847 million, while Supply Chain Digital reports that it grossed $1.2 billion.  Either way, Zappos was Amazon’s largest acquisition ever.

Also, Zappos utilized Kiva’s then-widely-used robots to fully automate their warehouses.

Then, in March 2012, Amazon acquired Kiva for $775 million, Amazon’s second largest acquisition to date.

On March 31, 2014, Supply Chain Digest reported that Amazon would keep Kiva unavailable to its competitors for at least two years. In other words, Kiva remains for internal use only.

 

The downside of warehouse automation and the automation of other labor-intensive products and services is a negative flux in human capital demand.

Gartner paints a dystopic future for 2020:

By 2020, the labor reduction effect of digitization will cause social unrest and a quest for new economic models in several mature economies. Near Term Flag: A larger scale version of an “Occupy Wall Street”-type movement will begin by the end of 2014, indicating that social unrest will start to foster political debate.

Digitization is reducing labor content of services and products in an unprecedented way, thus fundamentally changing the way remuneration is allocated across labor and capital. Long term, this makes it impossible for increasingly large groups to participate in the traditional economic system — even at lower prices — leading them to look for alternatives such as a bartering-based (sub)society, urging a return to protectionism or resurrecting initiatives like Occupy Wall Street, but on a much larger scale. Mature economies will suffer most as they don’t have the population growth to increase autonomous demand nor powerful enough labor unions or political parties to (re-)allocate gains in what continues to be a global economy.


NOAM CHOMSKY The Atlantic was brave enough to publish a conversation with the legendary or notorious Noam Chomsky, titled “Noam Chomsky on Where Artificial Intelligence Went Wrong.”

The following is a brief excerpt from the introduction, which basically says that while AI is innovative by sifting through mountains of data, it can’t capture the biologically-rooted creativity (e.g., language) of the human brain.

Skinner’s approach stressed the historical associations between a stimulus and the animal’s response — an approach easily framed as a kind of empirical statistical analysis, predicting the future as a function of the past. Chomsky’s conception of language, on the other hand, stressed the complexity of internal representations, encoded in the genome, and their maturation in light of the right data into a sophisticated computational system, one that cannot be usefully broken down into a set of associations. Behaviorist principles of associations could not explain the richness of linguistic knowledge, our endlessly creative use of it, or how quickly children acquire it with only minimal and imperfect exposure to language presented by their environment. The “language faculty,” as Chomsky referred to it, was part of the organism’s genetic endowment, much like the visual system, the immune system and the circulatory system, and we ought to approach it just as we approach these other more down-to-earth biological systems.

….

In May of last year, during the 150th anniversary of the Massachusetts Institute of Technology, a symposium on “Brains, Minds and Machines” took place, where leading computer scientists, psychologists and neuroscientists gathered to discuss the past and future of artificial intelligence and its connection to the neurosciences.

The gathering was meant to inspire multidisciplinary enthusiasm for the revival of the scientific question from which the field of artificial intelligence originated: how does intelligence work? How does our brain give rise to our cognitive abilities, and could this ever be implemented in a machine?

Noam Chomsky, speaking in the symposium, wasn’t so enthused. Chomsky critiqued the field of AI for adopting an approach reminiscent of behaviorism, except in more modern, computationally sophisticated form. Chomsky argued that the field’s heavy use of statistical techniques to pick regularities in masses of data is unlikely to yield the explanatory insight that science ought to offer. For Chomsky, the “new AI” — focused on using statistical learning techniques to better mine and predict data — is unlikely to yield general principles about the nature of intelligent beings or about cognition.

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