Artificial Intelligence  

Soon computers will be able to design themselves. Richard Waters of the Financial Times recently joined SciTech Now host Hari Sreenivasan to discuss the ramifications of truly intelligent computers.

Artificial intelligence has been heading in one direction for 60 years according to Financial Times West Coast Editor, Richard Waters.  And that direction is for the technology becoming, "as smart as people."

In this respect, the history of AI has been one of disappointment, as the early practitioners were way ahead of themselves in terms of the capabilities they could achieve. This was especially pronounced in the so-called AI Winter of the 80's and 90's, when it seemed that the HAL 9000 system of the movies was never going to be achieved.

"How will they explain to us, with our smaller brains, what they are doing to improve themselves?"

Now, the technology has caught up, and the field of artificial intelligence, including deep learning, is in a boom time. "Its not as though computers are as smart as people yet, but they've started to do things, particularly in the last two-to-three years that no-one thought was possible," says Waters.

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Waters points to IBM's Watson defeating human competitors at Jeopardy! as one example, but beyond gaming, he points to the power of machine learning and Big Data to find answers that humans have a hard time finding on our own.  Medicine is one key example. Drug discovery and unraveling the complex interactions of biology are an area where AI might help out.

Commenting on the threat of artificial intelligence, Waters says, "How will they explain to us, with our smaller brains, what they are doing to improve themselves?" thinking about the potential for recursive self-improving computer systems.

Sreenivasan asks how this is going to impact society and democracy as super-intelligent machines begin to have more of an impact (in a time span of five-to-ten years, according to Waters).

SOURCE  SciTech Now

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 Artificial Intelligence  

As we track developments in artificial intelligence there is undoubtedly a greater awareness around the current capabilities of the technology and the path forward.  It is now widely agreed that we are in an unprecedented era of large-scale development in the field.

In the history of artificial intelligence research, there have been some especially dark periods when the deliverables did not match the hype. The popular name for these periods like two major spans 1974–80 and 1987–93 is "AI winter".  In the last few years though significant progress, enthusiasm and funding has led to a warmer environment for AI, or a new spring.

For investor Aydin Senkut, "The reality is, there are a very limited number of AI and machine learning experts in the world, which is one reason why it's been getting so much attention."

Although most experts agree that artificial general intelligence (AGI) is still far off on the horizon, the current development push may accelerate its arrival.

At the recent IARPA-sponsored conference, exploring the future of research into Machine Intelligence from Cortical Networks (MICrONS), experts were asked to propose MICrONS-related projects that aim to "create a new generation of machine learning algorithms derived from high-fidelity representations of cortical microcircuits to achieve human-like performance on complex information processing tasks."

"The intelligence community puts money into it, the government puts money into it. The environment is pretty warm right now, it's a new spring."

According to The Register's Jack Clark, conference attendees were especially guarded in how transformational the artificial intelligence community is progressing.

In spite of this, the conference looked to get answers and direction for the lofty research areas involving creating a new generation of machine learning algorithms derived from high-fidelity representations of cortical microcircuits to achieve human-like performance on complex information processing tasks.

To achieve this goal, the conference looked to:

• Propose an algorithmic framework for information processing that is consistent with existing neuroscience data, but that cannot be fully realized without additional specific knowledge about the data representations, computations, and network architectures employed by the brain;
• Collect and analyze high-resolution data on the structure and function of cortical microcircuits believed to embody the cortical computing primitives underlying key components of the proposed framework;
• Generate computational neural models of cortical microcircuits informed and constrained by this data and by the existing neuroscience literature to elucidate the nature of the cortical computing primitives; and
• Implement novel machine learning algorithms that use mathematical abstractions of the identified cortical computing primitives as their basis of operation.

Using data and techniques derived from neuroscience and connectomics, the direction from IARPA seems to be to duplicate the brain's wiring to improve machine learning.  Far from working in isolation, today's AI researchers are looking to biology for inspiration, especially at the only known actual intelligence in the universe: our brain. MICrONS conference attendees included (presumably) Jeff Hawkins from Numenta, who is well known for his theory of intelligence derived from the study of the neocortex.

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Hawkins' former colleague Dileep George and his company, Vicarious have, in the meantime, been expanding their funding for their mostly under-wraps work in artificial intelligence. Their list of investors includes Elon Musk, Peter Thiel, Mark Zuckerberg, Jeff Bezos, Ashton Kutcher, and Dustin Moskowitz.

Some of the initial buzz around Vicarious involves their system's cracking of CAPTCHA tests.  "We should be careful not to underestimate the significance of Vicarious crossing this milestone," said Facebook co-founder and board member Moskovitz. "This is an exciting time for artificial intelligence research, and they are at the forefront of building the first truly intelligent machines."

London-based DeepMind is working on similar projects as Vicarious, like advanced image recognition, though they too are quite secretive about what exactly they're up to. The company was recently acquired by Google.  Google is gobbling up AI and robotics companies as well as researchers such as Geoff Hinton.

The other big corporate names in Silicon Valley are also hungry for AI expertise.  Facebook recently started up an AI endeavor headed by Yann LeCun, and Amazon is known to be heavily researching the area.  Microsoft has progressed well beyond the days of "Clippy," with Cortana about to be widely released and Microsoft Research publishing impressive results.

IBM, long the leader in cutting-edge AI has spun off Watson into a billion dollar company, with new partnerships for the cognitive computing engine springing up all the time.  The company has even partnered with long-time rival Apple to develop applications that will use IBM's technology.

The AI research community is now used to taking a long view on things, despite the hype around the technological Singularity. Time will tell how productive and revolutionary this new AI spring has been, but the effort is large

"The intelligence community puts money into it, the government puts money into it," said one attendee at MIRCrONS. "The environment is pretty warm right now, it's a new spring."


SOURCES  The Register, Business Insider

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