The Rise of the Thinking Machine

By Michael Feldman

August 25, 2011

This year has seen some notable advancements in computer-based brain mimicry, not just on the artificial intelligence (AI) front, but also related to in silico brain simulations.

Watson’s vanquishing of Jeopardy champions Brad Rutter and Ken Jennings in February set the stage for the year.  The now world-famous IBM super exhibited a sophisticated understanding of language semantics along with the ability to integrate that understanding into a complex analytics engine.  Since the Jeopardy match, IBM has been looking to take the technology into the commercial realm, most notably in the health care arena. 

Meanwhile projects like FACETS (Fast Analog Computing with Emergent Transient States) and SpiNNaker are working to uncover the nature of the brain at the level of the neuron.  The goal here is not to create any kind of artificial intelligence system a la Watson, but rather to simulate the neuronal network of the brain for basic science research.

SpiNNaker, a multi-year project run out of the UK at the University of Manchester, also is attempting to map the brain’s low-level biological structure and function. In June, the project received its first batch of custom-built ARM processors that will eventually power a 50 thousand-node neural network supercomputer.

The FACETS project, managed by the University of Heidelberg, actually wrapped up last year. It’s sequel, BrainScaleS project booted up in January 2011, with the idea of developing of a “brain-inspired computer architecture” based on a custom-designed neural network hardware.  BrainScaleS has links to Henry Markram’s famous Blue Brain work.

Blue Brain, based at the École Polytechnique Fédérale in Lausanne (EPFL), is perhaps the best-known of the brain mimicry projects. The idea is to perform detailed simulations of the brain at the scale of the neuronal network.  In this case though, the work was done with conventional supercomputing hardware (if you can call Blue Gene conventional). The project has successfully simulated a rat cortical column.

The follow-on to Blue Brain, also headed by Markram, is the Human Brain Project. The goal here is to move from rats to human and simulate the entire brain.

The other bookend to the Watson AI story is also from IBM. Last week, the company unveiled their cognitive computing chips.  This is basic research as well, but IBM is aiming the technology at developing thinking machines, rather than just using it to elucidate the workings of the brain.

I queried Markram about the significance to IBM’s latest chippery, who responded thusly: “This is a very important technology step. There are still many challenges ahead, but neuromorphic chips like IBM’s are bound to become key processing units in hybrid architectures of future computers.”  He also recognized the work at FACETS/BrainScaleS and SpiNNaker as contributing to this growing body of knowledge.

So what does it all mean?  For those of you who read about such development in the popular press, there has been plenty of speculation about the future of artificial brains.  A lot of this is centered around how such technology will impact the human condition, particular how intelligent computers will displace human labor.

The big question is if such technology will ultimately benefit people or merely make them superfluous.  Edward Tenner,  a historian of technology and culture with a Ph.d in European history, believes it will be the former.  From a piece he penned in The Atlantic:

 
Will people be obsolete? I doubt it. The economic theory of comparative advantage explains why. Assuming there will still be people, even if the computers are running everything, it will pay for them to let people do what they are relatively better at. There’s likely to be a higher opportunity cost for computers to do more intuitive analysis for which human brain-body system has evolved and concentrate on tasks at which their abilities are an even high-multiple than people’s. In the case of computers and people, as I suggested about IBM’s Watson and Jeopardy! there will always be elements of tacit knowledge and common sense that will be extremely expensive to achieve electronically.

His premise is that it will always be cheaper and more effective to have a real live human provide answers that involve intuition.  “So even if, for example, computers surpass physicians on diagnostic reasoning,” he writes, “it will be cheaper, more effective, and safer to have their judgment double-checked by a real doctor.

Maybe.  But I think one of the article’s commenters nailed it pretty well when he suggests that the real question is not whether computers will replace all labor, but how many jobs will be displaced by intelligent machines and how that impacts our traditional economic model.  He writes:

In classical economics, employers furnish the capital, and workers produce raw materials and finished goods or services.  There is tension between worker and management: both need each other, but both want a bigger piece of the profits from work; each has a strong bargaining position, and the compromise they reach determines wages and benefits.  But what’s playing out on the world stage isn’t classical economics at all.  With every passing year, owners of capital are relying less on workers and more on machines.  The balance has shifted in favor of owners of capital.

We don’t have to wait for the future to see this play out.  It’s been happening for decades, as businesses large and small have adopted IT.  The commenter notes that multinational tech manufacture Foxconn will be shedding a million of its million and half workers manufacturing circuit boards over the next two years, thanks to assembly line robotics.

We’ve certainly seen similar downsizing across the manufacturing sector in general. A century ago, the same process happened in agriculture, a sector whose labor base continues to decline.  It’s not that the industries are shrinking, just their labor force.

With the introduction of more sophisticated computing,  machines are moving higher up the food chain. For example, over the last three decades at JP Morgan, profitability has risen by a factor of 30, but employee head count has only doubled. That’s directly attributable to computer technology raising productivity.

The advent of really intelligent machines like Watson and its neuromorphic brethren will accelerate all this, in ways we can only imagine.  Even industries that are enjoying relatively rapid job growth today, like professional services, education, and health care, will eventually be impacted.

From my perspective, the key problem is that our social and economic systems are not ready for this.  While everyone is fixated on globalization, I think that’s a side show compared to what will happen — and is happening — as intelligent technology displaces human labor worldwide.

It’s not just that people who have invested years of specialized training will find their jobs threatened.  As the commenter noted above, the balance between capital and labor is shifting rapidly in favor of capital as the labor force is squeezed into fewer and fewer jobs that resist automation.  The hope is that other industries will emerge to engage the masses again, as happened after the agricultural and industrial revolutions.  But this time may be different.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

China’s Expanding Effort to Win in Microchips

July 27, 2017

The global battle for preeminence, or at least national independence, in semiconductor technology and manufacturing continues to heat up with Europe, China, Japan, and the U.S. all vying for sway. A fascinating article ( Read more…

By John Russell

Hyperion: Storage to Lead HPC Growth in 2016-2021

July 27, 2017

Global HPC external storage revenues will grow 7.8% over the 2016-2021 timeframe according to an updated forecast released by Hyperion Research this week. HPC server sales, by comparison, will grow a modest 5.8% to $14.8 Read more…

By John Russell

Exascale FY18 Budget – The Senate Provides Their Input

July 27, 2017

In the federal budgeting world, “regular order” is a meaningful term that is fondly remembered by members of both the Congress and the Executive Branch. Regular order is the established process whereby an Administrat Read more…

By Alex R. Larzelere

HPE Extreme Performance Solutions

HPE Servers Deliver High Performance Remote Visualization

Whether generating seismic simulations, locating new productive oil reservoirs, or constructing complex models of the earth’s subsurface, energy, oil, and gas (EO&G) is a highly data-driven industry. Read more…

India Plots Three-Phase Indigenous Supercomputing Strategy

July 26, 2017

Additional details on India's plans to stand up an indigenous supercomputer came to light earlier this week. As reported in the Indian press, the Rs 4,500-crore (~$675 million) supercomputing project, approved by the Ind Read more…

By Tiffany Trader

Exascale FY18 Budget – The Senate Provides Their Input

July 27, 2017

In the federal budgeting world, “regular order” is a meaningful term that is fondly remembered by members of both the Congress and the Executive Branch. Reg Read more…

By Alex R. Larzelere

India Plots Three-Phase Indigenous Supercomputing Strategy

July 26, 2017

Additional details on India's plans to stand up an indigenous supercomputer came to light earlier this week. As reported in the Indian press, the Rs 4,500-crore Read more…

By Tiffany Trader

Tuning InfiniBand Interconnects Using Congestion Control

July 26, 2017

InfiniBand is among the most common and well-known cluster interconnect technologies. However, the complexities of an InfiniBand (IB) network can frustrate the Read more…

By Adam Dorsey

NSF Project Sets Up First Machine Learning Cyberinfrastructure – CHASE-CI

July 25, 2017

Earlier this month, the National Science Foundation issued a $1 million grant to Larry Smarr, director of Calit2, and a group of his colleagues to create a comm Read more…

By John Russell

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in Read more…

By Tiffany Trader

Fujitsu Continues HPC, AI Push

July 19, 2017

Summer is well under way, but the so-called summertime slowdown, linked with hot temperatures and longer vacations, does not seem to have impacted Fujitsu's out Read more…

By Tiffany Trader

Researchers Use DNA to Store and Retrieve Digital Movie

July 18, 2017

From abacus to pencil and paper to semiconductor chips, the technology of computing has always been an ever-changing target. The human brain is probably the com Read more…

By John Russell

The Exascale FY18 Budget – The Next Step

July 17, 2017

On July 12, 2017, the U.S. federal budget for its Exascale Computing Initiative (ECI) took its next step forward. On that day, the full Appropriations Committee Read more…

By Alex R. Larzelere

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. Read more…

By Tiffany Trader

Leading Solution Providers

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

Reinders: “AVX-512 May Be a Hidden Gem” in Intel Xeon Scalable Processors

June 29, 2017

Imagine if we could use vector processing on something other than just floating point problems.  Today, GPUs and CPUs work tirelessly to accelerate algorithms Read more…

By James Reinders

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of Read more…

By Alex Woodie

Six Exascale PathForward Vendors Selected; DoE Providing $258M

June 15, 2017

The much-anticipated PathForward awards for hardware R&D in support of the Exascale Computing Project were announced today with six vendors selected – AMD Read more…

By John Russell

Top500 Results: Latest List Trends and What’s in Store

June 19, 2017

Greetings from Frankfurt and the 2017 International Supercomputing Conference where the latest Top500 list has just been revealed. Although there were no major Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This