The California research outfit OpenAI is back with another gigantic deep learning model, GPT-3. While it shows that bigger can be better in natural language processing, it also points to a potential absolute limit on the whole practice of language modeling.
If you are interested in artificial general intelligence (AGI), then I have a panel discussion to recommend. My friend, David Wood, has done a masterful job of selecting three panelists with deep insight into possible regulation of AGI. One of the panelists was my friend, Dan Faggella, who was eloquent and informative as usual. For this session of the London Futurists, David Wood selected two other panelists with significantly different opinions on how to properly restrain AGI.
As research around the world proceeds to improve the power, the scope, and the generality of AI systems, should developers adopt regulatory frameworks to help steer progress?
What are the main threats that such regulations should be guarding against? In the midst of an intense international race to obtain better AI, are such frameworks doomed to be ineffective? Might such frameworks do more harm than good, hindering valuable innovation? Are there good examples of precedents, from other fields of technology, of international agreements proving beneficial? Or is discussion of frameworks for the governance of AGI (Artificial General Intelligence) a distraction from more pressing issues, given the potential long time scales ahead before AGI becomes a realistic prospect?
Houston oil field software firm Bluware has landed an artificial intelligence deal with British oil major BP.
Photo: Marc Morrison / ST
GUANGZHOU/TOKYO — Tech startups in Shenzhen, known as China’s Silicon Valley, are set to experience a range of outcomes as the novel coronavirus pandemic appears to near its end, with some seeing their businesses thrive while others face headwinds following significantly reduced investment.
AI and robot companies feel positive impact, while some face harsh climate.
Graphcore, the four-year-old startup based in Bristol, England, has a chip that takes the kinds of math that a neural network processes and splits them across 1,216 tiny computers, each of which does its work in parallel with its brethren. The company believes its chip makes new kinds of AI possible.
A revolution is underway in the development of autonomous wireless sensors, low-power consumer electronics, smart homes, domotics and the Internet of Things. All the related technologies require efficient and easy-to-integrate energy harvesting devices for their power. Billions of wireless sensors are expected to be installed in interior environments in coming decades.
Microsoft just built a huge supercomputer for AI research organization OpenAI. It’s a dream-machine for the company. What will they do with it?
There aren’t many computer chips that you have to build a life support system for.
But when you’re combining actual living brain cells with inorganic silicon chips, you can’t feed them just electricity. You actually need to supply everything they would normally get in a fully biological body.
Why bother?
Chinese tech giant’s Paddle Quantum development toolkit now is available on GitHub, enabling developers to build and train quantum neural network models, and includes quantum computing applications.
The Dawn of AI :
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In the past few videos in this series, we have delved quite deep into the field of machine learning, discussing both supervised and unsupervised learning.
