Lifeboat Foundation

In the distant past, there was a proverbial “digital divide” that bifurcated workers into those who knew how to use computers and those who didn’t.[1] Young Gen Xers and their later millennial companions grew up with Power Macs and Wintel boxes, and that experience made them native users on how to make these technologies do productive work. Older generations were going to be wiped out by younger workers who were more adaptable to the needs of the modern digital economy, upending our routine notion that professional experience equals value.

Of course, that was just a narrative. Facility with using computers was determined by the ability to turn it on and log in, a bar so low that it can be shocking to the modern reader to think that a “divide” existed at all. Software engineering, computer science and statistics remained quite unpopular compared to other academic programs, even in universities, let alone in primary through secondary schools. Most Gen Xers and millennials never learned to code, or frankly, even to make a pivot table or calculate basic statistical averages.

There’s a sociological change underway though, and it’s going to make the first divide look quaint in hindsight.

Chinese electric startup Kandi announces that its small K27 electric car has been approved for California roads and it is going to cost only $7,999 in the state after incentives.

Several Chinese automakers are currently looking to expand outside of China, and that’s especially true of electric vehicle makers.

Even foreign automakers, like Volvo and BMW, are now producing electric vehicles in China and exporting them globally. The Chinese-made Polestar 2 is due later this year. BMW is also looking at bringing Chinese made EVs to the US.

Researchers at the University of Rochester and Cornell University have taken an important step toward developing a communications network that exchanges information across long distances by using photons, mass-less measures of light that are key elements of quantum computing and quantum communications systems.

The research team has designed a nanoscale node made out of magnetic and semiconducting materials that could interact with other nodes, using laser light to emit and accept photons.

The development of such a quantum network—designed to take advantage of the physical properties of light and matter characterized by quantum mechanics—promises faster, more efficient ways to communicate, compute, and detect objects and materials as compared to networks currently used for computing and communications.

About $950 million worth of bitcoin believed to have originated from the site was moved out of a bitcoin wallet, according to Elliptic.

NASA reestablished communication with the Voyager 2 probe that is more than 11.6 billion miles, after the agency went dark in March to complete hardware upgrades on its satellite.

O,.o!

Canon reveals new industrial cameras that can see in the dark, in illumination less than 0.0005 lux!

Artificial intelligence helps scientists make discoveries, but not everyone can understand how it reaches its conclusions. One UMaine computer scientist is developing deep neural networks that explain their findings in ways users can comprehend, applying his work to biology, medicine and other fields.

Interpretable machine learning, or AI that creates explanations for the findings it reaches, defines the focus of Chaofan Chen’s research. The assistant professor of computer science says interpretable machine learning also allows AI to make comparisons among images and predictions from data, and at the same time, elaborate on its reasoning.

Scientists can use interpretable machine learning for a variety of applications, from identifying birds in images for wildlife surveys to analyzing mammograms.

How do you *feel* about that?

Much of today’s discussion around the future of artificial intelligence is focused on the possibility of achieving artificial general intelligence. Essentially, an AI capable of tackling an array of random tasks and working out how to tackle a new task on its own, much like a human, is the ultimate goal. But the discussion around this kind of intelligence seems less about if and more about when at this stage in the game. With the advent of neural networks and deep learning, the sky is the actual limit, at least that will be true once other areas of technology overcome their remaining obstructions.

For deep learning to successfully support general intelligence, it’s going to need the ability to access and store much more information than any individual system currently does. It’s also going to need to process that information more quickly than current technology will allow. If these things can catch up with the advancements in neural networks and deep learning, we might end up with an intelligence capable of solving some major world problems. Of course, we will still need to spoon-feed it since it only has access to the digital world, for the most part.

Continue reading “The Future of AI is Artificial Sentience” »

Humanoid robots of tomorrow could feel pain just like us.