Lifeboat Foundation

Reward-seeking behavior drove evolution on Earth. Can it do the same in AI?

When you put these three factors together—the bounty of technological advances, the compressed restructuring timetable due to covid-19, and an economy finally running at full capacity—the ingredients are in place for a productivity boom. This will not only boost living standards directly, but also frees up resources for a more ambitious policy agenda.

AI and other digital technologies have been surprisingly slow to improve economic growth. But that could be about to change.

Researchers at Oxford University have developed an AI-enabled system that can comprehensively identify people in videos by conducting detective-like, multi-domain investigations as to who they might be, from context, and from a variety of publicly available secondary sources, including the matching of audio sources with visual material from the internet.

Though the research centers on the identification of public figures, such as people appearing in television programs and films, the principle of inferring identity from context is theoretically applicable to anyone whose face, voice, or name appears in online sources.

Indeed, the paper’s own definition of fame is not limited to show business workers, with the researchers declaring ‘We denote people with many images of themselves online as famous‘.

WASHINGTON—The Biden administration launched an initiative Thursday aiming to make more government data available to artificial intelligence researchers, part of a broader push to keep the U.S. on the cutting edge of the crucial new technology.

The National Artificial Intelligence Research Resource Task Force, a group of 12 members from academia, government, and industry led by officials from the White House Office of Science and Technology Policy and the National Science Foundation, will draft a strategy for creating an AI research resource that could, in part, give researchers secure access to stores of anonymous data about Americans, from demographics to health and driving habits.

They would also look to make available computing power to analyze the data, with the goal of allowing access to researchers across the country.

(Bloomberg) — On a Wednesday afternoon in May, an Uber driver in San Francisco was about to run out of charge on his Nissan Leaf. Normally this would mean finding a place to plug in and wait for a half hour, at least. But this Leaf was different.

Instead of plugging in, the driver pulled into a swapping station near Mission Bay, where a set of robot arms lifted the car off of the ground, unloaded the depleted batteries and replaced them with a fully charged set. Twelve minutes later the Leaf pulled away with 32 kilowatt hours of energy, enough to drive about 130 miles, for a cost of $13.

A swap like this is a rare event in the U.S. The Leaf’s replaceable battery is made by Ample, one of the only companies offering a service that’s more popular in markets in Asia. In March, Ample announced that it had deployed five stations around the Bay Area. Nearly 100 Uber drivers are using them, the company says, making an average of 1.3 swaps per day. Ample’s operation is tiny compared to the 100000 public EV chargers in the U.S.—not to mention the 150000 gas stations running more than a million nozzles. Yet Ample’s founders Khaled Hassounah and John de Souza are convinced that it’s only a matter of time before the U.S. discovers that swapping is a necessary part of the transition to electric vehicles.

COVID 19 pandemic, automation and 6G could end the metropolitan era from building high sky scrapers for companies. Companies can operate like a network from home to home without going to office. This will help a lot to bring down Urban Heat Islands and make our cities more efficient in transportation and communication to send the data even faster.

Tom Marzetta is the director of NYU Wireless, New York University’s research center for cutting-edge wireless technologies. Prior to joining NYU Wireless, Marzetta was at Nokia Bell Labs, where he developed massive MIMO. Massive MIMO (short for “multiple-input multiple-output”) allows engineers to pack dozens of small antennas into a single array. The high number of antennas means more signals can be sent and received at once, dramatically boosting a single cell tower’s efficiency.

Massive MIMO is becoming an integral part of 5G, as is an independent development that came out of NYU Wireless by the center’s founding director Ted Rappaport: Millimeter waves. And now the professors and students at NYU Wireless are already looking ahead to 6G and beyond.

Continue reading “Heres What 6G Will Be, According to the Creator of Massive MIMO” »

Featureless “cost functions” prevent quantum machine learning algorithms from reconstructing scrambled information.

Glow is an iconic interesting research about deep neural networks that can generalize with small training sets.

Since the early days of machine learning, artificial intelligence scenarios have faced with two big challenges in order to experience mainstream adoption. First, we have the data efficiency problem that requires machine or deep learning models to be trained using large and accurate datasets which, as we know, are really expensive to build and maintain. Secondly, we have the generalization problem which AI agents face in order to build new knowledge that is different from the training data. Humans, by contrast, are incredibly efficient learning with minimum supervision and rapidly generalizing knowledge from a few data examples.

Generative models are one of the deep learning disciplines that focuses on addressing the two challenges mentioned above. Conceptually, generative models are focused on observing an initial dataset, like a set of pictures, and try to learn how the data was generated. Using more mathematical terms, generative models try to infer all dependencies within very high-dimensional input data, usually specified in the form of a full joint probability distribution. Entire deep learning areas such as speech synthesis or semi-supervised learning are based on generative models. Recently, generative models such as generative adversarial networks(GANs) have become extremely popular within the deep learning community. Recently, OpenAI experimented with a not-very well-known technique called Flow-Based Generative Models in order to improve over existing methods.

Continue reading “This Neural Networkfrom OpenAI can Learn from Small Datasets” »

In your business you need to learn how to distinguish between chatbots and conversational AI, here are some tips on how to do that.