Lifeboat Foundation

If you don’t like what a robot is doing with your mug, there are no takebacks.

Tesla has posted new jobs for its Tesla Bot team on its Careers page. Most of the Tesla Bot jobs are located in California except one located in Austin, Texas.

A few of the openings have been posted for quite some time. Tesla has been steadily posting jobs for the Tesla Bot team since the project was announced during Artificial Intelligence or AI Day back in August. Most of the new jobs seem to be related to software development for the Tesla Bot, hinting at the company’s progress with the humanoid robot.

The new Tesla Bot jobs are listed below with their responsibilities.

One of China’s biggest AI solution providers SenseTime is a step closer to its initial public offering. SenseTime has received regulatory approval to list on the Hong Kong Stock Exchange, according to media reports. Founded in 2014, SenseTime was christened as one of China’s four “AI Dragons” alongside Megvii, CloudWalk, and Yitu. In the second half of the 2010s, their algorithms found much demand from businesses and governments hoping to turn real-life data into actionable insights. Cameras embedded with their AI models watch city streets 24 hours. Malls use their sensing solutions to track and predict crowds on the premises.

SenseTime’s three rivals have all mulled plans to sell shares either in mainland China or Hong Kong. Megvii is preparing to list on China’s Nasdaq-style STAR board after its HKEX application lapsed.

The window for China’s data-rich tech firms to list overseas has narrowed. Beijing is making it harder for companies with sensitive data to go public outside China. And regulators in the West are wary of facial recognition companies that could aid mass surveillance.

Continue reading “China’s AI giant SenseTime readies Hong Kong IPO” »

Rarely does scientific software spark such sensational headlines. “One of biology’s biggest mysteries ‘largely solved’ by AI”, declared the BBC. Forbes called it “the most important achievement in AI — ever”. The buzz over the November 2020 debut of AlphaFold2, Google DeepMind’s (AI) system for predicting the 3D structure of proteins, has only intensified since the tool was made freely available in July.

The excitement relates to the software’s potential to solve one of biology’s thorniest problems — predicting the functional, folded structure of a protein molecule from its linear amino-acid sequence, right down to the position of each atom in 3D space. The underlying physicochemical rules for how proteins form their 3D structures remain too complicated for humans to parse, so this ‘protein-folding problem’ has remained unsolved for decades.

Researchers have worked out the structures of around 160,000 proteins from all kingdoms of life. They have been using experimental techniques, such as X-ray crystallography and cryo-electron microscopy (cryo-EM), and then depositing their 3D information in the Protein Data Bank. Computational biologists have made steady gains in developing software that complements these methods, and have correctly predicted the 3D shapes of some molecules from well-studied protein families.

New ways to measure the top supercomputers’ smarts in the AI field include searching for dark energy, predicting hurricanes, and finding new materials for energy storage.

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This new reality promises robotic dogs to enforce social distancing and publicly owned flying taxis to provide transportation since private vehicles are only available to the rich. The technology is currently being rolled out in other western nations, including Canada.

On a hard disk somewhere in the surveillance archives of Singapore’s Changi prison is a video of Jolovan Wham, naked, alone, performing Hamlet.

GauGAN2, a new AI-powered tool from Nvidia, can translate text prompts into high-quality landscape images.

Over the past several decades, researchers have moved from using electric currents to manipulating light waves in the near-infrared range for telecommunications applications such as high-speed 5G networks, biosensors on a chip, and driverless cars. This research area, known as integrated photonics, is fast evolving and investigators are now exploring the shorter—visible—wavelength range to develop a broad variety of emerging applications. These include chip-scale LIDAR (light detection and ranging), AR/VR/MR (augmented/virtual/mixed reality) goggles, holographic displays, quantum information processing chips, and implantable optogenetic probes in the brain.

The one device critical to all these applications in the visible range is an optical phase modulator, which controls the phase of a light wave, similar to how the phase of radio waves is modulated in wireless computer networks. With a phase modulator, researchers can build an on-chip optical switch that channels light into different waveguide ports. With a large network of these optical switches, researchers could create sophisticated integrated optical systems that could control light propagating on a tiny chip or light emission from the chip.

But phase modulators in the visible range are very hard to make: there are no materials that are transparent enough in the visible spectrum while also providing large tunability, either through thermo-optical or electro-optical effects. Currently, the two most suitable materials are silicon nitride and lithium niobate. While both are highly transparent in the visible range, neither one provides very much tunability. Visible-spectrum phase modulators based on these materials are thus not only large but also power-hungry: the length of individual waveguide-based modulators ranges from hundreds of microns to several mm and a single modulator consumes tens of mW for phase tuning. Researchers trying to achieve large-scale integration—embedding thousands of devices on a single microchip—have, up to now, been stymied by these bulky, energy-consuming devices.

NVIDIA’s GauGAN2 artificial intelligence (AI) can now use simple written phrases to generate a fitting photorealistic image. The deep-learning model is able to craft different scenes in just three or four words.

GauGAN is NVIDIA’s AI program that was used to turn simple doodles into photorealistic masterpieces in 2019, a technology that was eventually turned into the NVIDIA Canvas app earlier this year. Now NVIDIA has advanced the AI even further to where it only needs a brief description in order to generate a “photo.”