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Rideshare provider Uber has signed a 10-year, multi-market commercial agreement with Motional – a developer of driverless robotaxis. As part of the agreement, Uber will deploy Motional’s IONIQ 5 electric robotaxis in select markets at first, with the potential to reach millions of customers by providing both ride-hailing and delivery services autonomously.

Motional is an autonomous driving technology developer that exists as a joint venture between Hyundai Motor Group and Aptiv – specialists in advanced safety, electrification, and vehicle connectivity. It is headquartered in Boston with more recent offices in Santa Monica, California, where the company has been testing its driverless robotaxis built upon Hyundai IONIQ 5 EVs.

In 2021, the public got the first glimpse of Motional’s IONIQ 5 robotaxis, which have completed a fully-autonomous cross-country drive in the US, in addition to 100,000 public rides. Prior to today’s news, Motional has already had a working relationship with Uber Technologies ($UBER) that has consisted of autonomous food deliveries in the Los Angeles area.

This technique involves having participants place their finger over the camera and flash of a smartphone, which uses a deep-learning algorithm to decipher the blood oxygen levels from the blood flow patterns in the resulting video.

Conditions like asthma or COVID-19 make it harder for bodies to absorb oxygen from the lungs. This leads to oxygen saturation percentages dropping to 90% or below, indicating that medical attention is needed.

In a clinic, doctors monitor oxygen saturation using pulse oximeters — those clips you put over your fingertip or ear. But monitoring oxygen saturation at home multiple times a day could help patients keep an eye on COVID symptoms, for example.

Continue reading “A smartphone’s camera and flash could help people measure blood oxygen levels at home” »

A new study has revealed that researchers have used artificial intelligence to create a map that allows them to predict the distribution of dark matter throughout the universe.

The new study has been published in the Astrophysical Journal and shows that researchers have taken a different approach to creating a model of the distribution of dark matter. So far, researchers know that dark matter makes up 80% of the universe, and creating a model of the distribution of dark matter allows cosmologists to construct what is called a “cosmic web”.

With this cosmic web, cosmologists and researchers will be able to see how dark matter impacts the motion of galaxies in the past, present, and future. Researchers in the new study used machine learning, a branch of artificial intelligence, to construct a new model. The AI was fed a large set of galaxy simulations that include galaxies, dark matter, visible matter, and gases.

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Artificial intelligence is quickly advancing in the field of video generation. That could have a profound effect on our social media feeds one day.

CEO Elon Musk unveiled two prototypes of the Tesla robot at his company’s second annual AI Day — and while the bots are nowhere near as impressive as some of the humanoids we’ve seen before, they could have a bigger impact on the future of work.

The Tesla robot: In 2021, Tesla held its first AI Day, a livestreamed event to present its latest developments in AI hardware and software, with a focus on Tesla cars’ Autopilot system.

Continue reading “Elon Musk demos humanoid robot, which will cost less than $20K” »

Scientists have long looked to the brain as an inspiration for designing computing systems. Some researchers have recently gone even further by making computer hardware with a brain-like structure. These “neuromorphic chips” have already shown great promise, but they have used conventional digital electronics, limiting their complexity and speed. As the chips become larger and more complex, the signals between their individual components become backed up like cars on a gridlocked highway and reduce computation to a crawl.

Now, a team at the National Institute of Standards and Technology (NIST) has demonstrated a solution to these communication challenges that may someday allow artificial neural systems to operate 100,000 times faster than the human brain.

The human brain is a network of about 86 billion cells called neurons, each of which can have thousands of connections (known as synapses) with its neighbors. The neurons communicate with each other using short electrical pulses called spikes to create rich, time-varying activity patterns that form the basis of cognition. In neuromorphic chips, electronic components act as artificial neurons, routing spiking signals through a brain-like network.

Researchers have reported a nano-sized neuromorphic memory device that emulates neurons and synapses simultaneously in a unit cell, another step toward completing the goal of neuromorphic computing designed to rigorously mimic the human brain with semiconductor devices.

Neuromorphic computing aims to realize artificial intelligence (AI) by mimicking the mechanisms of neurons and synapses that make up the human brain. Inspired by the cognitive functions of the human brain that current computers cannot provide, neuromorphic devices have been widely investigated. However, current Complementary Metal-Oxide Semiconductor (CMOS)-based neuromorphic circuits simply connect artificial neurons and synapses without synergistic interactions, and the concomitant implementation of neurons and synapses still remains a challenge. To address these issues, a research team led by Professor Keon Jae Lee from the Department of Materials Science and Engineering implemented the biological working mechanisms of humans by introducing the neuron-synapse interactions in a single memory cell, rather than the conventional approach of electrically connecting artificial neuronal and synaptic devices.

Similar to commercial graphics cards, the artificial synaptic devices previously studied often used to accelerate parallel computations, which shows clear differences from the operational mechanisms of the human brain. The research team implemented the synergistic interactions between neurons and synapses in the neuromorphic memory device, emulating the mechanisms of the biological neural network. In addition, the developed neuromorphic device can replace complex CMOS neuron circuits with a single device, providing high scalability and cost efficiency.

Learn how your company can create applications to automate tasks and generate further efficiencies through low-code/no-code tools on November 9 at the virtual Low-Code/No-Code Summit. Register here.

With the increasing digitization of services across multiple industries, large corporations are pushing for new security measures to keep their customers’ documents and sensitive information secure. Among these measures are passwordless logins, with new authentication methods adding an extra layer of data protection.

The transition to passwordless logins is undeniable, with approximately 60% of large and global enterprises and 90% of midsize enterprises predicted to adopt passwordless methods in at least 50% of use cases, according to a recent Gartner study. This comes as no surprise, as security problems associated with password-only authentication are among the digital world’s biggest vulnerabilities. Consumers are often tempted to reuse passwords across different services due to the difficulty of managing so many passwords.