Lifeboat Foundation

Discover what we do when robots break and learn how we take the opportunity to rebuild more robust robots. https://bit.ly/2WsZnGm

Now, the robot geologist reached an exciting area with mountain layers that may reveal how the ancient environment within Gale Crater dried up over time. More: https://go.nasa.gov/2W48e0U

https://reut.rs/37ZGOMe

Towards raising young developers across the Urhobo Nation, the Urhobo Innovation Hub has completed the training of 40 youths on Website design, Internet of Things (IOT), Robotics and Virtual Reality.

The boot camp training, which drew its participants from Urhobo youths within the age bracket of 13–38 years old, held at the Michael and Cecilia Ibru University, Agbaro-Otor, Delta State.

The Hub is a brainchild of the Urhobo Economic and Investment Summit (Ekpobaro) and was initiated to raise young entrepreneurs of Urhobo extraction who will key into the reality of the new normal and raise seasoned developers to make Urhobo Nation proud.

Continue reading “UIH Trains 40 Aspiring Developers” »

Much of the recent progress in AI has come from building ever-larger neural networks. A new chip powerful enough to handle “brain-scale” models could turbo-charge this approach.

Chip startup Cerebras leaped into the limelight in2019when it came out of stealth to reveal a 1.2-trillion-transistor chip. The size of a dinner plate, the chip is called the Wafer Scale Engine and was the world’s largest computer chip. Earlier this year Cerebras unveiled the Wafer Scale Engine 2 (WSE-2), which more than doubled the number of transistors to 2.6 trillion.

Now the company has outlined a series of innovations that mean its latest chip can train a neural network with up to 120 trillion parameters. For reference, OpenAI’s revolutionary GPT-3 language model contains 175 billion parameters. The largest neural network to date, which was trained by Google, had 1.6 trillion.

As a recent New York Times article highlighted, self-driving cars are taking longer to come to market than many experts initially predicted. Automated vehicles where riders can sit back, relax, and be delivered to their destinations without having to watch the road are continuously relegated to the “not-too-distant future.”

There’s not just debate on when this driverless future will arrive, there’s also a lack of consensus on how we’ll get there, that is, which technologies are most efficient, safe, and scalable to take us from human-driven to computer-driven (Tesla is the main outlier in this debate). The big players are lidar, cameras, ultrasonic sensors, and radar. Last week, one lidar maker showcased some new technology that it believes will tip the scales.

Continue reading “An ‘Uncrashable’ Car? Luminar Says Its Lidar Can Get There” »