Lifeboat Foundation

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers.

📝 The paper “LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action” is available below. Note that this is a collaboration between UC Berkeley, University of Warsaw, and Robotics at Google.
https://sites.google.com/view/lmnav.

Continue reading “Google’s New Self-Driving Robot Is Amazing! 🤖” »

My head is currently swirling and whirling with a cacophony of conceptions. This maelstrom of meditations was triggered by NVIDIA’s recent announcement of their Jetson Orin Nano system-on-modules that deliver up to 80x the performance over the prior generation, which is, in their own words, “setting a new standard for entry-level edge AI and robotics.”

One of my contemplations centers on their use of the “entry level” qualifier in this context. When I was coming up, this bodacious beauty would have qualified as the biggest, baddest supercomputer on the planet.

I’m being serious. In 1975, which was the year I entered university, Cray Research announced their Cray-1 Supercomputer. Conceived by Seymore Cray, this was the first computer to successfully implement a vector processing architecture.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Watch here.

We are on the cusp of a paradigm shift brought by generative AI — but it isn’t about making creativity “quick and easy.” Generative technology opens new heights of human expression and helps creators find their authentic voices.

How we create is changing. The blog you read earlier today may have been made with generative AI. Within 10 years, most creative content will be produced with generative technologies.

Researchers at QuTech—a collaboration between the Delft University of Technology and TNO—have engineered a record number of six, silicon-based, spin qubits in a fully interoperable array. Importantly, the qubits can be operated with a low error-rate that is achieved with a new chip design, an automated calibration procedure, and new methods for qubit initialization and readout. These advances will contribute to a scalable quantum computer based on silicon. The results are published in Nature today.

Different materials can be used to produce qubits, the quantum analog to the bit of the classical computer, but no one knows which material will turn out to be best to build a large-scale quantum computer. To date there have only been smaller demonstrations of silicon quantum chips with high quality qubit operations. Now, researchers from QuTech, led by Prof. Lieven Vandersypen, have produced a six qubit chip in silicon that operates with low error-rates. This is a major step towards a fault-tolerant quantum computer using silicon.

To make the qubits, individual electrons are placed in a linear array of six “quantum dots” spaced 90 nanometers apart. The array of quantum dots is made in a silicon chip with structures that closely resemble the transistor—a common component in every computer chip. A quantum mechanical property called spin is used to define a qubit with its orientation defining the 0 or 1 logical state. The team used finely-tuned microwave radiation, magnetic fields, and electric potentials to control and measure the spin of individual electrons and make them interact with each other.

Have you ever been faced with a problem where you had to find an optimal solution out of many possible options, such as finding the quickest route to a certain place, considering both distance and traffic?

If so, the problem you were dealing with is what is formally known as a “combinatorial optimization problem.” While mathematically formulated, these problems are common in the real world and spring up across several fields, including logistics, network routing, machine learning, and materials science.

Continue reading “Scalable and fully coupled quantum-inspired processor solves optimization problems” »

Artificial Intelligence (AI), a term first coined at a Dartmouth workshop in 1956, has seen several boom and bust cycles over the last 66 years. Is the current boom different?

The most exciting advance in the field since 2017 has been the development of “Large Language Models,” giant neural networks trained on massive databases of text on the web. Still highly experimental, Large Language Models haven’t yet been deployed at scale in any consumer product — smart/voice assistants like Alexa, Siri, Cortana, or the Google Assistant are still based on earlier, more scripted approaches.

Continue reading “Blaise Aguera y Arcas and Melanie Mitchell: How Close Are We to AI?” »

A new field of science has been emerging at the intersection of neuroscience and high-performance computing — this is the takeaway from the 2022 BrainComp conference, which took place in Cetraro, Italy from the 19th to the 22nd of September. The meeting, which featured international experts in brain mapping, machine learning, simulation, research infrastructures, neuro-derived hardware, neuroethics and more, strengthened the current collaborations in this emerging field and forged new ones.

Now in its 5th edition, BrainComp first started in 2013 and is jointly organised by the Human Brain Project and the EBRAINS digital research infrastructure, University of Calabria in Italy, the Heinrich Heine University of Düsseldorf and the Forschungszentrum Jülich in Germany. It is attended by researchers from inside and outside the Human Brain Project. This year was dedicated to the computational challenges of brain connectivity. The brain is the most complex system in the observable universe due to the tight connections between areas down to the wiring of the individual neurons: decoding this complexity through neuroscientific and computing advances benefits both fields.

Hosted by the organising committee of Katrin Amunts, Scientific Research Director of the HBP, Thomas Lippert, Leader of EBRAINS Computing Services from the Juelich Supercomputing Centre and Lucio Grandinetti from the University of Calabria, the sessions included a variety of topics over four days.

A well-known game studio is allegedly using AI voices for a video game. A clarification includes a commitment to human creativity. It’s another footnote in the debate over the value of human labor that will become more common in the future.

It’s the very debate that has erupted so vehemently around AI-generated images in recent months. Are AI images art? If so, can they be equated with human art? Are they detrimental to art? Are they even plagiarism, because the AI examines human works during training – in the inspiration phase, so to speak – and then imitates them in trace elements?

Continue reading “AI audio is on the rise and will spark new debates about the value of human effort” »

Tesla will be showing off a working prototype of its Optimus humanoid robot at AI Day next week. Should we be afraid for our lives?