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This Article Is Based On The Research Paper ‘GraphWorld: Fake Graphs Bring Real Insights for GNNs’. All Credit For This Research Goes To The Researchers 👏👏👏 Please Don’t Forget To Join Our ML Subreddit A graph is a structure consisting of a set of items in which some pairings of the objects are in some […].

Please describe the mindset needed to be a roboticist.

In order to be a roboticist, you need to have an open mindset and an insatiable sense of curiosity. You need to be able to understand the needs of people and how they accomplish tasks before you can develop robots that can meet these requirements. That’s how we can maximise usefulness in the robots of tomorrow.

When you walk through our office, you will also notice that roboticists come from all walks of life and many different disciplines. So we encourage anyone who is interested in robots to apply for a job with us.

Future quantum computers are expected not only to solve particularly tricky computing tasks, but also to be connected to a network for the secure exchange of data. In principle, quantum gates could be used for these purposes. But until now, it has not been possible to realize them with sufficient efficiency. By a sophisticated combination of several techniques, researchers at the Max Planck Institute of Quantum Optics (MPQ) have now taken a major step towards overcoming this hurdle.

For decades, computers have been getting faster and more powerful with each new generation. This development makes it possible to constantly open up new applications, for example in systems with artificial intelligence. But further progress is becoming increasingly difficult to achieve with established computer technology. For this reason, researchers are now setting their sights on alternative, completely new concepts that could be used in the future for some particularly difficult computing tasks. These concepts include quantum computers.

Their function is not based on the combination of digital zeros and ones—the classical bits—as is the case with conventional, microelectronic computers. Instead, a quantum computer uses quantum bits, or qubits for short, as the basic units for encoding and processing information. They are the counterparts of bits in the quantum world—but differ from them in one crucial feature: qubits can not only assume two fixed values or states such as zero or one, but also any values in between. In principle, this offers the possibility to carry out many computing processes simultaneously instead of processing one logical operation after the other.

Inspired by progress in large-scale language modelling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens.

During the training phase of Gato, data from different tasks and modalities are serialised into a flat sequence of tokens, batched, and processed by a transformer neural network similar to a large language model. The loss is masked so that Gato only predicts action and text targets.

When deploying Gato, a prompt, such as a demonstration, is tokenised, forming the initial sequence. Next, the environment yields the first observation, which is also tokenised and appended to the sequence. Gato samples the action vector autoregressively, one token at a time.

Over the years, supercomputers have played a pivotal role in pushing the frontiers of science. Earlier this year, Meta launched one of the fastest AI supercomputers, the AI Research SuperCluster (RSC), to build sophisticated AI models that can learn from trillions of examples; navigate hundreds of different languages; seamlessly analyse text, images, and video together; build AR tools etc.

However, the quest for something even faster than supercomputers led to the development of quantum computers. Last year, the University of Science and Technology of China (USTC) introduced the world’s fastest programmable superconducting quantum computer; Zuchongzhi 2.1 is a million times faster than a conventional computer.

At last year’s I/O conference, Google unveiled a Quantum AI campus in Santa Barbara, California, complete with a quantum data centre, quantum hardware research labs, and quantum processor chip fab facilities. The tech giant plans to build a useful, error-corrected quantum computer within a decade.

At this year’s IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), the Alexa AI automatic speech recognition organization is rep… See more.

Multimodal training, signal-to-interpretation, and BERT rescoring are just a few topics covered by Amazon’s 21 speech-related papers.

For a human, one of the first signs someone is getting old is the inability to remember little things; maybe they misplace their keys, or get lost on an oft-taken route. For a laboratory mouse, it’s forgetting that when bright lights and a high-pitched buzz flood your cage, an electric zap to the foot quickly follows.

But researchers at Stanford University discovered that if you transfuse cerebrospinal fluid from a young mouse into an old one, it will recover its former powers of recall and freeze in anticipation. They also identified a protein in that cerebrospinal fluid, or CSF, that penetrates into the hippocampus, where it drives improvements in memory.

The tantalizing breakthrough, published Wednesday in Nature, suggests that youthful factors circulating in the CSF, or drugs that target the same pathways, might be tapped to slow the cognitive declines of old age. Perhaps even more importantly, it shows for the first time the potential of CSF as a vehicle to get therapeutics for neurological diseases into the hard-to-reach fissures of the human brain.

Lensless cameras have many potential use-cases but have generally been held back by lengthy processing requirements and low-resolution images. A research from a team at the Tokyo Institute of Technology is looking to change that.