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3D bioprinting inside the human body could be possible thanks to new soft robot

Engineers from UNSW Sydney have developed a miniature and flexible soft robotic arm which could be used to 3D print biomaterial directly onto organs inside a person’s body.

3D bioprinting is a process whereby biomedical parts are fabricated from so-called bioink to construct natural tissue-like structures.

Bioprinting is predominantly used for research purposes such as tissue engineering and in the development of new drugs — and normally requires the use of large 3D printing machines to produce cellular structures outside the living body.

Paper Advanced Sciences:

Advanced soft robotic system for in situ 3D bioprinting and endoscopic surgery.

https://onlinelibrary.wiley.com/doi/10.1002/advs.

FUTURE OF AI — The Fate Of Human Value — 4K

The future of artificial intelligence is the question on all of our minds right now. AI has the potential of replacing us in every conceivable industry, leading to a potential dystopia. Humanity is suddenly gripped with this massive anxiety, but this is also our greatest opportunity.

Will this be the end of meaning?

Or is this humanity’s greatest gift in the fulfillment of a larger purpose?

What will be the fate of human value?

Join me as we explore both the dystopian nightmare and the utopian dream scenario.

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Watch Syntiant’s 1-milliwatt Chip Play Doom

At the 2023 IEEE International Solid State Circuits Conference (ISSCC) in San Francisco this week, Irvine, Calif.–based Syntiant detailed the NDP200. This is an ultralow-power chip designed to run neural networks that monitor video and wake other systems when it spots something important. That may be its core purpose, but the NDP200 can also mow down the spawn of hell, if properly trained.

What Happens If You Run A Transformer Model With An Optical Neural Network?

The exponentially expanding scale of deep learning models is a major force in advancing the state-of-the-art and a source of growing worry over the energy consumption, speed, and, therefore, feasibility of massive-scale deep learning. Recently, researchers from Cornell talked about Transformer topologies, particularly how they are dramatically better when scaled up to billions or even trillions of parameters, leading to an exponential rise in the utilization of deep learning computing. These large-scale Transformers are a popular but expensive solution for many tasks because digital hardware’s energy efficiency has not kept up with the rising FLOP requirements of cutting-edge deep learning models. They also perform increasingly impressively in other domains, such as computer vision, graphs, and multi-modal settings.

Also, they exhibit transfer learning skills, which enable them to quickly generalize to certain activities, sometimes in a zero-shot environment with no additional training required. The cost of these models and their general machine-learning capabilities are major driving forces behind the creation of hardware accelerators for effective and quick inference. Deep learning hardware has previously been extensively developed in digital electronics, including GPUs, mobile accelerator chips, FPGAs, and large-scale AI-dedicated accelerator systems. Optical neural networks have been suggested as solutions that provide better efficiency and latency than neural-network implementations on digital computers, among other ways. At the same time, there is also significant interest in analog computing.

Even though these analog systems are susceptible to noise and error, neural network operations can frequently be carried out optically for a much lower cost, with the main cost typically being the electrical overhead associated with loading the weights and data amortized in large linear operations. The acceleration of huge-scale models like Transformers is thus particularly promising. Theoretically, the scaling is asymptotically more efficient regarding energy per MAC than digital systems. Here, they demonstrate how Transformers use this scaling more and more. They sampled operations from a real Transformer for language modeling to run on a real spatial light modulator-based experimental system. They then used the results to create a calibrated simulation of a full Transformer running optically. This was done to show that Transformers may run on these systems despite their noise and error characteristics.

Dr Ben Goertzel — Will Artificial Intelligence Kill Us? Part 1 of 2

https://youtube.com/watch?v=1Uxaq-p0oHs

First Broadcast: July 29, 2019
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Dr Ben Goertzel is the Founder and CEO of SingularityNET and Chief Science Advisor for Hanson Robotics.

He is one of the world’s leading experts in Artificial General Intelligence (AGI), with decades of expertise in applying AI to practical problems like natural language processing, data mining, video gaming, robotics, national security and bioinformatics.

He was part of the Hanson team which developed the AI software for the humanoid Sophia robot, which can communicate with humans and display more than 50 facial expressions. Today he also serve as Chairman of the AGI Society, the Decentralized AI Alliance and the futurist nonprofit organisation Humanity+.

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Google lays off 100 robot workers used to clean its cafeterias, says report

I guess humans aren’t the only ones getting the boot.🤣


Topics Google | Sundar Pichai.

According to a Wired report, Alphabet’s ‘Everyday Robots’ project — an unit under Google’s experimental X laboratories — has been shut down by Google CEO Sundar Pichai. It had trained 100 one-armed, wheeled robots to help clean the company’s cafeterias. Several of these robot prototypes were transported out of the lab and were doing useful duties throughout Google’s Bay Area facilities.

Almost-unbeatable AI comes to Gran Turismo 7

Last year, Sony AI and Polyphony Digital, the developers of Gran Turismo, developed a new AI agent that is able to race at a world-class level. At the time, the experiment was described in a paper in Nature, where the researchers showed that this AI was not only capable of driving very fast—something other AI have done in the past—but also learned tactics, strategy, and even racing etiquette.

At the time, GT Sophy—the name of the AI—wasn’t quite ready for prime time. For example, it often passed opponents at the earliest opportunity on a straight, allowing itself to be overtaken in the next braking zone. And unlike human players, GT Sophy would try to overtake players with impending time penalties—humans would just wait for that penalized car to slow to gain the place.