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Archive for the ‘robotics/AI’ category: Page 934

Dec 9, 2022

DeepMind’s AlphaCode Can Outcompete Human Coders

Posted by in category: robotics/AI

AlphaCode received an average ranking in the top 54.3% in simulated evaluations in recent coding competitions on the Codeforces competitive coding platform when limited to generation 10 solutions per problem. 66% of those problems, however, were solved using its first submission.

That might not sound all that impressive, particularly when compared to seemingly stronger model performances against humans in complex board games, though the researchers note that succeeding at coding competitions are uniquely difficult. To succeed, AlphaCode had to first understand complex coding problems in natural languages and then “reason” about unforeseen problems rather than simply memorizing code snippets. AlphaCode was able to solve problems it hadn’t seen before, and the researchers claim they found no evidence that their model simply copied core logix from the training data. Combined, the researchers say those factors make AlphaCode’s performance a “big step forward.”

Dec 9, 2022

DeepMind’s latest AI project solves programming challenges like a newb

Posted by in categories: information science, robotics/AI

Google’s DeepMind AI division has tackled everything from StarCraft to protein folding. So it’s probably no surprise that its creators have eventually turned to what is undoubtedly a personal interest: computer programming. In Thursday’s edition of Science, the company describes a system it developed that produces code in response to programming typical of those used in human programming contests.

On an average challenge, the AI system could score near the top half of participants. But it had a bit of trouble scaling, being less likely to produce a successful program on problems where more code is typically required. Still, the fact that it works at all without having been given any structural information about algorithms or programming languages is a bit of a surprise.

Dec 9, 2022

GPT-3 + Sheets — LifeArchitect.ai LIVE

Posted by in categories: ethics, robotics/AI

https://lifearchitect.ai/sheets/
https://sheets.new/
https://beta.openai.com/account/api-keys.

Mentioned in this stream:
https://jalammar.github.io/how-gpt3-works-visualizations-animations/
https://c4-search.apps.allenai.org/?q=%22James+Gosling%22
https://beta.openai.com/codex-javascript-sandbox.
https://lifearchitect.ai/leta/#prompt.
https://galactica.org/explore/
https://lifearchitect.ai/roadmap/

Continue reading “GPT-3 + Sheets — LifeArchitect.ai LIVE” »

Dec 9, 2022

How Starlink & T-Mobile’s partnership will impact 5G for the better for AI cameras

Posted by in categories: mobile phones, robotics/AI

Starlink and T-Mobile’s partnership will be revolutionary for cellular service and Smarter AI CEO Chris Piche had some thoughts on how the new partnership will impact 5G capability for the automotive industry.

Chris, who has created services including AT&T TV, BBM Video, Poly Video, and STUN/TURN/ICE shared his thoughts on the effect of 5G on vehicles and telecommunications in an interview with Teslarati.

AI CAMERAS, TESLA, STARLINK & AUTONOMOUS VEHICLES Before founding Smarter AI, the Top 40 under 40 entrepreneur’s company created a technology that BlackBerry licensed to enable voice and video calling. This gave Chris a front-row seat to witness the speed at which technology can transform markets.

Dec 9, 2022

This U.S. company manufactures low-cost, high-quality robotic limbs for Ukraine-Russia war victims

Posted by in categories: business, finance, robotics/AI

Thousands of Ukrainian soldiers and civilians are suffering unimaginable injuries in the country’s war with Russia. Many of the more seriously wounded have lost one or more limbs. Now a company in New York is stepping up to help. For access to live and exclusive video from CNBC subscribe to CNBC PRO: https://cnb.cx/2NGeIvi.

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Dec 9, 2022

How AI found the words to kill cancer cells

Posted by in categories: biotech/medical, robotics/AI

Using new machine learning techniques, researchers at UC San Francisco (UCSF), in collaboration with a team at IBM Research, have developed a virtual molecular library of thousands of “command sentences” for cells, based on combinations of “words” that guided engineered immune cells to seek out and tirelessly kill cancer cells.

The work, published online Dec. 8, 2022, in Science, represents the first time such sophisticated computational approaches have been applied to a field that until now has progressed largely through ad hoc tinkering and engineering cells with existing—rather than synthesized—molecules.

The advance allows scientists to predict which elements—natural or synthesized—they should include in a cell to give it the precise behaviors required to respond effectively to complex diseases.

Dec 9, 2022

Competition-level code generation with AlphaCode

Posted by in category: robotics/AI

AI gradually taking over coding. Coders tryin to circle the wagons, but its already too late. AI coders will be best in world in probably mid 2025.


Modern machine learning systems can achieve average human-level performance in popular competitive programming contests.

Dec 8, 2022

Pentagon picked four tech companies to form $9B cloud computing network

Posted by in categories: military, robotics/AI, satellites

In a press conference that Ars attended today, Department of Defense officials discussed the benefits of partnering with Google, Oracle, Microsoft, and Amazon to build the Pentagon’s new cloud computing network. The multi-cloud strategy was described as a necessary move to keep military personnel current as technology has progressed and officials’ familiarity with cloud technology has matured.

Air Force Lieutenant General Robert Skinner said that this Joint Warfighting Cloud Capability (JWCC) contract—worth $9 billion—would help quickly expand cloud capabilities across all defense departments. He described new accelerator capabilities like preconfigured templates and infrastructure as code that will make it so that even “people who don’t understand cloud can leverage cloud” technologies. Such capabilities could help troops on the ground easily access data gathered by unmanned aircraft or space communications satellites.

“JWCC is a multiple-award contract vehicle that will provide the DOD the opportunity to acquire commercial cloud capabilities and services directly from the commercial Cloud Service Providers (CSPs) at the speed of mission, at all classification levels, from headquarters to the tactical edge,” DOD’s press release said.

Dec 8, 2022

Future of Humanity: AI & Robotics | Free Documentary

Posted by in categories: education, robotics/AI

Humanity Augmented: https://youtu.be/Hc2FMNMPiNQ

Mankind has always looked for ways to reduce manual labor and repetitive tasks. To that end, and in the absence of technology, civilization exploited various me-thods, often by taking advantage of their fellow humans. Robots, as a potential solution, have long fascinated mankind, capturing our imagination for centuries. Even in Greek mythology, the god Hephaestus had « mechanical » servants. But not until recently, has artificial intelligence finally progressed to a level that will become more and more life-changing for the future of humanity.
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Dec 8, 2022

Researchers at Stanford developed an Artificial Intelligence (AI) Model called ‘RoentGen,’ based on Stable Diffusion and fine-tuned on a Large Chest X-ray and Radiology Dataset

Posted by in categories: biotech/medical, robotics/AI

Latent diffusion models (LDMs), a subclass of denoising diffusion models, have recently acquired prominence because they make generating images with high fidelity, diversity, and resolution possible. These models enable fine-grained control of the image production process at inference time (e.g., by utilizing text prompts) when combined with a conditioning mechanism. Large, multi-modal datasets like LAION5B, which contain billions of real image-text pairs, are frequently used to train such models. Given the proper pre-training, LDMs can be used for many downstream activities and are sometimes referred to as foundation models (FM).

LDMs can be deployed to end users more easily because their denoising process operates in a relatively low-dimensional latent space and requires only modest hardware resources. As a result of these models’ exceptional generating capabilities, high-fidelity synthetic datasets can be produced and added to conventional supervised machine learning pipelines in situations where training data is scarce. This offers a potential solution to the shortage of carefully curated, highly annotated medical imaging datasets. Such datasets require disciplined preparation and considerable work from skilled medical professionals who can decipher minor but semantically significant visual elements.

Despite the shortage of sizable, carefully maintained, publicly accessible medical imaging datasets, a text-based radiology report often thoroughly explains the pertinent medical data contained in the imaging tests. This “byproduct” of medical decision-making can be used to extract labels that can be used for downstream activities automatically. However, it still demands a more limited problem formulation than might otherwise be possible to describe in natural human language. By prompting pertinent medical terms or concepts of interest, pre-trained text conditional LDMs could be used to synthesize synthetic medical imaging data intuitively.

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