Lifeboat Foundation

http://2045.com http://gf2045.com.
In February of 2012 the first Global Future 2045 Congress was held in Moscow. There, over 50 world leading scientists from multiple disciplines met to develop a strategy for the future development of humankind. One of the main goals of the Congress was to construct a global network of scientists to further research on the development of cybernetic technology, with the ultimate goal of transferring a human’s individual consciousness to an artificial carrier.

2012–2013. The global economic and social crises are exacerbated. The debates on the global paradigm of future development intensifies.

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Nvidia omniverse to predict an alternate future of the world by forecasting productivity with AI, FIFA integrates full body tracking AI to help referees make calls, and new Meta AI translates 200 languages with highest degree of accuracy.

AI News Timestamps:
0:00 Nvidia Omniverse AI Predicts Alternate Future of The World.
3:01 FIFA Uses Full Body Tracking AI
4:05 Meta AI Translates 200 Languages.

Continue reading “Nvidia Omniverse AI Predicts Alternate Future of The World | FIFA Uses Full Body Tracking AI” »

Large language models are widely adopted in a range of natural language tasks, such as question-answering, common sense reasoning, and summarization. These models, however, have had difficulty with tasks requiring quantitative reasoning, such as resolving issues in mathematics, physics, and engineering.

Researchers find quantitative reasoning an intriguing application for language models as they put language models to the test in various ways. The ability to accurately parse a query with normal language and mathematical notation, remember pertinent formulas and constants and produce step-by-step answers requiring numerical computations and symbolic manipulation are necessary for solving mathematical and scientific problems. Therefore, scientists have believed that machine learning models will require significant improvements in model architecture and training methods to solve such reasoning problems.

A new Google research introduces Minerva, a language model that uses sequential reasoning to answer mathematical and scientific problems. Minerva resolves such problems by providing solutions incorporating numerical computations and symbolic manipulation.

The invasion of Ukraine has prompted militaries to update their arsenals—and Silicon Valley stands to capitalize.

As training regimes go, being forced to watch eight years’ worth of someone else playing Minecraft feels pretty harsh. When the revolution comes I fear OpenAI could be first against the wall after the Robot uprising after what it’s put its latest AI through in order to get it to play the standard version of Minecraft.

OpenAI neural network can now craft a diamond pickaxe off its own back. The detailed blog post on the OpenAI site explains how it managed to teach the network to play Minecraft, and it’s some fascinating stuff. Not least how, of those 70,000 hours of Minecraft gameplay footage, it paid $160,000 to a team of contractors to create and tag up 2,000 hours of footage with labels so the AI could understand what it was looking at and how that related to its actions in the game.

The method is called Video PreTraining (VPT) and it claims its model can learn to craft diamond tools, which it says takes a proficient human around 20 minutes.

Meta’s AI translation work could provide a killer app for AR.

Social media conglomerate Meta has created a single AI model capable of translating across 200 different languages, including many not supported by current commercial tools. The company is open-sourcing the project in the hopes that others will build on its work.

The AI model is part of an ambitious R&D project by Meta to create a so-called “universal speech translator,” which the company sees as important for growth across its many platforms — from Facebook and Instagram, to developing domains like VR and AR. Machine translation not only allows Meta to better understand its users (and so improve the advertising systems that generate 97 percent of its revenue) but could also be the foundation of a killer app for future projects like its augmented reality glasses.

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The artificial intelligence program at Google has apparently hired a lawyer, which brings up a host of scary questions.

Meta, the Facebook parent company, has announced its new, artificial intelligence (AI)-driven language translation model, which claims to be able to translate 200 languages worldwide, in real-time. In a blog post from earlier today, Meta said that this is the first AI language translator model that brings a large number of fringe and lesser known languages from around the world — including fringe dialects from Asia and Africa.

The AI model can also carry out these translations without needing to first translate a language to English, and then translate it to the originally intended language. This, Meta said, does not only help in speeding up the translation time, but is a breakthrough of sorts since many of the 200 languages that its AI model can understand had little to no available public data for AI to train on.

The initiative is part of the company’s No Language Left Behind (NLLB) project, which it announced in February this year. The new AI model, called NLLB-200, has achieved up to 44% higher BLEU (Bilingual Evaluation Understudy) score in terms of its accuracy and quality of translation results. For Indian dialects, NLLB-200 is 70% better than existing AI models.

Circa 2018

Debugging code is drudgery. But SapFix, a new AI hybrid tool created by Facebook engineers, can significantly reduce the amount of time engineers spend on debugging, while also speeding up the process of rolling out new software. SapFix can automatically generate fixes for specific bugs, and then propose them to engineers for approval and deployment to production.

SapFix has been used to accelerate the process of shipping robust, stable code updates to millions of devices using the Facebook Android app — the first such use of AI-powered testing and debugging tools in production at this scale. We intend to share SapFix with the engineering community, as it is the next step in the evolution of automating debugging, with the potential to boost the production and stability of new code for a wide range of companies and research organizations.

Continue reading “Finding and fixing software bugs automatically with SapFix and Sapienz” »