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Category: robotics/AI – Page 1,589

In 2009—four years after it was published—I read Ray Kurzweil’s The Singularity Is Near. It is an optimistic view of the future—a future that depends on computational technology. A future of superintelligent machines. It is also a future where humans will transcend our present biological limits.

I had to read the book twice—once for the sense and once for the detail.

After that, just for my own interest, year-in, year-out, I started to track this future; that meant a weekly read through New Scientist, Wired, the excellent technology pieces in the New York Times and the Atlantic, as well as following the money via the Economist and Financial Times. I picked up any new science and tech books that came out, but it wasn’t enough for me. I felt I wasn’t seeing the bigger picture.

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As technology rapidly progresses, some proponents of artificial intelligence believe that it will help solve complex social challenges and offer immortality via virtual humans.

But AI’s critics are sounding the alarm, going so far as to call its development an “existential threat” to mankind. Is this the stuff of science fiction? Could the “Terminator” become reality, or will these fears prevent the next technological revolution?

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Circa 2020 Kawasaki has successfully verified the Close-range Subsea Pipeline Inspection by Autonomous Underwater Vehicle (AUV) #kawasaki

Powering your potential. Kawasaki is committed to providing customers unique business solutions with our innovative technologies to meet diverse societal needs worldwide. Kawasaki, ‘working as one for the good of the planet’.

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Microsoft’s blog post on Megatron-Turing says the algorithm is skilled at tasks like completion prediction, reading comprehension, commonsense reasoning, natural language inferences, and word sense disambiguation. But stay tuned—there will likely be more skills added to that list once the model starts being widely utilized.

GPT-3 turned out to have capabilities beyond what its creators anticipated, like writing code, doing math, translating between languages, and autocompleting images (oh, and writing a short film with a twist ending). This led some to speculate that GPT-3 might be the gateway to artificial general intelligence. But the algorithm’s variety of talents, while unexpected, still fell within the language domain (including programming languages), so that’s a bit of a stretch.

However, given the tricks GPT-3 had up its sleeve based on its 175 billion parameters, it’s intriguing to wonder what the Megatron-Turing model may surprise us with at 530 billion. The algorithm likely won’t be commercially available for some time, so it’ll be a while before we find out.

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Artificial intelligence (AI) is increasingly becoming a tool for researchers in other science and technology fields, forging collaborations across disciplines. Stanford University in California, which produces an index that tracks AI-related data, finds in its 2021 report that the number of AI journal publications grew by 34.5% from 2019 to 2020; up from 19.6% between 2018 and 2019 (see go.nature.com/3mdt2yq). AI publications represented 3.8% of all peer-reviewed scientific publications worldwide in 2,019 up from 1.3% in 2011.

Five AI researchers describe the fruits of these collaborations, beyond journal publications, and talk about how they are helping to break down barriers between disciplines. across disciplines are growing, and artificial intelligence is helping to make joint working more effective.

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According to the report, the global AI market will be worth US$284.4 billion by 2026.

Today, the artificial intelligence platform has become a way for computer systems to perform tasks like human intelligence including decision-making and speech recognition. Globally, problem-solving, social intelligence, and general intelligence are being achieved with the help of the artificial intelligence platform. Moreover, rising high-level computer languages are helping various industries to work efficiently on the artificial intelligence platform.

Full Story:

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AI startups can rake in investment by hiding how their systems are powered by humans. But such secrecy can be exploitative.

The nifty app CamFind has come a long way with its artificial intelligence. It uses image recognition to identify an object when you point your smartphone camera at it. But back in 2015 its algorithms were less advanced: The app mostly used contract workers in the Philippines to quickly type what they saw through a user’s phone camera, CamFind’s co-founder confirmed to me recently. You wouldn’t have guessed that from a press release it put out that year which touted industry-leading “deep learning technology,” but didn’t mention any human labelers.

The practice of hiding human input in AI systems still remains an open secret among those who work in machine learning and AI. A 2019 analysis of tech startups in Europe by London-based MMC Ventures even found that 40% of purported AI startups showed no evidence of actually using artificial intelligence in their products.

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