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Facebook (now Meta) popularized the Silicon Valley ethos with the saying “Move fast and break things”. This approach might have worked when disrupting the social media business, but it’s causing all sorts of problems for them as well as other major AI players. Breaking things and moving fast might be the reason why so many AI projects are failing. According to an MIT study, over 85% of AI projects fail to deliver their stated objectives, and 70% of data science projects never make it to fruition. Clearly moving fast and breaking things doesn’t work if you’re not getting closer to success.

There’s a difference between Iterating to Success and Breaking Things.

Early AI winners align organizational and business strategies to build value and manage risk.

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The tool can identify symptoms of dengue, malaria, leptospirosis, and scrub typhus.

The study investigates both statistical and machine learning approaches. WHO has categorized dengue as a “neglected tropical disease.”

A prediction tool based on multi-nominal regression analysis and a machine learning algorithm was developed.

Accurate diagnosis is essential for the proper treatment and ensuring the well-being of patients. However, some diseases present with similar clinical symptoms and laboratory results, making diagnosing them more challenging.

Continue reading “AI could revolutionize healthcare but can we trust it?” | >

This video will address the hypothesis that advances in artificial intelligence (AI) and neurotechnology could trigger a technological singularity. The singularity could involve the development of artificial intelligence (AI) that is superior to human intelligence, effectively blurring or perhaps removing the distinction between humans and machines.

There is no agreement on when artificial superintelligence will be achieved. Still, one thing is sure: we need to think about our collective goals before the alarming trend of technological singularity makes them irrelevant. Whether powered by AI or some other technical method, the singularity will result in a technological tsunami that will pose unprecedented challenges to human civilization and the physical world on all scales.

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Inspired by Technological Singularity: Will A.I. Take Over?

Continue reading “The Technological Singularity is Near” | >

How expensive and difficult does hyperscale-class AI training have to be for a maker of self-driving electric cars to take a side excursion to spend how many hundreds of millions of dollars to go off and create its own AI supercomputer from scratch? And how egotistical and sure would the company’s founder have to be to put together a team that could do it?

Like many questions, when you ask these precisely, they tend to answer themselves. And what is clear is that Elon Musk, founder of both SpaceX and Tesla as well as a co-founder of the OpenAI consortium, doesn’t have time – or money – to waste on science projects.

Just like the superpowers of the world underestimated the amount of computing power it would take to fully simulate a nuclear missile and its explosion, perhaps the makers of self-driving cars are coming to the realization that teaching a car to drive itself in a complex world that is always changing is going to take a lot more supercomputing. And once you reconcile yourself to that, then you start from scratch and build the right machine to do this specific job.

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Summary: A newly developed artificial intelligence model can detect Parkinson’s disease by reading a person’s breathing patterns. The algorithm can also discern the severity of Parkinson’s disease and track progression over time.

Source: MIT

Parkinson’s disease is notoriously difficult to diagnose as it relies primarily on the appearance of motor symptoms such as tremors, stiffness, and slowness, but these symptoms often appear several years after the disease onset.

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Meta is developing a machine learning model that scans these citations and cross-references their content to Wikipedia articles to verify that not only the topics line up, but specific figures cited are accurate.

This isn’t just a matter of picking out numbers and making sure they match; Meta’s AI will need to “understand” the content of cited sources (though “understand” is a misnomer, as complexity theory researcher Melanie Mitchell would tell you, because AI is still in the “narrow” phase, meaning it’s a tool for highly sophisticated pattern recognition, while “understanding” is a word used for human cognition, which is still a very different thing).

Meta’s model will “understand” content not by comparing text strings and making sure they contain the same words, but by comparing mathematical representations of blocks of text, which it arrives at using natural language understanding (NLU) techniques.

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A new study in Science overthrew the whole gamebook. Led by Dr. David Baker at the University of Washington, a team tapped into an AI’s “imagination” to dream up a myriad of functional sites from scratch. It’s a machine mind’s “creativity” at its best—a deep learning algorithm that predicts the general area of a protein’s functional site, but then further sculpts the structure.

As a reality check, the team used the new software to generate drugs that battle cancer and design vaccines against common, if sometimes deadly, viruses. In one case, the digital mind came up with a solution that, when tested in isolated cells, was a perfect match for an existing antibody against a common virus. In other words, the algorithm “imagined” a hotspot from a viral protein, making it vulnerable as a target to design new treatments.

The algorithm is deep learning’s first foray into building proteins around their functions, opening a door to treatments that were previously unimaginable. But the software isn’t limited to natural protein hotspots. “The proteins we find in nature are amazing molecules, but designed proteins can do so much more,” said Baker in a press release. The algorithm is “doing things that none of us thought it would be capable of.”

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