Lifeboat Foundation: Safeguarding Humanity
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Blog – Page 11,610

In Brief:

  • Researchers have created a heuristically trained neural network that outperformed conventional machine learning algorithms by 160 percent and its own training by 9 percent.
  • This new teaching method could enable AI to make correct classifications of data that’s previously unknown or unclassified, learning information beyond its data set.

Machine learning technology in neural networks has been pushing artificial intelligence (AI) development to new heights. Most AI systems learn to do things using a set of labelled data provided by their human programmers. Parham Aarabi and Wenzhi Guo, engineers from the University of Toronto, Canada have taken machine learning to a different level, developing an algorithm that can learn things on its own, going beyond its training.

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Progress in real science is steady, follows proper methodology and respects engineering safety. We live in an amazing world where medical progress is advancing rapidly, sadly we also have those willing to jump the gun hawking unproven experimental therapies without sufficient data.

Unproven therapies and people jumping the gun to make a quick buck are a plague in the aging research field. Real science is slow and methodical but ultimately gets results that ensure safe therapies can be deployed in the healthcare arena. At Lifespan.io we are passionate about supporting the progress of science that is conducted properly.

“The life science community should embrace the discrediting of unproven therapies promoted without data for economic gain, and instead focus on the promise of research held to the highest standards.”

#aging #crowdfundthecure

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Intel today unveiled new hardware and software targeting the artificial intelligence (AI) market, which has emerged as a focus of investment for the largest data center operators. The chipmaker introduced an FPGA accelerator that offers more horsepower for companies developing new AI-powered services.

The Intel Deep Learning Inference Accelerator (DLIA) combines traditional Intel CPUs with field programmable gate arrays (FPGAs), semiconductors that can be reprogrammed to perform specialized computing tasks. FPGAs allow users to tailor compute power to specific workloads or applications.

The DLIA is the first hardware product emerging from Intel’s $16 billion acquisition of Altera last year. It was introduced at SC16 in Salt Lake City, Utah, the annual showcase for high performance computing hardware. Intel is also rolling out a beefier model of its flagship Xeon processor, and touting its Xeon Phi line of chips optimized for parallelized workloads.

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