In 2011, scientists made one of the most important discoveries in the history of AI development. They found that graphics processing units (GPUs) are far better at simulating biological learning than central processing units (CPUs).
In retrospect, it seems obvious. Human brains are much more like GPUs than CPUs. Both brains and GPUs rely on parallel processing that simulates and predicts real world physics.
In light of this, AI developers created powerful deep neural networks (DNNs) that emulate human brain function. All the major advances in self-driving cars and language translation are the result of this.
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Below is a chart from Nvidia. It shows the explosive progress of AI running the Caffe deep learning platform:
In the meantime, pharma is tapping into this breakthrough to cure the diseases of aging and cut healthcare costs.
Deep Learning and Aging
One company that took on aging is InSilico Medicine. InSilico is one of the biotech companies I write about in my weekly publication, Tech Digest (subscribe here for free). The company uses DNNs to sort through huge amounts of biological data. The DNNs look for biomarkers (measurable indicators of your biological state such as those included in blood tests) that correlate with aging. For humans, this would be an impossibly complicated and time-consuming task.
InSilico has developed a program that will guess your age within a few years based on a standard blood test. Why should you care?
The reason is that biomarkers indicating old age can be altered. We can identify compounds that can make your biomarkers—and you—younger and healthier. A few of these compounds are already known. But scientists lacked the technology to identify the most powerful ones… until now.
Today, there is a race to find the most effective and safest geroprotectors. These are compounds that can restore you and your biomarkers to a more youthful profile.
One player in this field is the Life Extension Foundation (LEF). It’s on a mission to find safe and effective natural geroprotectors without regulatory delays and costs.
Last week, InSilico Medicine announced a product called Ageless Cell. It was developed in collaboration with LEF’s head of R&D Andrew Swick, PhD, former Director of Cardiovascular and Metabolic Diseases at Pfizer Global Research and Development. The supplement contains four natural compounds that DNNs have shown can rejuvenate older cells.
LEF has access to blood tests from its customers who take the product. That means data should be available in less than a year. If it works, we can expect other DNN-developed geroprotectors.
Many biotech companies, including startups, are using AI. That means investors can bet on this trend without exposure to the volatility of biotech startups.
Nvidia (NVDA) is the obvious way to play the growth of DNN applications. The company may have been overvalued at one point, but several negative reports drove its price back to bargain levels.
One indication of the potential of this company comes from Insilico Medicine’s CEO. He told me that the company ran calculations for the LEF supplement on a 40 Tflop 4xNvidia Titan X (Pascal architecture) supercomputer. Since then, Insilico built another GPU cluster using 16xTitan X (Pascal), delivering 160 Tflops in GPU computing power.
Despite a collaborative relationship with Nvidia, Insilico couldn’t get all the Nvidia GPUs it needed for the project due to high demand for the processors. This points to strong growth potential in this market.
While Nvidia pioneered dedicated GPUs for AI, chip giant Intel (INTC) is catching up. Last year, Intel bought AI startup Nervana and integrated its deep learning architecture into Xeon microprocessors. Intel also benefits from an arrangement with Google to advance AI in multicloud environments.
One tech giant that doesn’t seem to be riding this wave is IBM. Big Blue made a big mistake by underestimating the market for AI computer components. Instead, IBM invested in its own cognitive system called Watson. IBM may expand its AI product line, but we’ll have to see if the shift comes in time to compete with Nvidia and Intel.
In any case, advanced AI is not just a marginal improvement in analytical tools. It’s the only technology capable of exploiting the massive knowledge gained from human genome sequencing.
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