Combining Machine Learning and Your Gut
The link between the gut biome and age is described by longevity researcher Alex Zhavoronkov and a team of his colleagues at Insilico Medicine, an artificial intelligence startup focused on drug discovery, biomarker development, and aging research.
If the forecast calls for rain, you’ll probably pack an umbrella. If it calls for cold, you may bring your mittens. That same kind of preparation happens in buildings, where sophisticated heating and cooling systems adjust themselves based on the predicted weather.
But when the forecast is imperfect—as it often is—buildings can end up wasting energy, just as we may find ourselves wet, cold or burdened with extra layers we don’t need.
A new approach developed by Fengqi You, professor in energy systems engineering at Cornell University, predicts the accuracy of the weather forecast using a machine learning model trained with years’ worth of data on forecasts and actual weather conditions. You combined that predictor with a mathematical model that considers building characteristics including the size and shape of rooms, the construction materials, the location of sensors and the position of windows.
A team of researchers including Neil Johnson, a professor of physics at the George Washington University, has discovered that decentralized systems work better when the individual parts are less capable.
Dr. Johnson was interested in understanding how systems with many moving parts can reach a desired target or goal without centralized control. This explores a common theory that decentralized systems, those without a central brain, would be more resilient against damage or errors.
This research has the potential to inform everything from how to effectively structure a company, build a better autonomous vehicle, optimize next-generation artificial intelligence algorithms—and could even transform our understanding of evolution. The key lies in understanding how the “sweet spot” between decentralized and centralized systems varies with how clever the pieces are, Dr. Johnson said.
Forensics is on the cusp of a third revolution in its relatively young lifetime. The first revolution, under the brilliant but complicated mind of J. Edgar Hoover, brought science to the field and was largely responsible for the rise of criminal justice as we know it today. The second, half a century later, saw the introduction of computers and related technologies in mainstream forensics and created the subfield of digital forensics.
We are now hurtling headlong into the third revolution with the introduction of Artificial Intelligence (AI) – intelligence exhibited by machines that are trained to learn and solve problems. This is not just an extension of prior technologies. AI holds the potential to dramatically change the field in a variety of ways, from reducing bias in investigations to challenging what evidence is considered admissible.
AI is no longer science fiction. A 2016 survey conducted by the National Business Research Institute (NBRI) found that 38% of enterprises are already using AI technologies and 62% will use AI technologies by 2018. “The availability of large volumes of data—plus new algorithms and more computing power—are behind the recent success of deep learning, finally pulling AI out of its long winter,” writes Gil Press, contributor to Forbes.com.
Lawmakers and experts are sounding the alarm about “deepfakes,” forged videos that look remarkably real, warning they will be the next phase in disinformation campaigns.
The manipulated videos make it difficult to distinguish between fact and fiction, as artificial intelligence technology produces fake content that looks increasingly real.
The issue has the attention of lawmakers from both parties on Capitol Hill.
Posted in robotics/AI
Robots are already capable of doing many things for us. As this video shows, mobile robots can be used to automate tasks in the lab. They use tags and picture recognition to handle items. The robots are also capable of accessing lab devices.
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