Prominent cardiologist Dr. Eric Topol explains how artificial intelligence and technological advances are ushering in a new age of healthcare and medicine.
By Laurie Mathena
Samsung disclosed that it will introduce an “artificial human” called Neon at CES 2020 on Jan. 7 that is totally different than its artificial intelligence (AI) assistant Bixby, Digital Trends reported.
The limited information about Neon was shared on Samsung’s Twitter account, and includes an “Artificial Human” teaser in several languages with the tagline “Have you met an ‘artificial’?”
Continue reading “Samsung Launches ‘Artificial Human’ Called Neon” »
“When you’re out there, and you can hear them moving around, but you can’t see them, it’s like, ‘Where are they going to come from?’,” she said. “It’s a little nerve-racking at first.”
Amazon is increasingly requiring warehouse employees to get used to working with robots. The company now has more than 200,000 robotic vehicles it calls “drives” that are moving goods through its delivery-fulfillment centers around the U.S. That’s double the number it had last year and up from 15,000 units in 2014.
Its rivals have taken notice. Many are adding their own robots in a race to speed up productivity and bring down costs.
Read writing from Amelie Schreiber in Towards Data Science. CEO & Founder of The Singularity: Quantum Machine Learning Hiring, Business Integration, and R&D Consultant.
A pair of researchers, one with the Max Planck Institute for Astronomy, the other with the Jet Propulsion Laboratory at CIT, has found a way to estimate how long it will take already launched space vehicles to arrive at other star systems. The pair, Coryn Bailer-Jones and Davide Farnocchia have written a paper describing their findings and have uploaded it to the arXiv preprint server.
Back in the 1970s, NASA sent four unmanned space probes out into the solar system—Pioneer 10 and 11, and Voyager 1 and 2—which, after completion of their missions, kept going—all four are on their way out of the solar system or have already departed. But what will become of them? Will they make their way to other star systems, and if so, how long might it take them? This is what Bailer-Jones and Davide Farnocchia wondered. To find some possible answers, they used the Gaia space telescope. It was launched by the European Space Agency back in 2013 and has been stationed at a point just outside of Earth’s orbit around the sun. It has been collecting information on a billion stars, including their paths through space. The latest dataset was released just last year on 7.2 million stars.
With data describing the paths of the four spacecraft and data describing the paths of a host of stars in hand, the researchers were able to work out when the paths of the four spacecraft might approach very far away star systems.
Microsoft researchers propose an approach to training AI that uses smiles as a reward signal. They say it achieves superior efficiency.
So, what can businesses do to address this? A big part of the answer lies in emerging technologies such as artificial intelligence (AI) and automation, which will drive new ways of securing the cloud in 2020 and beyond.
That might explain things…
There may soon be serious financial and legal ramifications to the proliferation of deepfake technology. The Wall Street Journal reported last week that a UK energy company’s chief executive was tricked into wiring €200,000 to a Hungarian supplier because he believed his boss was instructing him to do so. Instead, it was a thief using deepfake tech.
https://youtube.com/watch?v=CMREUiQZSIs
To start, Volvo’s Vera will ferry goods from a logistics center to a port in Gothenburg, Sweden. But more Veras will eventually mean fewer trucking jobs.
In this age of “big data,” artificial intelligence (AI) has become a valuable ally for scientists. Machine learning algorithms, for instance, are helping biologists make sense of the dizzying number of molecular signals that control how genes function. But as new algorithms are developed to analyze even more data, they also become more complex and more difficult to interpret. Quantitative biologists Justin B. Kinney and Ammar Tareen have a strategy to design advanced machine learning algorithms that are easier for biologists to understand.
The algorithms are a type of artificial neural network (ANN). Inspired by the way neurons connect and branch in the brain, ANNs are the computational foundations for advanced machine learning. And despite their name, ANNs are not exclusively used to study brains.
Biologists, like Tareen and Kinney, use ANNs to analyze data from an experimental method called a “massively parallel reporter assay” (MPRA) which investigates DNA. Using this data, quantitative biologists can make ANNs that predict which molecules control specific genes in a process called gene regulation.
