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Category: information science – Page 317

How AI is helping us discover materials faster than ever

Another problem is that we still don’t have enough data about every compound, according to Wolverton, and a lack of data means algorithms aren’t very smart. That said, he and Mehta are now interested in using their method on other types of materials beside metallic glass. And they hope that one day, you won’t need a human to do experiments at all, it’ll just be AI and robots. “We can create really a completely autonomous system,” Wolverton says, “without any human being involved.

For hundreds of years, new materials were discovered through trial and error, or luck and serendipity. Now, scientists are using artificial intelligence to speed up the process.

How Music Generated

There is an enduring fear in the music industry that artificial intelligence will replace the artists we love, and end creativity as we know it.

As ridiculous as this claim may be, it’s grounded in concrete evidence. Last December, an AI-composed song populated several New Music Friday playlists on Spotify, with full support from Spotify execs. An entire startup ecosystem is emerging around services that give artists automated songwriting recommendations, or enable the average internet user to generate customized instrumental tracks at the click of a button.

But AI’s long-term impact on music creation isn’t so cut and dried. In fact, if we as an industry are already thinking so reductively and pessimistically about AI from the beginning, we’re sealing our own fates as slaves to the algorithm. Instead, if we take the long view on how technological innovation has made it progressively easier for artists to realize their creative visions, we can see AI’s genuine potential as a powerful tool and partner, rather than as a threat.

Google made an AR microscope that can help detect cancer

In a talk given today at the American Association for Cancer Research’s annual meeting, Google researchers described a prototype of an augmented reality microscope that could be used to help physicians diagnose patients. When pathologists are analyzing biological tissue to see if there are signs of cancer — and if so, how much and what kind — the process can be quite time-consuming. And it’s a practice that Google thinks could benefit from deep learning tools. But in many places, adopting AI technology isn’t feasible. The company, however, believes this microscope could allow groups with limited funds, such as small labs and clinics, or developing countries to benefit from these tools in a simple, easy-to-use manner. Google says the scope could “possibly help accelerate and democratize the adoption of deep learning tools for pathologists around the world.”

The microscope is an ordinary light microscope, the kind used by pathologists worldwide. Google just tweaked it a little in order to introduce AI technology and augmented reality. First, neural networks are trained to detect cancer cells in images of human tissue. Then, after a slide with human tissue is placed under the modified microscope, the same image a person sees through the scope’s eyepieces is fed into a computer. AI algorithms then detect cancer cells in the tissue, which the system then outlines in the image seen through the eyepieces (see image above). It’s all done in real time and works quickly enough that it’s still effective when a pathologist moves a slide to look at a new section of tissue.

Google’s latest AI experiments let you talk to books and test word association skills

Google today announced a pair of new artificial intelligence experiments from its research division that let web users dabble in semantics and natural language processing. For Google, a company that’s primary product is a search engine that traffics mostly in text, these advances in AI are integral to its business and to its goals of making software that can understand and parse elements of human language.

The website will now house any interactive AI language tools, and Google is calling the collection Semantic Experiences. The primary sub-field of AI it’s showcasing is known as word vectors, a type of natural language understanding that maps “semantically similar phrases to nearby points based on equivalence, similarity or relatedness of ideas and language.” It’s a way to “enable algorithms to learn about the relationships between words, based on examples of actual language usage,” says Ray Kurzweil, notable futurist and director of engineering at Google Research, and product manager Rachel Bernstein in a blog post. Google has published its work on the topic in a paper here, and it’s also made a pre-trained module available on its TensorFlow platform for other researchers to experiment with.

The first of the two publicly available experiments released today is called Talk to Books, and it quite literally lets you converse with a machine learning-trained algorithm that surfaces answers to questions with relevant passages from human-written text. As described by Kurzweil and Bernstein, Talk to Books lets you “make a statement or ask a question, and the tool finds sentences in books that respond, with no dependence on keyword matching.” The duo add that, “In a sense you are talking to the books, getting responses which can help you determine if you’re interested in reading them or not.”

Using an algorithm to reduce energy bills—rain or shine

Researchers proposed implementing the residential energy scheduling algorithm by training three action dependent heuristic dynamic programming (ADHDP) networks, each one based on a weather type of sunny, partly cloudy, or cloudy. ADHDP networks are considered ‘smart,’ as their response can change based on different conditions.

“In the future, we expect to have various types of supplies to every household including the grid, windmills, and biogenerators. The issues here are the varying nature of these power sources, which do not generate electricity at a stable rate,” said Derong Liu, a professor with the School of Automation at the Guangdong University of Technology in China and an author on the paper. “For example, power generated from windmills and solar panels depends on the weather, and they vary a lot compared to the more stable power supplied by the grid. In order to improve these power sources, we need much smarter algorithms in managing/scheduling them.”

The details were published on the January 10th issue of IEEE/CAA Journal of Automatica Sinica, a joint bimonthly publication of the IEEE and the Chinese Association of Automation.

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