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Rewriting the Code of Life

The New Yorker

Early on an unusually blustery day in June, Kevin Esvelt climbed aboard a ferry at Woods Hole, bound for Nantucket Island. Esvelt, an assistant professor of biological engineering at the Massachusetts Institute of Technology, was on his way to present to local health officials a plan for ridding the island of one of its most persistent problems: Lyme disease. He had been up for much of the night working on his slides, and the fatigue showed. He had misaligned the buttons on his gray pin-striped shirt, and the rings around his deep-blue eyes made him look like a sandy-haired raccoon. Esvelt, who is thirty-four, directs the "sculpting evolution" group at M.I.T., where he and his colleagues are attempting to design molecular tools capable of fundamentally altering the natural world. If the residents of Nantucket agree, Esvelt intends to use those tools to rewrite the DNA of white-footed mice to make them immune to the bacteria that cause Lyme and other tick-borne diseases. He and his team would breed the mice in the laboratory and then, as an initial experiment, release them on an uninhabited island. If the number of infected ticks begins to plummet, he would seek permission to repeat the process on Nantucket and on nearby Martha's Vineyard. More than a quarter of Nantucket's residents have been infected with Lyme, which has become one of the most rapidly spreading diseases in the United States. The illness is often accompanied by a red bull's-eye rash, along with fever and chills. When the disease is caught early enough, it can be cured in most cases with a single course of antibiotics. For many people, though, pain and neurological symptoms can persist for years. In communities throughout the Northeast, the fear of ticks has changed the nature of summer itself--few parents these days would permit a child to run barefoot through the grass or wander blithely into the woods. "What if we could wave our hands and make this problem go away?" Esvelt asked the two dozen officials and members of the public who had assembled at the island's police station for his presentation. He explained that white-footed mice are the principal reservoir of Lyme disease, which they pass, through ticks, to humans.


The Chatbot Will See You Now

#artificialintelligence

In March of 2016, a twenty-seven-year-old Syrian refugee named Rakan Ghebar began discussing his mental health with a counsellor. Ghebar, who has lived in Beirut since 2014, lost a number of family members to the civil war in Syria and struggles with persistent nervous anxiety. Before he fled his native country, he studied English literature at Damascus University; now, in Lebanon, he works as the vice-principal at a school for displaced Syrian children, many of whom suffer from the same difficulties as he does. When Ghebar asked the counsellor for advice, he was told to try to focus intently on the present. By devoting all of his energy to whatever he was doing, the counsellor said, no matter how trivial, he could learn to direct his attention away from his fears and worries.


NEW BUZZ about the 6 p.m. MSNBC slot -- 14-0 vote against Israel; Trump vows CHANGE -- ASSANGE on Trump -- WEEKEND READS -- ROB SALITERMAN engaged -- B'DAY: Dan Pfeiffer

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REVOLVING DOOR -- "Trump appoints his business attorney to manage international negotiations," by CNN's Elise Labott and Teddy Schleifer: "Jason Greenblatt, the executive vice president and chief legal officer for Trump's business empire, will take on the title of special representative for international negotiations. A source familiar with the appointment told CNN that Greenblatt will primarily will be working on Israel-Palestinian peace process, the American relationship with Cuba and trade agreements."


80% of businesses want chatbots by 2020

#artificialintelligence

Businesses are beginning to see the benefits of using chatbots for their consumer-facing products, according to a survey by Oracle. The survey included responses from 800 decision makers including chief marketing officers, chief strategy officers, senior marketers, and senior sales executives from France, the Netherlands, South Africa, and the UK. When asked which emerging technologies they are already using and which they intended to implement, 80% of respondents said they already used or planned to use chatbots by 2020. Chatbots are interactive software platforms that reside in apps, live chat, email, and SMS and can behave in a human-like manner. Additionally, the survey shows that business leaders and decision makers are turning to the broader umbrella of automation technologies, which includes chatbots, for things like sales, marketing, and customer service.


How robots moved from science fiction into the real world in 2016

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Millions of jobs could be lost to robots and automation by the year 2020 as part of the so-called "fourth industrial revolution," according to a World Economic Forum report from January. Jobs such as manufacturing and production are expected to be affected by the rise of the machines, while a whole new line of jobs will also be created, including I.T. and data analysis. However, the net loss is expected to be around five million jobs within the next three years. There may be a smidgen of hyperbole in that statement, but you only have to look at some of the developments of the past twelve months to realize that robots are here to stay and they will start adversely affecting employment. Here, we take a look back at some of the landmark moments and trends from across the robot realm in 2016.


Optimality and Sub-optimality of PCA for Spiked Random Matrices and Synchronization

arXiv.org Machine Learning

A central problem of random matrix theory is to understand the eigenvalues of spiked random matrix models, in which a prominent eigenvector is planted into a random matrix. These distributions form natural statistical models for principal component analysis (PCA) problems throughout the sciences. Baik, Ben Arous and P\'ech\'e showed that the spiked Wishart ensemble exhibits a sharp phase transition asymptotically: when the signal strength is above a critical threshold, it is possible to detect the presence of a spike based on the top eigenvalue, and below the threshold the top eigenvalue provides no information. Such results form the basis of our understanding of when PCA can detect a low-rank signal in the presence of noise. However, not all the information about the spike is necessarily contained in the spectrum. We study the fundamental limitations of statistical methods, including non-spectral ones. Our results include: I) For the Gaussian Wigner ensemble, we show that PCA achieves the optimal detection threshold for a variety of benign priors for the spike. We extend previous work on the spherically symmetric and i.i.d. Rademacher priors through an elementary, unified analysis. II) For any non-Gaussian Wigner ensemble, we show that PCA is always suboptimal for detection. However, a variant of PCA achieves the optimal threshold (for benign priors) by pre-transforming the matrix entries according to a carefully designed function. This approach has been stated before, and we give a rigorous and general analysis. III) For both the Gaussian Wishart ensemble and various synchronization problems over groups, we show that inefficient procedures can work below the threshold where PCA succeeds, whereas no known efficient algorithm achieves this. This conjectural gap between what is statistically possible and what can be done efficiently remains open.


Artificial intelligence could help farmers diagnose crop diseases

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A network of computers fed a large image dataset can learn to recognize specific plant diseases with a high degree of accuracy, potentially paving the way for field-based crop-disease identification using smartphones, according to a team of researchers at Penn State and the Swiss Federal Institute of Technology (EPFL), in Lausanne, Switzerland. The technology could have particular benefits for producers in developing countries, such as in sub-Saharan Africa, who often do not have the research infrastructure or agricultural extension systems to support smallholder farmers, the researchers said. "Global food security is threatened by a number of factors, not the least of which is plant diseases that can reduce yields or even wipe out a crop," said study co-author David Hughes, assistant professor of entomology and biology, College of Agricultural Sciences and Eberly College of Science, Penn State. In addition, Hughes said, plant diseases can have disastrous consequences for smallholder farmers whose livelihoods depend on healthy crops. In the developing world, more than 80 percent of agricultural production is generated by smallholder farmers, and as many as half of hungry people live in smallholder farming households.


Investorideas.com - #Tech #Stocks in #AI/ #Robotics Just Added: $MBLY, $YASKY, $IRBT, $EKSO, $CGNX, $ISRG, $BKFS, $ROK, $PH, $DLPH, $MGA, $ARAY, $LECO, $FARO

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Newswire) Investorideas.com, a global news source and investor resource covering actively traded sectors announces this week's additions to its membership global stock directories in technology, mining, energy, biotech and marijuana/hemp. The biggest addition is an entire new section to the Tech Stocks lists featuring Artificial intelligence (AI) and Robotics companies. Robotics have been with us for some time now - assisting with simple chores (like the Roomba vacuum) all the way though space flight and to police bomb squad and military uses. Some names in our list will be recognizable as having been in the tech sector for some time and robotics/AI is just another branch for them - companies like Amazon, Apple, Google, Microsoft and Facebook that have become an everyday part of our lives. Also included are assembly-line robotics companies and companies making robotic parts, all the way to makers of machine vision technologies and automotive intelligence tech.


Machine learning is helping researchers decipher bat speech

Engadget

Egyptian fruit bats are widespread throughout Africa and often roost together in colonies of 1,000 or more individuals. With that many neighbors packed together, it's no wonder they're such a noisy bunch. And thanks to some exciting machine learning research from Tel Aviv University, we now understand a bit of what they're saying. The research, published Thursday in the journal, Scientific Reports, explains how they did it. First, the team spent 75 days recording two groups of 11 bats held in separate cages.