Health & Medicine

Artificial intelligence identifies an unknown human ancestor


The new research comes from Institute of Evolutionary Biology (IBE), the Centro Nacional de Análisis Genómico (CNAG-CRG) of the Centre for Genomic Regulation (CRG) and the Institute of Genomics at the University of Tartu. In studies researchers have applied deep learning algorithms and statistical methods to establish the footprint of a new hominid. The application of human DNA computational analysis indicates that the extinct species was a hybrid of Neanderthals and Denisovans. At some stage this hominid cross bred with'Out of Africa' modern humans within the region of the world that is now Asia. The scientific theory of recent African origin of modern humans is the most widely accepted model of the geographic origin and early migration of anatomically modern humans (Homo sapiens).

Driving AI's potential in organizations


For some organizations, harnessing artificial intelligence's full potential begins tentatively with explorations of select enterprise opportunities and a few potential use cases. While testing the waters this way may deliver valuable insights, it likely won't be enough to make your company a market maker (rather than a fast follower). To become a true AI-fueled organization, a company may need to fundamentally rethink the way humans and machines interact within working environments. Executives should also consider deploying machine learning and other cognitive tools systematically across every core business process and enterprise operation to support data-driven decision-making. Likewise, AI could drive new offerings and business models. These are not minor steps, but as AI technologies standardize rapidly across industries, becoming an AI-fueled organization will likely be more than a strategy for success--it could be table stakes for survival. In his new book The AI Advantage, Deloitte Analytics senior adviser Thomas H. Davenport describes three stages in the journey that companies can take toward achieving full utilization of artificial intelligence.1 In the first stage, which Davenport calls assisted intelligence, companies harness large-scale data programs, the power of the cloud, and science-based approaches to make data-driven business decisions. Today, companies at the vanguard of the AI revolution are already working toward the next stage--augmented intelligence--in which machine learning (ML) capabilities layered on top of existing information management systems work to augment human analytical competencies. According to Davenport, in the coming years, more companies will progress toward autonomous intelligence, the third AI utilization stage, in which processes are digitized and automated to a degree whereby machines, bots, and systems can directly act upon intelligence derived from them. The journey from the assisted to augmented intelligence stages, and then on to fully autonomous intelligence, is part of a growing trend in which companies transform themselves into "AI-fueled organizations."

Swine flu kills at least 40 in India's Rajasthan state

Al Jazeera

At least 40 people have died and more than 1,000 have tested positive for swine flu since the beginning of the year in a western Indian state popular with foreigners, according to authorities. The highly contagious H1N1 virus that spreads from human-to-human killed around 1,100 people and infected 15,000 across the country last year. "Total deaths are 40 and positive cases are 1,036 as from January 1 to 17 in Rajasthan. One of the deaths occurred on Thursday," a statement by the Rajasthan health department said on Friday. Rajasthan's Jodhpur district recorded the highest death toll with 16 fatalities and 225 people testing positive.

Israeli AI transforms cervical cancer screening - Israel News - Jerusalem Post


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Bacteria-inspired Swiss microbot will be able to deliver drugs inside human body

The Japan Times

LONDON - A tiny sliver of elastic material swims along a narrow tube, coiling up and changing shape in response to the thickness of fluid and the contours of the tube around it as it moves toward its goal. The miniature robot -- the brainchild of scientists in Switzerland seeking new methods to deliver drugs to diseased tissue -- is designed to wend its way through blood vessels and other systems in the body. The team is led by Selman Sakar at the Ecole Polytechnique Federale de Lausanne and Bradley Nelson at ETH Zurich, two leading research institutes for science and technology. "Nature has evolved a multitude of micro-organisms that change shape as their environmental conditions change. This basic principle inspired our microrobot design," Nelson said.

What Everyone Should Know About Cognitive Computing


Artificial intelligence has been a far-flung goal of computing since the conception of the computer, but we may be getting closer than ever with new cognitive computing models. Cognitive computing comes from a mashup of cognitive science -- the study of the human brain and how it functions -- and computer science, and the results will have far-reaching impacts on our private lives, healthcare, business, and more. The goal of cognitive computing is to simulate human thought processes in a computerized model. Using self-learning algorithms that use data mining, pattern recognition and natural language processing, the computer can mimic the way the human brain works. While computers have been faster at calculations and processing than humans for decades, they haven't been able to accomplish tasks that humans take for granted as simple, like understanding natural language, or recognizing unique objects in an image.

Machine learning could reduce testing, improve treatment for intensive care patients


Doctors in intensive care units face a continual dilemma: Every blood test they order could yield critical information, but also adds costs and risks for patients. To address this challenge, researchers from Princeton University are developing a computational approach to help clinicians more effectively monitor patients' conditions and make decisions about the best opportunities to order lab tests for specific patients. Using data from more than 6,000 patients, graduate students Li-Fang Cheng and Niranjani Prasad worked with Associate Professor of Computer Science Barbara Engelhardt to design a system that could both reduce the frequency of tests and improve the timing of critical treatments. The team presented their results on Jan. 6 at the Pacific Symposium on Biocomputing in Hawaii. The analysis focused on four blood tests measuring lactate, creatinine, blood urea nitrogen and white blood cells.

This AI identifies genetic disorders by looking at face shape


Ideally, Face2Gene would be able to correctly identify a disorder every time. To get closer to that goal, the FDNA team needs more training data, which it hopes to generate by making the app available to healthcare professionals for free. It also needs that training data to include more non-Caucasian faces -- a 2017 study using Face2Gene to identify Down Syndrome found the healthcare app was 80 accurate in its diagnosis if a photo featured a white Belgian child, but only 37 accurate if it featured a black Congolese child. Even at its current rate of accuracy, though, the app has already impressed at least one rare disease specialist: the University of Oxford's Christoffer Nellåker, who was not associated with the research. "The real value here is that for some of these ultra-rare diseases, the process of diagnosis can be many, many years," he told New Scientist.

Deep Learning on Graphs


Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. However, traditionally machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph. In this talk I will discuss methods that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. I will provide a conceptual review of key advancements in this area of representation learning on graphs, including random-walk based algorithms, and graph convolutional networks.

Machines that listen


A group of scientists from the Massachusetts Institute of Technology (United States) has created a machine learning system that processes sounds like people. This model can understand the meaning of a word and classify a song according to its genre or style: classical, jazz, pop, rock, blues, soul, hip hop, techno, house, etc. It is the first invention of this type that mimics the way the brain works. As the experiments carried out at MIT show, it can compete in precision with humans. The research, published in the journal Neuron, is based on deep neural networks, that is, a structure inspired by brain cells that analyses information by layers.