In this industry and in everyday life, the best work and interactions comes from people solving problems for other people. IBM's Watson was created to answer questions on the TV gameshow Jeopardy! But now IBM are using Watsons question answering abilities to help nurses in a lung cancer treatment ward in the Memorial Sloan Kettering Cancer Center, New York, to get diagnoses quicker. Instead of giving bots and AI a soul, give the being with a soul more support with bots and AI.
Notably, Novartis (NYSE:NVS), which has also been involved in AI for two or three years, recently signed a deal with IBM Watson to explore the technology's use in breast cancer care. The collaboration's aims include identifying better treatment sequences or predictors of response, Pascal Touchon, Novartis' global head of oncology strategy, told EP Vantage. Also looking for patterns is London-based BenevolentAI, which hopes its machine-based learning approach to processing academic research, clinical studies and other health-related data will help identify correlations in data that could lead to new drugs and significantly speed up the process of drug development. With plenty of other companies clamoring to get into healthcare, including tech giants like IBM Watson and Alphabet, how will medtech and pharma groups compete in the AI space?
Richard Dabate told police a masked intruder assaulted him and killed his wife in their Connecticut home. Detectives suspected foul play and obtained data from Bates's Amazon Echo device. Smart cars, fridges, doorbells, watches, phones, Fitbits, sneakers, televisions, gaming consoles, coffee makers, Pacemakers – a fast proliferating list – all can monitor, record and be used as evidence. "I think everyone realises – good guys, bad guys, cops, robbers – that everything is being videotaped or tracked somehow," Andy Kleinick, the head of the Los Angeles police department's cyber crimes section, and a supervisor for the secret service's LA electronic crimes task force, said in an interview.
I am incredibly proud and excited to present the very first public product of Peptone, the Database of Structural Propensities of Proteins. Database of Structural Propensities of Proteins (dSPP) is the world's first interactive repository of structural and dynamic features of proteins with seamless integration for leading Machine Learning frameworks, Keras and Tensorflow. As opposed to binary (logits) secondary structure assignments available in other protein datasets for experimentalists and the machine learning community, dSPP data report on protein structure and local dynamics at the residue level with atomic resolution, as gauged from continuous structural propensity assignment in a range -1.0 to 1.0. Seamless dSPP integration with Keras and Tensorflow machine learning frameworks is achieved via dspp-keras Python package, available for download and setup in under 60 seconds time.
As the CEO of Silicon Valley Artificial Intelligence, Pete Kane has founded multiple startups such as Healthcare Minnesota and Startup Venture Loft, which led to his most recent collaborative creation Silicon Valley Artificial Intelligence. The community group uses machine learning (ML) and artificial intelligence (AI) to collaborate on research projects that can make landmark discoveries in science and healthcare. Silicon Valley AI will host the Genomics Hackathon from Friday through Sunday at Google Launchpad in San Francisco. In the future, I believe AI will play a leading role in areas like drug discovery, personalized medicine and cancer genomics.
To simplify, this can be revealed with the Implicit Association Test, where subjects look at pictures of humans or trolls, coupled with words with positive or negative connotations. Recent work, adapting the Implicit Association Test to another species, suggests that even other primates have implicit negative associations with Others. And monkeys would look longer at pairings discordant with their biases (e.g., pictures of members of their own group with pictures of spiders). Thus, the strength of Us/Them-ing is shown by the: speed and minimal sensory stimuli required for the brain to process group differences; tendency to group according to arbitrary differences, and then imbue those differences with supposedly rational power; unconscious automaticity of such processes; and rudiments of it in other primates.
Plenty of research has documented the adverse impact of a parent's sudden job loss on the average child, in terms of mental health and economic prospects. A 1 percent sudden statewide loss in jobs affects 1.5 percent of students directly -- and indirectly led the remaining 98.5 percent of students to experience "learning losses ... that are about one-third the size of those experienced by children whose parents lose jobs." More specifically, that 1 percent job loss lowered the state's eighth-grade math test scores by 0.057 standard deviations, an amount roughly the same size as the increase that results from intervention efforts intended to boost test scores. "What I see as one of the main points in our study is that the effects on people who lost their job or the children of people who lost their jobs -- there are spillover effects," said Dania Francis, one of the study's authors and an assistant professor of economics at the University of Massachusetts at Amherst.
If memory works the way most neuroscientists think it does--by altering the strength of connections between neurons--storing all that information would be way too energy-intensive, especially if memories are encoded in Shannon information, high fidelity signals encoded in binary. That assumption leads some scientists--mind-body dualists--to argue that we won't learn much by studying the physical brain. Over time, our memories are physically encoded in our brains in spidery networks of neurons--software building new hardware, in a way. That's because the street lamp infrastructure in the two halves of the city remain different, to this day--West Berlin street lamps use bright white mercury bulbs and East Berlin uses tea-stained sodium vapor bulbs.
Just using an individual's brain activity – specifically, their P300 response – we could determine a subject's preferences for things like favorite coffee brand or favorite sports. The potential ability to determine individuals' preferences and personal information using their own brain signals has spawned a number of difficult but pressing questions: Should we be able to keep our neural signals private? Putting ethicists in labs alongside engineers – as we have done at the CSNE – is one way to ensure that privacy and security risks of neurotechnology, as well as other ethically important issues, are an active part of the research process instead of an afterthought. The goal should be that the ethical standards and the technology will mature together to ensure future BCI users are confident their privacy is being protected as they use these kinds of devices.
For example, if the robot brain has roughly the same number of human neurons as a typical human brain, then could it, or should it, have rights similar to those of a person? Also, if such robots have far more human neurons than in a typical human brain--for example, a million times more neurons--would they, rather than humans, make all future decisions? With those cases, the situation isn't straightforward, as patients receive abilities that normal humans don't have--for example, the ability to move a cursor on a computer screen using nothing but neural signals. It's clear that connecting a human brain with a computer network via an implant could, in the long term, open up the distinct advantages of machine intelligence, communication, and sensing abilities to the individual receiving the implant.