Scientists at MIT are using Wi-Fi and AI to determine your emotional state. Without that tether, EQ Radio can't make assumptions about your heartbeat. The AI behind EQ Radio could figure out that you're stressed and cue the music without you even knowing you needed it. There's probably a pretty sizable market for parents as well – does your current router provide real-time EKG quality information about your sleeping newborn?
Using a computational model, the researchers found that older women's ability to "grandmother"--that is, devote their resources to grandchildren--and use their cognitive abilities to support their offspring may have been crucial to the evolution of menopause Existing hypotheses suggest that menopause protects humans from risky pregnancies at an older age (the Maternal Hypothesis) or might allow older mothers to invest their energy in supporting the survival of their grandchildren (the Grandmother Hypothesis). For example, to test whether the model supported the Grandmother Hypothesis, they removed the variables allowing the woman to offer more support to children who had more children of their own. When the neural network's model prevented women from caring for their grandchildren--or assumed that cognitive resources didn't affect offspring's skills--menopause did not evolve and women continued reproducing into old age. Aime acknowledges that there are some limitations to the team's neural network model--for example, it only simulates women, so in future studies, she hopes to expand the model to include men.
However, comparative studies on the effectiveness of machine learning–based decision support systems (ML-DSS) in medicine are lacking, especially regarding the effects on health outcomes. It also could lead to reduced interest in and decreased ability to perform holistic evaluations of patients, with loss of valuable and irreducible aspects of the human experience such as psychological, relational, social, and organizational issues. Failing to include difficult to represent factors into medical decision making may lead to other similar contextual errors, and overreliance on ML-DSS may enhance the odds of the occurrence of these types of errors when contextual factors cannot be easily integrated. This observer variability is related not only to interpretive deficiencies, but also to an intrinsic ambiguity in the observed phenomena.7 However, the intrinsic uncertainty of medical observations and interpretations that are part of input to "optimize" machine learning models is not usually considered.
A team of researchers from the University of Alberta, Canada and tech giant IBM has developed artificial intelligence and machine learning algorithms, which can diagnose schizophrenia by studying the blood flow of the brain. The study of the human brain has been a challenging medical field, especially brain related ailments such as Schizophrenia. The team behind the research also aims to employ the algorithm in research for other diseases such as Huntington's disease, and provide a better insight into a brain afflicted with them. The company's AI software called Watson is being employed in genomics research for cancer.
Currently, most AI systems are based on layers of mathematics that are only loosely inspired by the way the human brain works. Building AI that can perform general tasks, rather than niche ones, is a long-held desire in the world of machine learning. It argues that deep learning, which uses layers of artificial neurons to understand inputs, and reinforcement learning, where systems learn by trial and error, both owe a great deal to neuroscience. The solution, Hassabis and his colleagues argue, is a renewed "exchange of ideas between AI and neuroscience [that] can create a'virtuous circle' advancing the objectives of both fields."
The connection will be achieved through the so-called brain-to-computer interface.Credit: Natural Science Foundation Javier Mínguez and Luis Montesano, researchers from BitBrain, a company specializing in applied neuro-technologies, explain to OpenMind that "it is not that we are close to connecting our brains to technologies to interact with the exterior. Leading centres in Europe and the United States have been working in this discipline for years, including big technology companies such as Facebook or Neuralink from the magnate Elon Musk, the founder of Tesla and SpaceX. What is new is that the connection of a human brain to a computer with implantable microelectrodes is now a real scientific option," explained Jens Caluse of the Institute of Ethics and History of Medicine at the University of Tübingen (Germany) in a publication in the journal Nature. The connection of a human brain to a computer with implantable microelectrodes is now a real scientific option.
Other robots will be able to carry heavy loads, said Marc Raibert, Boston Dynamics chief executive. Another of Son's partner ventures, Guardant Health, offers blood biopsies, which are safer and quicker than tissue biopsies, to detect cancers in their early stages. "Those who rule chips will rule the entire world. Those who rule data will rule the entire world."
A few years ago, I wrote about a fascinating Italian project to use mobile phone data to predict the onset of bipolar disorder. It isn't the only work utilizing AI to help those with bipolar disorder, as a recent paper from the University of Cincinnati outlined an approach to accurately predict treatment outcomes by using AI. The authors suggest that existing models of treatment predict the response to lithium treatment with an accuracy of no more than 75%. The Australian research team used the kind of AI algorithms that underpin many modern dating sites to try and improve organ acceptance and ensure a more accurate connection between organ donors and recipients.
However, pioneering research conducted by IBM and the University of Alberta could soon help doctors diagnose the onset of the disease and the severity of its symptoms using a simple MRI scan and a neural network built to look at blood flow within the brain. The research team first trained its neural network on a 95-member dataset of anonymized fMRI images from the Function Biomedical Informatics Research Network which included scans of both patients with schizophrenia and a healthy control group. From this data, the neural network cobbled together a predictive model of the likelihood that a patient suffered from schizophrenia based on the blood flow. What's more, the model managed to also predict the severity of symptoms once they set in.
The total number of neurons in the human brain falls in the same ballpark of the number of galaxies in the observable universe. Interestingly enough, the total number of neurons in the human brain falls in the same ballpark of the number of galaxies in the observable universe. Researchers regularly use a technique called power spectrum analysis to study the large-scale distribution of galaxies. Based on the latest analysis of the connectivity of the brain network, independent studies have concluded that the total memory capacity of the adult human brain should be around 2.5 petabytes, not far from the 1-10 petabyte range estimated for the cosmic web!