AI News, upiih artificial intelligence
President Trump’s Artificial Intelligence Executive Order: Analysis
Feb 19, 2019 Artificial intelligence (AI) recently received an endorsement from President Trump who this month signed an executive order directing federal agencies to allocate more resources for research and development, promotion and training in the emerging technology.
And, in 2017, the researcher put together a list of 80 privately-held cybersecurity companies using AI and operating in nine areas, spanning identity management to mobile, predictive, behavioral, automated, app security and more.
Of note (although it’s debatable how much), Wired noticed that in Alphabet’s (Google’s parent) latest SEC filing, it cautioned investors specifically about AI technology, warning that “new products and services, including those that incorporate or utilize artificial intelligence and machine learning, can raise new or exacerbate existing ethical, technology, legal and other challenges which may negatively affect our brands and demand for our products and services and adversely affect our revenues and operating results.” (via Wired) Six months earlier, as Wired noted, Microsoft issued a similar statement in its August SEC filing: “AI algorithms may be flawed.
These deficiencies could undermine the decisions, predictions, or analysis AI applications produce, subjecting us to competitive harm, legal liability, and brand or reputational harm.” In other words, where it concerns AI, both companies don’t yet know the full extent of what they don’t know.
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Researchers Made an AI Whose Performance Increases if They Let It Sleep And Dream
Of course, artificial neural networks (ANNs) - a type of artificial intelligence based on biological neural networks - don't automatically and instinctively fall asleep and dream.
'Inspired by sleeping and dreaming mechanisms in mammal brains, we propose an extension of this model displaying the standard on-line (awake) learning mechanism (that allows the storage of external information in terms of patterns) and an off-line (sleep) unlearning &
So the team worked out a way to mathematically implement human sleep patterns - rapid-eye movement sleep and slow-wave sleep, the former of which is thought to remove unnecessary memories, and the latter of which is thought to consolidate important ones.
Extensive testing using simulations validated the result - showing that allowing a neural network to nap once in a while (using the correct napping algorithm) could result in improved performance.
Researchers use artificial neural networks to streamline materials testing
The work, led by Nikhil Gupta, associate professor of mechanical and aerospace engineering at NYU Tandon, with Ph.D.
The Tandon team found a way to bypass this process by designing an ANN-based approach that builds a model and then feeds it data from DMA -- a test of a material's response to a given temperature and loading frequency (a measure of load applied in cycles) -- to predict how it will respond to any other temperature and pressure combination.
'Applying an artificial neural network approach to predict the properties of nanocomposites can help in developing an approach where modeling can guide the material and application development and reduce the cost over time,' continued Gupta.
Es investigador miembro del Sistema Nacional de Investigadores, con el Nivel SNI III, también es miembro senior del IEEE, de la IEICE y de la Academia mexicana de ciencia.
En 1991 recibió el IEICE Excellent Paper Award y, en 2000, el Premio de Investigación del IPN y el Diploma de Investigación del IPN.
Sus principales áreas de investigación son sistemas adaptativos, procesamiento de imágenes, reconocimiento de patrones, marcas de agua y campos relacionados.