Computational Neuroscience Coursera

@machinelearnbot

This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning. We will make use of Matlab/Octave/Python demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is primarily aimed at third- or fourth-year undergraduates and beginning graduate students, as well as professionals and distance learners interested in learning how the brain processes information.


How the Next Generation is Building Artificial Intelligence - iQ by Intel

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Teen scientists use machine learning and neural networks to detect and diagnose diseases, track space debris, design drones and justify conclusions at Intel ISEF 2017. While sentient computer beings like HAL from the classic 2001: A Space Odyssey or Samantha from the 2013 film Her may still be on the distant horizon, some forms of artificial intelligence (AI) are already improving lives. At the 2017 Intel International Science and Engineering Fair (ISEF) – where nearly 1,800 high school students gathered to present original research and compete for more than $4 million in prizes – the next generation of scientists used machine learning and artificial neural networks to find solutions to some of today's most vexing problems. "AI is critical to our future," said Christopher Kang, a budding computer scientist from Richland, Washington, who won an ISEF award in the robotics and intelligent machines category. "Humans have a limit as to how much data we can analyze," he said.


6 ways to future-proof universities

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The members of the Global University Leaders Forum community convened at the World Economic Forum Annual Meeting 2019 to discuss their role in our ever-changing world. Here are six topics that were top of the agenda as the members considered the future of the university and its role in society. Today data is omnipresent and often overwhelming. By way of example, Domo's Data Never Sleeps 6.0 reported that in 2018 Google conducted an average 3.8 million searches per minute. Though not all graduates will enter data-related fields, universities are starting to work towards increasing data literacy in their student body by adding data science courses and challenges for social science majors so that graduates can effectively communicate with their data-oriented peers and co-workers.


Ethical Dimensions of Using Artificial Intelligence in Health Care

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An artificially intelligent computer program can now diagnose skin cancer more accurately than a board-certified dermatologist.1 Better yet, the program can do it faster and more efficiently, requiring a training data set rather than a decade of expensive and labor-intensive medical education. While it might appear that it is only a matter of time before physicians are rendered obsolete by this type of technology, a closer look at the role this technology can play in the delivery of health care is warranted to appreciate its current strengths, limitations, and ethical complexities. Artificial intelligence (AI), which includes the fields of machine learning, natural language processing, and robotics, can be applied to almost any field in medicine,2 and its potential contributions to biomedical research, medical education, and delivery of health care seem limitless. With its robust ability to integrate and learn from large sets of clinical data, AI can serve roles in diagnosis,3 clinical decision making,4 and personalized medicine.5 For example, AI-based diagnostic algorithms applied to mammograms are assisting in the detection of breast cancer, serving as a "second opinion" for radiologists.6


A list of artificial intelligence tools you can use today -- for industry specific (3/3)

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Part 3. Here's a look at industry specific companies that utilise various forms of artificial intelligence to solve some really interesting and particular problems for different markets. Basket -- e-commerce shopping cart chatbot Choice.ai AltSchool -- a platform made to improve learning capabailities Content Technologies (CTI) -- research and development company Coursera -- online courses from top universities Gradescope -- streamlines the tedious parts of grading Hugh -- helps library users find any book quickly Ivy.ai -- customer service chatbot for higher education Knewton -- personalised learning for high and primary schools Volley -- makes training and development more engaging and effective AlphaSense -- highly intelligent search functionality Alta5 -- scriptable trading automation for your online brokerage account Analytic.ai Atomwise -- for novel small molecule discovery Babylon -- online doctor consultations using AI BuddiHealth -- helps improve process, payment systems and costs with RCM Behold.ai Imagia -- helps detect changes in cancer early Kuznech -- computer vision products range Lunit Inc. -- a range of medical imaging software Zebra Medical Vision -- medical imaging to help physicians and practitioners Cape Analytics -- identify property attributes at scale for underwriting Underwrite.ai