Out of all the machine learning algorithms I have come across, KNN has easily been the simplest to pick up. Despite it's simplicity, it has proven to be incredibly effective at certain tasks (as you will see in this article). It can be used for both classification and regression problems! It's far more popularly used for classification problems, however. I have seldom seen KNN being implemented on any regression task.

With the help of a five-year, $2.7 million grant from the National Institute of Mental Health, researchers at Vanderbilt University Medical Center will use computational methods to shed light on suicidal ideation and its relationship to attempted suicide, predict suicidal ideation and suicide attempt using routine electronic health records (EHRs) and explore the genetic underpinnings of both. From 1999 to 2017, the all-ages suicide rate in the United States increased 33%, from 10.5 to 14.0 per 100,000 population. In 2017 there were 47,173 recorded suicides, making it the nation's 10th leading cause of death. The principal investigators for the study are internist and clinical informatician Colin Walsh, MD, MA, assistant professor of Biomedical Informatics, Medicine, and Psychiatry and Behavioral Sciences, and geneticist and computational biologist Douglas Ruderfer, PhD, MS, assistant professor of Medicine, Psychiatry and Behavioral Sciences, and Biomedical Informatics. In previous work Walsh and colleagues used EHR data and machine learning techniques to develop predictive algorithms for attempted suicide.

Mostly, you are given a model which has been created by years of engineering and expertise and you cannot change its architecture nor can you retrain it. So how to do you go about interpreting a model about which you have no clue? TCAV is a technique which aims to handle such scenarios. Most Machine Learning models are designed to operate on low-level features like edges and lines in a picture or say the colour of a single pixel. This is very different from the high-level concepts more familiar to humans like stripes in a zebra.

In this tutorial, we show you how to configure TensorFlow with Keras on a computer and build a simple linear regression model. If you have access to a modern NVIDIA graphics card (GPU), you can enable tensorflow-gpu to take advantage of the parallel processing afforded by CUDA. The field of Artificial Intelligence (AI) has been around for quite some time. As we move to build an understanding and use cases for Edge AI, we first need to understand some of the popular frameworks for building machine learning models on personal computers (and servers!). These models can then be deployed to edge devices, such as single-board computers (like the Raspberry Pi) and microcontrollers.

Voleon Group, one of the best known machine-learning hedge funds, returned 7% last year in its flagship strategy after drawing inflows on the back of a stellar performance in 2018. The Berkeley, California-based firm now oversees $6.5 billion overall compared with $5.1 billion in mid-2019, according to people familiar with the matter who asked not to be identified because the information is private. Voleon's Investors Fund gained 14% in 2018, when many of its competitors were hit by the global market tumult that saw the S&P 500 Index drop 6.2%. The group is one of the few systematic players to have built a reputation on strategies run exclusively by artificial intelligence. While proponents say machine-learning can detect multifaceted links between economic forces and security prices, most quants are still struggling to apply the technology to complex financial markets.

It was almost ten years ago when Sherry Turkle warned that the world was headed for a place where humans would be interacting socially with machines, like robots. Turkle is a MIT professor and social scientist who has been working on human-technology interaction and what it will mean for the human race. She is the author of several books including Alone Together and Reclaiming Conversation which explore the impact of technology on some of the aspects that actually make humans humans. Over the years, through her books and numerous talks, Sherry Turkle has explained the dangers of people trying to replace each other with machines including the smartphone and robots, but the world seems to have taken little heed as today we see companies inventing robots for all sorts of tasks and even for human relationships. Remember the Chinese inventor of a female robot whom he married in 2017?

Augmented intelligence (AI) promises to be a transformational force in health care, especially within primary care. Experts outline ways that innovations driven by AI--often called artificial intelligence--can aid rather than subvert the patient-physician relationship. "AI implemented poorly risks pushing humanity to the margins; done wisely, AI can free up physicians' cognitive and emotional space for patients, and shift the focus away from transactional tasks to personalized care," wrote the authors of an article published in the Journal of General Internal Medicine. The AMA is committed to helping physicians harness AI in ways that safely and effectively improve patient care. The authors--Steven Y. Lin, MD, and Megan R. Mahoney, MD, associate clinical professor of medicine and clinical professor of medicine, respectively, in the Division of Primary Care and Population Health at Stanford University School of Medicine, and AMA vice president of professional satisfaction Christine A. Sinsky, MD--reviewed promising AI inventions in 10 distinct problem areas.

Many developments show that states have turned AI technology into a part of the arms race. The "Summary of the 2018 Department of Defense Artificial Intelligence Strategy" report prepared by the U.S. Department of Defense highlighted Chinese and Russian investments in AI weapons technologies and stated the steps to be taken within the framework of such competition. Moreover, the Pentagon's budget for AI arming, worth $2 billion, and the "Executive Order on Maintaining American Leadership in AI" published by U.S. President Donald Trump reveal the importance of arming in AI technology. U.S. Defense Secretary Mark Esper recently said that the growing threats posed by great power competitors such as China and Russia warrant refocusing on high-intensity conflict across all of the military services. Esper also stressed the necessity of modernizing the military in AI, robotics, directed energy and hypersonic technologies.

Artificial intelligence is here to stay, but as with any helpful new tool, there are notable flaws and consequences to blindly adapting it. From the esoteric worlds of predictive health care and cybersecurity to Google's e-mail completion and translation apps, the impacts of AI are increasingly being felt in our everyday lived experience. The way it has crepted into our lives in such diverse ways and its proficiency in low-level knowledge shows that AI is here to stay. But like any helpful new tool, there are notable flaws and consequences to blindly adapting it. AI is a tool--not a cure-all to modern problems.

Recently, human being's curiosity has been expanded from the land to the sky and the sea. Besides sending people to explore the ocean and outer space, robots are designed for some tasks dangerous for living creatures. Take the ocean exploration for an example. There are many projects or competitions on the design of Autonomous Underwater Vehicle (AUV) which attracted many interests. Authors of this article have learned the necessity of platform upgrade from a previous AUV design project, and would like to share the experience of one task extension in the area of fish detection. Because most of the embedded systems have been improved by fast growing computing and sensing technologies, which makes them possible to incorporate more and more complicated algorithms. In an AUV, after acquiring surrounding information from sensors, how to perceive and analyse corresponding information for better judgement is one of the challenges. The processing procedure can mimic human being's learning routines. An advanced system with more computing power can facilitate deep learning feature, which exploit many neural network algorithms to simulate human brains. In this paper, a convolutional neural network (CNN) based fish detection method was proposed.