Life sciences companies are likely to begin experimenting further with AI in their workflows in the coming years, but they face challenges in AI adoption due to strict regulations. Machine learning has a "black box" problem, meaning that it's in many cases impossible to know how a machine learning algorithm comes to its conclusions. An AI application that detects cancer, for example, may not be able to show an oncologist how it determined the presence of cancer in a patient's body. As a result, if the oncologist used the application to diagnose a patient, they wouldn't be able to explain to the patient what makes them sure they have cancer. This issue relegates AI applications in life sciences to experiments and pilots, and widespread adoption, although likely inevitable, may not come for a while as public opinion shifts toward accepting that its diagnoses are informed by decision-making artificial intelligence and regulations evolve to match.
In a story Nov. 24, The Associated Press reported that an artificial intelligence program featuring a talking image of the Greek philosopher Aristotle is starting to help University of Southern California students cope with stress. The program's designers recently removed that image based on student feedback and are considering replacing it with another character.
High-resolution radar and night vision cameras may help scientists protect bats from untimely deaths at wind farms, according to new research. Researchers are using these technologies to provide more specific details about the number of bats killed by wind turbines in Iowa. These details will improve scientists' understanding of bat activity and potentially save their lives, said Jian Teng, a graduate researcher at the University of Iowa who presented the work this week at the 2019 American Geophysical Union Fall Meeting in San Francisco. This work has broad impacts, according to Teng. "The more bats you kill, the more insects you have on farms; then, farmers will put more pesticides; and then, people will eat more pesticides," he said.
In the race to develop fully autonomous vehicles, Israeli start-Up Lirhot Systems says they "see" the road ahead and assess potential hazards. While most leading industry actors have relied on and heavily invested in laser-based LiDAR (light detection and ranging) three-dimensional sensors for self-driving navigation, Tesla CEO Elon Musk has been the primary – and vocal – proponent of navigation based on using inexpensive cameras and radar. While developers continue to argue among themselves regarding the pros and cons of the two systems, Rehovot-based robotic vision start-up Lirhot Systems says it has developed a third method of navigation: a camera-like sensor inspired by insect navigation. "In nature, you have bugs and insects that navigate in a specific way, and we're copying that to enable autonomous vehicles to see," Lirhot CEO Shlomi Voro, an applied physicist with dozens of patents in the field of quantum physics, told The Jerusalem Post. "We were inspired by the heads of bees, their artificial intelligence-like neural network, size, accuracy of navigation, and how they see the world through their five eyes – two for vision and three for navigation."
California's passage of their "GDPR-lite" caught people off guard. We think this is part of a trend we've studied for a long time. Much of the current analysis misses key points, so it seems worth explaining. About two years ago, we asked several thought leaders in the U.S. about the odds we'd see legislation like the E.U. GDPR provides clear rights to E.U citizens, controlling data captured on-line.
Deepfakes are spreading fast, and while some have playful intentions, others can cause serious harm. We stepped inside this deceptive new world to see what experts are doing to catch this altered content. Chances are you've seen a deepfake; Donald Trump, Barack Obama, and Mark Zuckerberg have all been targets of the computer-generated replications. A deepfake is a video or an audio clip where deep learning models create versions of people saying and doing things that have never actually happened. A good deepfake can chip away at our ability to discern fact from fiction, testing whether seeing is really believing.
For many R users, it's obvious why you'd want to use R with big data, but not so obvious how. In fact, many people (wrongly) believe that R just doesn't work very well for big data. In this article, I'll share three strategies for thinking about how to use big data in R, as well as some examples of how to execute each of them. By default R runs only on data that can fit into your computer's memory. Hardware advances have made this less of a problem for many users since these days, most laptops come with at least 4-8Gb of memory, and you can get instances on any major cloud provider with terabytes of RAM.
Deep learning will radically change aspects of our medical care. How well do we need to understand how AI tools work? In clinics around the world, a type of artificial intelligence called deep learning is starting to supplement or replace humans in common tasks such as analyzing medical images. Already, at Massachusetts General Hospital in Boston, "every one of the 50,000 screening mammograms we do every year is processed through our deep learning model, and that information is provided to the radiologist," says Constance Lehman, chief of the hospital's breast imaging division. In deep learning, a subset of a type of artificial intelligence called machine learning, computer models essentially teach themselves to make predictions from large sets of data.
Key point: Faster, more capable processors will enable the aircraft's avionics, radar, sensors and communications technologies to better identify and attack enemy targets. Faster computer processors, AI-infused algorithms able to merge or "fuse" sensor information and automated maintenance and checklists are informing emerging pilot tactics aimed at anticipating future threat environments. Various applications of AI now perform a wide range of functions not purely restricted to conventional notions of IT or cyberspace; computer algorithms are increasingly able to almost instantaneously access vast pools of data, compare and organize information and perform automated procedural and analytical functions for human decision-makers. When high-volume, redundant tasks are performed through computer automation, humans are freed up to expend energy pursuing a wider range of interpretive or conceptual work. "The bottom line is the next big thing that is going to enable the US to maintain its qualitative edge is the seamless and ubiquitous sharing of information.