If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Artificial intelligence (AI) is the science of programming computers to perceive their environment and make rational, cognitive decisions in order to achieve a goal. It is one of the most rapidly progressing and sought after technologies in the world. It is, however, a rather general term. When most people talk about artificial intelligence, they are usually talking about machine learning. At its most basic definition, machine learning is a method of teaching computers to make predictions based on data.
In September 1955, John McCarthy, a young assistant professor of mathematics at Dartmouth College, boldly proposed that "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." McCarthy called this new field of study "artificial intelligence," and suggested that a two-month effort by a group of 10 scientists could make significant advances in developing machines that could "use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves." At the time, scientists optimistically believed we would soon have thinking machines doing any work a human could do. Now, more than six decades later, advances in computer science and robotics have helped us automate many of the tasks that previously required the physical and cognitive labor of humans. But true artificial intelligence, as McCarthy conceived it, continues to elude us.
The problem is not only in the lack of educational institutions for bringing up this kind of specialists. There is also the absence of a general concept to solve the problem of training qualified personnel. Sceptics believe that the creation of added value in the era of artificial intelligence depends on the genius of the individual -- a creative expert who cannot be taught to manage the processes of artificial intelligence. Internet gurus such as Mark Zuckerberg or Pavel Durov are often cited as examples. However, people forget that the above-mentioned creators are only the developers of the concept, which is implemented by an horde of medium-level managers.
When women are over-represented in the workforce, it tends be in industries of assistance – cleaning, nursing, secretarial work and, now, the world of virtual assistants. Research by Unesco has shown that using default female voices in AI – as Microsoft has done with Cortana, Amazon with Alexa, Google with Google Assistant and Apple with Siri – is furthering the belief that women exist merely to help men to get on with more important things. There is no real reason for AI technologies to be gendered at all, but we are at the mercy of tech companies "staffed by overwhelmingly male engineering teams", fixated on living out a Captain Kirk fantasy and delegating to the subservient, silky-voiced computers of Star Trek. These systems are unapologetically built by men, for men. They can even struggle to understand the "breathy" voices of women as software is often developed with male voice samples.
Wolfram Research today announced free access to the engine that powers its technology stack. The Wolfram Engine is available to developers for free, assuming it is used for non-production development. Wolfram Research is best known for creating the modern technical computing system Mathematica and the computational knowledge engine Wolfram Alpha (stylized Wolfram Alpha). Founded by computer scientist Stephen Wolfram, the company celebrated the 10-year anniversary of Wolfram Alpha just last week. "The Wolfram Engine is the heart of all our products," Stephen Wolfram explains.
The Defense Advanced Research Projects Agency (DARPA) is funding research that could give a future generation of soldiers the power to control machines and weapons with their minds. The agency said it will fund six organizations through the Next-Generation Nonsurgical Neurotechnology (N3) program who will work to design and build interfaces for application in the U.S. military, that could be worn be soldiers and translate their brain signals into instructions. Those instructions could be used to control swarms of unmanned aerial vehicles, wield cyber defense systems, or facilitate military communications. Soldiers may be able to control vehicles and more by using only their minds under a new initiative from the U.S. Department of Defense. While the feat may sound firmly in the realm of science fiction, according to DARPA it is setting a completion date within four years.
One of lung cancer's most lethal attributes is its ability to trick radiologists. Some nodules appear threatening but turn out to be false positives. Others escape notice entirely, and then spiral without symptoms into metastatic disease. On Monday, however, Google unveiled an artificial intelligence system that -- in early testing -- demonstrated a remarkable talent for seeing through lung cancer's disguises. A study published in Nature Medicine reported that the algorithm, trained on 42,000 patient CT scans taken during a National Institutes of Health clinical trial, outperformed six radiologists in determining whether patients had cancer.
The race to build fully autonomous cars has gone into hyper-drive, with major car-makers such as GM, Daimler, BMW and Audi promising SAE Level 5 autonomous driving by sometime in 2021. Goldman Sachs predicts that robo taxis will grow the ride-hailing and sharing business from $5 billion in revenue today to $285 billion by 2030. Autonomous driving will re-define mobility, and historic earning streams are sure to be toppled. Even with all the road testing the car-makers are doing, the only way the car companies can meet their ambitious goals is by leveraging the power of analytics and artificial intelligence (AI) to learn on real-world roads and accelerate development using simulations. The auto-makers are using simulation techniques such as hardware-in-the-loop (HIL) and software-in-the-loop (SIL) to make this happen.
Researchers at UC Davis and UC San Francisco have found a way to teach a computer to precisely detect one of the hallmarks of Alzheimer's disease in human brain tissue, delivering a proof of concept for a machine-learning approach capable of automating a key component of Alzheimer's research. Amyloid plaques are clumps of protein fragments in the brains of people with Alzheimer's disease that destroy nerve cell connections. Much like the way Facebook recognizes faces based on captured images, the machine learning tool developed by a team of University of California scientists can "see" if a sample of brain tissue has one type of amyloid plaque or another -- and do it very quickly. The findings, published May 15, 2019 in Nature Communications, suggest that machine learning can augment the expertise and analysis of an expert neuropathologist. The tool allows them to analyze thousands of times more data and ask new questions that would not be possible with the limited data processing capabilities of even the most highly trained human experts.