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 technology is advancing and bringing opportunities for society but also profound challenges for individual freedom. AI is a powerful enabler of surveillance technology, such as facial recognition, and many countries are grappling with appropriate rules for use, weighing the security benefits against privacy risks. Authoritarian regimes, however, lack strong institutional mechanisms to protect individual privacy--a free and independent press, civil society, an independent judiciary--and the result is the widespread use of AI for surveillance and repression. This dynamic is most acute in China, where the Chinese government is pioneering new uses of AI to monitor and control its population. China has already begun to export this technology along with laws and norms for illiberal uses to other nations.
"November 8th is National STEM day and we honor the dedication, creativity, and passion of JAIC's science, technology, engineering, and math (STEM) professionals who drive our AI capability delivery forward." The Department of Defense stands at a rare moment of historical significance. Just as in 1957 when the world was caught off-guard as we watched Sputnik launch into space, our nation must again align our STEM capabilities to our national defense needs. We have entered a time when AI technologies will transform not only society, but the very character of warfare. Our country's leadership role is dependent on our next-generation STEM expertise.
The role of the development of artificial intelligence in geopolitics usually means competition between the United States and China. While reports are inconclusive about which country will ultimately win (if this is the right term), there is a glaring shortcoming in the United States that China will largely avoid. In 2017 China accounted for 48% of global AI venture capital while the US only accounted for 38%. But only two years later the trend reversed, possibly because of headwinds from the trade war. Neither is it talent acquisition and a brain drain.
WASHINGTON, D.C. – Today, the U.S. Department of Energy's (DOE's) Advanced Research Projects Agency-Energy (ARPA-E) announced $15 million in funding for 23 projects to accelerate the incorporation of machine learning and artificial intelligence into the energy technology and product design processes as part of the Design Intelligence Fostering Formidable Energy Reduction (and) Enabling Novel Totally Impactful Advanced Technology Enhancements (DIFFERENTIATE) program. Launched in April of this year, the DIFFERENTIATE program aims to develop streamlined solutions to next-generation energy challenges. The program identified three general mathematical optimization problems that are common to many design processes. The selected projects then conceptualized machine learning and artificial intelligence-based solutions to help engineers execute and solve these problems in a manner that dramatically accelerates the pace of energy innovation. "The incorporation of AI and Machine Learning into our energy technology design and engineering processes has great potential to increase the productivity of our nation's engineers and scientists," said Secretary of Energy Rick Perry.
The operator of a self-driving Uber that hit and killed a pedestrian in Tempe last year was the primary cause of the accident because she was watching "The Voice" on her phone instead of the road. That's the finding from the National Transportation Safety Board, although the federal agency identified several other contributory causes in its final report submitted on Tuesday. The board also recommended new federal and state requirements for testing autonomous cars on public roads. Beyond the driver, the board found plenty of blame to go around for the nation's first pedestrian fatality involving a self-driving car. Officials called out Uber's lax safety culture, the pedestrian who was high on methamphetamine, and the state of Arizona's lack of safety requirements for the cars.
Imagine your body is like a loaf of sliced bread. During an MRI scan, a powerful magnet and radio waves create detailed images of each "slice" of your body, then a computer puts the slices together to show a full picture of your anatomy. But before the slicing comes the choosing. Before an MRI technologist can scan a patient, they have to manually specify the slices they want the MRI to acquire. This process can take several minutes of tweaking and adjusting, leaving a patient waiting anxiously in the MRI scanner and adding unnecessary steps to set up each scan.
As police embrace new facial recognition technology, many fear false matches could lead to wrongful arrests. The fight over the use of our faces is far from done. A raging battle over controversial facial recognition software used by law enforcement and the civil rights of Americans might be heading to a courtroom. The latest salvo includes the American Civil Liberties Union suing the FBI, the Department of Justice and the Drug Enforcement Agency for those federal agencies' records to see if there is any secret surveillance in use nationwide. The lawsuit, filed Oct. 31, comes as organizations and law enforcement are going toe-to-toe over what is private and what isn't.
This August, the Department of Housing and Urban Development put forth a proposed ruling that could potentially turn back the clock on the Fair Housing Act (FHA). This ruling states that landlords, lenders, and property sellers who use third-party machine learning algorithms to decide who gets approved for a loan or who can purchase or rent a property would not be held responsible for any discrimination resulting from these algorithms. The Fair Housing Act (FHA) is a part of the Civil Rights Act of 1968. This stated that people should not be discriminated against for the purchase of a home, rental of a property or qualification of a lease based on race, national origin or religion. In 1974, this was expanded to include gender, and in 1988, disability.
Connect, download a free E-Book, watch a keynote, or browse my blog. Recently, I discussed how Artificial Intelligence (AI) and a new breed of Creative Machines was being used to help design everything from cities to NASA planetary rovers, and now architecture studio Wallgren Arkitekter and Swedish construction company BOX Bygg have created an AI design tool called Finch that can generate new building floor plans and adapt them according to the space available – and while this might sound like quirky work, as we begin to 3D print everything from military barracks through to family homes and 80 storey skyscrapers, having an AI that can help design buildings will no doubt come in very handy indeed. Furthermore, as AI and drone technology helps us develop the world's first fully autonomous construction sites this additional development could mean one day machines control the entire construction process – from initial building concept and design, through to final construction and fit outs. Finch, that you can see working below, will be launched in 2020 as a plug-in to visual programming tool Grasshopper within 3D computer graphics software Rhino. "The idea of Finch is to create a more user-friendly tool for architects to be able to enjoy the benefits of parametric design without any knowledge of Grasshopper or coding," said Pamela Wallgren, co-founder of Wallgren Arkitekter.
Argonne researchers have created a neural architecture search that automates the development of deep-learning-based predictive models for cancer data. While increasing swaths of collected data and growing scales of computing power are helping to improve our understanding of cancer, further development of data-driven methods for the disease's diagnosis, detection and prognosis is necessary. There is a particular need to develop deep-learning methods -- that is, machine learning algorithms capable of extracting science from unstructured data. Researchers from the U.S. Department of Energy's (DOE) Argonne National Laboratory have made strides toward accelerating such efforts by presenting a method for the automated generation of neural networks. As detailed in a paper for presentation at the SC19 conference, the researchers, utilizing resources from the Argonne Leadership Computing Facility (ALCF), a DOE Office of Science User Facility, have established a neural architecture search (NAS) that, for a class of representative cancer data, automates the development of deep-learning-based predictive models.