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) …
Are you a junior researcher with the potential to become a world-class machine learning scientist? Apply to become a Vector Institute Postdoctoral Fellow and conduct cutting-edge fundamental research in machine learning and deep learning algorithms and their applications. Postdoctoral fellows at the Vector Institute are junior researchers with the potential to become world-class researchers. Like postdoctoral researchers in a University lab, postdoctoral fellows at the Vector Institute are tasked with and supported in carrying out state-of-the-art research, publishing at the highest international level, and contributing to the academic life and reputation of the Institute. In addition, postdoctoral fellows at the Vector Institute have access to the resources of a well-funded institute dedicated solely to machine learning and deep learning, and are encouraged to work with any of our over 25 world-class faculty in machine learning and deep learning, though they will typically work primarily with 1–2 faculty members.
The consequences of this issue are well documented, from hate speech-spewing chatbots to racial bias in facial recognition. The report says that these failings -- attributed to a lack of diversity within the AI sector -- have created a "moment of reckoning." Report author Kate Crawford said that the industry needs to acknowledge the gravity of the situation, and that the use of AI systems for classification, detection and predication of race and gender "is in urgent need of re-evaluation." Indeed, the report found that more than 80 percent of AI professors are men -- a figure that reflects a wider problem across the computer science landscape. In 2015 women comprised only 24 percent of the computer and information sciences workforce.
The Omnitek deep learning processing unit (DPU) employs a novel mathematical framework combining low-precision fixed point maths with floating point maths to achieve 135 GOPS/W at full 32-bit floating point accuracy when running the VGG-16 CNN in an Arria 10 GX 1150. Scalable across a wide range of Arria 10 GX and Stratix 10 GX devices, the DPU can be tuned for low cost or high performance in either embedded or data centre applications. The DPU is fully software programmable in C/C or Python using standard frameworks such as TensorFlow, enabling it to be configured for a wide range of standard CNN models including GoogLeNet, ResNet-50 and VGG-16 as well as custom models. No FPGA design expertise is required to do this. "We are very excited to apply this unique innovation, resulting from our joint research program with Oxford University, to reducing the cost of a whole slew of AI-enabled applications, particularly in video and imaging where we have a rich library of highly optimised IP to complement the DPU and create complete systems on a chip", commented Roger Fawcett, CEO at Omnitek.
Utah Jazz head coach Quin Snyder had heavy praise for reigning MVP James Harden ahead of Game 2. Snyder had compared Harden to artificial intelligence on Tuesday and was asked to expand on that before Wednesday night's game. "The way he plays, there's an artistic nature to it… Obviously he's skilled, but I think the way he processes the game… He literally sees the whole court." I think the way he plays, there's an artistic nature to it. The feel that he has for different things on the court. He's able to put the ball on different locations that he wants, to manipulate spacing.
TEL AVIV, Israel, 16 April 2019--Israel's reams of electronic medical records –health data on its population of around 8.9 million people-- are proving fruitful for a growing number of digital health startups training algorithms to do things like early detection of diseases and produce more accurate medical diagnoses. According to a new report by Start-Up Nation Central, the growth in the number of Israeli digital health startups –537 companies, up from 327 in 2014--has drawn in new investors, including Israeli VCs who have never previously invested in healthcare. This has driven financing in the sector to a record $511M in 2018, up 32% year on year. By the first quarter of 2019 the amount raised was already at $214M. Of the $511M, over 50% ($285M) went to companies in decision support and diagnostics which rely heavily on data crunching.
"Artificial intelligence" can be defined as the theory and development of computer systems able to perform tasks that normally require human intervention. Artificial intelligence (AI) is being used in new products and services across numerous industries and for a variety of policy-related purposes, raising questions about the resulting legal implications, including its effect on individual privacy. Aspects of AI related to privacy concerns are the ability of systems to make decisions and to learn by adjusting their code in response to inputs received over time, using large volumes of data. Following the European Commission's declaration on AI in April 2018, its High-Level Expert Group on Artificial Intelligence (AI HLEG) published Draft Ethics Guidelines for Trustworthy AI in December 2018. A consultation process regarding this working document concluded on February 1, 2019, and a revised draft of the document based on the comments that were received is expected to be delivered to the European Commission in April 2019.
Boston Dynamics has taught its fleet of SpotMini robot dogs a new trick: The robotics company posted a new video featuring ten of the mechanical canines hauling a cargo truck up a slightly inclined hill like a team of sled dogs. The video was shared to the company's popular YouTube page on Tuesday. In the above video, two lines of SpotMini's are tethered together and marched in unison to inch a semi-truck in neutral gear forward. As with pretty much every video Boston Dynamics publishes, it elicited a flurry of Black Mirror, Terminator, and robot overlord-themed references across social media. But Boston Dynamics promises that its famous SpotMinis are here to help humans, not to rule them.
Whenever we start to talk about artificial intelligence, machine learning, or deep learning, the cautionary tales from science fiction cinema arise: HAL 9000 from 2001: A Space Odyssey, the T-series robots from Terminator, replicants from Blade Runner, there are hundreds of stories about computers learning too much and becoming a threat. The crux of these movies always has one thing in common: there are things that computers do well and things that humans can do well, and they don't necessarily intersect. Computers are really good at crunching numbers and statistical analysis (deductive reasoning) and humans are really good at recognizing patterns and making inductive decisions using deductive data. Both have their strengths and their role. With the massive proliferation of data across platforms, types, and collection schedules, how are geospatial specialists supposed to address this apparently insurmountable task?
Science fiction didn't do a great job in preparing us for our first real encounters with AI. Most people probably still envision AI in the form of a sentient robot that can talk, move around, and experience feelings – something like WALL-E or C-3PO from the movies. Although that still may be the dream, it turns out that the current iteration of AI is actually quite different. With modern AI, all the "thinking" gets done in the cloud, and the algorithms aren't tied to the identity of a physical machine like we would have expected from the big screen. The modern iteration of AI works silently in the background without a face, and it's starting to impact everything it touches.