Most of the daily news and recently published scientific papers on research, innovations, and applications in artificial intelligence (AI) refer to what is known as machine learning--algorithms using massive amounts of data and various methodologies to find patterns, support decisions, make predictions, or, for the deep learning part, self-identify important features in data. However, AI is a complex concept to grasp, and most people have little understanding of what it really is. AI was founded as an academic discipline in 1956 and, despite its youth, already has a rich history [1,2]. In more than 60 years of exploration and progress, AI has become a large field of research and development involving multidisciplinary approaches to address many challenges, from theoretical frameworks, methods, and tools to real implementations, risk analysis, and impact measures. The definition of AI is a moving target and changes over time with the evolution of the field. Since its early days, the field of AI has allowed the development of many techniques supporting decision support and prediction, as it is usually made by humans. As early as 1958, a perceptron was expected to be able "to walk, talk, see, write, reproduce itself and be conscious of its existence," which led a large scientific controversy between neural network and symbolic reasoning approaches [3].

On December 10, 2019, Phillip Goter and Joseph Herriges hosted the webinar "Autonomous Vehicles: Technical Advancements and Legal Considerations." If you were not able to attend the webinar, you can find a partial summary of its contents in the Q&A below. Transportation system elements in this context include other vehicles, pedestrians, and cyclists, as well as the vehicle's environment, such as roadway infrastructure, buildings, signs, pavement markings, and weather conditions. The safe operation of an AV requires connectivity between the vehicle and other elements of the transportation system. AVs are enabled by artificial intelligence systems and connectivity.

Everything we know about memory requirements in machine learning may be wrong. Today, when data scientists process deep learning models using a "throughput computing" device like a GPU, TPU, or similar hardware accelerator, they're likely faced with a decision to shrink their model or input size to fit within the device's memory limitations. Training a large, deep neural network (or even a wide, shallow one) on a single GPU, in many cases, may be impossible. Ever wonder why on the original Resnet 152, the winner of the ILSVRC-2015 image detection competition had 152 layers and not 153? Is it a coincidence that the parameters in 152 layers have a memory footprint of slightly less than 12G, while 153 layers go beyond 12G (the standard size of GPU memory at the time)?

Late last year, the European Commission's Expert Group on Liability and New Technologies – New Technologies Formation (NTF) released a report on Liability for Artificial Intelligence. The report focuses on liability regimes across European Union (EU) member states and offers high-level recommendations on how those liability regimes can be adapted to meet challenges posed by artificial intelligence (AI) and other digital technologies. Insights from this report may inform legislative and regulatory changes in the EU and elsewhere, including in Canada. Here's what you need to know. The NTF first convened in June 2018.

"One of the agency's top priorities is to ensure that the United States maintains its leadership in innovation, especially in emerging technologies such as artificial intelligence (AI). To that end, the USPTO has been actively engaging with the innovation community and experts in AI to determine whether further guidance is needed to promote the predictability and reliability of intellectual property rights relating to AI technology and to encourage further innovation in and around this critical area." Browse USPTO leadership's speeches, blogs, and events about AI and learn more about our approach. Find our Federal Register Notices (FRNs) on AI and responses to see how policy is shaped. Discover other resources for AI and learn about cross-government goals shared by other federal entities.

SAN FRANCISCO – A former Google star engineer charged with stealing trade secrets from its self-driving car program has agreed to plead guilty in a deal with prosecutors, according to court documents filed Thursday. Anthony Levandowski, 39, was a founding member of an autonomous vehicle project in 2009 called "Chauffeur," one of Google's more ambitious undertakings. Several years later Levandowski began thinking of leaving Google for another self-driving endeavor that was eventually named "Otto," the plea deal said. He began negotiating with ride-sharing giant Uber to invest in or buy Otto while he was still working at Google, and admits having downloaded a whole series of documents a few months before his resignation in January 2016. "Prior to my departure, I downloaded thousands of files related to Project Chauffeur," Levandowski said in court documents.

Artificial intelligence (AI) experimentation is now prolific across South African companies, with many businesses demonstrating enthusiasm for AI. According to Business Tech (2019), over 45% of South African businesses say that they're already actively piloting AI within their organisations. Metaphorical robots are infiltrating organisations and reinventing business processes due to the rapid rise in Robotic Process Automation (RPA), which has become a readily available solution offered by ICT service providers. A year ago, says 4IR guru Arthur Goldstuck, "only 6% of South African enterprises were using robotics. Then came the RPA explosion. Now the figure stands at 37% (in Engineering News, 2019)."

Imagine being scared to breathe the air around you. An unusual concept for us here in the UK, but it is a genuine concern for communities all over the world with air pollution killing an estimated seven million people every year. A team of Loughborough University computer scientists are hoping to help eradicate this fear with a new artificial intelligence (AI) system they have developed that can predict air pollution levels hours in advance. The technology is novel for a number of reasons, one being that it has the potential to provide new insight into the environmental factors that have significant impacts on air pollution levels. Professor Qinggang Meng and Dr. Baihua Li are leading the project which is focused on using AI to predict PM2.5--particulate matter of less than 2.5 microns (10-6 m) in diameter--that is often characterized as reduced visibility in cities and hazy-looking air when levels are high.

When VentureBeat asked Andrew Burt why he was starting an AI-focused law firm, Burt was quick to clarify that it's about AI and analytics. But that didn't answer the underlying question of why the world needs a law firm focused so precisely on this one key area. "The thesis behind the law firm is that traditional legal expertise on its own is not sufficient," said Burt, a Yale Law School alum. His partner is data scientist Patrick Hall, and together they aim to provide legal acumen around AI and analytics that's bolstered by technical understanding. "If we are going to successfully manage the risks of AI and advanced analytics, we need both of these types of expertise commingled," added Burt. Called bnh.ai (techy shorthand for "Burt and Hall"), the firm is located in Washington, D.C., which Burt says confers a key advantage.

An AI startup is suing Facebook and one of its employees for allegedly stealing proprietary software that allows machine learning workloads to run faster on standard processors, eliminating the need for more expensive custom hardware. Neural Magic, founded in 2017 by Nir Shavit and Alex Matveev, describes itself as a "no-hardware AI" company. Instead of relying on GPU chips that are able to crunch through matrix maths operations to run machine-learning models quickly, the Boston-based upstart employs nifty software tricks to achieve similar speeds on CPUs. Court documents filed (PDF) in the District Court of Massachusetts last week claim that Neural Magic's first employee, Aleksandar Zlateski, breached the non-disclosure and non-competition agreement he signed when he joined as the company's technology director. Zlateski left to join Facebook and allegedly stole his former employer's secret algorithms to give to his new team. That code, describing how to perform low-precision matrix multiplication to run trained computer vision models, was then published by Facebook engineers on GitHub last year in November.