It is possible to train just a neural network to answer questions about a scene by feeding in millions of examples as training data. But a human child doesn't require such a vast amount of data in order to grasp what a new object is or how it relates to other objects. Also, a network trained that way has no real understanding of the concepts involved--it's just a vast pattern-matching exercise. So such a system would be prone to making very silly mistakes when faced with new scenarios. This is a common problem with today's neural networks and underpins shortcomings that are easily exposed (see "AI's language problem").

It will still be a while before you are able to order drone-delivered packages, however. The news: The Federal Aviation Administration has granted UPS's drone business a Part 135 certification, meaning it is treated as a full-fledged airline, able to operate as many drones in as many locations as it wishes (although there are a lot of obstacles and caveats before that can happen in reality). UPS has dubbed its new drone airline "UPS Flight Forward," and it's the first in the US to gain official recognition. Currently: UPS has been providing a drone delivery service at the WakeMed hospital and campus in Raleigh, North Carolina, since March, moving medical samples around the site about 10 times a day. This new certification means UPS can expand beyond this site.

Work to bring driverless cars to Britain's streets has reached a milestone with the first demonstration of an autonomous fleet driving in a "complex urban environment" in London. Ford Mondeos fitted with autonomous technology from the UK tech firm Oxbotica operated on public roads around the former Olympic Park in Stratford this week. Driven programme, a partially government-funded consortium, said it had "exceeded their initial plan" and was a significant step in confirming autonomous vehicles could operate in real-life situations in a large European city. Oxbotica said first passenger trials of a separate venture, an autonomous ride-sharing taxi service planned with the cab firm Addison Lee in the capital, could now start in June 2020. The Driven team – a combination of local authority planners, insurers, cyber-security and data experts, as well as Oxbotica – have been conducting trials in Oxford to examine what they called the "ecosystem" around autonomous vehicles, such as potential problems with hackers, communications technology and the legal framework. For now, the cars are operated with a safety driver in the front seat ready to take control, and prompted by the technology to decide whether to intervene in difficult situations.

Prolific science and science fiction writer Isaac Asimov (1920–1992) developed the Three Laws of Robotics in the hope of guarding against potentially dangerous artificial intelligence. They first appeared in his 1942 short story Runaround. "Many computer engineers use the three laws as a tool for how they think about programming," says Chris Stokes, a philosopher at Wuhan University in China. But the trouble is, they don't work. In "Why the Three Laws of Robotics Do Not Work," published in the International Journal of Research in Engineering and Innovation, Stokes writes that "the Three Laws are not sufficient when it comes to controlling an artificial intelligence."

Prosthetic legs with sensors can help people avoid unseen obstacles underfoot. Three people who have had a leg amputated found that they perceived such prostheses as an extension of their own body and were able to climb stairs more quickly than they could with a conventional prosthetic leg. Prosthetic limbs are often abandoned due to people's poor mobility when using them. The devices don't restore sensation, leaving people to rely on touch feedback from the stump meeting the socket. Stanisa Raspopovic at the Swiss Federal Institute of Technology in Zurich and his colleagues modified a commercially available prosthetic leg by adding sensors to an insole on the foot and inside the knee.

The story of Alberta AI starts on April 1, 1964, with the opening of the University of Alberta's department of computing science. Researchers such as Randy Goebel would graduate from the department and set the foundation for early work on the science of natural language processing and AI in the 80s, the latter of which took the form of studying games like chess. By the '90s, however, several'AI winters' -- periods of funding constraints for research and lack of press interest to generate public excitement -- inhibited the discoveries coming out of the university. "The symptoms of the winter are more that industries [that] bought into the idea that expert systems could help them found that it was way too expensive to implement," said Goebel, now a U of A professor and principal investigator at the Alberta Machine Intelligence Institute (Amii). "If you scale up, it costs a lot to have you and me write down processes for chemistry, for example."

Last year the United States Food and Drug Administration (FDA) cleared a total of 12 AI tools that use machine learning for health (ML4H) algorithms to inform medical diagnosis and treatment for patients. The tools are now allowed to be marketed, with millions of potential users in the US alone.Because ML4H tools directly affect human health, their development from experiments in labs to deployment in hospitals progresses under heavy scrutiny. A critical component of this process is reproducibility. A team of researchers from MIT, University of Toronto, New York University, and Evidation Health have proposed a number of "recommendations to data providers, academic publishers, and the ML4H research community in order to promote reproducible research moving forward" in their new paper Reproducibility in Machine Learning for Health. Just as boxers show their strength in the ring by getting up again after being knocked to the canvas, researchers test their strength in the arena of science by ensuring their work's reproducibility.

Despite 60% of Marketers Demanding Control of the'Digital Experience', Many Do Not Understand Common Digital Terms Despite 60% of marketers wanting to'own' the digital experience, many admit that they don't fully understand digital terminology such as API, big data and machine learning. The research, which surveyed over 200 IT professionals and 200 marketers, explores the growing disconnect between each group as they struggle to decide who should'own' the emerging digital experience sector. Magnolia found that 24% of marketers don't understand what'machine learning' is, and 23% say they don't know what the term'big data' means. A third of marketers also confess to not know what API stands for. IT teams are also suffering from a similar disconnect, with 77% saying they don't understand the buzzwords marketers use.

Over the last decade, companies have begun to grasp and unlock the potential that artificial intelligence (AI) and machine learning (ML) can bring. While still in its infancy, companies are starting to understand the significant impact this technology can bring, helping them make better, faster and more efficient decisions. Of course, AI and ML is no silver bullet to help businesses embrace innovation. In fact, the success of these algorithms is only as good as their foundations -- specifically, quality data. Without it, businesses will see the very objective they've installed AI and ML to do fail, with the unforeseen consequences of bad data causing irreversible damage to the business both in terms of its efficiency and reputation.

The complete part of the earthquake frequency–magnitude distribution, above the completeness magnitude mc, is well described by the Gutenberg–Richter law. On the other hand, incomplete data does not follow any specific law, since the shape of the frequency–magnitude distribution below max(mc) is function of mc heterogeneities that depend on the seismic network spatiotemporal configuration. This paper attempts to solve this problem by presenting an asymmetric Laplace mixture model, defined as the weighted sum of Laplace (or double exponential) distribution components of constant mc, where the inverse scale parameter of the exponential function is the detection parameter κ below mc, and the Gutenberg–Richter β-value above mc. Using a variant of the Expectation-Maximization algorithm, the mixture model confirms the ontology proposed by Mignan [2012, https://doi.org/10.1029/2012JB009347], The performance of the proposed mixture model is analysed, with encouraging results obtained in simulations and in eight real earthquake catalogues that represent different seismic network spatial configurations.