The administration of U.S. President Barack Obama on Monday released an overview of the federal government's automated vehicles policy, which includes a checklist for makers on various aspects of the cars they are developing, as well as guidelines to states on evolving a common framework for regulating the new technologies. "Automated vehicles have the potential to save tens of thousands of lives each year," wrote Obama in an op-ed in the Pittsburgh Post-Gazette on Monday. That's what harnessing technology for good can look like. But we have to get it right," he added. Obama wrote that the quickest way to slam the brakes on innovation is if the public loses confidence in the safety of the new technology, and the responsibility of both government and industry is to make sure it doesn't happen.

SAN FRANCISCO -- Federal regulators, faced with a growing number of self-driving car tests on roads across the U.S., plan to issue a flurry of new guidelines Tuesday aimed at automakers and tech companies. The U.S. Department of Transportation will require any new tech to meet a 15-point safety assessment, consider new powers to allow administrators to limit the deployment of experimental vehicles, and will issue a model for state self-driving car policies aimed at developing a cohesive set of national regulations. Officials will solicit public comments on the topic of self-driving car regulations for the next 60 days on the Transportation Department website and plan to update self-driving car policies annually. "We're laying it out there, what we care about, and inviting the industry to show us how they meet those standards," Department of Transportation Secretary Anthony Foxx told reporters during a briefing late Monday. "Some companies haven't dealt with us, but they'll learn quickly we can go really deep on these topics. We want the public to be safe."

Non-stationary continuous time Bayesian networks are introduced. They allow the parents set of each node to change over continuous time. Three settings are developed for learning non-stationary continuous time Bayesian networks from data: known transition times, known number of epochs and unknown number of epochs. A score function for each setting is derived and the corresponding learning algorithm is developed. A set of numerical experiments on synthetic data is used to compare the effectiveness of non-stationary continuous time Bayesian networks to that of non-stationary dynamic Bayesian networks. Furthermore, the performance achieved by non-stationary continuous time Bayesian networks is compared to that achieved by state-of-the-art algorithms on four real-world datasets, namely drosophila, saccharomyces cerevisiae, songbird and macroeconomics.

We introduce a new artificial intelligence (AI) approach called, the 'Digital Synaptic Neural Substrate' (DSNS). It uses selected attributes from objects in various domains (e.g. chess problems, classical music, renowned artworks) and recombines them in such a way as to generate new attributes that can then, in principle, be used to create novel objects of creative value to humans relating to any one of the source domains. This allows some of the burden of creative content generation to be passed from humans to machines. The approach was tested in the domain of chess problem composition. We used it to automatically compose numerous sets of chess problems based on attributes extracted and recombined from chess problems and tournament games by humans, renowned paintings, computer-evolved abstract art, photographs of people, and classical music tracks. The quality of these generated chess problems was then assessed automatically using an existing and experimentally-validated computational chess aesthetics model. They were also assessed by human experts in the domain. The results suggest that attributes collected and recombined from chess and other domains using the DSNS approach can indeed be used to automatically generate chess problems of reasonably high aesthetic quality. In particular, a low quality chess source (i.e. tournament game sequences between weak players) used in combination with actual photographs of people was able to produce three-move chess problems of comparable quality or better to those generated using a high quality chess source (i.e. published compositions by human experts), and more efficiently as well. Why information from a foreign domain can be integrated and functional in this way remains an open question for now. The DSNS approach is, in principle, scalable and applicable to any domain in which objects have attributes that can be represented using real numbers.

Composition conventions are guidelines used by human composers in composing chess problems. They are particularly significant in composition tournaments. Examples include, not having any check in the first move of the solution and not dressing up the board with unnecessary pieces. Conventions are often associated or even directly conflated with the overall aesthetics or beauty of a composition. Using an existing experimentally-validated computational aesthetics model for three-move mate problems, we analyzed sets of computer-generated compositions adhering to at least 2, 3 and 4 comparable conventions to test if simply conforming to more conventions had a positive effect on their aesthetics, as is generally believed by human composers. We found slight but statistically significant evidence that it does, but only to a point. We also analyzed human judge scores of 145 three-move mate problems composed by humans to see if they had any positive correlation with the computational aesthetic scores of those problems. We found that they did not. These seemingly conflicting findings suggest two main things. First, the right amount of adherence to composition conventions in a composition has a positive effect on its perceived aesthetics. Second, human judges either do not look at the same conventions related to aesthetics in the model used or emphasize others that have less to do with beauty as perceived by the majority of players, even though they may mistakenly consider their judgements beautiful in the traditional, non-esoteric sense. Human judges may also be relying significantly on personal tastes as we found no correlation between their individual scores either.

The Netflix logo is shown in this illustration photograph. Editor's Note: This is the third in a series of excerpts we are publishing from sociologist Jerry Davis's new book, "The Vanishing American Corporation: Navigating the Hazards of a New Economy." For more on the topic, watch last week's Making Sen e report below. Suppose you wanted to start an enterprise without leaving your couch. Imagine a hypothetical product: the iPhone Remote Drone Assassin App.

Ride-hailing service Uber may be anchored in California's Silicon Valley, but it's the latest auto-related firm to say it will make sure to have a presence in the Motor City. Uber will open a new research center in metro Detroit by the end of this year to accommodate increased work with auto suppliers and other technology companies involved in autonomous vehicle development, said Sherif Marakby, Uber vice president for global vehicle development, said Monday at a conference in Novi, Mich. Uber has a large presence in Pittsburgh where last week it began operated about 20 fully autonomous Ford Fusion hybrids on its most heavily traveled routes in that city. "We have having engineers come up to Detroit to meet with...suppliers. We've also had discussions with automakers," said Marakby, who began working for Uber in April after 25 years at Ford.

Since blogging that I Need an AI BS-Meter a number of people have sent me pointers to a subset of AI I loosely think of as Result Explainers -- everything from pending government regulations (EU's Global Data Protection Regulations -- GDPR) to the latest in academic research (Local Interpretable Model-agnostic Explanations -- LIME). As the authors of the EU's GDPR state, widespread adoption of AI cannot occur until vendors are able to communicate results in a "concise, intelligible and easily accessible form, using clear and plain language." This got me thinking, "What should Result Explainers look like?" Should they generate trust scores, a series of Google-Maps like directions that get you from data to results, a series of diagrams? And as my colleague Patrick at Lab41 has pointed out, "Why should we trust a Result Explainer if we don't trust AI to begin with? As you might expect there isn't one right answer. That said, recent advances in recommenders, digital assistants, user interface design and initiatives like DARPA's recently announced Explainable Artificial Intelligence (XAI) grand challenge suggest we may be on the brink of a few breakthroughs. Again, as the authors of the EU's General Data Protection Regulations note, while the resulting classifiers, models, predictors, etc. can be very powerful they also frequently confound explanation -- e.g., the output of SVMs and Gaussian processes can be difficult to render, ensemble methods hide information as a result of aggregation and averaging, neural nets create high data dimensionality, and so on. End users care a lot more about results than they do about models. Unfortunately assessing result quality takes us right back to the models, as nonparametric models are only as good as the data used to train them (along with the type of model structure and associated parameters that were selected). But these models frequently hide information. Part of the magic of AI is that it finds stuff based on features that previously may not have been well understood. Unfortunately, the features models train on are frequently unclear. Assigning labels to pre-trained models can help mitigate some of this ambiguity -- e.g., "This model was trained with over 100,000 high-res color images of cats." These labels may be misleading though, as the model may contain feature biases that are not well understood -- e.g., "the training data is dominated by images of "well-fed, indoor cats from Japan."

While Star Trek-style teleportation is still a long way off, researchers have revealed a major breakthrough in the field of quantum travel. Two separate teams have transferred quantum information over several miles of fibre optic networks in an urban network. The results could lead to more secure bank accounts, a faster internet and possibly even open the door to the controversial idea of human teleportation. One of the potential applications for quantum teleportation is a network of quantum computers (illustrated) and a'quantum internet' that is far faster and much more secure than current networks When atoms are'entangled' the measurement of one of the atoms will not only cause it to'pick' one state, but it will also instantaneously cause the atoms it is entangled with to do the same, even if that atom has not been measured itself. This means we automatically know information about all the atoms that are entangled at once, just by measuring one, and it does not matter how far apart in space the two entangled atoms are.

As you went about your day quietly humming it, perhaps someone else heard you and complained minutes later that you'd gotten the tune stuck in their head. The song's hook seems to have the ability to jump from one brain to another. And perhaps, to jump from the web browser you are using right now to your brain. In fact, you may be singing the hook to yourself right now. Something similar happens on the internet when things go viral – seeming to follow no rhyme or reason, people are compelled to like, share, retweet or participate in things online.