Tax laws, for example, don't currently take automated workers into account. While human employees contribute payroll and income taxes, an automated "employee" doesn't, Abbott noted. Governments could lose out on quite a bit of income tax as AI becomes more prevalent and possibly displaces more human workers. Granted, that argument only works if displaced employees don't find other jobs. Abbott predicted that that may happen as AI becomes smarter at a rate that outpaces people's ability to learn new skills or find job training.

The US city of Portland, Maine on Wednesday voted to ban the use of facial recognition by police and city officials. The Bangor Daily News reported that voters passed a ballot initiative "bolstering a ban on facial recognition by city agencies". The initiative follows a city council vote in August, which put a preliminary ban in place as an ordinance. The citizens are entitled to a minimum of $1,000 in fines if they are subjected to a facial recognition scan by police. Portland joins Boston, San Francisco; Portland, Oregon and the city of Oakland in Northern California in banning the use of facial recognition technology by the authorities.

The Mars Reconnaissance Orbiter's HiRISE camera captured this impact crater on Mars. On July 15, 1965, the Mariner 4 spacecraft snapped a series of photographs of Mars during its flyby of the Red Planet. These were the first "close-up" images taken of another planet from outer space, according to NASA. One of these first grainy photographs depicted a massive crater nearly 100 miles in diameter. Now, NASA's Jet Propulsion Laboratory (JPL) is tapping artificial intelligence (AI) to help with its cosmic cartography efforts, using these technologies to identify "fresh craters" on Mars.

Best listening experience is on Chrome, Firefox or Safari. The Defense Department's data strategy released just a few weeks ago says improving data management will help it fight and win wars. It says artificial intelligence will become an important component of data-fueled digital modernization. For an assessment, the CEO of data analysis company Govini, Tara Murphy Dougherty joined Federal Drive with Tom Temin. Insight by BOX: Federal News Network showcases several examples of agencies and industry partnering to create and evolve the future of work in this exclusive ebook.

The report makes recommendations on how the arms control community could build on existing responsible AI initiatives and export control and …

The Morning Download delivers daily insights and news on business technology from the CIO Journal team. "I wouldn't fire the pollsters, but I would direct them to try to leverage machine learning, data mining and AI in their work more to get better projections," said Oren Etzioni, chief executive of the Allen Institute for AI, a nonprofit research center in Seattle. The size of this year's polling error is still unknown as the vote count continues. But polls generally predicted clear Democratic gains, not cliffhangers. No person or algorithm can predict human behavior accurately all the time, said Heidi Messer, chairman of New York-based Collective[i], which offers AI and predictive technologies for sales teams.

The current practice of manually processing features for high-dimensional and heterogeneous aviation data is labor-intensive, does not scale well to new problems, and is prone to information loss, affecting the effectiveness and maintainability of machine learning (ML) procedures. This research explored an unsupervised learning method, autoencoder, to extract effective features for aviation machine learning problems. The study explored variants of autoencoders with the aim of forcing the learned representations of the input to assume useful properties. A flight track anomaly detection autoencoder was developed to demonstrate the versatility of the technique. The research results show that the autoencoder can not only automatically extract effective features for the flight track data, but also efficiently deep clean data, thereby reducing the workload of data scientists. Moreover, the research leveraged transfer learning to efficiently train models for multiple airports. Transfer learning can reduce model training times from days to hours, as well as improving model performance. The developed applications and techniques are shared with the whole aviation community to improve effectiveness of ongoing and future machine learning studies.

We consider a commonly studied supervised classification of a synthetic dataset whose labels are generated by feeding a one-layer neural network with random iid inputs. We study the generalization performances of standard classifiers in the high-dimensional regime where $\alpha=n/d$ is kept finite in the limit of a high dimension $d$ and number of samples $n$. Our contribution is three-fold: First, we prove a formula for the generalization error achieved by $\ell_2$ regularized classifiers that minimize a convex loss. This formula was first obtained by the heuristic replica method of statistical physics. Secondly, focussing on commonly used loss functions and optimizing the $\ell_2$ regularization strength, we observe that while ridge regression performance is poor, logistic and hinge regression are surprisingly able to approach the Bayes-optimal generalization error extremely closely. As $\alpha \to \infty$ they lead to Bayes-optimal rates, a fact that does not follow from predictions of margin-based generalization error bounds. Third, we design an optimal loss and regularizer that provably leads to Bayes-optimal generalization error.

The widespread interest in autonomous driving technology in recent years [2] has motivated extensive research in multiagent navigation in driving domains. One of the most challenging driving domains [3] is the uncontrolled intersection, i.e., a street intersection that features no traffic signs or signals. Within this domain, we focus on scenarios in which agents do not communicate explicitly or implicitly through e.g., turn signals. This model setup gives rise to challenging multi-vehicle encounters that mimic real-world situations (arising due to human distraction, violation of traffic rules or special emergencies) that result in fatal accidents [3]. The frequency and severity of such situations has motivated vivid research interest in uncontrolled intersections [4, 5, 6]. In the absence of explicit traffic signs, signals, rules or explicit communication among agents, avoiding collisions at intersections relies on the ability of agents to predict the dynamics of interaction amongst themselves. One prevalent way to model multiagent dynamics is via trajectory prediction. However, multistep multiagent trajectory prediction is NPhard [7], whereas the sample complexity of existing learning algorithms effectively prohibits the extraction of practical models. Our key insight is that the geometric structure of the intersection and the incentive of agents to move efficiently and avoid collisions with each other (rationality) compress the space of possible multiagent trajectories, effectively simplifying inference.

The Morning Download delivers daily insights and news on business technology from the CIO Journal team. "I wouldn't fire the pollsters, but I would direct them to try to leverage machine learning, data mining and AI in their work more to get better projections," said Oren Etzioni, chief executive of the Allen Institute for AI, a nonprofit research center in Seattle. The size of this year's polling error is still unknown as the vote count continues. But polls generally predicted clear Democratic gains, not cliffhangers. No person or algorithm can predict human behavior accurately all the time, said Heidi Messer, chairman of New York-based Collective[i], which offers AI and predictive technologies for sales teams.