Last fall, the MIT Stephen A. Schwarzman College of Computing embarked on a project to design and construct a new building on Vassar Street in Cambridge, at the former site of Building 44. Working with Skidmore, Owings & Merrill (SOM), the design for the new building is taking shape, with plans for the exterior façade now complete. The proposed project will establish a home for the MIT Schwarzman College of Computing, providing state-of-the-art space for computing research and education. The building's central location in the Vassar Street block between Main Street and Massachusetts Avenue will help form a new cluster of connectivity, and will enable the space to have a multifaceted role. The project has been reviewed extensively with city planning staff and will be presented to the Cambridge Planning Board for review and approval.

The Army of the future will involve humans and autonomous machines working together to accomplish the mission. According to Army researchers, this vision will only succeed if artificial intelligence is perceived to be ethical. Researchers, based at the U.S. Army Combat Capabilities Development Command, now known as DEVCOM, Army Research Laboratory, Northeastern University and the University of Southern California, expanded existing research to cover moral dilemmas and decision making that has not been pursued elsewhere. This research, featured in Frontiers in Robotics and AI, tackles the fundamental challenge of developing ethical artificial intelligence, which, according to the researchers, is still mostly understudied. "Autonomous machines, such as automated vehicles and robots, are poised to become pervasive in the Army," said DEVCOM ARL researcher Dr. Celso de Melo, who is located at the laboratory's ARL West regional site in Playa Vista, California.

When it comes to integrating AI-based processes into security, it isn't just useful; it's become essential and is rapidly becoming mission critical to organizations of all sizes. The rapidly expanding attack surface of virtually every enterprise, with the proliferation of Internet of Things (IoT) devices and cloud systems is a leading reason why artificial intelligence (AI) has become essential in security. Organizations have moved from securing thousands of devices to potentially millions. Within this new surge in network traffic are billions of time-varying signals, all of which must be analyzed to assess risk. Security is becoming incredibly more complex in just a handful of years because there is far more to attack.

Katharina McFarland, National Security Commission on Artificial Intelligence, will provide an overview of the commission's latest report on the state of …

Building operations represent a significant percentage of the total primary energy consumed in most countries due to the proliferation of Heating, Ventilation and Air-Conditioning (HVAC) installations in response to the growing demand for improved thermal comfort. Reducing the associated energy consumption while maintaining comfortable conditions in buildings are conflicting objectives and represent a typical optimization problem that requires intelligent system design. Over the last decade, different methodologies based on the Artificial Intelligence (AI) techniques have been deployed to find the sweet spot between energy use in HVAC systems and suitable indoor comfort levels to the occupants. This paper performs a comprehensive and an in-depth systematic review of AI-based techniques used for building control systems by assessing the outputs of these techniques, and their implementations in the reviewed works, as well as investigating their abilities to improve the energy-efficiency, while maintaining thermal comfort conditions. This enables a holistic view of (1) the complexities of delivering thermal comfort to users inside buildings in an energy-efficient way, and (2) the associated bibliographic material to assist researchers and experts in the field in tackling such a challenge. Among the 20 AI tools developed for both energy consumption and comfort control, functions such as identification and recognition patterns, optimization, predictive control. Based on the findings of this work, the application of AI technology in building control is a promising area of research and still an ongoing, i.e., the performance of AI-based control is not yet completely satisfactory. This is mainly due in part to the fact that these algorithms usually need a large amount of high-quality real-world data, which is lacking in the building or, more precisely, the energy sector.

Anomaly detection techniques are growing in importance at the Large Hadron Collider (LHC), motivated by the increasing need to search for new physics in a model-agnostic way. In this work, we provide a detailed comparative study between a well-studied unsupervised method called the autoencoder (AE) and a weakly-supervised approach based on the Classification Without Labels (CWoLa) technique. We examine the ability of the two methods to identify a new physics signal at different cross sections in a fully hadronic resonance search. By construction, the AE classification performance is independent of the amount of injected signal. In contrast, the CWoLa performance improves with increasing signal abundance. When integrating these approaches with a complete background estimate, we find that the two methods have complementary sensitivity. In particular, CWoLa is effective at finding diverse and moderately rare signals while the AE can provide sensitivity to very rare signals, but only with certain topologies. We therefore demonstrate that both techniques are complementary and can be used together for anomaly detection at the LHC.

We find that the way we choose to represent data labels can have a profound effect on the quality of trained models. For example, training an image classifier to regress audio labels rather than traditional categorical probabilities produces a more reliable classification. This result is surprising, considering that audio labels are more complex than simpler numerical probabilities or text. We hypothesize that high dimensional, high entropy label representations are generally more useful because they provide a stronger error signal. We support this hypothesis with evidence from various label representations including constant matrices, spectrograms, shuffled spectrograms, Gaussian mixtures, and uniform random matrices of various dimensionalities. Our experiments reveal that high dimensional, high entropy labels achieve comparable accuracy to text (categorical) labels on the standard image classification task, but features learned through our label representations exhibit more robustness under various adversarial attacks and better effectiveness with a limited amount of training data. These results suggest that label representation may play a more important role than previously thought. Image classification is a well-established task in machine learning. The standard approach takes an input image and predicts a categorical distribution over the given classes. The most popular method to train these neural network is through a cross-entropy loss with backpropagation. Deep convolutional neural networks (Lecun et al., 1998; Krizhevsky et al., 2012; Simonyan & Zisserman, 2014; He et al., 2015; Huang et al., 2016) have achieved extraordinary performance on this task, while some even surpass human level performance.

In this paper, we investigate the so-called ODP-problem that has been formulated by Caragiannis and Micha [10]. Here, we are in a setting with two election alternatives out of which one is assumed to be correct. In ODP, the goal is to organise the delegations in the social network in order to maximize the probability that the correct alternative, referred to as ground truth, is elected. While the problem is known to be computationally hard, we strengthen existing hardness results by providing a novel strong approximation hardness result: For any positive constant $C$, we prove that, unless $P=NP$, there is no polynomial-time algorithm for ODP that achieves an approximation guarantee of $\alpha \ge (\ln n)^{-C}$, where $n$ is the number of voters. The reduction designed for this result uses poorly connected social networks in which some voters suffer from misinformation. Interestingly, under some hypothesis on either the accuracies of voters or the connectivity of the network, we obtain a polynomial-time $1/2$-approximation algorithm. This observation proves formally that the connectivity of the social network is a key feature for the efficiency of the liquid democracy paradigm. Lastly, we run extensive simulations and observe that simple algorithms (working either in a centralized or decentralized way) outperform direct democracy on a large class of instances. Overall, our contributions yield new insights on the question in which situations liquid democracy can be beneficial.

Machine learning, artificial intelligence, and deep learning have advanced significantly over the past decade. Nonetheless, humans possess unique abilities such as creativity, intuition, context and abstraction, analytic problem solving, and detecting unusual events. To successfully tackle pressing scientific and societal challenges, we need the complementary capabilities of both humans and machines. The Federal Government could accelerate its priorities on multiple fronts through judicious integration of citizen science and crowdsourcing with artificial intelligence (AI), Internet of Things (IoT), and cloud strategies.

Artificial intelligence salaries benefit from the perfect recipe for a sweet paycheck: a hot field and high demand for scarce talent. It's the ever-reliable law of supply and demand, and right now, anything artificial intelligence-related is in very high demand. According to Indeed.com, the average IT salary -- the keyword is "artificial intelligence engineer" -- in the San Francisco area ranges from approximately $134,135 per year for "software engineer" to $169,930 per year for "machine learning engineer." Check out our editorial recommendations on the best machine learning books. However, it can go much higher if you have the credentials firms need.