About a year ago, I wrote that mounting AI hype would likely give way to yet another AI winter. Now, according to the panelists at "the world's leading academic AI conference" the temperature is already falling. Most recent advances in AI have come through a pair of related technologies: Deep Learning and Neural Networks. The ideas beneath these, however, are more than 70 years old. Warren McCulloch and Walter Pitts (1943) opened the subject by creating a computational model for neural networks…The first functional networks with many layers were published by Ivakhnenko and Lapa in 1965, as the Group Method of Data Handling.

Look in the wrong place, and you'll get the wrong answers. Five years ago, Elon Musk said "With Artificial Intelligence (AI) we are summoning the demon". A report by the Brookings Institution's AI and Emerging Tech Initiative notes that fearful warnings about new technology like this are nothing new. When C. Babbage created the first "computer" in the mid-19th century: "The idea that God-given human reason could be replaced by a machine was fearfully received by Victorian England in a manner similar to today's concern about machines being able to think like humans." However, while Musk called for (preventive) "[r]egulatory oversight… just to make sure that we don't do something foolish", the report warns that regulation of AI would be meaningful only if it "focused on the tangible effects of the technology."

Sitting at the nexus of data science, computer vision, and machine learning, artificial intelligence (AI) has the promise to provide insights from large, high-dimensional datasets that can stay otherwise hidden from traditional data analysis approaches. However, its application in materials science has been comparatively slower than some fields due to the specialized knowledge required to apply AI to physical systems, as well as the wide variety of problems and data types encountered. In an effort to push forward the state-of-the-art in materials science research, Carnegie Mellon University (CMU) and the Air Force Research Laboratory (AFRL) are establishing a collaborative Center of Excellence. The center will leverage the strengths of the two institutions to develop next-generation aerospace materials, establish a pipeline of research talent with both AI and materials science expertise, and advance the materials science field by integrating AI into materials research and design. The 5-year, $7.5M joint Center of Excellence, named Data-Driven Discovery Of Optimized Multifunctional Material Systems (D3OM2S), is supported by an award from the Air Force Office of Scientific Research (AFOSR) and the AFRL Materials and Manufacturing Directorate.

I will also discuss the common technical challenges of executing A/B tests on ML algorithms, such as infrastructure requirements, connecting online and offline metrics, and handling ramp up periods for online learning algorithms. Overall, the goal of this talk will be to motivate ML practitioners to use A/B testing when evaluating their algorithms and provide them with high-level guidelines on how to do it. Profile Pavel Dmitriev is a Vice President of Data Science at Outreach, where he works on enabling data driven decision making in sales through experimentation and machine learning. He was previously a Principal Data Scientist with Microsoft's Analysis and Experimentation team, where he worked on scaling experimentation in Bing, Skype, and Windows OS. Pavel co-authored numerous papers at top-tier data mining and machine learning conferences, such as WWW, ICSE, KDD, has given keynotes and tutorials at WWW, SIGIR, SEAA, and KDD.

SPRINGFIELD, MASSACHUSETTS – Police departments around the U.S. are asking citizens to trust them to use facial recognition software as another handy tool in their crime-fighting toolbox. But some lawmakers -- and even some technology giants -- are hitting the brakes. Are fears of an all-seeing, artificially intelligent security apparatus overblown? Not if you look at China, where advancements in computer vision applied to vast networks of street cameras have enabled authorities to track members of ethnic minority groups for signs of subversive behavior. American police officials and their video surveillance industry partners contend that won't happen here.

Toyota Motor Corp. plans to first deploy advanced self-driving features in commercial vehicles before adding them to cars meant for personal use, a senior official at the Japanese auto major said on Tuesday. It will be easier to apply self-driving technology that does not require constant and direct human-monitoring to taxis and vehicles Toyota is developing, including on-demand ride services, mobile shops and ambulatory hospitals, said James Kuffner, chief of Toyota Research Institute-Advanced Development (TRI-AD). The operators of these vehicles could control when and where they are deployed and oversee their maintenance, he told reporters at the opening of its new offices in Tokyo. "It will take more time to achieve'Level 4' for a personally-owned vehicle," Kuffner said, referring to the automation level at which vehicles can drive themselves under limited conditions. "Level 4 is really what we're striving for to first appear in mobility as a service," he added.

The simultaneous control of multiple coordinated robotic agents represents an elaborate problem. If solved, however, the interaction between the agents can lead to solutions to sophisticated problems. The concept of swarming, inspired by nature, can be described as the emergence of complex system-level behaviors from the interactions of relatively elementary agents. Due to the effectiveness of solutions found in nature, bio-inspired swarming-based control techniques are receiving a lot of attention in robotics. One method, known as swarm shepherding, is founded on the sheep herding behavior exhibited by sheepdogs, where a swarm of relatively simple agents are governed by a shepherd (or shepherds) which is responsible for high-level guidance and planning. Many studies have been conducted on shepherding as a control technique, ranging from the replication of sheep herding via simulation, to the control of uninhabited vehicles and robots for a variety of applications. We present a comprehensive review of the literature on swarm shepherding to reveal the advantages and potential of the approach to be applied to a plethora of robotic systems in the future.

When and why can a neural network be successfully trained? This article provides an overview of optimization algorithms and theory for training neural networks. First, we discuss the issue of gradient explosion/vanishing and the more general issue of undesirable spectrum, and then discuss practical solutions including careful initialization and normalization methods. Second, we review generic optimization methods used in training neural networks, such as SGD, adaptive gradient methods and distributed methods, and theoretical results for these algorithms. Third, we review existing research on the global issues of neural network training, including results on bad local minima, mode connectivity, lottery ticket hypothesis and infinite-width analysis.

Neural network compression methods have enabled deploying large models on emerging edge devices with little cost, by adapting already-trained models to the constraints of these devices. The rapid development of AI-capable edge devices with limited computation and storage requires streamlined methodologies that can efficiently satisfy the constraints of different devices. In contrast, existing methods often rely on heuristic and manual adjustments to maintain accuracy, support only coarse compression policies, or target specific device constraints that limit their applicability. We address these limitations by proposing the TOlerance-based COmpression (TOCO) framework. TOCO uses an in-depth analysis of the model, to maintain the accuracy, in an active learning system. The results of the analysis are tolerances that can be used to perform compression in a fine-grained manner. Finally, by decoupling compression from the tolerance analysis, TOCO allows flexibility to changes in the hardware.

Fingerprinting-based positioning, one of the promising indoor positioning solutions, has been broadly explored owing to the pervasiveness of sensor-rich mobile devices, the prosperity of opportunistically measurable location-relevant signals and the progress of data-driven algorithms. One critical challenge is to controland improve the quality of the reference fingerprint map (RFM), which is built at the offline stage and applied for online positioning. The key concept concerningthe quality control of the RFM is updating the RFM according to the newly measured data. Though varies methods have been proposed for adapting the RFM, they approach the problem by introducing extra-positioning schemes (e.g. PDR orUGV) and directly adjust the RFM without distinguishing whether critical changes have occurred. This paper aims at proposing an extra-positioning-free solution by making full use of the redundancy of measurable features. Loosely inspired by random sampling consensus (RANSAC), arbitrarily sampled subset of features from the online measurement are used for generating multi-resamples, which areused for estimating the intermediate locations. In the way of resampling, it can mitigate the impact of the changed features on positioning and enables to retrieve accurate location estimation. The users location is robustly computed by identifying the candidate locations from these intermediate ones using modified Jaccardindex (MJI) and the feature-wise change belief is calculated according to the world model of the RFM and the estimated variability of features. In order to validate our proposed approach, two levels of experimental analysis have been carried out. On the simulated dataset, the average change detection accuracy is about 90%. Meanwhile, the improvement of positioning accuracy within 2 m is about 20% by dropping out the features that are detected as changed when performing positioning comparing to that of using all measured features for location estimation. On the long-term collected dataset, the average change detection accuracy is about 85%.