The United Nations Educational, Scientific, and Cultural Organization (UNESCO) today unveiled its latest Science Report. The massive undertaking -- this year's report totals 762 pages, compiled by 70 authors from 52 countries over 18 months -- is published every five years to examine current trends in science governance. This latest edition includes discussion of the rapid progress toward Industry 4.0 and, for the first time, a deep analysis of AI and robotics research around the globe. Going beyond just the global leaders, it offers an overview of almost two dozen countries and global regions, examining AI research, funding, strategies, and more. Overall, the report determines "it is the field of AI and robotics that dominated scientific output" in recent years.

Convolutional Neural Networks (CNN) have shown impressive performance in computer vision, natural language processing, and many other applications, but they exhibit high computations and substantial memory requirements. To address these limitations, especially in resource-constrained devices, the use of cloud computing for CNNs is becoming more popular. This comes with privacy and latency concerns that have motivated the designers to develop embedded hardware accelerators for CNNs. However, designing a specialized accelerator increases the time-to-market and cost of production. Therefore, to reduce the time-to-market and access to state-of-the-art techniques, CNN hardware mapping and deployment on embedded accelerators are often outsourced to untrusted third parties, which is going to be more prevalent in futuristic artificial intelligence of things (AIoT) systems. These AIoT systems anticipate horizontal collaboration among different resource-constrained AIoT node devices, where CNN layers are partitioned and these devices collaboratively compute complex CNN tasks Therefore, there is a dire need to explore this attack surface for designing secure embedded hardware accelerators for CNNs. Towards this goal, in this paper, we exploited this attack surface to propose an HT-based attack called FeSHI. This attack exploits the statistical distribution i.e., Gaussian distribution, of the layer-by-layer feature maps of the CNN to design two triggers for stealthy HT with a very low probability of triggering. To illustrate the effectiveness of the proposed attack, we deployed the LeNet and LeNet-3D on PYNQ to classify the MNIST and CIFAR-10 datasets, respectively, and tested FeSHI. The experimental results show that FeSHI utilizes up to 2% extra LUTs, and the overall resource overhead is less than 1% compared to the original designs

Trust region methods are widely applied in single-agent reinforcement learning problems due to their monotonic performance-improvement guarantee at every iteration. Nonetheless, when applied in multi-agent settings, the guarantee of trust region methods no longer holds because an agent's payoff is also affected by other agents' adaptive behaviors. To tackle this problem, we conduct a game-theoretical analysis in the policy space, and propose a multi-agent trust region learning method (MATRL), which enables trust region optimization for multi-agent learning. Specifically, MATRL finds a stable improvement direction that is guided by the solution concept of Nash equilibrium at the meta-game level. We derive the monotonic improvement guarantee in multi-agent settings and empirically show the local convergence of MATRL to stable fixed points in the two-player rotational differential game. To test our method, we evaluate MATRL in both discrete and continuous multiplayer general-sum games including checker and switch grid worlds, multi-agent MuJoCo, and Atari games. Results suggest that MATRL significantly outperforms strong multi-agent reinforcement learning baselines.

Large-scale models for learning fixed-dimensional cross-lingual sentence representations like LASER (Artetxe and Schwenk, 2019b) lead to significant improvement in performance on downstream tasks. However, further increases and modifications based on such large-scale models are usually impractical due to memory limitations. In this work, we introduce a lightweight dual-transformer architecture with just 2 layers for generating memory-efficient cross-lingual sentence representations. We explore different training tasks and observe that current cross-lingual training tasks leave a lot to be desired for this shallow architecture. To ameliorate this, we propose a novel cross-lingual language model, which combines the existing single-word masked language model with the newly proposed cross-lingual token-level reconstruction task. We further augment the training task by the introduction of two computationally-lite sentence-level contrastive learning tasks to enhance the alignment of cross-lingual sentence representation space, which compensates for the learning bottleneck of the lightweight transformer for generative tasks. Our comparisons with competing models on cross-lingual sentence retrieval and multilingual document classification confirm the effectiveness of the newly proposed training tasks for a shallow model.

To understand the promise and peril of artificial intelligence for food safety, consider the story of Larry Brilliant. Brilliant is a self-described "spiritual seeker," "social change addict," and "rock doc." During his medical internship in 1969, he responded to a San Francisco Chronicle columnist's call for medical help to Native Americans then occupying Alcatraz. Then came Warner Bros.' call to have him join the cast of Medicine Ball Caravan, a sort-of sequel to Woodstock Nation. That caravan ultimately led to a detour to India, where Brilliant spent 2 years studying at the foot of the Himalayas in a monastery under guru Neem Karoli Baba. Toward the end of the stay, Karoli Baba informed Brilliant of his calling: join the World Health Organization (WHO) and eradicate smallpox. He joined the WHO as a medical health officer, as a part of a team making over 1 billion house calls collectively. In 1977, he observed the last human with smallpox, leading WHO to declare the disease eradicated. After a decade battling smallpox, Brilliant went on to establish and lead foundations and start-up companies, and serve as a professor of international health at the University of Michigan. As one corporate brand manager wrote, "There are stories that are so incredible that not even the creative minds that fuel Hollywood could write them with a straight face."[1]

As drones become a bigger part of modern warfare, fighting forces are devising creative ways to disable them. The U.S. military recently demonstrated a drone interceptor that fires pink'Silly String'-like streamers at unmanned craft, gumming up their rotors and bringing them crashing down to Earth. The goal is to devise anti-drone technology that doesn't cause as much collateral damage as explosives, according to the Defense Advanced Research Projects Agency (DARPA), and would be used in populated areas. DARPA began developing the interceptor, known as Counter-Unmanned Air System (C-UAS), four years ago as a means to stop small self-guided unmanned aircraft without the kind of major collateral damage caused by gunfire or explosives. In a video posted this week, DARPA demonstrated C-UAS at Eglin Air Force Base outside Valparaiso, Florida.

On March 18, 2018, at around 10 p.m., Elaine Herzberg was wheeling her bicycle across a street in Tempe, Arizona, when she was struck and killed by a self-driving car. Although there was a human operator behind the wheel, an autonomous system--artificial intelligence--was in full control. This incident, like others involving interactions between people and AI technologies, raises a host of ethical and proto-legal questions. What moral obligations did the system's programmers have to prevent their creation from taking a human life? And who was responsible for Herzberg's death? "Artificial intelligence" refers to systems that can be designed to take cues from their environment and, based on those inputs, proceed to solve problems, assess risks, make predictions, and take actions. In the era predating powerful computers and big data, such systems were programmed by humans and followed rules of human invention, but advances in technology have led to the development of new approaches.

The Boston area has long been home to innovation that leads to impactful new drugs. But manufacturing those drugs for clinical trials often involves international partners and supply chains. The vulnerabilities of that system have become all too apparent during the Covid-19 pandemic. Now Snapdragon Chemistry, co-founded by MIT Professor and Associate Provost Tim Jamison, is helping pharmaceutical companies manufacture drugs locally to shorten the time it takes for new drugs to get to patients. Snapdragon essentially starts as a chemistry lab, running experiments on behalf of pharmaceutical customers to create molecules of interest.

While deep and large pre-trained models are the state-of-the-art for various natural language processing tasks, their huge size poses significant challenges for practical uses in resource constrained settings. Recent works in knowledge distillation propose task-agnostic as well as task-specific methods to compress these models, with task-specific ones often yielding higher compression rate. In this work, we develop a new task-agnostic distillation framework XtremeDistilTransformers that leverages the advantage of task-specific methods for learning a small universal model that can be applied to arbitrary tasks and languages. To this end, we study the transferability of several source tasks, augmentation resources and model architecture for distillation. We evaluate our model performance on multiple tasks, including the General Language Understanding Evaluation (GLUE) benchmark, SQuAD question answering dataset and a massive multi-lingual NER dataset with 41 languages. We release three distilled task-agnostic checkpoints with 13MM, 22MM and 33MM parameters obtaining SOTA performance in several tasks.

We develop a novel method for carrying out model selection for Bayesian autoencoders (BAEs) by means of prior hyper-parameter optimization. Inspired by the common practice of type-II maximum likelihood optimization and its equivalence to Kullback-Leibler divergence minimization, we propose to optimize the distributional sliced-Wasserstein distance (DSWD) between the output of the autoencoder and the empirical data distribution. The advantages of this formulation are that we can estimate the DSWD based on samples and handle high-dimensional problems. We carry out posterior estimation of the BAE parameters via stochastic gradient Hamiltonian Monte Carlo and turn our BAE into a generative model by fitting a flexible Dirichlet mixture model in the latent space. Consequently, we obtain a powerful alternative to variational autoencoders, which are the preferred choice in modern applications of autoencoders for representation learning with uncertainty. We evaluate our approach qualitatively and quantitatively using a vast experimental campaign on a number of unsupervised learning tasks and show that, in small-data regimes where priors matter, our approach provides state-of-the-art results, outperforming multiple competitive baselines.