This observation--that to understand Proust's text requires knowledge of various kinds--is not a new one. We came across it before, in the context of the Cyc project. Remember that Cyc was supposed to be given knowledge corresponding to the whole of consensus reality, and the Cyc hypothesis was that this would yield human-level general intelligence. Researchers in knowledge-based AI would be keen for me to point out to you that, decades ago, they anticipated exactly this issue. But it is not obvious that just continuing to refine deep learning techniques will address this problem.

Problems of cooperation--in which agents seek ways to jointly improve their welfare--are ubiquitous and important. They can be found at scales ranging from our daily routines--such as driving on highways, scheduling meetings, and working collaboratively--to our global challenges--such as peace, commerce, and pandemic preparedness. Arguably, the success of the human species is rooted in our ability to cooperate. Since machines powered by artificial intelligence are playing an ever greater role in our lives, it will be important to equip them with the capabilities necessary to cooperate and to foster cooperation. We see an opportunity for the field of artificial intelligence to explicitly focus effort on this class of problems, which we term Cooperative AI. The objective of this research would be to study the many aspects of the problems of cooperation and to innovate in AI to contribute to solving these problems. Central goals include building machine agents with the capabilities needed for cooperation, building tools to foster cooperation in populations of (machine and/or human) agents, and otherwise conducting AI research for insight relevant to problems of cooperation. This research integrates ongoing work on multi-agent systems, game theory and social choice, human-machine interaction and alignment, natural-language processing, and the construction of social tools and platforms. However, Cooperative AI is not the union of these existing areas, but rather an independent bet about the productivity of specific kinds of conversations that involve these and other areas. We see opportunity to more explicitly focus on the problem of cooperation, to construct unified theory and vocabulary, and to build bridges with adjacent communities working on cooperation, including in the natural, social, and behavioural sciences.

Automation, cloud-based systems, internet-enabled devices, API-centric environments -- all of these things within software application development have paved the way for greater enterprise efficiency, productivity and innovation. But they have also opened up new avenues for cybercriminals to target private, sensitive information and compromise the systems that process it. Security pros and hackers tend to stay neck and neck in a race against each other. As new security innovations emerge, hackers crop up almost immediately, finding new ways to get around them. The only way for the good guys to pull ahead in the race is to shift their security and risk management approach from reactive to proactive.

This post is dedicated to John Horton Conway and Tom Fawcett, who recently passed away, for their noted contributions to the field of cellular automata and machine learning. With the advent of fast data streams, real-time machine learning has become a challenging task. They can be affected by the concept drift effect, by which stream learning methods have to detect changes and adapt to these evolving conditions. Several emerging paradigms such as the so-called "Smart Dust", "Utility Fog", "TinyML" or "Swarm Robotics" are in need for efficient and scalable solutions in real-time scenarios. Cellular Automata (CA), as low-bias and robust-to-noise pattern recognition methods with competitive classification performances, meet the requirements imposed by the aforementioned paradigms mainly due to their simplicity and parallel nature.

State-of-the-art deep classifiers are intriguingly vulnerable to universal adversarial perturbations: single disturbances of small magnitude that lead to misclassification of most inputs. This phenomena may potentially result in a serious security problem. Despite the extensive research in this area, there is a lack of theoretical understanding of the structure of these perturbations. In image domain, there is a certain visual similarity between patterns, that represent these perturbations, and classical Turing patterns, which appear as a solution of non-linear partial differential equations and are underlying concept of many processes in nature. In this paper, we provide a theoretical bridge between these two different theories, by mapping a simplified algorithm for crafting universal perturbations to (inhomogeneous) cellular automata, the latter is known to generate Turing patterns. Furthermore, we propose to use Turing patterns, generated by cellular automata, as universal perturbations, and experimentally show that they significantly degrade the performance of deep learning models. We found this method to be a fast and efficient way to create a data-agnostic quasi-imperceptible perturbation in the black-box scenario.

Sensory information can only be used meaningfully in the brain when integrated with and compared with internally generated top-down signals. However, we know little about the brainwide afferents that convey such top-down signals, their information content, and learning-related plasticity. Pardi et al. identified the higher-order thalamus as a major source of top-down input to mouse auditory cortex and investigated a circuit in cortical layer 1 that facilitates plastic changes and flexible responses. These results demonstrate how top-down feedback information can reach cortical areas through a noncortical structure that has received little attention despite its widespread connections with the cortex. Science , this issue p. [844][1] The sensory neocortex is a critical substrate for memory. Despite its strong connection with the thalamus, the role of direct thalamocortical communication in memory remains elusive. We performed chronic in vivo two-photon calcium imaging of thalamic synapses in mouse auditory cortex layer 1, a major locus of cortical associations. Combined with optogenetics, viral tracing, whole-cell recording, and computational modeling, we find that the higher-order thalamus is required for associative learning and transmits memory-related information that closely correlates with acquired behavioral relevance. In turn, these signals are tightly and dynamically controlled by local presynaptic inhibition. Our results not only identify the higher-order thalamus as a highly plastic source of cortical top-down information but also reveal a level of computational flexibility in layer 1 that goes far beyond hard-wired connectivity. [1]: /lookup/doi/10.1126/science.abc2399

Deep learning has proved an effective means to capture the non-linear associations of user preferences. However, the main drawback of existing deep learning architectures is that they follow a fixed recommendation strategy, ignoring users' real time-feedback. Recent advances of deep reinforcement strategies showed that recommendation policies can be continuously updated while users interact with the system. In doing so, we can learn the optimal policy that fits to users' preferences over the recommendation sessions. The main drawback of deep reinforcement strategies is that are based on predefined and fixed neural architectures. To shed light on how to handle this issue, in this study we first present deep reinforcement learning strategies for recommendation and discuss the main limitations due to the fixed neural architectures. Then, we detail how recent advances on progressive neural architectures are used for consecutive tasks in other research domains. Finally, we present the key challenges to fill the gap between deep reinforcement learning and adaptive neural architectures. We provide guidelines for searching for the best neural architecture based on each user feedback via reinforcement learning, while considering the prediction performance on real-time recommendations and the model complexity.

Classical conditioning is a psychological process through which animals or humans pair desired or unpleasant stimuli (e.g., food or a painful experiences) with a seemingly neutral stimulus (e.g., the sound of a bell, the flash of a light, etc.) after these two stimuli are repeatedly presented together. Russian psychologist Ivan Pavlov studied classical conditioning in great depth and introduced the idea of "associative memory," which entails building strong associations between the pleasant/unpleasant and neutral stimuli. Pavlov is renowned for his studies on dogs, in which he gave the animals food after they heard a specific sound for several trials. Interestingly, he observed that the dogs would eventually start salivating (i.e., anticipating the food) after hearing the sound, even if the food had not yet been presented to them. This suggests that they had learned to associate the sound with the arrival of food.

Neural Architecture Search (NAS) automates network architecture engineering. It aims to learn a network topology that can achieve best performance on a certain task. Although most popular and successful model architectures are designed by human experts, it doesn't mean we have explored the entire network architecture space and settled down with the best option. We would have a better chance to find the optimal solution if we adopt a systematic and automatic way of learning high-performance model architectures. Automatically learning and evolving network topologies is not a new idea (Stanley & Miikkulainen, 2002). In recent years, the pioneering work by Zoph & Le 2017 and Baker et al. 2017 has attracted a lot of attention into the field of Neural Architecture Search (NAS), leading to many interesting ideas for better, faster and more cost-efficient NAS methods. As I started looking into NAS, I found this nice survey very helpful by Elsken, et al 2019. They characterize NAS as a system with three major components, which is clean & concise, and also commonly adopted in other NAS papers. The NAS search space defines a set of basic network operations and how operations can be connected to construct valid network architectures.

The shift to cloud networks and a wider attack surface brought about by new working practices during the COVID-19 pandemic have made traditional security strategies unfit for purpose, according to Steven Tee, principal solutions architect at Infoblox, speaking during a session at the Infosecurity Online event. He made the case that there needs to be much greater use of automated tools such as machine learning to effectively detect and combat cyber-attacks in the current age. Tee began by outlining the alarming increase and impact of cybercrime over recent years. "Cybercrime is a problem that either directly or indirectly affects everyone," he said. He noted that the average cost of a data breach in 2019 was almost $4m.