Session-based recommendation has received growing attention recently due to the increasing privacy concern. Despite the recent success of neural session-based recommenders, they are typically developed in an offline manner using a static dataset. However, recommendation requires continual adaptation to take into account new and obsolete items and users, and requires "continual learning" in real-life applications. In this case, the recommender is updated continually and periodically with new data that arrives in each update cycle, and the updated model needs to provide recommendations for user activities before the next model update. A major challenge for continual learning with neural models is catastrophic forgetting, in which a continually trained model forgets user preference patterns it has learned before. To deal with this challenge, we propose a method called Adaptively Distilled Exemplar Replay (ADER) by periodically replaying previous training samples (i.e., exemplars) to the current model with an adaptive distillation loss. Experiments are conducted based on the state-of-the-art SASRec model using two widely used datasets to benchmark ADER with several well-known continual learning techniques. We empirically demonstrate that ADER consistently outperforms other baselines, and it even outperforms the method using all historical data at every update cycle. This result reveals that ADER is a promising solution to mitigate the catastrophic forgetting issue towards building more realistic and scalable session-based recommenders.

We present a dynamic prediction framework for binary sequences that is based on a Bernoulli generalization of the auto-regressive process. Our approach lends itself easily to variants of the standard link prediction problem for a sequence of time dependent networks. Focusing on this dynamic network link prediction/recommendation task, we propose a novel problem that exploits additional information via a much larger sequence of auxiliary networks and has important real-world relevance. To allow discovery of links that do not exist in the available data, our model estimation framework introduces a regularization term that presents a trade-off between the conventional link prediction and this discovery task. In contrast to existing work our stochastic gradient based estimation approach is highly efficient and can scale to networks with millions of nodes. We show extensive empirical results on both actual product-usage based time dependent networks and also present results on a Reddit based data set of time dependent sentiment sequences.

Foreign manipulation campaigns on social media can be spotted by looking at clues in the timing and length of posts and the URLs they contain, researchers have found. From the 2016 US presidential election to Brexit, a growing number of political events are thought to have been targeted by foreign activity on social media platforms such as Facebook, Twitter and Reddit. Now researchers say they have developed an automated machine learning system – a type of artificial intelligence – that can spot such posts, based on their content. "We can use machine learning to automatically identify the content of troll postings and track an online information operation without human intervention," said Dr Meysam Alizadeh, of Princeton University, a co-author of the research. The team say the approach differs from simply detecting bots, which they say is important since such campaigns often include posts by humans.

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Foreign manipulation campaigns on social media can be spotted by looking at features such as the timing and length of posts, and the URLs they contain, researchers have found. From the 2016 US presidential election to Brexit, a growing number of major political events are thought to have been targeted by foreign activity on social media platforms such as Facebook, Twitter and Reddit. Now researchers say they have developed an automated machine learning system – a type of artificial intelligence – that can spot such posts, based on their content. "We can use machine learning to automatically identify the content of troll postings and track an online information operation without human intervention," said Dr Meysam Alizadeh of Princeton University, co-author of the research. The team say the approach differs from simply detecting bots, which they say is important since such campaigns often include posts by humans.

While artificial intelligence capabilities have been evolving, the COVID-19 pandemic has accelerated adoption of these tools and made intelligent machines part of our new normal lives, according to a new report from Capgemini. More than half of the consumers surveyed (54%) use AI daily–compared to just 21% in 2018, the report, "The art of customer-centric artificial intelligence," finds. Sweden, Brazil, and the US have the highest daily interactions with AI. Contactless or non-touch interfaces are finding their way into numerous sectors, the report said. Over three-quarters (77%) of respondents expect to increase the use of touchless interfaces--such as voice assistants and facial recognition--to avoid direct interactions with humans or touchscreens during COVID-19, and 62% will continue to do so post-COVID.

Artificial intelligence (AI) has been successfully applied in numerous scientific domains including biomedicine and healthcare. Here, it has led to several breakthroughs ranging from clinical decision support systems, image analysis to whole genome sequencing. However, training an AI model on sensitive data raises also concerns about the privacy of individual participants. Adversary AIs, for example, can abuse even summary statistics of a study to determine the presence or absence of an individual in a given dataset. This has resulted in increasing restrictions to access biomedical data, which in turn is detrimental for collaborative research and impedes scientific progress. Hence there has been an explosive growth in efforts to harness the power of AI for learning from sensitive data while protecting patients' privacy. This paper provides a structured overview of recent advances in privacy-preserving AI techniques in biomedicine. It places the most important state-of-the-art approaches within a unified taxonomy, and discusses their strengths, limitations, and open problems.

Nvidia and the University of Florida (UF) today announced plans to build the fastest AI supercomputer in academia. By enhancing the capabilities of UF's existing HiPerGator supercomputer with the DGX SuperPod architecture, Nvidia claims the system -- which it expects will be up and running by early 2021 -- will deliver 700 petaflops (one quadrillion floating point operations per second) of performance. Some researchers within the AI community believe that capable computers, in conjunction with reinforcement learning and other techniques, can achieve paradigm-shifting AI advances. A paper recently published by researchers at the Massachusetts Institute of Technology, MIT-IBM Watson AI Lab, Underwood International College, and the University of Brasilia found that deep learning improvements have been "strongly reliant" on increases in compute. And in 2018, OpenAI researchers released an analysis showing that from 2012 to 2018, the amount of compute used in the largest AI training runs grew more than 300,000 times with a 3.5-month doubling time, far exceeding the pace of Moore's law.

Gabriel Guimaraes grew up in Vitória, Brazil, in a yellow house surrounded by star-fruit trees and chicken coops. His father, who wrote software for a local bank, instilled in him an interest in computers. On weekends, when Guimaraes got bored with Nintendo video games, he programmed his own. In grade school, he built a humanoid robot and wrote enough assembly code to make it zip around his home. In Vitória, an island city, his most ambitious peers dreamed of attending university in São Paulo, an hour away by plane.

Researchers from MIT recently collaborated with the University of Brasilia and Yonsei University to estimate the computational limits of deep learning (DL). They stated, "The computational needs of deep learning scale so rapidly that they will quickly become burdensome again." The researchers analysed 1,058 research papers from the arXiv pre-print repository and other benchmark references in order to understand how the performance of deep learning techniques depends on the computational power of several important application areas. They stated, "To understand why DL is so computationally expensive, we analyse its statistical as well as computational scaling in theory. We show DL is not computationally expensive by accident, but by design." They added, "The same flexibility that makes it excellent at modelling the diverse phenomena as well as outperforming the expert models also makes it more computationally expensive in nature.