You are in an era where you can see the fast evolution of technology from being reactive to proactive. You are witness to the time when computers are slowly taking up space in every aspect of your life. You will have some form of electronic intelligence around you. Why is the song you hear on your favourite music app driven by algorithms that use artificial intelligence? What is this artificial intelligence, and in what other ways does this technology help?

The top-k recommendation is a fundamental task in recommendation systems which is generally learned by comparing positive and negative pairs. The Contrastive Loss (CL) is the key in contrastive learning that has received more attention recently and we find it is well suited for top-k recommendations. However, it is a problem that CL treats the importance of the positive and negative samples as the same. On the one hand, CL faces the imbalance problem of one positive sample and many negative samples. On the other hand, positive items are so few in sparser datasets that their importance should be emphasized. Moreover, the other important issue is that the sparse positive items are still not sufficiently utilized in recommendations. So we propose a new data augmentation method by using multiple positive items (or samples) simultaneously with the CL loss function. Therefore, we propose a Multi-Sample based Contrastive Loss (MSCL) function which solves the two problems by balancing the importance of positive and negative samples and data augmentation. And based on the graph convolution network (GCN) method, experimental results demonstrate the state-of-the-art performance of MSCL. The proposed MSCL is simple and can be applied in many methods. We will release our code on GitHub upon the acceptance.

A machine intelligence pipeline usually consists of six components: problem, representation, model, loss, optimizer and metric. Researchers have worked hard trying to automate many components of the pipeline. However, one key component of the pipeline--problem definition--is still left mostly unexplored in terms of automation. Usually, it requires extensive efforts from domain experts to identify, define and formulate important problems in an area. However, automatically discovering research or application problems for an area is beneficial since it helps to identify valid and potentially important problems hidden in data that are unknown to domain experts, expand the scope of tasks that we can do in an area, and even inspire completely new findings. This paper describes Problem Learning, which aims at learning to discover and define valid and ethical problems from data or from the machine's interaction with the environment. We formalize problem learning as the identification of valid and ethical problems in a problem space and introduce several possible approaches to problem learning. In a broader sense, problem learning is an approach towards the free will of intelligent machines. Currently, machines are still limited to solving the problems defined by humans, without the ability or flexibility to freely explore various possible problems that are even unknown to humans. Though many machine learning techniques have been developed and integrated into intelligent systems, they still focus on the means rather than the purpose in that machines are still solving human defined problems. However, proposing good problems is sometimes even more important than solving problems, because a good problem can help to inspire new ideas and gain deeper understandings. The paper also discusses the ethical implications of problem learning under the background of Responsible AI.

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Deciding which of Netflix's thousands of shows and movies to grant your all-important click can be a paralyzing task for many of us, so there was something brilliant, or cynical--or in all likelihood, both--about the streaming service coming out with a show called Clickbait. It's announcing itself as potentially dishonest and exploitative and daring you to click anyway, and the gambit clearly worked: As of Tuesday, the limited series, which premiered on the streaming service last week, was topping Netflix's most-watched list. Whether you don't want to give Clickbait the satisfaction of your click or you've already clicked many times over, let's talk about it--and there is a lot to talk about--spoilers and all. In the first episode of the eight-episode series, a video surfaces online of Nick Brewer (Adrian Grenier), an improbably perfect husband and father, being held hostage and holding a series of signs: One says he abuses women. Another says that if the video gets to 5 million views, he will die.

The voices on Amazon's Alexa, Google Assistant and other AI assistants are far ahead of old-school GPS devices, but they still lack the rhythms, intonation and other qualities that make speech sound, well, human. NVIDIA has unveiled new research and tools that can capture those natural speech qualities by letting you train the AI system with your own voice, the company announced at the Interspeech 2021 conference. To improve its AI voice synthesis, NVIDIA's text-to-speech research team developed a model called RAD-TTS, a winning entry at an NAB broadcast convention competition to develop the most realistic avatar. The system allows an individual to train a text-to-speech model with their own voice, including the pacing, tonality, timbre and more. Another RAD-TTS feature is voice conversion, which lets a user deliver one speaker's words using another person's voice.

If you missed the previous sale earlier this month, you have another chance to grab one of Amazon's Echo Show 5s for less. Both the first- and second-gen versions of the compact smart display are on sale right now, with the original Echo Show 5 going for $45 and the updated version, which came out earlier this year, only $10 more. Unlike the new Echo Show 8, the Show 5s are designed to fit neatly on your nightstand and act as smart alarm clocks. Each have a 5.5-inch, 960 x 480 touchscreen that shows the time along with things like weather forecasts, news headlines and more. They are also capable of making video calls thanks to their built-in camera and mics, and if you have multiple Alexa devices in your home, the Show 5s can be part of your larger intercom system.

Has been programmed to do, reticently.

We investigate whether model extraction can be used to "steal" the weights of sequential recommender systems, and the potential threats posed to victims of such attacks. This type of risk has attracted attention in image and text classification, but to our knowledge not in recommender systems. We argue that sequential recommender systems are subject to unique vulnerabilities due to the specific autoregressive regimes used to train them. Unlike many existing recommender attackers, which assume the dataset used to train the victim model is exposed to attackers, we consider a data-free setting, where training data are not accessible. Under this setting, we propose an API-based model extraction method via limited-budget synthetic data generation and knowledge distillation. We investigate state-of-the-art models for sequential recommendation and show their vulnerability under model extraction and downstream attacks. We perform attacks in two stages. (1) Model extraction: given different types of synthetic data and their labels retrieved from a black-box recommender, we extract the black-box model to a white-box model via distillation. (2) Downstream attacks: we attack the black-box model with adversarial samples generated by the white-box recommender. Experiments show the effectiveness of our data-free model extraction and downstream attacks on sequential recommenders in both profile pollution and data poisoning settings.

We consider the query recommendation problem in closed loop interactive learning settings like online information gathering and exploratory analytics. The problem can be naturally modelled using the Multi-Armed Bandits (MAB) framework with countably many arms. The standard MAB algorithms for countably many arms begin with selecting a random set of candidate arms and then applying standard MAB algorithms, e.g., UCB, on this candidate set downstream. We show that such a selection strategy often results in higher cumulative regret and to this end, we propose a selection strategy based on the maximum utility of the arms. We show that in tasks like online information gathering, where sequential query recommendations are employed, the sequences of queries are correlated and the number of potentially optimal queries can be reduced to a manageable size by selecting queries with maximum utility with respect to the currently executing query. Our experimental results using a recent real online literature discovery service log file demonstrate that the proposed arm selection strategy improves the cumulative regret substantially with respect to the state-of-the-art baseline algorithms.