Check out all the on-demand sessions from the Intelligent Security Summit here. As the world increasingly relies on technology, the way we shop has also undergone a significant transformation. Gone are the days of physically visiting a store to make a purchase -- now, we can shop from the comfort of our homes, thanks to ecommerce. However, even ecommerce-based shopping is set to change with the emergence of AI-powered conversational commerce. In retail, artificial intelligence is quickly becoming a widely used tool to provide more efficient and personalized customer service.

Abstract: Transformer-based sequential recommenders are very powerful for capturing both short-term and long-term sequential item dependencies. This is mainly attributed to their unique self-attention networks to exploit pairwise item-item interactions within the sequence. However, real-world item sequences are often noisy, which is particularly true for implicit feedback. For example, a large portion of clicks do not align well with user preferences, and many products end up with negative reviews or being returned. As such, the current user action only depends on a subset of items, not on the entire sequences.

Researchers at the universities of California, Virginia, and Microsoft have devised a new poisoning attack that could trick AI-based coding assistants into suggesting dangerous code. Named'Trojan Puzzle,' the attack stands out for bypassing static detection and signature-based dataset cleansing models, resulting in the AI models being trained to learn how to reproduce dangerous payloads. Given the rise of coding assistants like GitHub's Copilot and OpenAI's ChatGPT, finding a covert way to stealthily plant malicious code in the training set of AI models could have widespread consequences, potentially leading to large-scale supply-chain attacks. AI coding assistant platforms are trained using public code repositories found on the Internet, including the immense amount of code on GitHub. Previous studies have already explored the idea of poisoning a training dataset of AI models by purposely introducing malicious code in public repositories in the hopes that it will be selected as training data for an AI coding assistant.

Matrix factorization-based recommender system is in effect an angle preserving dimensionality reduction technique. Since the frequency of items follows power-law distribution, most vectors in the original dimension of user feature vectors and item feature vectors lie on the same hyperplane. However, it is very difficult to reconstruct the embeddings in the original dimension analytically, so we reformulate the original angle preserving dimensionality reduction problem into a distance preserving dimensionality reduction problem. We show that the geometric shape of input data of recommender system in its original higher dimension are distributed on co-centric circles with interesting properties, and design a paraboloid-based matrix factorization named ParaMat to solve the recommendation problem. In the experiment section, we compare our algorithm with 8 other algorithms and prove our new method is the most fair algorithm compared with modern day recommender systems such as ZeroMat and DotMat Hybrid.

We're past the point where utilizing the latest technology is confined to big businesses with budgets to spare. The pandemic with its accompanying mandated lockdowns and changes in the demands and requirements of customers and markets are forcing companies to adapt to digital transformation. Put bluntly, those who want to remain in business have to keep up with the latest digital transformation trends. Some of them might already be familiar because they belong to innovations that have been in development for a long time. However, most will soon be ready for application and will be taking center stage in 2021. So we will be detailing each one to help you understand how these trends can affect your business in the coming years. With innovations being developed left and right, technological evolution comes into play. New technologies radically transform our lives into something that seemed unthinkable in the old times. However, it's not just our personal lives that are being altered by modernization. Businesses are also grabbing what they can when it comes to technological advancement. With the onset of the pandemic, they have to leverage new technologies to remain relevant in the digital age.

I have been interested in Artificial Intelligence (AI) for a long time, ever since I started learning about technology, design, media, and gaming. In a previous post, I introduced Smart Home Technologies. Some subscribers asked my opinion about AI, so I decided to create this short post to respond to them. AI refers to the ability of computers or machines to perform tasks that would typically require human intelligence, such as learning, problem-solving, decision-making, and language understanding. Recently, I have come across different types of AI, including narrow AI and general AI.

Session-based Recommendation (SBR) is to predict users' next interested items based on their previous browsing sessions. Existing methods model sessions as graphs or sequences to estimate user interests based on their interacted items to make recommendations. In recent years, graph-based methods have achieved outstanding performance on SBR. However, none of these methods consider temporal information, which is a crucial feature in SBR as it indicates timeliness or currency. Besides, the session graphs exhibit a hierarchical structure and are demonstrated to be suitable in hyperbolic geometry. But few papers design the models in hyperbolic spaces and this direction is still under exploration. In this paper, we propose Time-aware Hyperbolic Graph Attention Network (TA-HGAT) - a novel hyperbolic graph neural network framework to build a session-based recommendation model considering temporal information. More specifically, there are three components in TA-HGAT. First, a hyperbolic projection module transforms the item features into hyperbolic space. Second, the time-aware graph attention module models time intervals between items and the users' current interests. Third, an evolutionary loss at the end of the model provides an accurate prediction of the recommended item based on the given timestamp. TA-HGAT is built in a hyperbolic space to learn the hierarchical structure of session graphs. Experimental results show that the proposed TA-HGAT has the best performance compared to ten baseline models on two real-world datasets.

Developers and teams building avatars and virtual assistants can now register to join the early-access program for NVIDIA Omniverse Avatar Cloud Engine (ACE), a suite of cloud-native AI microservices that make it easier to build and deploy intelligent virtual assistants and digital humans at scale. Omniverse ACE eases avatar development, delivering the AI building blocks necessary to add intelligence and animation to any avatar, built on virtually any engine and deployed on any cloud. These AI assistants can be designed for organizations across industries, enabling organizations to enhance existing workflows and unlock new business opportunities. ACE is one of several generative AI applications that will help creators accelerate the development of 3D worlds and the metaverse. Members who join the program will receive access to the prerelease versions of NVIDIA's AI microservices, as well as the tooling and documentation needed to develop cloud-native AI workflows for interactive avatar applications.

With the recent release of ChatGPT, AI-inspired modern art and the infusion of Elon Musk…everywhere, it's no surprise that AI continues to dominate the discourse. As the use cases for artificial intelligence grow, it's inevitable that we'll discover more ways it can improve our lives. And the space has plenty of oomph: The global AI software market is expected to reach $22.6 billion by 2025. With AI's popularity on the rise, we thought we'd explore a few especially promising applications of artificial intelligence. AI, or artificial intelligence, is a complex topic with many layers.

With Christmas over and'cuffing season' drawing to a close, happy couples who spent the end of 2022 snuggled around a fireplace may have finally had their day. Singletons, therefore, are rising up, and today dating apps will see their busiest day of the year as swathes open them up for a swiping session. This has historically been the first Sunday in January and, as New Year's Day fell on a Sunday this year, this makes today the official'Dating Sunday' of 2023. Tinder has revealed that Dating Sunday sees 30 per cent more matches being made than usual on its app. Sundays in January are known to be particularly busy on dating apps like Tinder, Bumble and Hinge, as many singletons start to follow up on their New Year's Resolutions (stock image) Tinder is the world's most popular dating app, and has been downloaded more than 450 million times since launching back in 2012.