Could an artificial intelligence (AI) be sentient? A recent opinion piece in the New York Times claims that AIs are not sentient.(1) The article raises an interesting question but does not adequately answer it, in part because it conflates sentience and intelligence and in part because its language is confused. There is no evidence this technology is sentient or conscious -- two words that describe an awareness of the surrounding world. This sentence is little more than a tautology, since in English "conscious" and "aware" mean the same thing.(2)

To make Sequential Recommendation (SR) successful, recent works focus on designing effective sequential encoders, fusing side information, and mining extra positive self-supervision signals. The strategy of sampling negative items at each time step is less explored. Due to the dynamics of users' interests and model updates during training, considering randomly sampled items from a user's non-interacted item set as negatives can be uninformative. As a result, the model will inaccurately learn user preferences toward items. Identifying informative negatives is challenging because informative negative items are tied with both dynamically changed interests and model parameters (and sampling process should also be efficient). To this end, we propose to Generate Negative Samples (items) for SR (GenNi). A negative item is sampled at each time step based on the current SR model's learned user preferences toward items. An efficient implementation is proposed to further accelerate the generation process, making it scalable to large-scale recommendation tasks. Extensive experiments on four public datasets verify the importance of providing high-quality negative samples for SR and demonstrate the effectiveness and efficiency of GenNi.

With recent technological developments, artificial intelligence is quickly evolving and becoming more advanced to offer a higher level of accommodation. It's continued to become more versatile and now has many different uses in all types of businesses. There are a few important facts to understand when it comes to machine learning and AI assistants in business as it's integrated into every industry. Although everyone has heard of AI, not everyone understands its potential and how it can be integrated into different organizations. Artificial intelligence consists of software that mimics human interaction and engages in activities.

Today's Narrow Artificial Intelligence, "AI," is showing up in everything we do including language, vision, robotics, and game playing. Can human-level Artificial General Intelligence, "AGI," be far away?

Tag-aware recommendation is a task of predicting a personalized list of items for a user by their tagging behaviors. It is crucial for many applications with tagging capabilities like last.fm or movielens. Recently, many efforts have been devoted to improving Tag-aware recommendation systems (TRS) with Graph Convolutional Networks (GCN), which has become new state-of-the-art for the general recommendation. However, some solutions are directly inherited from GCN without justifications, which is difficult to alleviate the sparsity, ambiguity, and redundancy issues introduced by tags, thus adding to difficulties of training and degrading recommendation performance. In this work, we aim to simplify the design of GCN to make it more concise for TRS. We propose a novel tag-aware recommendation model named Light Folksonomy Graph Collaborative Filtering (LFGCF), which only includes the essential GCN components. Specifically, LFGCF first constructs Folksonomy Graphs from the records of user assigning tags and item getting tagged. Then we leverage the simple design of aggregation to learn the high-order representations on Folksonomy Graphs and use the weighted sum of the embeddings learned at several layers for information updating. We share tags embeddings to bridge the information gap between users and items. Besides, a regularization function named TransRT is proposed to better depict user preferences and item features. Extensive hyperparameters experiments and ablation studies on three real-world datasets show that LFGCF uses fewer parameters and significantly outperforms most baselines for the tag-aware top-N recommendations.

Designing recommendation systems that serve content aligned with time varying preferences requires proper accounting of the feedback effects of recommendations on human behavior and psychological condition. We argue that modeling the influence of recommendations on people's preferences must be grounded in psychologically plausible models. We contribute a methodology for developing grounded dynamic preference models. We demonstrate this method with models that capture three classic effects from the psychology literature: Mere-Exposure, Operant Conditioning, and Hedonic Adaptation. We conduct simulation-based studies to show that the psychological models manifest distinct behaviors that can inform system design. Our study has two direct implications for dynamic user modeling in recommendation systems. First, the methodology we outline is broadly applicable for psychologically grounding dynamic preference models. It allows us to critique recent contributions based on their limited discussion of psychological foundation and their implausible predictions. Second, we discuss implications of dynamic preference models for recommendation systems evaluation and design. In an example, we show that engagement and diversity metrics may be unable to capture desirable recommendation system performance.

Debdoot Mukherjee is the Chief Data Scientist and Head of AI at Meesho, the Indian origin social commerce platform at the forefront of the boundaryless workplace model that became a norm in the aftermath of the Covid-19 pandemic. Upon completing his postgraduate degree from IIT-Delhi, Mukherjee began his career in the research division at IBM, where he attained expertise in Information Retrieval and Machine Learning techniques. He then journeyed on to work in impactful roles at companies like Hike, Myntra and ShareChat before leading the AI and data science division at Meesho. In an exclusive interview with Analytics India Magazine, Debdoot Mukherjee opened up about his journey into data science, machine learning and everything AI. AIM: What attracted you to this field?

A brand-new area of computer science was initially referred to as "artificial intelligence" in 1955. Many daily jobs are being replaced by artificial intelligence, requiring less human involvement. But what precisely is this new AI technology? AI refers to the process of teaching a computer system to function and think like a human brain. This is often accomplished through reinforcement learning, in which the computer learns from past errors and observed patterns.

Looking at the latest TRDR Pocket video games console, you would never think it hosted one of the most sophisticated AI voice assistants on the market today. The £399 Gameboy-like device, which is the second handheld console developed by British company Go Games, has a boxy, retro appearance, with a shiny aluminium body and a 3.5-inch touchscreen display. It runs Android, giving users access to over 600,000 games and apps from the Google Play Store, including Fortnite and Call of Duty. The original TRDR Pocket, which was released in 2021 in partnership with American rapper and social media influencer Soulja Boy, shipped over 100,000 units. However, the reviews were far from glowing - with some pointing out it was almost identical to the Retroid Pocket, while others complained that the small touch screen made it awkward to type text and play certain games like shooters.

For decades, science fiction authors, futurists, and movie makers alike have been predicting the amazing (and sometimes catastrophic) changes that will arise with the advent of widespread artificial intelligence. So far, AI hasn't made any such crazy waves, and in many ways has quietly become ubiquitous in numerous aspects of our daily lives. From the intelligent sensors that help us take perfect pictures, to the automatic parking features in cars, to the sometimes frustrating personal assistants in smartphones, artificial intelligence of one kind of another is all around us, all the time. While we've yet to create self-aware robots like those that pepper popular movies like 2001: A Space Odyssey and Star Wars, we have made smart and often significant use of AI technology in a wide range of applications that, while not as mind-blowing as androids, still change our day-to-day lives. One place where artificial intelligence is poised to make big changes (and in some cases already is) is in education.