Wine recommendation apps are now using AI to help better match people with a bottle they will like.

When you were a kid, you probably had piano lessons at some point. Back then, it may not have seemed cool enough to stick with, but in adulthood, you might be wishing you had some musical skill. Skoove is an innovative new way to learn the piano, trusted by more than one million people worldwide. This interactive program offers lessons for beginners, intermediate, and advanced players, utilizing artificial intelligence to recognize the notes you play and give you real-time feedback. The system learns your weaknesses and plans your next exercises, giving you a fully personalized plan to learn and practice notes, chords, and techniques.

Brits have flocked to Somerset in their thousands this weekend to enjoy Glastonbury Festival in the sun. Elton John and Lana Del Rey among numerous famous faces to perform, with fans eagerly camping out across the humongous field. But amidst a so-called'artificial intelligence (AI) revolution', will Glastonbury always stay the same? Chart-topping artists like Megan Thee Stallion and Lil Nas X are already using the power of virtual reality (VR) to put on a show, with fans watching with just a headset. And ABBA have raked in millions after selling out lifelike'hologram' performances of their younger selves.

The reconstruction of micro-Doppler signatures of human movements is a key enabler for fine-grained activity recognition with radio-frequency sensing. In this work, we focus on Joint Communication and Sensing (JCS) systems where, unlike in dedicated radar sensing systems, a suitable tradeoff between sensing accuracy and communication overhead has to be attained. It follows that the micro-Doppler has to be reconstructed from sparse and noisy channel estimates obtained from communication packets, limiting as much as possible the transmission of additional probing signals for the purpose of sensing. Existing approaches exploit compressed sensing, but produce very poor reconstructions when only a few channel measurements are available, which is often the case in real communication patterns. In addition, the large number of iterations they need to converge hinders their use in real-time systems. Here, we present STAR, a lightweight neural network that combines a single unrolled iterative hard-thresholding layer with an attention mechanism. Our new approach exploits the temporal correlation of the micro-Doppler to accurately reconstruct microDoppler sequences from human movement even from very sparse channel measurements. In doing so, it combines model-based and data-driven approaches into an interpretable and low-complexity architecture, which is amenable to real-time implementations. We evaluate STAR on a public JCS dataset of 60 GHz IEEE 802.11ay channel measurements of human activity traces. Experimental results show that it substantially outperforms state-of-the-art solutions in terms of the reconstructed microDoppler quality. Remarkably, STAR enables human activity recognition with satisfactory accuracy even with 90%-sparse channel measurements, for which existing techniques fail.

One impressive emergent capability of large language models (LLMs) is generation of code, including Structured Query Language (SQL) for databases. For the task of converting natural language text to SQL queries, Text-to-SQL, adaptation of LLMs is of paramount importance, both in in-context learning and fine-tuning settings, depending on the amount of adaptation data used. In this paper, we propose an LLM-based Text-to-SQL model SQL-PaLM, leveraging on PaLM-2, that pushes the state-of-the-art in both settings. Few-shot SQL-PaLM is based on an execution-based self-consistency prompting approach designed for Text-to-SQL, and achieves 77.3% in test-suite accuracy on Spider, which to our best knowledge is the first to outperform previous state-of-the-art with fine-tuning by a significant margin, 4%. Furthermore, we demonstrate that the fine-tuned SQL-PALM outperforms it further by another 1%. Towards applying SQL-PaLM to real-world scenarios we further evaluate its robustness on other challenging variants of Spider and demonstrate the superior generalization capability of SQL-PaLM. In addition, via extensive case studies, we demonstrate the impressive intelligent capabilities and various success enablers of LLM-based Text-to-SQL.

The rapid advancement in machine learning has led to a surge in automatic data generation, making it increasingly challenging to differentiate between naturally or human-generated data and machine-generated data. Despite these advancements, the generation of creative data remains a challenge. This paper aims to investigate and comprehend the essence of creativity, both in general and within the context of natural language generation. We review various approaches to creative writing devices and tasks, with a specific focus on the generation of poetry. We aim to shed light on the challenges and opportunities in the field of creative data generation.

Large language models (LLMs) that have been trained on multilingual but not parallel text exhibit a remarkable ability to translate between languages. We probe this ability in an in-depth study of the pathways language model (PaLM), which has demonstrated the strongest machine translation (MT) performance among similarly-trained LLMs to date. We investigate various strategies for choosing translation examples for few-shot prompting, concluding that example quality is the most important factor. Using optimized prompts, we revisit previous assessments of PaLM's MT capabilities with more recent test sets, modern MT metrics, and human evaluation, and find that its performance, while impressive, still lags that of state-of-the-art supervised systems. We conclude by providing an analysis of PaLM's MT output which reveals some interesting properties and prospects for future work.

Your eyes can reveal more than you might think, as researchers can now use computer vision technology to reconstruct 3D images of a scene from the reflections on a person's eyeballs. Jia-Bin Huang and his colleagues at the University of Maryland, College Park, developed a computer vision model that takes between five and 15 digital photographs from different angles of an individual's face while they look at a scene, and reconstructs that scene from the reflections in their eyes. The method adapts a technique called neural radiance fields (NeRF), which uses neural networks to determine the density and colour of objects the computer "sees". NeRF usually operates by directly looking at a scene, rather than viewing one reflected in a person's eyeballs. Huang's version builds the scene by extrapolating from a square of, on average, 20 by 20 pixels in each eye.

A novelist responds to Jeff Hewitt's "The Big Four v. ORWELL." For the first time in history, a machine is capable of crafting flash fiction stories, poems, parody Bible verses, and spoof My Little Pony episode summaries, to everyone's delight (or horror). Narrative art, once thought the sole province of humans, has been invaded by large language models. Hollywood writers have told me they're terrified that studios will fire them all and fill writers' rooms with robots in a few years. Before we've even had a chance to absorb the fact that the Turing test (used to determine if an artificial intelligence can pass as human) has been demolished, it seems we writers are being handed pink slips.

The digital revolution has led to the digitization of human behavior, creating unprecedented opportunities to understand observable actions on an unmatched scale. Emerging phenomena such as crowdfunding and crowdsourcing have further illuminated consumer behavior while also introducing new behavioral patterns. However, the sheer volume and complexity of this data present significant challenges for marketing researchers and practitioners. Traditional methods used to analyze consumer data fall short in handling the breadth, precision, and scale of emerging data sources. To address this, computational methods have been developed to manage the "big data" associated with consumer behavior, which typically includes structured data, textual data, audial data, and visual data. These methods, particularly machine learning, allow for effective parsing and processing of multi-faceted data. Given these recent developments, this review article seeks to familiarize researchers and practitioners with new data sources and analysis techniques for studying consumer behavior at scale. It serves as an introduction to the application of computational social science in understanding and leveraging publicly available consumer data.