When you purchase through links in our articles, we may earn a small commission. Where were the flying drone cams and Alexa robot dogs? Amazon's fall hardware events can usually be counted on for a couple of things: an avalanche of new devices, and at least one new product that's genuinely nuts. Take the Amazon Astro, a two-wheeled Alexa-powered robot that was rolled out--literally--during Amazon's fall 2021 hardware event. A year prior, there was the Ring Always Home Cam, an indoor airborne drone that could patrol your home in a preset flight pattern.

Demis Hassabis, CEO of Google DeepMind, says that reaching artificial general intelligence or AGI--a fuzzy term typically used to describe machines with human-like cleverness--will mean honing some of the nascent abilities found in Google's flagship Gemini models. Google announced a slew of AI upgrades and new products at its annual I/O event today in Mountain View, California. The search giant revealed upgraded versions of Gemini Flash and Gemini Pro, Google's fastest and most capable models, respectively. Hassabis said that Gemini Pro outscores other models on LMArena, a widely used benchmark for measuring the abilities of AI models. Hassabis showed off some experimental AI offerings that reflect a vision for artificial intelligence that goes far beyond the chat window.

--Consumers often heavily rely on online product reviews, analyzing both quantitative ratings and textual descriptions to assess product quality. However, existing research hasn't adequately addressed how to systematically encourage the creation of comprehensive reviews that capture both customers sentiment and detailed product feature analysis. This paper presents CPR, a novel methodology that leverages the power of Large Language Models (LLMs) and T opic Modeling to guide users in crafting insightful and well-rounded reviews. Our approach employs a three-stage process: first, we present users with product-specific terms for rating; second, we generate targeted phrase suggestions based on these ratings; and third, we integrate user-written text through topic modeling, ensuring all key aspects are addressed. We evaluate CPR using text-to-text LLMs, comparing its performance against real-world customer reviews from Walmart. Our results demonstrate that CPR effectively identifies relevant product terms, even for new products lacking prior reviews, and provides sentiment-aligned phrase suggestions, saving users time and enhancing reviews quality. Quantitative analysis reveals a 12.3% improvement in BLEU score over baseline methods, further supported by manual evaluation of generated phrases. We conclude by discussing potential extensions and future research directions. I NTRODUCTION Product reviews play a crucial role for retailers, as they help build trust among potential customers by providing social proof. They influence purchase decisions [7], [9], [19], [25] by offering information on the quality and suitability of the product. Reviews also provide valuable feedback for retailers, allows them to improve their products and enhance customer satisfaction. Furthermore, product reviews contribute to product search optimization efforts [8], giving retailers a competitive advantage and fostering customer engagement and loyalty. Product review phrase suggestion is a sub-task of text-to-text generation in natural language processing (NLP). Online shopping is increasingly popular. However, customers often lack the motivation to write constructive reviews.

SHOPPING – Contains affiliated content. Products featured in this Shopping Finder article are selected by our shopping writers. If you make a purchase using links on this page, Dailymail.co.uk will earn an affiliate commission. The day Apple fans have been waiting for is nearly here. After many months of rumours, the tech giant is finally due to unveil a slew of new products on Wednesday.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. There are myriad reasons to loathe Elon Musk, the CEO of Tesla, who has become a top ally of Donald Trump. OG haters have long accused Musk of endangering road users by exaggerating the capabilities of Tesla's navigation assistance systems, misleadingly named Autopilot and Full-Self Driving. The ranks of the angry have steadily grown, fueled by Musk's habit of amplifying trans-bashing and antisemitism as well as his demolition of Twitter. Now, as Musk cozies up to extremists across Europe, wields the Department of Government Efficiency as a wrecking ball against the federal government, and generally acts as an unelected leader, the furor is reaching a fever pitch.

The fast fashion industry's insatiable demand for new styles and rapid production cycles has led to a significant environmental burden. Overproduction, excessive waste, and harmful chemicals have contributed to the negative environmental impact of the industry. To mitigate these issues, a paradigm shift that prioritizes sustainability and efficiency is urgently needed. Integrating learning-based predictive analytics into the fashion industry represents a significant opportunity to address environmental challenges and drive sustainable practices. By forecasting fashion trends and optimizing production, brands can reduce their ecological footprint while remaining competitive in a rapidly changing market. However, one of the key challenges in forecasting fashion sales is the dynamic nature of consumer preferences. Fashion is acyclical, with trends constantly evolving and resurfacing. In addition, cultural changes and unexpected events can disrupt established patterns. This problem is also known as New Fashion Products Performance Forecasting (NFPPF), and it has recently gained more and more interest in the global research landscape. Given its multidisciplinary nature, the field of NFPPF has been approached from many different angles. This comprehensive survey wishes to provide an up-to-date overview that focuses on learning-based NFPPF strategies. The survey is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological flow, allowing for a systematic and complete literature review. In particular, we propose the first taxonomy that covers the learning panorama for NFPPF, examining in detail the different methodologies used to increase the amount of multimodal information, as well as the state-of-the-art available datasets. Finally, we discuss the challenges and future directions.

-- In this paper, we propose a novel reinforcement learning algorithm for inventory management of newly launched products with no historical demand information. The algorithm follows the classic Dyna-Q structure, balancing the model-free and model-based approaches, while accelerating the training process of Dyna-Q and mitigating the model discrepancy generated by the model-based feedback. Based on the idea of transfer learning, warm-start information from the demand data of existing similar products can be incorporated into the algorithm to further stabilize the early-stage training and reduce the variance of the estimated optimal policy. Our approach is validated through a case study of bakery inventory management with real data. The adjusted Dyna-Q shows up to a 23.7% reduction in average daily cost compared with Q-learning, and up to a 77.5% reduction in training time within the same horizon compared with classic Dyna-Q . By using transfer learning, it can be found that the adjusted Dyna-Q has the lowest total cost, lowest variance in total cost, and relatively low shortage percentages among all the benchmarking algorithms under a 30-day testing. I. INTRODUCTION Inventory management is crucial for supply chain operations, overseeing and controlling the order, storage, and usage of goods in business [1]. In inventory management, the cold-start setting refers to predicting demand and formulating appropriate inventory strategies when new products are introduced or new market demands arise due to the lack of historical data [2].

Four iPhones and a new iPad have already been released in the past two months, but now Apple has announced yet another new product. With its'strikingly thin design', the tech giant calls its new hardware the'best in the world' in its category – and it's been built for AI. The company's new 1,299 iMac desktop computer has a 24-inch display, a front-facing 12-megapixel camera and four USB-C ports. It's fitted with the M4 chip that powers AI jobs, meaning it will be able to run Apple Intelligence. Apple Intelligence is the firm's suite of AI software that includes image editing, 'Genmoji' and an integration with ChatGPT. The new iMac comes in'playful' colors – green, yellow, orange, pink, purple, blue and silver.

From a high level, we need something akin to the medical Hippocratic oath, which governs doctors to do no harm. It's for others to decide whether that's regulation or something else, but we need a framing commitment. I often come at things from a narrative place, and I've always been struck by writer Isaac Asimov's Robot series, in which he weaves meditations around how societal principles and protections are included in the laws of robotics on an almost engineered basis. Similarly, we need someone to assert a foundational principle for all of us that AI shouldn't do harm. On balance, at the phase we're in right now, I see far more benefits than any actual realized negatives. I think what's going on in medicine alone should give people a lot of enthusiasm for the positive potential in AI.

Apple is slated to unveil its latest iPhone and a slew of other new hardware on Monday during its biggest product launch event of the year. The event, held at Apple's headquarters in Cupertino, California, features the tagline "It's Glowtime" with the company's logo surrounded by a colorful aura. New colors for the iPhone and other Apple products are rumored to be coming. Apple's fall product launch has become one of the company's most important annual events as it typically showcases a series of new devices and sets the tone for the holiday shopping season, Apple's busiest and most profitable period. These new products are routinely the cause of immense speculation inside the tech industry and among the company's fans.