Amazon's long-awaited update to its assistant is almost here. About 18 months after the company first previewed the "next-gen Alexa" built with generative AI, it unveiled Alexa, and early access will be available starting in March. Alexa will exist alongside the older Alexa and will cost 20 a month, unless you have a Prime membership, which will make it free to use. The new assistant will come with all the modern upgrades that its contemporaries like the redesigned Siri or Gemini offer, like more conversational interaction, better contextual understanding and the ability to "summarize complex topics" and "make suggestions based on your interests." But it does one thing differently, and it's the way Amazon purports to integrate with third-party apps and the rest of the internet that could set it apart.

Real-time personalization has advanced significantly in recent years, with platforms utilizing machine learning models to predict user preferences based on rich behavioral data on each individual user. Traditional approaches usually rely on embedding-based machine learning models to capture user preferences, and then reduce the final optimization task to nearest-neighbors, which can be performed extremely fast. However, these models struggle to capture complex user behaviors, which are essential for making accurate recommendations. Transformer-based models, on the other hand, are known for their practical ability to model sequential behaviors, and hence have been intensively used in personalization recently to overcome these limitations. However, optimizing recommendations under transformer-based models is challenging due to their complicated architectures. In this paper, we address this challenge by considering a specific class of transformers, showing its ability to represent complex user preferences, and developing efficient algorithms for real-time personalization. We focus on a particular set of transformers, called simple transformers, which contain a single self-attention layer. We show that simple transformers are capable of capturing complex user preferences. We then develop an algorithm that enables fast optimization of recommendation tasks based on simple transformers. Our algorithm achieves near-optimal performance in sub-linear time. Finally, we demonstrate the effectiveness of our approach through an empirical study on datasets from Spotify and Trivago. Our experiment results show that (1) simple transformers can model/predict user preferences substantially more accurately than non-transformer models and nearly as accurately as more complex transformers, and (2) our algorithm completes simple-transformer-based recommendation tasks quickly and effectively.

For me, one of the most surprising things at Amazon's lavish Alexa event earlier this week was what didn't happen. Oh no, Amazon goes big at these events, aiming a firehose of products at the quivering journalists in attendance. The parade goes at a breathless pace, one after another, so fast that you can barely keep up. Uncharacteristically, Amazon held its fire last fall, skipping its usual September event in favor of a smaller, Kindles-only gathering in October, featuring ex-Microsoft exec and new Amazon devices chief Panos Panay. So when Amazon announced it was having an "Alexa-focused" event this week, I braced myself.

The keyword for the iPhone 16e seems to be "compromise." In this episode, Devindra chats with Cherlynn about her iPhone 16e review and try to figure out who this phone is actually for. Also, they dive into Amazon's Alexa event, where we finally learned more about the company's AI-powered voice assistant. Alexa seems useful, but can we trust it? Listen below or subscribe on your podcast app of choice. If you've got suggestions or topics you'd like covered on the show, be sure to email us or drop a note in the comments! And be sure to check out our other podcast, Engadget News! Framework unveils a cheap 2-in-1 laptop and a…modular desktop? Devindra: This week, it's the iPhone 16e, which Cherlynn has reviewed. We're going to get her full thoughts on that thing. And also, Amazon held an AI event this week. We expected a lot of devices, but they spent 75 minutes talking about Alexa plus, which is the AI powered Alexa. Cherlynn: we expected a lot of devices. Cherlynn: one, at least one it's been a while. Devindra: Mr. Panos Panay was there, the father of the service and no devices, just him talking about AI. Cherlynn: Oh, and stay tuned at the end of this episode. Uh, I, we included an interview that I did with, um, the vice president of Alexa to talk more about the new Alexa plus. Devindra: Anyway, folks, if you're enjoying the show, please be sure to subscribe to us on iTunes or your podcaster of choice, leave us a review on iTunes and drop us an email at podcast@engadget.com. You can also join us on our live [00:01:00] stream on Thursday mornings, typically around 11 a. m. Um, you'll see our faces. Sometimes we'll do Q& A and show off devices as well. This week, uh, Sherilyn has the iPhone 16e, which is the least, um, impressive thing to show off. It's just like, Hey, you have an iPhone from 10 years ago, five, a while ago, Devindra: last, was there a single camera back iPhone? Cherlynn: Oh God, before that was 11. So, you know, it's like a flashback. So let's talk about this thing, Sherlynn. And I checked out your review. First of all, you gave it a really, um, I think serviceable score. Your title is what's your acceptable compromise. And really when we were talking about it last week, it really was like compromise seemed like the key word. The thing we kept coming back to was like just one camera, no mag safe, no fast wireless charging. What are your overall thoughts on this thing? Cherlynn: I mean, so that headline is like all thanks to our EIC, Aaron [00:02:00]Souppouris, because I was like, where, where do I go from here? How do I, so, so he's right. It is like, instead of what's in your wallet, it's like, what are you willing to take out your wallet? I'll tell you the story. So yesterday I was at the Amazon devices and services event where there were no devices and A bunch of other reporters had gathered and we were all like, you know, the, like, review's going up soon, right?

In Tuesday's newsletter, I laid out how to watch (and what to expect from) Amazon's Alexa press event. But aside from unveiling what Alexa will be capable of, there was no silly hardware and no upgraded Echos, but lots of demos. We learned Alexa will be included with an Amazon Prime subscription, and the company will also offer the enhanced digital assistant separately, for 20 per month. Meanwhile, Apple's new entry-level iPhone, the 16e, launches online and in stores today. The 599 phone is arguably 100 too expensive, but it packs a processor that can deliver Apple Intelligence to the masses.

The new Alexa is powered by a more responsive AI. (iStock) Amazon is taking Alexa to the next level with the help of AI. Amazon just announced Alexa, an updated assistant powered by generative AI. The idea is to make Alexa more human, so she can help you control all your devices and get more done. The U.S. Alexa launch is set to happen over the next few weeks, and will start with the Echo Show 8, 10, 15, and 21 devices. It can have more in-depth conversations, understand colloquial expressions and think through complex ideas.

In this short paper we address issues related to building multimodal AI systems for human performance support in manufacturing domains. We make two contributions: we first identify challenges of participatory design and training of such systems, and secondly, to address such challenges, we propose the ACE paradigm: "Action and Control via Explanations". Specifically, we suggest that LLMs can be used to produce explanations in the form of human interpretable "semantic frames", which in turn enable end users to provide data the AI system needs to align its multimodal models and representations, including computer vision, automatic speech recognition, and document inputs. ACE, by using LLMs to "explain" using semantic frames, will help the human and the AI system to collaborate, together building a more accurate model of humans activities and behaviors, and ultimately more accurate predictive outputs for better task support, and better outcomes for human users performing manual tasks.

--The growth of Educational T echnology (EdT ech) has enabled highly personalized learning experiences through Artificial Intelligence (AI)-based recommendation systems tailored to each student's needs. However, these systems can unintentionally introduce biases, potentially limiting fair access to learning resources. This study presents a recommendation system for K-12 students, combining graph-based modeling and matrix factorization to provide personalized suggestions for extracurricular activities, learning resources, and volunteering opportunities. T o address fairness concerns, the system includes a framework to detect and reduce biases by analyzing feedback across protected student groups. This work highlights the need for continuous monitoring in educational recommendation systems to support equitable, transparent, and effective learning opportunities for all students. I NTRODUCTION The rapid advancement of Educational Technology (EdTech) has significantly reshaped traditional learning environments, enabling the delivery of personalized educational experiences tailored to individual students' needs. According to the U.S. Department of Education Office of Educational Technology, leveraging AI-based modern educational technologies has been pivotal in providing personalized pathways for learning, supporting adaptive and individualized instruction, and enhancing student engagement through innovative digital solutions 1 . This trend toward personalization in education underscores the importance of leveraging advanced recommendation systems to support student exploration and growth.

This paper provides a theoretical analysis of a new learning problem for recommender systems where users provide feedback by comparing pairs of items instead of rating them individually. We assume that comparisons stem from latent user and item features, which reduces the task of predicting preferences to learning these features from comparison data. Similar to the classical matrix factorization problem, the main challenge in this learning task is that the resulting loss function is nonconvex. Our analysis shows that the loss function exhibits (restricted) strong convexity near the true solution, which ensures gradient-based methods converge exponentially, given an appropriate warm start. Importantly, this result holds in a sparse data regime, where each user compares only a few pairs of items. Our main technical contribution is to extend certain concentration inequalities commonly used in matrix completion to our model. Our work demonstrates that learning personalized recommendations from comparison data is computationally and statistically efficient.

Conversational assistants often require a question rewriting algorithm that leverages a subset of past interactions to provide a more meaningful (accurate) answer to the user's question or request. However, the exact rewriting approach may often depend on the use case and application-specific tasks supported by the conversational assistant, among other constraints. In this paper, we systematically investigate two different approaches, denoted as rewriting and fusion, on two fundamentally different generation tasks, including a text-to-text generation task and a multimodal generative task that takes as input text and generates a visualization or data table that answers the user's question. Our results indicate that the specific rewriting or fusion approach highly depends on the underlying use case and generative task. In particular, we find that for a conversational question-answering assistant, the query rewriting approach performs best, whereas for a data analysis assistant that generates visualizations and data tables based on the user's conversation with the assistant, the fusion approach works best. Notably, we explore two datasets for the data analysis assistant use case, for short and long conversations, and we find that query fusion always performs better, whereas for the conversational text-based question-answering, the query rewrite approach performs best.