Smart glasses are traditionally long on promise, short on delivery, especially at these sorts of consumer electronics shindigs. Theres always a steady stream of companies promising were on the cusp of having our very own Gary-from-Veep attached to our faces before fading away. The weight of promises Halliday has laid upon the table is a sign of braggadocio, but itll take a while before we know if its deserved or not.Halliday has turned up at CES 2025 in Las Vegas with a pair of eponymous smart glasses filled to the brim with technology. Theres a waveguide display in the right eyecup that will project the equivalent of a 3.5-inch screen into the wearers view. This display is also easy to read in strong light and the company promises the hardware is invisible to onlookers. The company adds the glasses weigh just 35 grams and promise eight hours of battery life on a single charge.HallidayTheres no outward-facing camera, but Halliday says its product comes with a proactive AI assistant, anticipating your needs before you ask. The glasses have built-in microphones that are listening to your conversations, analyzing them and answering prompts as they come up. If you were to wear one of these in a meeting, say, youd be able to ask the system to produce a summary of said meeting immediately afterward. (And yes, we are curious about the privacy implications of such a system.)As well as barking instructions to your glasses, the sides are touch sensitive, but its more likely your main mode of interaction will be with the bundled trackpad ring. You should be able to discreetly control what the AI is pumping to your eyes without attracting attention.HallidayTheres a fairly long list of tasks Halliday says the glasses will be able to grease the wheels for you. As well as listening out for questions in conversation and throwing up answers from the internet, you can use the screen as a hidden teleprompter. It can also translate 40 different languages, offer real-time directions and play music with the accompanying on-screen lyrics.Of course, none of this is anything but sweet words until weve been able to see how this performs in the real world. Halliday says that pre-orders for the glasses will begin at the end of CES, with shipping starting at some point before March 2025. We dont know the price yet, but the company says itll be between $399 and $499.This article originally appeared on Engadget at https://www.engadget.com/wearables/halliday-promises-its-smart-wayfarers-have-a-proactive-ai-assistant-inside-010007688.html?src=rss

Its not as if any period in human history was particularly gentle, but we do seem to live in very stressful times. Maybe its not the stress thats changed, but that were more able than ever to notice, monitor and analyze whats going on in our bodies. Here at CES 2025, startup Eli Health is launching Hormometer, an at-home kit that tests your cortisol and progesterone levels.Hormometer is a wand with a collection tip at one end that you hold in your mouth for 60 seconds. Once its absorbed your spit, youll sit it down on a table for 20 minutes while the cartridges network of built-in microfluidics gets to work. After that, the assay will start displaying lines in a specific color, which can be read and interpreted by your smartphone camera.The app will then be able to, presumably once its established your individual baseline levels, offer helpful suggestions for how to better manage your health. Its likely those will get more detailed over time, as Eli Health says its creating the first data set of its kind. Which, if youre unaware, means your hormone levels are likely going into a very large database with all of its users. The company will have to answer many questions about how its planning to protect the privacy of its users, especially in the US.Cortisol, of course, is better known as the stress hormone, which increases when youre in a tough spot. Its cortisol thats behind your fight-or-flight response, spiking your blood sugar to get you ready for fighting or fleeing, suppressing other critical bodily functions to do so. High cortisol levels will weaken your immune system, waste your muscles and prevent bone growth.Progesterone, meanwhile, is crucial for regulating the reproductive system, particularly for those able to become pregnant. Low levels can risk ectopic pregnancy, miscarriage or premature labor. Excessively high levels, meanwhile, can create problems with temperature and mood regulation as well as low fertility. Progesterone is sometimes given to transfeminine individuals as well, where it's believed to play a role in breast development.Its worth noting that saliva-testing gadgets arent particularly new, and weve reported on them in 2005, 2009 and 2016. Whats attractive about this device, and the rest of the current crop of saliva analysis tools, is the easier accessibility. Eli Health isnt stopping at just those two hormones, and says its already working to develop tests for testosterone and estradiol.The first tests will be available to buy in the US and Canada from January 2025, with various subscription plans available, including one where each test will cost $8 if you commit to a whole year.This article originally appeared on Engadget at https://www.engadget.com/wearables/this-saliva-testing-wand-knows-how-stressed-you-are-010002378.html?src=rss

Samsung has announced its 2025 Neo QLED mini-LED TV lineup at CES.

This year will be a year of change for the U.S. political landscape and that comes with big implications for the direction of travel for global AI regulation.

Choosing skincare products often comes down to a matter of trial and error.A tabletop device unveiled by LOral, called the LOral Cell BioPrint, is intended to change that. It will offer visitors to LOral-stocked beauty counters the ability to analyze their skin for signs of biological aging, responsiveness to particular active ingredients, and likelihood of conditions like dark spots or enlarged pores. As a result, the company claims, people will be able to choose the skincare products that are most effective for them.We have very good imaging systems that can tell you about what is on the surface of your skin, but what is on the surface of your skin has already happened, says Guive Balooch, global managing director of augmented beauty and open innovation at LOral Groupe. What matters for the future and for people to really get the right product is to understand their individual biology, and that has been something very challenging to do at scale.The new technology will let shoppers take a quick sample of their skin with a painless sticky tape and deposit it onto a test cartridge along with a drop of test solution, then insert it into the device for an analysis of various protein-based biomarkers. Within a few minutes, the user and an adviser at the beauty counter will be able to see a report on an in-store tablet they can use to guide their skincare product considerations, knowing what products are most likely to be effective and what skin issues may pop up in the future. That way, Balooch says, shoppers can shape their skincare routines to address issues like dark spots or wrinkles well before they actually crop up.[Photo: LOral]The goal is to let people be able to really, for the first time, get to know what they can do to correct the future of their skin health, says Balooch.LOrals experts have spent decades identifying biomarkers that can predict skin health, but it wasnt previously possible to do a real-time analysis or spot them in a sample without a traditional laboratory. The company worked with South Korean company NanoEntek, which has experience creating miniaturized biological analysis tools, to develop the technology, which Balooch says compares favorably to traditional lab-grade equipment. The analysis has been tested on roughly 1,000 different skin types and tones from across the world to ensure its helpful for a wide range of customers.Balooch says LOral intends to deploy the devices to thousands of beauty counters across the globe beginning in about a year. The exact business model, including any cost to the consumer, is yet to be determined. The company plans to offer training to help beauty advisers use the new equipment, though Balooch says its designed to be easy and quick to use. And as LOrals researchers continue to find new biomarkers that can inform skincare decisions, they can be added to the Cell BioPrint analysis, Balooch says.Advisers will also have a role in helping customers choose the right product based on their test results, since theyll also likely consider factors like how long a particular product lasts, thickness, and other factors beyond just the active ingredients, he says.Were making sure that the product recommendation is equally as strong as the assessment, Balooch says. Were working very hard on that so people get the right product.

Tezza is the photo-editing app that thinks it's a fashion brand.

LOral Groupe announced at CES 2025 theLOral Cell BioPrint, a hardware device that provides customized skin analysis in just five minutes.Read More

The development of multimodal large language models (MLLMs) has brought new opportunities in artificial intelligence. However, significant challenges persist in integrating visual, linguistic, and speech modalities. While many MLLMs perform well with vision and text, incorporating speech remains a hurdle. Speech, a natural medium for human interaction, plays an essential role in dialogue systems, yet the differences between modalitiesspatial versus temporal data representationscreate conflicts during training. Traditional systems relying on separate automatic speech recognition (ASR) and text-to-speech (TTS) modules are often slow and impractical for real-time applications.Researchers from NJU, Tencent Youtu Lab, XMU, and CASIA have introduced VITA-1.5, a multimodal large language model that integrates vision, language, and speech through a carefully designed three-stage training methodology. Unlike its predecessor, VITA-1.0, which depended on external TTS modules, VITA-1.5 employs an end-to-end framework, reducing latency and streamlining interaction. The model incorporates vision and speech encoders along with a speech decoder, enabling near real-time interactions. Through progressive multimodal training, it addresses conflicts between modalities while maintaining performance. The researchers have also made the training and inference code publicly available, fostering innovation in the field.Technical Details and BenefitsVITA-1.5 is built to balance efficiency and capability. It uses vision and audio encoders, employing dynamic patching for image inputs and downsampling techniques for audio. The speech decoder combines non-autoregressive (NAR) and autoregressive (AR) methods to ensure fluent and high-quality speech generation. The training process is divided into three stages:Vision-Language Training: This stage focuses on vision alignment and understanding, using descriptive captions and visual question answering (QA) tasks to establish a connection between visual and linguistic modalities.Audio Input Tuning: The audio encoder is aligned with the language model using speech-transcription data, enabling effective audio input processing.Audio Output Tuning: The speech decoder is trained with text-speech paired data, enabling coherent speech outputs and seamless speech-to-speech interactions.These strategies effectively address modality conflicts, allowing VITA-1.5 to handle image, video, and speech data seamlessly. The integrated approach enhances its real-time usability, eliminating common bottlenecks in traditional systems.Results and InsightsEvaluations of VITA-1.5 on various benchmarks demonstrate its robust capabilities. The model performs competitively in image and video understanding tasks, achieving results comparable to leading open-source models. For example, on benchmarks like MMBench and MMStar, VITA-1.5s vision-language capabilities are on par with proprietary models like GPT-4V. Additionally, it excels in speech tasks, achieving low character error rates (CER) in Mandarin and word error rates (WER) in English. Importantly, the inclusion of audio processing does not compromise its visual reasoning abilities. The models consistent performance across modalities highlights its potential for practical applications.ConclusionVITA-1.5 represents a thoughtful approach to resolving the challenges of multimodal integration. By addressing conflicts between vision, language, and speech modalities, it offers a coherent and efficient solution for real-time interactions. Its open-source availability ensures that researchers and developers can build upon its foundation, advancing the field of multimodal AI. VITA-1.5 not only enhances current capabilities but also points toward a more integrated and interactive future for AI systems.Check out the Paper and GitHub Page. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our60k+ ML SubReddit. FREE UPCOMING AI WEBINAR (JAN 15, 2025): Boost LLM Accuracy with Synthetic Data and Evaluation IntelligenceJoin this webinar to gain actionable insights into boosting LLM model performance and accuracy while safeguarding data privacy.The post VITA-1.5: A Multimodal Large Language Model that Integrates Vision, Language, and Speech Through a Carefully Designed Three-Stage Training Methodology appeared first on MarkTechPost.

Enhancing user experiences and boosting retention using recommendation systems is an effective and ever-evolving strategy used by many industries, such as e-commerce, streaming services, social media, etc. These systems must analyze complex relationships between users, items, and contextual factors to suggest precisely what the user might want. However, the existing recommendation systems are static, relying on substantial historical data to build connections effectively. In cold start scenarios, which are heavily prevalent, mapping the relationships becomes impossible, weakening these systems even further. Researchers from the Shanghai Jiao Tong University and Huawei Noahs Ark Lab have introduced AutoGraph to address these issues. This framework automatically builds graphs incorporating dynamic adjustments and leverages LLMs for better contextual understanding.Commonly, graph-based recommendation systems are employed. Current systems, however, require people to set the features manually and their connections in a graph, consuming much time. Also, rules are set beforehand, limiting how these graphs could adapt. Incorporating unstructured data, which potentially has rich semantic information about user preferences, is also a significant issue. Therefore, there is a need for a new method that can resolve the data sparsity issues and the failure to capture nuanced relationships and adjust to user preferences in real-time.AutoGraph is an innovative framework to enhance recommendation systems leveraging Large Language Models (LLMs) and Knowledge Graphs (KGs). The methodology of AutoGraph is based on these features:Utilization of Pre-trained LLMs: The framework leverages pre-trained LLMs to analyze user input. It can draw relationships based on the analysis of natural language, even those that are apparently hidden.Knowledge Graph Construction: After the relationship extraction, LLMs generate graphs, which can be seen as structured representations of user preferences. Algorithms optimize such graphs to remove less relevant connections in an attempt to maximize the quality of the graph in its entirety.Integration with Graph Neural Networks (GNNs): The final step of the proposed method is to integrate the created knowledge graph with regular Graph Neural Networks. GNNs can provide more accurate recommendations by using both node features and graph structure, and they are sensitive to personal preferences and more significant trends among users.To evaluate the proposed frameworks efficacy, authors benchmarked against traditional recommendation techniques using e-commerce and streaming services datasets. There was a significant gain in the precision of recommendations, which shows that the framework is competent enough to give relevant recommendations. The proposed method had improved scalability for dealing with large datasets. The framework demonstrated reduced computational requirements compared to traditional graph construction approaches. Process automation, along with the use of advanced algorithms, helped in lowering resource usage without compromising the quality of the results.The Autograph framework represents a significant leap forward in recommendation systems. Automating graph construction with LLMs addresses long-standing scalability, adaptability, and contextual awareness challenges. The frameworks success demonstrates the transformative potential of integrating LLMs into graph-based systems, setting a new benchmark for future research and applications in personalized recommendations. AutoGraph opens new avenues for personalized user experiences in diverse domains by automating the construction of dynamic, context-aware recommendation graphs. This innovation highlights the growing role of LLMs in addressing real-world challenges, revolutionizing how we approach recommendation systems.Check out the Paper. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our60k+ ML SubReddit. Afeerah Naseem+ postsAfeerah Naseem is a consulting intern at Marktechpost. She is pursuing her B.tech from the Indian Institute of Technology(IIT), Kharagpur. She is passionate about Data Science and fascinated by the role of artificial intelligence in solving real-world problems. She loves discovering new technologies and exploring how they can make everyday tasks easier and more efficient. Follow us on X (Twitter) to get regular AI Research and Dev Updates here...