The next time you conclude a conversation with ChatGPT, it will save what you said to memory, even if you don't ask it explicitly to do so. "We have greatly improved memory in chatgpt -- it can now reference all your past conversations!" OpenAI CEO Sam Altman wrote on Thursday in an X post spotted by The Verge. "This is a surprisingly great feature imo, and it points at something we are excited about: ai systems that get to know you over your life, and become extremely useful and personalized." OpenAI has been working on improving ChatGPT's memory since 2023 when the company began testing custom instructions, a feature that allows users to set preferences that ChatGPT will consider in future conversations.

For much of last year, US Marines conducting training exercises in the waters off South Korea, the Philippines, India, and Indonesia were also running an experiment. The service members in the unit responsible for sorting through foreign intelligence and making their superiors aware of possible local threats were for the first time using generative AI to do it, testing a leading AI tool the Pentagon has been funding. Two officers tell us that they used the new system to help scour thousands of pieces of open-source intelligence--nonclassified articles, reports, images, videos--collected in the various countries where they operated, and that it did so far faster than was possible with the old method of analyzing them manually. Though the US military has been developing computer vision models and similar AI tools since 2017, the use of generative AI--tools that can engage in human-like conversation--represent a newer frontier. The International Energy Agency states in a new report that AI could eventually reduce greenhouse-gas emissions, possibly by much more than the boom in energy-guzzling data center development pushes them up.

The ChatGPT developer OpenAI has countersued Elon Musk, accusing the billionaire of harassment and asking a US federal judge to stop him from "any further unlawful and unfair action" against the company. OpenAI was co-founded by Musk and its chief executive, Sam Altman, in 2015. However, the two men have been at loggerheads for years over its direction as it transitions from a complex non-profit structure into a more traditional for-profit business. Musk sued OpenAI over its restructuring plans about a year ago, accusing it of betraying its foundational mission by putting the pursuit of profit ahead of the benefit of humanity. He dropped the suit in June, but then filed a fresh one in August.

"We still need to validate the sources," says Lowdon. But the unit's commanders encouraged the use of large language models, he says, "because they provide a lot more efficiency during a dynamic situation." The generative AI tools they used were built by the defense-tech company Vannevar Labs, which in November was granted a production contract worth up to 99 million by the Pentagon's startup-oriented Defense Innovation Unit with the goal of bringing its intelligence tech to more military units. The company, founded in 2019 by veterans of the CIA and US intelligence community, joins the likes of Palantir, Anduril, and Scale AI as a major beneficiary of the US military's embrace of artificial intelligence--not only for physical technologies like drones and autonomous vehicles but also for software that is revolutionizing how the Pentagon collects, manages, and interprets data for warfare and surveillance. Though the US military has been developing computer vision models and similar AI tools, like those used in Project Maven, since 2017, the use of generative AI--tools that can engage in human-like conversation like those built by Vannevar Labs--represent a newer frontier.

A demonstration of an artificial intelligence-powered suitcase, designed to assist visually impaired individuals as a robotic alternative to guide dogs, will be conducted at the Osaka Expo, set to open on Sunday. The latest model incorporates generative AI technology, enabling it to describe the surrounding environment through voice feedback. Equipped with a built-in camera and sensors, the suitcase can analyze its surroundings and provide real-time guidance to users. In late January, an AI suitcase was demonstrated at the National Museum of Emerging Science and Innovation, known as Miraikan, in Tokyo. Resembling a regular suitcase, the device activated when Chieko Asakawa, the museum's chief executive director and a key member of the development team, grasped its handle at hip level.

Generative AI presents a profound challenge to traditional notions of human uniqueness, particularly in creativity. Fueled by neural network based foundation models, these systems demonstrate remarkable content generation capabilities, sparking intense debates about authorship, copyright, and intelligence itself. This paper argues that generative AI represents an alternative form of intelligence and creativity, operating through mathematical pattern synthesis rather than biological understanding or verbatim replication. The fundamental differences between artificial and biological neural networks reveal AI learning as primarily statistical pattern extraction from vast datasets crystallized forms of collective human knowledge scraped from the internet. This perspective complicates copyright theft narratives and highlights practical challenges in attributing AI outputs to individual sources. Rather than pursuing potentially futile legal restrictions, we advocate for human AI synergy. By embracing generative AI as a complementary tool alongside human intuition, context, and ethical judgment, society can unlock unprecedented innovation, democratize creative expression, and address complex challenges. This collaborative approach, grounded in realistic understanding of AIs capabilities and limitations, offers the most promising path forward. Additionally, recognizing these models as products of collective human knowledge raises ethical questions about accessibility ensuring equitable access to these tools could prevent widening societal divides and leverage their full potential for collective benefit.

The integration of Generative Artificial Intelligence (GenAI) within the Internet of Things (IoT) is garnering considerable interest. This growing attention stems from the continuous evolution and widespread adoption they are both having individually, enough to spontaneously reshape numerous sectors, including Healthcare, Manufacturing, and Smart Cities. Hence, their increasing popularity has catalyzed further extensive research for understanding the potential of the duo GenAI-IoT, how they interplay, and to which extent their synergy can innovate the state-of-the-art in their individual scenarios. However, despite the increasing prominence of GenAI for IoT Computing, much of the existing research remains focused on specific, narrowly scoped applications. This fragmented approach highlights the need for a more comprehensive analysis of the potential, challenges, and implications of GenAI integration within the broader IoT ecosystem. This survey exactly aims to address this gap by providing a holistic overview of the opportunities, issues, and considerations arising from the convergence of these mainstream paradigms. Our contribution is realized through a systematic literature review following the PRISMA methodology. A comparison framework is presented, and well-defined research questions are outlined to comprehensively explore the past, present, and future directions of GenAI integration with IoT Computing, offering valuable insights for both experts and newcomers.

Supporting learners' understanding of taught skills in online settings is a longstanding challenge. While exercises and chat-based agents can evaluate understanding in limited contexts, this challenge is magnified when learners seek explanations that delve into procedural knowledge ( how things are done) and reasoning ( why things happen). We hypothesize that an intelligent agent's ability to understand and explain learners' questions about skills can be significantly enhanced using the TMK (Task-Method-Knowledge) model, a Knowledge-based AI framework. We introduce Ivy, an intelligent agent that leverages an LLM and iterative refinement techniques to generate explanations that embody teleological, causal, and compositional principles. Our initial evaluation demonstrates that this approach goes beyond the typical shallow responses produced by an agent with access to unstructured text, thereby substantially improving the depth and relevance of feedback. This can potentially ensure learners develop a comprehensive understanding of skills crucial for effective problem-solving in online environments.

Risk management in finance involves recognizing, evaluating, and addressing financial risks to maintain stability and ensure regulatory compliance. Extracting relevant insights from extensive regulatory documents is a complex challenge requiring advanced retrieval and language models. This paper introduces RiskData, a dataset specifically curated for finetuning embedding models in risk management, and RiskEmbed, a finetuned embedding model designed to improve retrieval accuracy in financial question-answering systems. The dataset is derived from 94 regulatory guidelines published by the Office of the Superintendent of Financial Institutions (OSFI) from 1991 to 2024. We finetune a state-of-the-art sentence BERT embedding model to enhance domain-specific retrieval performance typically for Retrieval-Augmented Generation (RAG) systems. Experimental results demonstrate that RiskEmbed significantly outperforms general-purpose and financial embedding models, achieving substantial improvements in ranking metrics. By open-sourcing both the dataset and the model, we provide a valuable resource for financial institutions and researchers aiming to develop more accurate and efficient risk management AI solutions.

The countersuit opens up a new front in the high-stakes battle between two Silicon Valley heavyweights. "Elon's nonstop actions against us are just bad-faith tactics to slow down OpenAI and seize control of the leading AI innovations for his personal benefit," OpenAI said in a statement on Wednesday. "Today, we countersued to stop him." Last week, a federal judge in Oakland, California, set a March 2026 trial date in Mr Musk's suit in a bid to fast-track the legal fight. US District Judge Yvonne Gonzalez Rogers previously declined to grant Mr Musk an injunction that would temporarily halt OpenAI's conversion from a non-profit to a for-profit company.