If Google's generative AI Gemini Assistant chatbot is to surpass OpenAI's ChatGPT in popularity in the coming years, it may have to do so without some of the promotional partnerships that helped thrust Google search front and center into Americans' lives. In a US federal court filing on Friday, Google proposed a series of restrictions that for three years would bar the company from requiring its device manufacturer, browser, and wireless carrier licensees to distribute Gemini to their US users. Google also would give those partners more flexibility in how they set their default search provider for their users. Google's proposal counters last month's call from the US Justice Department for Google to not only loosen its grip over partners, but also share more data with competitors and divest its Chrome browser business. The company on Friday formally rejected the idea of selling off any piece of its business or turning over more information to rivals. And its proposed restrictions could be construed as narrower than those sought by the government.

Scientific discovery is a complex cognitive process that has driven human knowledge and technological progress for centuries. While artificial intelligence (AI) has made significant advances in automating aspects of scientific reasoning, simulation, and experimentation, we still lack integrated AI systems capable of performing autonomous long-term scientific research and discovery. This paper examines the current state of AI for scientific discovery, highlighting recent progress in large language models and other AI techniques applied to scientific tasks. We then outline key challenges and promising research directions toward developing more comprehensive AI systems for scientific discovery, including the need for science-focused AI agents, improved benchmarks and evaluation metrics, multimodal scientific representations, and unified frameworks combining reasoning, theorem proving, and data-driven modeling. Addressing these challenges could lead to transformative AI tools to accelerate progress across disciplines towards scientific discovery.

The age of Artificial Intelligence (AI) is marked by its transformative "generative" capabilities, distinguishing it from prior iterations. This burgeoning characteristic of AI has enabled it to produce new and original content, inherently showcasing its creative prowess. This shift challenges and requires a recalibration in the realm of arts education, urging a departure from established pedagogies centered on human-driven image creation. The paper meticulously addresses the integration of AI tools, with a spotlight on Stable Diffusion (SD), into university arts curricula. Drawing from practical insights gathered from workshops conducted in July 2023, which culminated in an exhibition of AI-driven artworks, the paper aims to provide a roadmap for seamlessly infusing these tools into academic settings. Given their recent emergence, the paper delves into a comprehensive overview of such tools, emphasizing the intricate dance between artists, developers, and researchers in the open-source AI art world. This discourse extends to the challenges and imperatives faced by educational institutions. It presents a compelling case for the swift adoption of these avant-garde tools, underscoring the paramount importance of equipping students with the competencies required to thrive in an AI-augmented artistic landscape.

The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustness of our models. In particular, our models can reason about our safety policies in context when responding to potentially unsafe prompts, through deliberative alignment. This leads to state-of-the-art performance on certain benchmarks for risks such as generating illicit advice, choosing stereotyped responses, and succumbing to known jailbreaks. Training models to incorporate a chain of thought before answering has the potential to unlock substantial benefits, while also increasing potential risks that stem from heightened intelligence. Our results underscore the need for building robust alignment methods, extensively stress-testing their efficacy, and maintaining meticulous risk management protocols. This report outlines the safety work carried out for the OpenAI o1 and OpenAI o1-mini models, including safety evaluations, external red teaming, and Preparedness Framework evaluations.

OpenAI's recent introduction of Reinforcement Fine-Tuning (RFT) showcases the potential of reasoning foundation model and offers a new paradigm for fine-tuning beyond simple pattern imitation. This technical report presents OpenRFT, our attempt to fine-tune generalist reasoning models for domain-specific tasks under the same settings as RFT. The evaluation is conducted on Sci-KnowEval, where OpenRFT achieves notable performance gains with only 100 domain-specific samples for each task. More experimental results will be updated continuously in later versions. OpenAI's o1 model has shown strong reasoning abilities in mathematics and programming, but its generalization to other tasks remains uncertain. The recent introduction of Reinforcement Fine-Tuning (RFT) (OpenAI, 2024) has provided a promising avenue for reasoning generalization. With only dozens of high-quality (question, answer) pairs, RFT enables the creation of customized reasoning models excelling at domain-specific tasks. The significance of RFT is at least two-fold: (1) It demonstrates the promise of using generalist reasoning models, like o1, as reasoning foundation models. By enabling the efficient creation of domain-specific reasoning models, RFT practically expands the applicability of reasoning models across diverse tasks. Unlike Supervised Fine-Tuning (SFT), which merely mimics patterns in training data, RFT leverages reasoning capabilities to facilitate thinking and trial-and-error learning.

Recent advancements in generative AI have flourished the development of highly adept Large Language Models (LLMs) that integrate diverse data types to empower decision-making. Among these, Multimodal Retrieval-Augmented Generation (RAG) applications are promising for their capability to combine the strengths of information retrieval and generative models, enhancing their utility across various domains, including biomedical research. This paper introduces AlzheimerRAG, a Multimodal RAG pipeline tool for biomedical research use cases, primarily focusing on Alzheimer's disease from PubMed articles. Our pipeline incorporates multimodal fusion techniques to integrate textual and visual data processing by efficiently indexing and accessing vast amounts of biomedical literature. Preliminary experimental results against benchmarks, such as BioASQ and PubMedQA, have returned improved results in information retrieval and synthesis of domain-specific information. We also demonstrate a case study with our RAG pipeline across different Alzheimer's clinical scenarios. We infer that AlzheimerRAG can generate responses with accuracy non-inferior to humans and with low rates of hallucination. Overall, a reduction in cognitive task load is observed, which allows researchers to gain multimodal insights, improving understanding and treatment of Alzheimer's disease.

OpenAI's new o3 artificial intelligence model has achieved a breakthrough high score on a prestigious AI reasoning test called the ARC Challenge, inspiring some AI fans to speculate that o3 has achieved artificial general intelligence (AGI). But even as ARC Challenge organisers described o3's achievement as a major milestone, they also cautioned that it has not won the competition's grand prize – and it is only one step on the path towards AGI, a term for hypothetical future AI with human-like intelligence. The o3 model is the latest in a line of AI releases that follow on from the large language models powering ChatGPT. "This is a surprising and important step-function increase in AI capabilities, showing novel task adaptation ability never seen before in the GPT-family models," said François Chollet, an engineer at Google and the main creator of the ARC Challenge, in a blog post. How does ChatGPT work and do AI-powered chatbots "think" like us? Chollet designed the Abstraction and Reasoning Corpus (ARC) Challenge in 2019 to test how well AIs can find correct patterns linking pairs of coloured grids. Such visual puzzles are intended to make AIs demonstrate a form of general intelligence with basic reasoning capabilities.

"Enterprises are trying to rush to figure out how to implement or incorporate generative AI into their business to gain efficiencies," says Will Fritcher, deputy chief client officer at TP. "But instead of viewing AI as a way to reduce expenses, they should really be looking at it through the lens of enhancing the customer experience and driving value." Doing this requires solving two intertwined challenges: empowering live agents by automating routine tasks and ensuring AI outputs remain accurate, reliable, and precise. Generative AI's potential impact on customer support is twofold: Customers stand to benefit from faster, more consistent service for simple requests, while also receiving undivided human attention for complex, emotionally charged situations. For employees, eliminating repetitive tasks boosts job satisfaction and reduces burnout.The tech can also be used to streamline customer support workflows and enhance service quality in various ways, including: Automated routine inquiries: AI systems handle straightforward customer requests, like resetting passwords or checking account balances. Real-time assistance: During interactions, AI pulls up contextually relevant resources, suggests responses, and guides live agents to solutions faster.

Generative AI is becoming a key component of business operations and customer service interactions today. According to Salesforce research, three out of five workers (61%) either currently use or plan to use generative AI in their roles. A full 68% of these employees are confident that the technology--which can churn out text, video, image, and audio content almost instantaneously--will enable them to provide more enriching customer experiences. Sixty percent of the surveyed employees believe that human oversight is indispensable for effective and trustworthy generative AI. Generative AI enables people and increases efficiencies in business operations, but using it to empower employees will make all the difference.

After nearly two weeks of announcements, OpenAI capped off its 12 Days of OpenAI livestream series with a preview of its next-generation frontier model. "Out of respect for friends at Telefónica (owner of the O2 cellular network), and in the grand tradition of OpenAI being really, truly bad at names, it's called o3," OpenAI CEO Sam Altman told those watching the announcement on YouTube. Instead, OpenAI is first making o3 available to researchers who want help with safety testing. OpenAI also announced the existence of o3 mini. Altman said the company plans to launch that model "around the end of January," with o3 following "shortly after that."