The advent of foundation models, which are pre-trained on vast datasets, has ushered in a new era of computer vision, characterized by their robustness and remarkable zero-shot generalization capabilities. Mirroring the transformative impact of foundation models like large language models (LLMs) in natural language processing, visual foundation models (VFMs) have become a catalyst for groundbreaking developments in computer vision. This review paper delineates the pivotal trajectories of VFMs, emphasizing their scalability and proficiency in generative tasks such as text-to-image synthesis, as well as their adeptness in discriminative tasks including image segmentation. While generative and discriminative models have historically charted distinct paths, we undertake a comprehensive examination of the recent strides made by VFMs in both domains, elucidating their origins, seminal breakthroughs, and pivotal methodologies. Additionally, we collate and discuss the extensive resources that facilitate the development of VFMs and address the challenges that pave the way for future research endeavors. A crucial direction for forthcoming innovation is the amalgamation of generative and discriminative paradigms. The nascent application of generative models within discriminative contexts signifies the early stages of this confluence. This survey aspires to be a contemporary compendium for scholars and practitioners alike, charting the course of VFMs and illuminating their multifaceted landscape.

Large Language Models represent state-of-the-art linguistic models designed to equip computers with the ability to comprehend natural language. With its exceptional capacity to capture complex contextual relationships, the LLaMA (Large Language Model Meta AI) family represents a novel advancement in the field of natural language processing by releasing foundational models designed to improve the natural language understanding abilities of the transformer architecture thanks to their large amount of trainable parameters (7, 13, and 70 billion parameters). In many natural language understanding tasks, these models obtain the same performances as private company models such as OpenAI Chat-GPT with the advantage to make publicly available weights and code for research and commercial uses. In this work, we investigate the possibility of Language Adaptation for LLaMA models, explicitly focusing on addressing the challenge of Italian Language coverage. Adopting an open science approach, we explore various tuning approaches to ensure a high-quality text generated in Italian suitable for common tasks in this underrepresented language in the original models' datasets. We aim to release effective text generation models with strong linguistic properties for many tasks that seem challenging using multilingual or general-purpose LLMs. By leveraging an open science philosophy, this study contributes to Language Adaptation strategies for the Italian language by introducing the novel LLaMAntino family of Italian LLMs.

Peer learning is a novel high-level reinforcement learning framework for agents learning in groups. While standard reinforcement learning trains an individual agent in trial-and-error fashion, all on its own, peer learning addresses a related setting in which a group of agents, i.e., peers, learns to master a task simultaneously together from scratch. Peers are allowed to communicate only about their own states and actions recommended by others: "What would you do in my situation?". Our motivation is to study the learning behavior of these agents. We formalize the teacher selection process in the action advice setting as a multi-armed bandit problem and therefore highlight the need for exploration. Eventually, we analyze the learning behavior of the peers and observe their ability to rank the agents' performance within the study group and understand which agents give reliable advice. Further, we compare peer learning with single agent learning and a state-of-the-art action advice baseline. We show that peer learning is able to outperform single-agent learning and the baseline in several challenging discrete and continuous OpenAI Gym domains. Doing so, we also show that within such a framework complex policies from action recommendations beyond discrete action spaces can evolve.

In modern collider experiments, the quest to explore fundamental interactions between elementary particles has reached unparalleled levels of precision. Signatures from particle physics detectors are low-level objects encoding the physics of collisions. The complete simulation of them in a detector is a memory and storage-intensive task. To address this computational bottleneck in particle physics, "Fast Simulation" has been introduced and refined over the years. The field has seen a surge in interest in surrogate modeling the detector simulation, fueled by the advancements in deep generative models. These models aim to generate responses that are statistically identical to the observed data. In this paper, we conduct a comprehensive and exhaustive taxonomic review of the existing literature on the simulation of detector signatures from both methodological and application-wise perspectives. Initially, we formulate the problem of detector signature simulation and discuss its different variations that can be unified. Next, we classify the state-of-the-art methods into four distinct categories based on their underlying model architectures, summarizing their respective generation strategies. We then identify and discuss three key application areas. Finally, we shed light on the challenges and opportunities that lie ahead in detector signature simulation, setting the stage for future research and development.

Through evolution, nature has presented a set of remarkable protein materials, including elastins, silks, keratins and collagens with superior mechanical performances that play crucial roles in mechanobiology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. Here we report a generative model that predicts protein designs to meet complex nonlinear mechanical property-design objectives. Our model leverages deep knowledge on protein sequences from a pre-trained protein language model and maps mechanical unfolding responses to create novel proteins. Via full-atom molecular simulations for direct validation, we demonstrate that the designed proteins are novel, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, as well as the detailed unfolding force-separation curves. Our model offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, using mechanical features as target to enable the discovery of protein materials with superior mechanical properties.

On the day after Thanksgiving this year, one ChatGPT user received an unusually lazy, human response from the AI chatbot: 'You can fill in the rest of the data.' Since then, ChatGPT's makers at OpenAI have fielded a wave of complaints about their large language model (LLM) AI behaving sluggishly over the past month -- leading to jokes and some sincere data analysis on the bot's'seasonal depression.' 'We've heard all your feedback about GPT4 getting lazier!' OpenAI's ChatGPT team posted to X. 'We haven't updated the model since Nov 11th, and this certainly isn't intentional,' the team said. 'Model behavior can be unpredictable, and we're looking into fixing it.' But one AI researcher ran an experiment asking ChatGPT's latest LLM model, GPT4 Turbo, to perform tasks as if it were May and then as if it were December - and he was shocked by the'wild result.' Since this past Thanksgiving, ChatGPT's makers at OpenAI have fielded a wave of complaints about their large language model (LLM) AI behaving sluggishly over the past month -- leading to jokes and some sincere data analysis on the bot's'seasonal depression' But one knowledgeable AI researcher, Rob Lynch, has run an experiment: asking ChatGPT's latest LLM model, GPT4 Turbo, to perform tasks, first as if it were May and then as if it were December.

OpenAI was founded on a promise to build artificial intelligence that benefits all of humanity--even when that AI becomes considerably smarter than its creators. Since the debut of ChatGPT last year and during the company's recent governance crisis, its commercial ambitions have been more prominent. Now, the company says a new research group working on wrangling the super-smart AIs of the future is starting to bear fruit. "AGI is very fast approaching," says Leopold Aschenbrenner, a researcher at OpenAI involved with the Superalignment research team established in July. "We're gonna see superhuman models, they're gonna have vast capabilities and they could be very, very dangerous, and we don't yet have the methods to control them."

Less than a month after OpenAI was rocked by a crisis when its CEO, Sam Altman, was fired by its oversight board (in an apparent coup led by chief scientist Ilya Sutskever) and then reinstated three days later, the message is clear: it's back to business as usual. Yet OpenAI's business is not usual. Many researchers still question whether machines will ever match human intelligence, let alone outmatch it. OpenAI's team takes machines' eventual superiority as given. "AI progress in the last few years has been just extraordinarily rapid," says Leopold Aschenbrenner, a researcher on the superalignment team.

A glimpse toward this future is beginning to emerge in products like Grok, an AI-powered plush toy in the shape of a rocket that can converse with your child. Grok is the first product from a Silicon Valley start-up called Curio that is partnering with OpenAI on a line of toys Curio's founders say will be capable of long-running, fully interactive conversation, allowing a child to view it almost as a peer or friend.

Every day, there seems to be new generative AI news, and while it can often be serious and quite technical, this time around it's just plain fun. Instagram has launched a new generative AI-powered tool called backdrop that lets you create a new image in the, yes, background of your Story. Meta's generative AI lead, Ahmad Al-Dahle, announced the feature on Threads alongside a video tutorial. Instagram's backdrop tool appears once you upload or capture content for your Story. It sits alongside existing icons at the top of your screen, like text and music, represented by an image of a person with a rectangular frame behind them.