Yesterday afternoon, OpenAI teased Sora, a video-generation model that promises to convert written text prompts into highly realistic videos. Footage released by the company depicts such examples as "a Shiba Inu dog wearing a beret and black turtleneck" and "in an ornate, historical hall, a massive tidal wave peaks and begins to crash." The excitement from the press has been reminiscent of the buzz surrounding the image creator DALL-E or ChatGPT in 2022: Sora is described as "eye-popping," "world-changing," and "breathtaking, yet terrifying." The imagery is genuinely impressive. At a glance, one example of an animated "fluffy monster" looks better than Shrek; an "extreme close up" of a woman's eye, complete with a reflection of the scene in front of her, is startlingly lifelike.

A coalition of 20 tech companies signed an agreement Friday to help prevent AI deepfakes in the critical 2024 elections taking place in more than 40 countries. OpenAI, Google, Meta, Amazon, Adobe and X are among the businesses joining the pact to prevent and combat AI-generated content that could influence voters. However, the agreement's vague language and lack of binding enforcement call into question whether it goes far enough. The list of companies signing the "Tech Accord to Combat Deceptive Use of AI in 2024 Elections" includes those that create and distribute AI models, as well as social platforms where the deepfakes are most likely to pop up. The signees are Adobe, Amazon, Anthropic, Arm, ElevenLabs, Google, IBM, Inflection AI, LinkedIn, McAfee, Meta, Microsoft, Nota, OpenAI, Snap Inc., Stability AI, TikTok, Trend Micro, Truepic and X (formerly Twitter).

ChatGPT generates believable text about nearly any subject, but there is a big difference between "believable" and "correct." ChatGPT, similarly to other LLMs, is trained on large swaths of publicly available data, in large part scraped from online forums such as Stack Overflow and Reddit. Given differences in the volume of available data, ChatGPT's performance naturally varies by topic and may in turn affect communities to different degrees. We observed ChatGPT's impact on Stack Overflow participation varies significantly across topics, aligning with its expected performance based on available training data. Those topics related to open-source tools and general-purpose programming languages (for example, Python, R) appeared to experience larger declines in participation and contribution than proprietary and closed technologies, such as those employed for enterprise server-side development (for example, Spring Framework, AWS, Azure).

Have you ever wanted to know what two golden retrievers podcasting on top of a mountain might look like? Or perhaps watch a bicycle race on the ocean with different animals riding the bicycles? OpenAI's latest generative artificial intelligence offering, Sora, can generate breathtakingly realistic videos that are up to a minute long from text prompts. OpenAI CEO Sam Altman announced the model's creation on X on Thursday. Sora is not yet available to the public. For now, OpenAI is only granting access to red teamers--individuals employed to look for issues--who will assess potential risks associated with the model's release, as well as a limited number of "visual artists, designers, and filmmakers to gain feedback on how to advance the model to be most helpful for creative professionals," according to a blog post.

OpenAI has built a striking new generative video model called Sora that can take a short text description and turn it into a detailed, high-definition film clip up to a minute long. Based on four sample videos that OpenAI shared with MIT Technology Review, the firm has pushed the envelope of what's possible with text-to-video generation (a hot new research direction that we flagged as a trend to watch in 2024). It's hard to know exactly how impressive a step this is until we get more information from OpenAI--and we may have a wait on our hands. The company has no plans to release it to the public currently, though it does hope to in future. For now, mindful of the potential for misuse, OpenAI will be doing extensive safety testing.

OpenAI, the creator of ChatGPT, has unveiled a new form of artificial intelligence that creates realistic video based on text prompts, prompting stunned reactions online. The text-to-video model, named Sora, has "a deep understanding of language" and can generate "compelling characters that express vibrant emotions," OpenAI said in a blog post on Thursday. "Sora is able to generate complex scenes with multiple characters, specific types of motion, and accurate details of the subject and background," the Microsoft-backed startup said. "The model understands not only what the user has asked for in the prompt, but also how those things exist in the physical world." OpenAI CEO Sam Altman on X invited users to suggest prompts for Sora before posting results that included realistic videos of two golden retrievers podcasting on top of a mountain, a grandmother making gnocchi, and marine animals taking part in a bicycle race on top of the ocean.

With the release of Generative AI systems such as ChatGPT, an increasing interest in using Artificial Intelligence (AI) has been observed across domains, including higher education. While emerging statistics show the popularity of using AI amongst undergraduate students, little is yet known about students' perceptions regarding AI including self-reported benefits and concerns from their actual usage, in particular in distance learning contexts. Using a two-step, mixed-methods approach, we examined the perceptions of ten online and distance learning students from diverse disciplines regarding the design of a hypothetical AI Digital Assistant (AIDA). In the first step, we captured students' perceptions via interviews, while the second step supported the triangulation of data by enabling students to share, compare, and contrast perceptions with those of peers. All participants agreed on the usefulness of such an AI tool while studying and reported benefits from using it for real-time assistance and query resolution, support for academic tasks, personalisation and accessibility, together with emotional and social support. Students' concerns related to the ethical and social implications of implementing AIDA, data privacy and data use, operational challenges, academic integrity and misuse, and the future of education. Implications for the design of AI-tailored systems are also discussed.

The design of novel protein sequences with targeted functionalities underpins a central theme in protein engineering, impacting diverse fields such as drug discovery and enzymatic engineering. However, navigating this vast combinatorial search space remains a severe challenge due to time and financial constraints. This scenario is rapidly evolving as the transformative advancements in AI, particularly in the realm of generative models and optimization algorithms, have been propelling the protein design field towards an unprecedented revolution. In this survey, we systematically review recent advances in generative AI for controllable protein sequence design. To set the stage, we first outline the foundational tasks in protein sequence design in terms of the constraints involved and present key generative models and optimization algorithms. We then offer in-depth reviews of each design task and discuss the pertinent applications. Finally, we identify the unresolved challenges and highlight research opportunities that merit deeper exploration.

A drug molecule is a substance that changes the organism's mental or physical state. Every approved drug has an indication, which refers to the therapeutic use of that drug for treating a particular medical condition. While the Large Language Model (LLM), a generative Artificial Intelligence (AI) technique, has recently demonstrated effectiveness in translating between molecules and their textual descriptions, there remains a gap in research regarding their application in facilitating the translation between drug molecules and indications, or vice versa, which could greatly benefit the drug discovery process. The capability of generating a drug from a given indication would allow for the discovery of drugs targeting specific diseases or targets and ultimately provide patients with better treatments. In this paper, we first propose a new task, which is the translation between drug molecules and corresponding indications, and then test existing LLMs on this new task. Specifically, we consider nine variations of the T5 LLM and evaluate them on two public datasets obtained from ChEMBL and DrugBank. Our experiments show the early results of using LLMs for this task and provide a perspective on the state-of-the-art. We also emphasize the current limitations and discuss future work that has the potential to improve the performance on this task. The creation of molecules from indications, or vice versa, will allow for more efficient targeting of diseases and significantly reduce the cost of drug discovery, with the potential to revolutionize the field of drug discovery in the era of generative AI.

Complex data analysis inherently seeks unexpected insights through exploratory \re{visual analysis} methods, transcending logical, step-by-step processing. However, \re{existing interfaces such as notebooks and dashboards have limitations in exploration and comparison for visual data analysis}. Addressing these limitations, we introduce a "design-like" intelligent canvas environment integrating generative AI into data analysis, offering rapid prototyping, iteration, and comparative visualization management. Our dual contributions include the integration of generative AI components into a canvas interface, and empirical findings from a user study (N=10) evaluating the effectiveness of the canvas interface.