A former ByteDance intern who was allegedly dismissed for professional misconduct, including sabotaging colleagues' work, was announced as a winner of one of the most prestigious annual awards for AI research this week. Keyu Tian, whose LinkedIn and Google Scholar pages list him as a master's student in computer science at Peking University, is the first author of one of two papers chosen Tuesday for the main "Best Paper Award" at the Neural Information Processing Systems (NeurIPS) conference, the largest gathering of machine learning researchers in the world. The paper, titled "Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction," presents a new method for creating AI-generated images that Tian and four coauthors--all affiliated with either ByteDance or Peking University--claim is faster and more efficient than its predecessors. "The overall quality of the paper presentation, experimental validation and insights (scaling laws) give compelling reasons to experiment with this model," the NeurIPS Best Paper Award committee wrote in a statement. The committee's decision to grant the honor to Tian, whom ByteDance reportedly sued for over 1 million in damages last month, claiming deliberate sabotage of other company research projects, quickly became the focus of wider discussions online about how NeurIPS is run and the way top AI researchers evaluate the work of their colleagues.

New Jersey residents frustrated with a lack of answers regarding dozens of potential drone sightings in the skies above their homes are threatening to take action on their own if the government doesn't start providing answers. James Ward, a Jersey Shore Realtor, shared video on Facebook that he said shows "SUV-size drones" above Island Beach State Park taken Sunday. It's difficult to judge their size in the clip, which shows a number of lights hovering in the sky. "Dozens of SUV-size drones in all directions," the caption says. "Emerging at same time and flying over the ocean and then heading in different directions – what do you think?" "A good shotgun will fix that problem," one commenter replied.

Mathematics olympiads are prestigious competitions, with problem proposing and solving highly honored. Building artificial intelligence that proposes and solves olympiads presents an unresolved challenge in automated theorem discovery and proving, especially in geometry for its combination of numerical and spatial elements. We introduce TongGeometry, a Euclidean geometry system supporting tree-search-based guided problem proposing and solving. The efficient geometry system establishes the most extensive repository of geometry theorems to date: within the same computational budget as the existing state-of-the-art, TongGeometry discovers 6.7 billion geometry theorems requiring auxiliary constructions, including 4.1 billion exhibiting geometric symmetry. Among them, 10 theorems were proposed to regional mathematical olympiads with 3 of TongGeometry's proposals selected in real competitions, earning spots in a national team qualifying exam or a top civil olympiad in China and the US. Guided by fine-tuned large language models, TongGeometry solved all International Mathematical Olympiad geometry in IMO-AG-30, outperforming gold medalists for the first time. It also surpasses the existing state-of-the-art across a broader spectrum of olympiad-level problems. The full capabilities of the system can be utilized on a consumer-grade machine, making the model more accessible and fostering widespread democratization of its use. By analogy, unlike existing systems that merely solve problems like students, TongGeometry acts like a geometry coach, discovering, presenting, and proving theorems.

This paper introduces the human-like embodied AI interviewer which integrates android robots equipped with advanced conversational capabilities, including attentive listening, conversational repairs, and user fluency adaptation. Moreover, it can analyze and present results post-interview. We conducted a real-world case study at SIGDIAL 2024 with 42 participants, of whom 69% reported positive experiences. This study demonstrated the system's effectiveness in conducting interviews just like a human and marked the first employment of such a system at an international conference. The demonstration video is available at https://youtu.be/jCuw9g99KuE.

Language is a symbolic capital that affects people's lives in many ways (Bourdieu, 1977, 1991). It is a powerful tool that accounts for identities, cultures, traditions, and societies in general. Hence, data in a given language should be viewed as more than a collection of tokens. Good data collection and labeling practices are key to building more human-centered and socially aware technologies. While there has been a rising interest in mid- to low-resource languages within the NLP community, work in this space has to overcome unique challenges such as data scarcity and access to suitable annotators. In this paper, we collect feedback from those directly involved in and impacted by NLP artefacts for mid- to low-resource languages. We conduct a quantitative and qualitative analysis of the responses and highlight the main issues related to (1) data quality such as linguistic and cultural data suitability; and (2) the ethics of common annotation practices such as the misuse of online community services. Based on these findings, we make several recommendations for the creation of high-quality language artefacts that reflect the cultural milieu of its speakers, while simultaneously respecting the dignity and labor of data workers.

GOP gubernatorial candidate Jack Ciattarelli says there's no reason why the feds shouldn't be able to identify the source of the drones flying over the Garden State as residents grow more fearful. New Jersey Gov. Phil Murphy says the federal government has the authority to shoot down the mysterious drones spotted flying over his state and that he "wouldn't be opposed" to them playing a "more robust role" in the matter. Murphy made the remark during an interview with WNYC as the public and lawmakers remain baffled over the source of the large drones. Rep. Chris Smith, R-N.J., told Fox News this week that a Coast Guard commander said "one of their 47-foot vessels, boats, was trailed very closely by more than a dozen of these drones." When the interviewer suggested whether one of the drones could be shot down so officials could get a closer look, Murphy said: "The feds have that authority, and I'd like to see them play a more robust role. I wouldn't be opposed to that. Let me put it that way." "I want folks out there to know -- listen, you're frustrated. But... we're going to stay at it, I promise you, this is our top priority. But based on everything we know, we don't see any evidence of a risk to public safety. And clearly, and that's largely based on the feds input. If that changes, we will shout it from the mountaintop," Murphy also said.

Quaternion contains one real part and three imaginary parts, which provided a more expressive hypercomplex space for learning knowledge graph. Existing quaternion embedding models measure the plausibility of a triplet either through semantic matching or geometric distance scoring functions. However, it appears that semantic matching diminishes the separability of entities, while the distance scoring function weakens the semantics of entities. To address this issue, we propose a novel quaternion knowledge graph embedding model. Our model combines semantic matching with entity's geometric distance to better measure the plausibility of triplets. Specifically, in the quaternion space, we perform a right rotation on head entity and a reverse rotation on tail entity to learn rich semantic features. Then, we utilize distance adaptive translations to learn geometric distance between entities. Furthermore, we provide mathematical proofs to demonstrate our model can handle complex logical relationships. Extensive experimental results and analyses show our model significantly outperforms previous models on well-known knowledge graph completion benchmark datasets. Our code is available at https://github.com/llqy123/DaBR.

This simple, intuitive methodology allows autoregressive (AR) transformers to learn visual distributions fast and generalize well: VAR, for the first time, makes GPT-style AR models surpass diffusion transformers in image generation. On ImageNet 256 256 benchmark, VAR significantly improve AR baseline by improving Frechet inception distance (FID) from 18.65 to 1.73, inception score (IS) from 80.4 to 350.2, with around 20x faster inference speed. It is also empirically verified that VAR outperforms the Diffusion Transformer (DiT) in multiple dimensions including image quality, inference speed, data efficiency, and scalability. Scaling up VAR models exhibits clear power-law scaling laws similar to those observed in LLMs, with linear correlation coefficients near -0.998 as solid evidence.

We consider the problem of how a trusted, but computationally bounded agent (a 'verifier') can learn to interact with one or more powerful but untrusted agents ('provers') in order to solve a given task. More specifically, we study the case in which agents are represented using neural networks and refer to solutions of this problem as neural interactive proofs. First we introduce a unifying framework based on prover-verifier games, which generalises previously proposed interaction protocols. We then describe several new protocols for generating neural interactive proofs, and provide a theoretical comparison of both new and existing approaches. Finally, we support this theory with experiments in two domains: a toy graph isomorphism problem that illustrates the key ideas, and a code validation task using large language models. In so doing, we aim to create a foundation for future work on neural interactive proofs and their application in building safer AI systems.

Artificial Intelligence (AI), like any transformative technology, has the potential to be a double-edged sword, leading either toward significant advancements or detrimental outcomes for society as a whole. As is often the case when it comes to widely-used technologies in market economies (e.g., cars and semiconductor chips), commercial interest tends to be the predominant guiding factor. The AI community is at risk of becoming polarized to either take a laissez-faire attitude toward AI development, or to call for government overregulation. Between these two poles we argue for the community of AI practitioners to consciously and proactively work for the common good. This paper offers a blueprint for a new type of innovation infrastructure including 18 concrete milestones to guide AI research in that direction. Our view is that we are still in the early days of practical AI, and focused efforts by practitioners, policymakers, and other stakeholders can still maximize the upsides of AI and minimize its downsides. We talked to luminaries such as recent Nobelist John Jumper on science, President Barack Obama on governance, former UN Ambassador and former National Security Advisor Susan Rice on security, philanthropist Eric Schmidt on several topics, and science fiction novelist Neal Stephenson on entertainment. This ongoing dialogue and collaborative effort has produced a comprehensive, realistic view of what the actual impact of AI could be, from a diverse assembly of thinkers with deep understanding of this technology and these domains. From these exchanges, five recurring guidelines emerged, which form the cornerstone of a framework for beginning to harness AI in service of the public good. They not only guide our efforts in discovery but also shape our approach to deploying this transformative technology responsibly and ethically.