OpenAI is testing a new search engine that uses generative artificial intelligence to produce results, raising the prospect of a significant challenge to Google's dominance of the online search market. SearchGPT will launch with a small group of users and publishers before a potential wider rollout, the company announced on Thursday. OpenAI ultimately intends to incorporate the search features into ChatGPT, rather offer a standalone product. OpenAI said SearchGPT is a temporary prototype that will combine the company's AI models, such as ChatGPT, with the ability to search the internet. It will respond conversationally to searches, while providing up-to-date information with "clear links to relevant sources".

A number of publishers and writers are in the middle of litigation against tech companies, claiming their intellectual property has been scraped into AI training data sets without their permission. The New York Times' ongoing case against OpenAI is probably the most high-profile of these. "There is a complete lack of transparency in terms of which content is used to train models, and we think this is preventing finding the right balance [between AI companies and content creators]," says Yves-Alexandre de Montjoye, an associate professor of applied mathematics and computer science at Imperial College London, who led the research. It was presented at the International Conference on Machine Learning, a top AI conference being held in Vienna this week. To create the traps, the team used a word generator to create thousands of synthetic sentences.

Even though computers were made to do maths faster than any human could manage, the top level of formal mathematics remains an exclusively human domain. But a breakthrough by researchers at Google DeepMind has brought AI systems closer than ever to beating the best human mathematicians at their own game. A pair of new systems, called AlphaProof and AlphaGeometry 2, worked together to tackle questions from the International Mathematical Olympiad, a global maths competition for secondary-school students that has been running since 1959. The Olympiad takes the form of six mind-bogglingly hard questions each year, covering fields including algebra, geometry and number theory. The combined efforts of DeepMind's two systems weren't quite in that league.

DeepMind's AlphaProof AI can tackle a range of mathematical problems An AI from Google DeepMind has achieved a silver medal score at this year's International Mathematical Olympiad (IMO), the first time any AI has made it to the podium. The IMO is considered the world's most prestigious competition for young mathematicians. Correctly answering its test questions requires mathematical ability that AI systems typically lack. In January, Google DeepMind demonstrated AlphaGeometry, an AI system that could answer some IMO geometry questions as well as humans. However, this was not from a live competition, and it couldn't answer questions from other mathematical disciplines, such as number theory, algebra and combinatorics, which is necessary to win an IMO medal.

Several years before ChatGPT began jibber-jabbering away, Google developed a very different kind of artificial intelligence program called AlphaGo that learned to play the board game Go with superhuman skill through tireless practice. Researchers at the company have now published research that combines the abilities of a large language model (the AI behind today's chatbots) with those of AlphaZero, a successor to AlphaGo also capable of playing chess, to solve very tricky mathematical proofs. Their new Frankensteinian creation, dubbed AlphaProof, has demonstrated its prowess by tackling several problems from the 2024 International Math Olympiad (IMO), a prestigious competition for high school students. AlphaProof uses the Gemini large language model to convert naturally phrased math questions into a programming language called Lean. This provides the training fodder for a second algorithm to learn, through trial and error, how to find proofs that can be confirmed as correct.

VideoPoet employs a decoder-only transformer architecture that processes multimodal inputs – including images, videos, text, and audio. The training protocol follows that of Large Language Models (LLMs), consisting of two stages: pretraining and task-specific adaptation. During pretraining, VideoPoet incorporates a mixture of multimodal generative objectives within an autoregressive Transformer framework. The pretrained LLM serves as a foundation that can be adapted for a range of video generation tasks. We present empirical results demonstrating the model's state-of-the-art capabilities in zero-shot video generation, specifically highlighting the ability to generate high-fidelity motions.

Recent advancements in machine learning, especially in Natural Language Processing (NLP), have led to the development of sophisticated models trained on vast datasets, but this progress has raised concerns about potential sensitive information leakage. In response, regulatory measures like the EU General Data Protection Regulation (GDPR) have driven the exploration of Machine Unlearning techniques, which aim to enable models to selectively forget certain data entries. While early approaches focused on pre-processing methods, recent research has shifted towards training-based machine unlearning methods. However, many existing methods require access to original training data, posing challenges in scenarios where such data is unavailable. Besides, directly facilitating unlearning may undermine the language model's general expressive ability. To this end, in this paper, we introduce the Iterative Contrastive Unlearning (ICU) framework, which addresses these challenges by incorporating three key components. We propose a Knowledge Unlearning Induction module for unlearning specific target sequences and a Contrastive Learning Enhancement module to prevent degrading in generation capacity. Additionally, an Iterative Unlearning Refinement module is integrated to make the process more adaptive to each target sample respectively. Experimental results demonstrate the efficacy of ICU in maintaining performance while efficiently unlearning sensitive information, offering a promising avenue for privacy-conscious machine learning applications.

One of the major aspects contributing to the striking performance of large language models (LLMs) is the vast amount of factual knowledge accumulated during pre-training. Yet, many LLMs suffer from self-inconsistency, which raises doubts about their trustworthiness and reliability. In this paper, we focus on entity type ambiguity and analyze current state-of-the-art LLMs for their proficiency and consistency in applying their factual knowledge when prompted for entities under ambiguity. To do so, we propose an evaluation protocol that disentangles knowing from applying knowledge, and test state-of-the-art LLMs on 49 entities. Our experiments reveal that LLMs perform poorly with ambiguous prompts, achieving only 80% accuracy. Our results further demonstrate systematic discrepancies in LLM behavior and their failure to consistently apply information, indicating that the models can exhibit knowledge without being able to utilize it, significant biases for preferred readings, as well as self inconsistencies. Our study highlights the importance of handling entity ambiguity in future for more trustworthy LLMs

Building communication systems that enable seamless and symbiotic communication between humans and AI agents is increasingly important. This research advances the field of human-AI interaction by developing an innovative approach to decode imagined speech using non-invasive high-density functional near-infrared spectroscopy (fNIRS). Notably, this study introduces MindGPT, the first thought-to-LLM (large language model) system in the world. This study focuses on enhancing human-AI communication by utilising fNIRS data to develop a proprietary AI model called MindGPT capable of decoding imagined speech. Hemodynamic responses representing neural activity were collected from four participants instructed to imagine three different sentences.

In the coming decade, artificial intelligence systems will continue to improve and revolutionise every industry and facet of human life. Designing effective, seamless and symbiotic communication paradigms between humans and AI agents is increasingly important. This paper reports a novel method for human-AI interaction by developing a direct brain-AI interface. We discuss a novel AI model, called MindSpeech, which enables open-vocabulary, continuous decoding for imagined speech. This study focuses on enhancing human-AI communication by utilising high-density functional near-infrared spectroscopy (fNIRS) data to develop an AI model capable of decoding imagined speech non-invasively. We discuss a new word cloud paradigm for data collection, improving the quality and variety of imagined sentences generated by participants and covering a broad semantic space. Utilising a prompt tuning-based approach, we employed the Llama2 large language model (LLM) for text generation guided by brain signals. Our results show significant improvements in key metrics, such as BLEU-1 and BERT P scores, for three out of four participants, demonstrating the method's effectiveness. Additionally, we demonstrate that combining data from multiple participants enhances the decoder performance, with statistically significant improvements in BERT scores for two participants. Furthermore, we demonstrated significantly above-chance decoding accuracy for imagined speech versus resting conditions and the identified activated brain regions during imagined speech tasks in our study are consistent with the previous studies on brain regions involved in speech encoding. This study underscores the feasibility of continuous imagined speech decoding. By integrating high-density fNIRS with advanced AI techniques, we highlight the potential for non-invasive, accurate communication systems with AI in the near future.