WhatsApp is testing the ability to create custom stickers using generative AI. According to WABetaInfo, the feature is available to a small number of testers through the Google Play Beta Program. Meta is said to be preparing to roll out the tool more broadly in the coming weeks. Those who are part of the test should see a Create button when they open the keyboard in the sticker tab. As with similar tools, you can enter a description of the sticker you'd like to use.

Beijing is poised to implement sweeping new regulations for artificial intelligence services this week, trying to balance state control of the technology with enough support that its companies can become viable global competitors. The government issued 24 guidelines that require platform providers to register their services and conduct a security review before they're brought to market. Seven agencies will take responsibility for oversight, including the Cyberspace Administration of China and the National Development and Reform Commission. The final regulations are less onerous than an original draft from April, but they show China, like Europe, moving ahead with government oversight of what may be the most promising -- and controversial -- technology of the last 30 years. The U.S., by contrast, has no legislation under serious consideration even after industry leaders warned that AI poses a "risk of extinction" and OpenAI's Sam Altman urged Congress in public hearings to get involved.

Recent progress in large language models (LLMs) like GPT-4 and PaLM-2 has brought significant advancements in addressing math reasoning problems. In particular, OpenAI's latest version of GPT-4, known as GPT-4 Code Interpreter, shows remarkable performance on challenging math datasets. In this paper, we explore the effect of code on enhancing LLMs' reasoning capability by introducing different constraints on the Code Usage Frequency of GPT-4 Code Interpreter. We found that its success can be largely attributed to its powerful skills in generating and executing code, evaluating the output of code execution, and rectifying its solution when receiving unreasonable outputs. Based on this insight, we propose a novel and effective prompting method, explicit code-based self-verification (CSV), to further boost the mathematical reasoning potential of GPT-4 Code Interpreter. This method employs a zero-shot prompt on GPT-4 Code Interpreter to encourage it to use code to self-verify its answers. In instances where the verification state registers as "False", the model shall automatically amend its solution, analogous to our approach of rectifying errors during a mathematics examination. Furthermore, we recognize that the states of the verification result indicate the confidence of a solution, which can improve the effectiveness of majority voting. With GPT-4 Code Interpreter and CSV, we achieve an impressive zero-shot accuracy on MATH dataset (53.9% 84.3%). Large language models (LLMs) (Brown et al., 2020; OpenAI, 2023; Anil et al., 2023) have shown impressive success in various tasks, such as common sense understanding and code generation. However, they still fall short in mathematical reasoning, often producing nonsensical or inaccurate content and struggling with complex calculations. Previous attempts to tackle these challenges include the Chain-of-Thought (CoT) (Wei et al., 2022) framework, which enhances LLMs' logical reasoning abilities by generating intermediate steps in their reasoning process.

The advent of Large Language Models (LLMs), such as OpenAI's ChatGPT, Codex, and GitHub's Copilot, affects the educational landscape at its core, as LLMs offer entirely new possibilities, but also challenges for educators, learners, and institutions. Even though LLMs have only appeared very recently to a broader audience, research has started to address their implications on computing education, particularly programming. The generative potential may be used by educators for the design of new programming tasks [Sa22], or for students to gather formative feedback [Ka23, Zh22]. At the same time, implications for programming pedagogy and assessments are being discussed [Be23, BK23, RTT23], as the lowthreshold availability of LLMs raises new questions with regard to adequate task designs, students' contribution, plagiarism, and ethical conduct. Educators and institutions will soon need to reconsider the design of (formative) assessments. In this context, it is crucial to investigate the capabilities and limitations of LLMs for novice learners of programming, whose challenges have a well-documented history [SS86, Mc01, Lu18].

Image reconstruction and captioning from brain activity evoked by visual stimuli allow researchers to further understand the connection between the human brain and the visual perception system. While deep generative models have recently been employed in this field, reconstructing realistic captions and images with both low-level details and high semantic fidelity is still a challenging problem. In this work, we propose UniBrain: Unify Image Reconstruction and Captioning All in One Diffusion Model from Human Brain Activity. For the first time, we unify image reconstruction and captioning from visual-evoked functional magnetic resonance imaging (fMRI) through a latent diffusion model termed Versatile Diffusion. Specifically, we transform fMRI voxels into text and image latent for low-level information and guide the backward diffusion process through fMRI-based image and text conditions derived from CLIP to generate realistic captions and images. UniBrain outperforms current methods both qualitatively and quantitatively in terms of image reconstruction and reports image captioning results for the first time on the Natural Scenes Dataset (NSD) dataset. Moreover, the ablation experiments and functional region-of-interest (ROI) analysis further exhibit the superiority of UniBrain and provide comprehensive insight for visual-evoked brain decoding.

Against this backdrop, we have a self-proclaimed pro-tech prime minister, Rishi Sunak. He is trying to entice the lucrative artificial intelligence sector - also largely US-based - to set up camp in the UK. A handful of them - Palantir, OpenAI and Anthropic - have agreed to open London headquarters.

Multivariate time series data for real-world applications typically contain a significant amount of missing values. The dominant approach for classification with such missing values is to impute them heuristically with specific values (zero, mean, values of adjacent time-steps) or learnable parameters. However, these simple strategies do not take the data generative process into account, and more importantly, do not effectively capture the uncertainty in prediction due to the multiple possibilities for the missing values. In this paper, we propose a novel probabilistic framework for classification with multivariate time series data with missing values. Our model consists of two parts; a deep generative model for missing value imputation and a classifier. Extending the existing deep generative models to better capture structures of time-series data, our deep generative model part is trained to impute the missing values in multiple plausible ways, effectively modeling the uncertainty of the imputation. The classifier part takes the time series data along with the imputed missing values and classifies signals, and is trained to capture the predictive uncertainty due to the multiple possibilities of imputations. Importantly, we show that na\"ively combining the generative model and the classifier could result in trivial solutions where the generative model does not produce meaningful imputations. To resolve this, we present a novel regularization technique that can promote the model to produce useful imputation values that help classification. Through extensive experiments on real-world time series data with missing values, we demonstrate the effectiveness of our method.

It looks like 2024 will be a pivotal year for democracy. There are elections taking place all over the free world – in South Africa, Ghana, Tunisia, Mexico, India, Austria, Belgium, Lithuania, Moldova and Slovakia, to name just a few. Of these, the last may be the most pivotal because: Donald Trump is a racing certainty to be the Republican candidate; a significant segment of the voting population seems to believe that the 2020 election was "stolen"; and the Democrats are, well… underwhelming. The consequences of a Trump victory would be epochal. It would mean the end (for the time being, at least) of the US experiment with democracy, because the people behind Trump have been assiduously making what the normally sober Economist describes as "meticulous, ruthless preparations" for his second, vengeful term.

Many workers across the U.S. are turning to ChatGPT to help with basic tasks, a Reuters/Ipsos poll found, despite fears that have led employers such as Microsoft and Google to curb its use. Companies worldwide are considering how to best make use of ChatGPT, a chatbot program that uses generative AI to hold conversations with users and answer myriad prompts. Security firms and companies have raised concerns, however, that it could result in intellectual property and strategy leaks. Anecdotal examples of people using ChatGPT to help with their day-to-day work include drafting emails, summarising documents and doing preliminary research.

In this paper, we explore potential uses of generative AI models, such as ChatGPT, for investment portfolio selection. Trusting investment advice from Generative Pre-Trained Transformer (GPT) models is a challenge due to model "hallucinations", necessitating careful verification and validation of the output. Therefore, we take an alternative approach. We use ChatGPT to obtain a universe of stocks from S&P500 market index that are potentially attractive for investing. Subsequently, we compared various portfolio optimization strategies that utilized this AI-generated trading universe, evaluating those against quantitative portfolio optimization models as well as comparing to some of the popular investment funds. Our findings indicate that ChatGPT is effective in stock selection but may not perform as well in assigning optimal weights to stocks within the portfolio. But when stocks selection by ChatGPT is combined with established portfolio optimization models, we achieve even better results. By blending strengths of AI-generated stock selection with advanced quantitative optimization techniques, we observed the potential for more robust and favorable investment outcomes, suggesting a hybrid approach for more effective and reliable investment decision-making in the future.