This white paper is the output of a multidisciplinary workshop in Nairobi (Nov 2023). Led by a cross-organisational team including Microsoft Research, NEPAD, Lelapa AI, and University of Oxford. The workshop brought together diverse thought-leaders from various sectors and backgrounds to discuss the implications of Generative AI for the future of work in Africa. Discussions centred around four key themes: Macroeconomic Impacts; Jobs, Skills and Labour Markets; Workers' Perspectives and Africa-Centris AI Platforms. The white paper provides an overview of the current state and trends of generative AI and its applications in different domains, as well as the challenges and risks associated with its adoption and regulation. It represents a diverse set of perspectives to create a set of insights and recommendations which aim to encourage debate and collaborative action towards creating a dignified future of work for everyone across Africa.

In this paper, we review legal testing methods based on Large Language Models (LLMs), using the OPENAI o1 model as a case study to evaluate the performance of large models in applying legal provisions. We compare current state-of-the-art LLMs, including open-source, closed-source, and legal-specific models trained specifically for the legal domain. Systematic tests are conducted on English and Chinese legal cases, and the results are analyzed in depth. Through systematic testing of legal cases from common law systems and China, this paper explores the strengths and weaknesses of LLMs in understanding and applying legal texts, reasoning through legal issues, and predicting judgments. The experimental results highlight both the potential and limitations of LLMs in legal applications, particularly in terms of challenges related to the interpretation of legal language and the accuracy of legal reasoning. Finally, the paper provides a comprehensive analysis of the advantages and disadvantages of various types of models, offering valuable insights and references for the future application of AI in the legal field.

Open AI is reportedly preparing the launch of a new AI agent, codenamed'Operator', which can perform tasks for users, such as writing code or booking travel. According to sources familiar with the project, the tool is planned to be released in January as a research version and via the company's API for developers. The launch is part of a larger trend in the AI industry towards developing agents, AI tools that can perform multi-step tasks with minimal supervision, Bloomberg reports. Competitor Anthropic has recently launched a similar agent that can handle real-time tasks on the user's computer. Microsoft, which also supports Open AI, has recently launched AI tools to automate tasks like sending emails and managing documents.

We present the MIDInfinite, a web application capable of generating symbolic music using a large-scale generative AI model locally on commodity hardware. Creating this demo involved porting the Anticipatory Music Transformer, a large language model (LLM) pre-trained on the Lakh MIDI dataset, to the Machine Learning Compilation (MLC) framework. Once the model is ported, MLC facilitates inference on a variety of runtimes including C++, mobile, and the browser. We envision that MLC has the potential to bridge the gap between the landscape of increasingly capable music AI models and technology more familiar to music software developers. As a proof of concept, we build a web application that allows users to generate endless streams of multi-instrumental MIDI in the browser, either from scratch or conditioned on a prompt. On commodity hardware (an M3 Macbook Pro), our demo can generate 51 notes per second, which is faster than real-time playback for 72.9% of generations, and increases to 86.3% with 2 seconds of upfront buffering.

Generative artificial intelligence is now a widely used tool in molecular science. Despite the popularity of probabilistic generative models, numerical experiments benchmarking their performance on molecular data are lacking. In this work, we introduce and explain several classes of generative models, broadly sorted into two categories: flow-based models and diffusion models. We select three representative models: Neural Spline Flows, Conditional Flow Matching, and Denoising Diffusion Probabilistic Models, and examine their accuracy, computational cost, and generation speed across datasets with tunable dimensionality, complexity, and modal asymmetry. Our findings are varied, with no one framework being the best for all purposes. In a nutshell, (i) Neural Spline Flows do best at capturing mode asymmetry present in low-dimensional data, (ii) Conditional Flow Matching outperforms other models for high-dimensional data with low complexity, and (iii) Denoising Diffusion Probabilistic Models appears the best for low-dimensional data with high complexity. Our datasets include a Gaussian mixture model and the dihedral torsion angle distribution of the Aib\textsubscript{9} peptide, generated via a molecular dynamics simulation. We hope our taxonomy of probabilistic generative frameworks and numerical results may guide model selection for a wide range of molecular tasks.

With the advancements of generative artificial intelligence (GenAI) models, their capabilities are expanding significantly beyond content generation and the models are increasingly being used across diverse applications. Particularly, GenAI shows great potential in addressing challenges in the electric vehicle (EV) ecosystem ranging from charging management to cyber-attack prevention. In this paper, we specifically consider Internet of electric vehicles (IoEV) and we categorize GenAI for IoEV into four different layers namely, EV's battery layer, individual EV layer, smart grid layer, and security layer. We introduce various GenAI techniques used in each layer of IoEV applications. Subsequently, public datasets available for training the GenAI models are summarized. Finally, we provide recommendations for future directions. This survey not only categorizes the applications of GenAI in IoEV across different layers but also serves as a valuable resource for researchers and practitioners by highlighting the design and implementation challenges within each layer. Furthermore, it provides a roadmap for future research directions, enabling the development of more robust and efficient IoEV systems through the integration of advanced GenAI techniques.

Kurt'CyberGuy' Knutsson on President-elect Trump's plan to deregulate cryptocurrency and A.I. in his second administration. 'DONALD TRUMP NEURON': Artificial intelligence recognizes images and the name of President-elect Donald Trump so much that the phenomenon is referred to as a "Donald Trump neuron," expert Chris Olah says. MUSK PETITION: An artificial intelligence (AI) advocacy group is urging President-elect Trump to make billionaire entrepreneur Elon Musk a special adviser to the White House focused on AI. INDIA - 2024/05/17: In this photo illustration, the OpenAI logo is seen displayed on a mobile phone screen with ChatGPT logo in the background. HELP FROM SILICON VALLEY: OpenAI has assembled a "blueprint" for artificial intelligence infrastructure that the company hopes will be considered by the incoming Trump administration and Congress – suggesting that the plan will help the United States maintain its lead in the field over competitors like China.

Kurt'CyberGuy' Knutsson on President-elect Trump's plan to deregulate cryptocurrency and A.I. in his second administration. OpenAI has assembled a "blueprint" for artificial intelligence (AI) infrastructure that the company hopes will be considered by the incoming Trump administration and Congress – suggesting that the plan will help the United States maintain its lead in the field over competitors like China. The company's Vice President of Global Affairs, Chris Lehane, announced the "Infrastructure Blueprint for the U.S." on Wednesday during an event hosted by the Center for Strategic and International Studies (CSIS). The company says AI's potential presents an "unmissable opportunity to revitalize the American Dream and reindustrialize the US." "Investments to extend the current U.S. lead in AI will yield tens of thousands of skilled-trade and other jobs, growth in productivity and GDP; a modernized grid including power generated by nuclear energy; a state-of-the-art network of semiconductor manufacturing facilities; and a new generation of AI-powered businesses and entrepreneurship," OpenAI claims. In this photo illustration, the OpenAI logo is seen displayed on a mobile phone screen with ChatGPT logo in the background.

Back in 2022, the tech community was buzzing over image-generating AI models, such as Midjourney, Stable Diffusion, and OpenAI's DALL-E 2, which could follow simple word prompts to depict fantasylands or whimsical chairs made of avocados. But artists saw this technological wonder as a new kind of theft. They felt the models were effectively stealing and replacing their work. Ben Zhao, a computer security researcher at the University of Chicago, was listening. He and his colleagues have built arguably the most prominent weapons in an artist's arsenal against nonconsensual AI scraping: two tools called Glaze and Nightshade that add barely perceptible perturbations to an image's pixels so that machine-learning models cannot read them properly.

On the call, artists shared details of how they had been hurt by the generative AI boom, which was then brand new. At that moment, AI was suddenly everywhere. The tech community was buzzing over image-generating AI models, such as Midjourney, Stable Diffusion, and OpenAI's DALL-E 2, which could follow simple word prompts to depict fantasylands or whimsical chairs made of avocados. But these artists saw this technological wonder as a new kind of theft. They felt the models were effectively stealing and replacing their work.