We introduce Embed to Control (E2C), a method for model learning and control of non-linear dynamical systems from raw pixel images. E2C consists of a deep generative model, belonging to the family of variational autoencoders, that learns to generate image trajectories from a latent space in which the dynamics is constrained to be locally linear. Our model is derived directly from an optimal control formulation in latent space, supports long-term prediction of image sequences and exhibits strong performance on a variety of complex control problems.

Deep generative models (DGMs) are effective on learning multilayered representations of complex data and performing inference of input data by exploring the generative ability. However, little work has been done on examining or empowering the discriminative ability of DGMs on making accurate predictions. This paper presents max-margin deep generative models (mmDGMs), which explore the strongly discriminative principle of max-margin learning to improve the discriminative power of DGMs, while retaining the generative capability. We develop an efficient doubly stochastic subgradient algorithm for the piecewise linear objective. Empirical results on MNIST and SVHN datasets demonstrate that (1) max-margin learning can significantly improve the prediction performance of DGMs and meanwhile retain the generative ability; and (2) mmDGMs are competitive to the state-of-the-art fully discriminative networks by employing deep convolutional neural networks (CNNs) as both recognition and generative models.

OpenAI CEO Sam Altman sits down with Shannon Bream to discuss the positives and potential negatives of artificial intelligence and the importance of maintaining a lead in the A.I. industry over China. OpenAI is staking out a plethora of new artificial intelligence (AI) policy proposals that the research organization believes will help the United States maintain its lead over the Chinese Communist Party (CCP). On Monday, OpenAI revealed the details of its AI "Economic blueprint," which the company hopes will be adopted by the incoming Trump administration and Congress. The blueprint will serve as a "living document" for responsible AI building and deployment. Speaking with Fox News Digital, Open AI's Vice President of Global Affairs, Chris Lehane, said it is "absolutely imperative" that the U.S. stays in command of AI innovation and production.

Fox News correspondent William La Jeunesse joins'Fox News Sunday' to discuss the evolution of AI and the push lawmakers are making to regulate it. FIRST ON FOX: A select group of tech industry titans and venture capitalists will gather in Washington, D.C., this week to welcome the incoming Trump administration and celebrate new opportunities for global innovation in artificial intelligence and entrepreneurship. Presidents and CEOs from companies on the cutting edge of AI tech and their big financial backers, along with personnel from the incoming administration, will attend a dinner on Thursday organized by Outside the Box Ventures, a firm founded last year by journalist-turned-investment banker Katherine Tarbox, along with Laurent Bili, the French ambassador to the U.S. The list of those invited to Thursday's dinner includes "DOGE" chief Elon Musk, Silicon Valley investor and GOP mega-donor Peter Thiel, NVCA chief executive Bobby Franklin, incoming White House AI and crypto czar David Sacks, OpenAI's Sam Altman, investor Joe Lonsdale and Narya co-founder Colin Greenspon. "This gathering represents more than discussion. We hope it symbolizes a new chapter in public-private collaboration to harness technology's transformative power for the nation's future," a source close to the planning told Fox News Digital.

Asking ChatGPT to write your emails is so two years ago. Generative AI tools are now going beyond the basic text-prompt phase. Take Google's NotebookLM, an experimental "AI research assistant" that lets you upload not just text but also videos, links and PDFs. It will provide a summary of the content, answer questions about it, and even make a podcast-like "AI overview" if you want it to – all while organising your original sources and notes. As AI tools advance, expect more features like this to be baked into everyday software.

YouTubers and other digital content creators are selling their unused video footage to artificial intelligence companies seeking exclusive videos to better train their AI algorithms, often netting thousands of dollars per deal. OpenAI, Alphabet's Google, AI media company Moonvalley and several other AI companies are collectively paying hundreds of content creators for access to their unpublished videos, according to people familiar with the negotiations. That content, which hasn't been posted elsewhere online, is considered valuable for training artificial intelligence systems since it's unique. AI companies are currently paying between 1 and 4 per minute of footage, the people said, with prices increasing depending on video quality or format. Videos that are shot in 4K, for example, go for a higher price, as does non-traditional footage like videos captured from drones or using 3D animations.

Multimodal GPTs represent a watershed in the interplay between Software Engineering and Generative Artificial Intelligence. GPT-4 accepts image and text inputs, rather than simply natural language. We investigate relevant use cases stemming from these enhanced capabilities of GPT-4. To the best of our knowledge, no other work has investigated similar use cases involving Software Engineering tasks carried out via multimodal GPTs prompted with a mix of diagrams and natural language.

This research-in-progress paper presents a new project management framework that utilises GenAI technology. The framework is designed to address the common challenge of uniform team compositions in academic and research project teams, particularly in universities and research institutions. It does so by integrating sociologically identified patterns of successful team member personalities and roles, using GenAI agents to fill gaps in team dynamics. This approach adds an additional layer of analysis to conventional project management processes by evaluating team members' personalities and roles and employing GenAI agents, fine-tuned on personality datasets, to fill specific team roles. Our initial experiments have shown improvements in the model's ability to understand and process personality traits, suggesting the potential effectiveness of GenAI teammates in real-world project settings. This paper aims to explore the practical application of AI in enhancing team diversity and project management

In recent years, AI red teaming has emerged as a practice for probing the safety and security of generative AI systems. Due to the nascency of the field, there are many open questions about how red teaming operations should be conducted. Based on our experience red teaming over 100 generative AI products at Microsoft, we present our internal threat model ontology and eight main lessons we have learned: 1. Understand what the system can do and where it is applied 2. You don't have to compute gradients to break an AI system 3. AI red teaming is not safety benchmarking 4. Automation can help cover more of the risk landscape 5. The human element of AI red teaming is crucial 6. Responsible AI harms are pervasive but difficult to measure 7. LLMs amplify existing security risks and introduce new ones 8. The work of securing AI systems will never be complete By sharing these insights alongside case studies from our operations, we offer practical recommendations aimed at aligning red teaming efforts with real world risks. We also highlight aspects of AI red teaming that we believe are often misunderstood and discuss open questions for the field to consider.

In recent years, Large Language Models (LLMs) have emerged as a transformative development in artificial intelligence (AI), drawing significant attention from industry and academia. Trained on vast datasets, these sophisticated AI systems exhibit impressive natural language processing and content generation capabilities. This paper explores the potential of LLMs to address key challenges in personal finance, focusing on the United States. We evaluate several leading LLMs, including OpenAI's ChatGPT, Google's Gemini, Anthropic's Claude, and Meta's Llama, to assess their effectiveness in providing accurate financial advice on topics such as mortgages, taxes, loans, and investments. Our findings show that while these models achieve an average accuracy rate of approximately 70%, they also display notable limitations in certain areas. Specifically, LLMs struggle to provide accurate responses for complex financial queries, with performance varying significantly across different topics. Despite these limitations, the analysis reveals notable improvements in newer versions of these models, highlighting their growing utility for individuals and financial advisors. As these AI systems continue to evolve, their potential for advancing AI-driven applications in personal finance becomes increasingly promising.