The video game industry has been in a troubled place for the past year, with studio closures and job security at the forefront of developer concerns. Increasing layoffs with seemingly no end paint a bleak picture for devs, while companies are busy pumping money into AI initiatives. According to a new report from the organizers of the Game Developers Conference, 52 percent of devs surveyed said they worked at companies that were using generative AI on their games. Of the 3,000 people surveyed, roughly half said they were concerned about the technology's impact on the industry and an increasing number reported they felt negatively about AI overall. The "State of the Game Industry" report, released Tuesday, is one of a series of surveys conducted each year by GDC organizers prior to their annual conference.

Some additional space could be spared e.g. by deleting the superfluous Sec.

Rakuten's chief dismissed skeptics who call the Japanese e-commerce pioneer's mobile foray a mistake and said the telecom arm is central for future growth through artificial intelligence. A decision to enter Japan's cutthroat wireless market has saddled Rakuten with four years of losses, weighing on its cash-churning online shopping mall and finance operations. But that mobile arm and its 8 million-plus users help train an AI poised to expand the conglomerate's business, according to billionaire founder Hiroshi Mikitani. The amount of exclusive data Rakuten gathers from its users is "extremely powerful," Mikitani said in an interview. "We have no intent to compete against OpenAI or Google. But we will actively build a more vertically integrated, specialized AI."

Most of the tools in the Humphrey suite are generative AI models - in this case, technology which takes large amounts of information and summarises it in a more digestible format - to be used by the civil service. Among them is Consult, which summarises people's responses to public calls for information. The government says this is currently done by expensive external consultants who bill the taxpayer "around 100,000 every time." Parlex, which the government says helps policymakers search through previous parliamentary debates on a certain topic, is described by The Times as "designed to avoid catastrophic political rows by predicting how MPs will respond". Other changes announced include more efficient data sharing between departments.

Novel capacities of generative AI to analyze and generate cultural artifacts raise inevitable questions about the nature and value of artistic education and human expertise. Has AI already leveled the playing field between professional artists and laypeople, or do trained artistic expressive capacity, curation skills and experience instead enhance the ability to use these new tools? In this pre-registered study, we conduct experimental comparisons between 50 active artists and a demographically matched sample of laypeople. We designed two tasks to approximate artistic practice for testing their capabilities in both faithful and creative image creation: replicating a reference image, and moving as far away as possible from it. We developed a bespoke platform where participants used a modern text-to-image model to complete both tasks. We also collected and compared participants' sentiments towards AI. On average, artists produced more faithful and creative outputs than their lay counterparts, although only by a small margin. While AI may ease content creation, professional expertise is still valuable - even within the confined space of generative AI itself. Finally, we also explored how well an exemplary vision-capable large language model (GPT-4o) would complete the same tasks, if given the role of an image generation agent, and found it performed on par in copying but outperformed even artists in the creative task. The very best results were still produced by humans in both tasks. These outcomes highlight the importance of integrating artistic skills with AI training to prepare artists and other visual professionals for a technologically evolving landscape. We see a potential in collaborative synergy with generative AI, which could reshape creative industries and education in the arts.

Generative artificial intelligence (GAI) has seen rapid adoption across diverse domains through its ability to produce high-quality text, images, and videos [1]. Vision-Language Models (VLMs) represent a significant advancement in this space, combining visual and linguistic understanding to generate contextually relevant images from textual descriptions [2]. They leverage vast datasets and sophisticated algorithms [2,3] to enable unprecedented creativity and efficiency, driving applications in marketing, entertainment, design, and more. Large Language Models (LLMs) and VLMs often inherit and perpetuate biases and stereotypes present in their training data [4-7], which is typically sourced from vast and diverse internet repositories [8-11]. The training datasets frequently contain implicit and explicit cultural stereotypes, societal biases, and skewed representations that the models learn during training.

Alongside the former presidents, family members and US officials you would expect to see at Donald Trump's inauguration, there have also been a host of faces familiar for less traditional reasons. We've seen OpenAI CEO Sam Altman taking selfies with influencer brothers Logan and Jake Paul, and controversial Irish mixed martial arts fighter Conor McGregor chatting to British politician Nigel Farage. Also in attendance are tech billionaires like Meta's Mark Zuckerberg and Amazon's Jeff Bezos, media tycoon Rupert Murdoch and FIFA president Gianni Infantino. We will continue spotting the notable and unusual names among the crowd as the day progresses.

Ask people building generative AI what generative AI is good for right now--what they're really fired up about--and many will tell you: coding. Everyone from established AI giants to buzzy startups is promising to take coding assistants to the next level. The upshot is that developers could essentially turn into managers, who may spend more time reviewing and correcting code written by a model than writing it from scratch themselves. Many of the people building generative coding assistants think that they could be a fast track to artificial general intelligence, the hypothetical superhuman technology that a number of top firms claim to have in their sights.Read the full story. When you think of AI's contributions to science, you probably think of AlphaFold, the Google DeepMind protein-folding program that earned its creator a Nobel Prize last year.

Copilot, a tool built on top of OpenAI's large language models and launched by Microsoft-backed GitHub in 2022, is now used by millions of developers around the world. "Today, more than a quarter of all new code at Google is generated by AI, then reviewed and accepted by engineers," Alphabet CEO Sundar Pichai claimed on an earnings call in October: "This helps our engineers do more and move faster." Expect other tech companies to catch up, if they haven't already. A bunch of new startups have entered this buzzy market too. Newcomers such as Zencoder, Merly, Cosine, Tessl (valued at 750 million within months of being set up), and Poolside (valued at 3 billion before it even released a product) are all jostling for their slice of the pie.

By composing graphical models with deep learning architectures, we learn generative models with the strengths of both frameworks. The structured variational autoencoder (SVAE) inherits structure and interpretability from graphical models, and flexible likelihoods for high-dimensional data from deep learning, but poses substantial optimization challenges. We propose novel algorithms for learning SVAEs, and are the first to demonstrate the SVAE's ability to handle multimodal uncertainty when data is missing by incorporating discrete latent variables. Our memory-efficient implicit differentiation scheme makes the SVAE tractable to learn via gradient descent, while demonstrating robustness to incomplete optimization. To more rapidly learn accurate graphical model parameters, we derive a method for computing natural gradients without manual derivations, which avoids biases found in prior work.