The cost of making further progress in artificial intelligence is becoming as startling as a hallucination by ChatGPT. Demand for the graphics chips known as GPUs needed for large-scale AI training has driven prices of the crucial components through the roof. OpenAI has said that training the algorithm that now powers ChatGPT cost the firm over 100 million. The race to compete in AI also means that data centers are now consuming worrying amounts of energy. The AI gold rush has a few startups hatching bold plans to create new computational shovels to sell.

Stability AI, the makers of the popular AI image generation model Stable Diffusion, had trained the model by feeding it with millions of images that had been "scraped" from the internet, without the consent of their creators. Newton-Rex, the head of Stability's audio team, disagreed. "Companies worth billions of dollars are, without permission, training generative AI models on creators' works, which are then being used to create new content that in many cases can compete with the original works. In December, the New York Times sued OpenAI in a Manhattan court, alleging that the creator of ChatGPT had illegally used millions of the newspaper's articles to train AI systems that are intended to compete with the Times as a reliable source of information. Meanwhile, in July 2023, comedian Sarah Silverman and other writers sued OpenAI and Meta, accusing the companies of using their writing to train AI models without their permission.

Since Sandberg's departure from the C-suite, the company has evolved into a dramatically different entity. Meta chief executive Mark Zuckerberg, after laying off tens of thousands of workers, has sought to remake its culture to become more efficient and focused. Meanwhile, Meta has thrust its resources toward big bets of generative artificial intelligence and virtual-reality-powered services -- and away from its original big blue app.

Bright banners tout the promise of artificial intelligence along the main promenade of Davos, but executives at the World Economic Forum (WEF) say they are grappling with how to turn early demos into money-makers. The arrival of OpenAI's viral ChatGPT triggered a frenzy of venture investment and an abrupt change of course inside the world's biggest technology companies since late 2022. This year, several CEOs at the WEF meeting in Davos have said that the latest generative AI still has a lot to prove.

In the financial services industry, forecasting the risk factor distribution conditional on the history and the current market environment is the key to market risk modeling in general and value at risk (VaR) model in particular. As one of the most widely adopted VaR models in commercial banks, Historical simulation (HS) uses the empirical distribution of daily returns in a historical window as the forecast distribution of risk factor returns in the next day. The objectives for financial time series generation are to generate synthetic data paths with good variety, and similar distribution and dynamics to the original historical data. In this paper, we apply multiple existing deep generative methods (e.g., CGAN, CWGAN, Diffusion, and Signature WGAN) for conditional time series generation, and propose and test two new methods for conditional multi-step time series generation, namely Encoder-Decoder CGAN and Conditional TimeVAE. Furthermore, we introduce a comprehensive framework with a set of KPIs to measure the quality of the generated time series for financial modeling. The KPIs cover distribution distance, autocorrelation and backtesting. All models (HS, parametric and neural networks) are tested on both historical USD yield curve data and additional data simulated from GARCH and CIR processes. The study shows that top performing models are HS, GARCH and CWGAN models. Future research directions in this area are also discussed.

Ed Newton-Rex says generative AI has an ethics problem. He ought to know, because he used to be part of the fast-growing industry. Newton-Rex was TikTok's head AI designer and then an executive at Stability AI until he quit in disgust in November over the company's stance on collecting training data. After his high-profile departure, Newton-Rex threw himself into conversation after conversation about what building AI ethically would look like in practice. "It struck me that there are a lot of people who want to use generative AI models that treat creators fairly," he says.

Samsung unveiled its Galaxy S24 devices at its first Unpacked of the year. As expected, the three smartphones have a heavy focus on artificial intelligence-powered features, from the likes of live translations to image editing. Galaxy AI, as Samsung is calling the devices' overarching AI system, is behind a number of communication-focused functions. For one thing, Galaxy S24 devices will natively support live, two-way translations on phone calls without the need for a third-party app, Samsung says. Since processing for most AI features is handled on-device with the help of the Snapdragon 8 Gen 3 Chipset and its neural processing unit, the conversations will stay private (well, aside from eavesdroppers who might catch one half of the chat).

In recent years Google has used the word "helpful" to describe new features added to its search product, its voice assistant, its generative AI tool Bard, even its Pixel earbuds. A keyword-search for the word "helpful" in Google's own corporate news blog brings up more than 1,200 results. Depending on what you're searching for, though, Google's main search service has become less helpful. To hear one columnist describe it, Google search is now a "tragedy" that is "bloated and overmonetized." The Financial Times notes that it's "cluttered with adverts"--less encyclopedia, more Yellow Pages.

OpenAI is working with the Pentagon on a number of projects including cybersecurity capabilities, a departure from the startup's earlier ban on providing its artificial intelligence to militaries. The ChatGPT maker is developing tools with the U.S. Defense Department on open-source cybersecurity software -- collaborating with DARPA for its AI Cyber Challenge announced last year -- and has had initial talks with the US government about methods to assist with preventing veteran suicide, Anna Makanju, the company's vice president of global affairs, said in an interview at Bloomberg House at the World Economic Forum in Davos on Tuesday. The company had recently removed language in its terms of service banning its AI from "military and warfare" applications. Makanju described the decision as part of a broader update of its policies to adjust to new uses of ChatGPT and its other tools. "Because we previously had what was essentially a blanket prohibition on military, many people thought that would prohibit many of these use cases, which people think are very much aligned with what we want to see in the world," she said.

In this paper, we investigate the intersection of large generative AI models and cloud-native computing architectures. Recent large models such as ChatGPT, while revolutionary in their capabilities, face challenges like escalating costs and demand for high-end GPUs. Drawing analogies between large-model-as-a-service (LMaaS) and cloud database-as-a-service (DBaaS), we describe an AI-native computing paradigm that harnesses the power of both cloud-native technologies (e.g., multi-tenancy and serverless computing) and advanced machine learning runtime (e.g., batched LoRA inference). These joint efforts aim to optimize costs-of-goods-sold (COGS) and improve resource accessibility. The journey of merging these two domains is just at the beginning and we hope to stimulate future research and development in this area.