Google's chief executive has said concerns about artificial intelligence keep him awake at night and that the technology can be "very harmful" if deployed wrongly. Sundar Pichai also called for a global regulatory framework for AI similar to the treaties used to regulate nuclear arms use, as he warned that the competition to produce advances in the technology could lead to concerns about safety being pushed aside. In an interview on CBS's 60 minutes programme, Pichai said the negative side to AI gave him restless nights. "It can be very harmful if deployed wrongly and we don't have all the answers there yet – and the technology is moving fast. So does that keep me up at night? Google's parent, Alphabet, owns the UK-based AI company DeepMind and has launched an AI-powered chatbot, Bard, in response to ChatGPT, a chatbot developed by the US tech firm OpenAI, which has become a phenomenon since its release in November. Pichai said governments would need to figure out global frameworks for regulating AI as it developed. Last month thousands of artificial intelligence experts, researchers and backers – including the Twitter owner Elon Musk – signed a letter calling for a pause in the creation of "giant" AIs for at least six months, amid concerns that development of the technology could get out of control. Asked if nuclear arms-style frameworks could be needed, Pichai said: "We would need that." The AI technology behind ChatGPT and Bard, known as a Large Language Model, is trained on a vast trove of data taken from the internet and is able to produce plausible responses to prompts from users in a range of formats, from poems to academic essays and software coding. The image-generating equivalent, in systems such as Dall-E and Midjourney, has also triggered a mixture of astonishment and alarm by producing realistic images such as the Pope sporting a puffer jacket. Pichai added that AI could cause harm through its ability to produce disinformation "It will be possible with AI to create, you know, a video easily.

Videogame companies say the kind of technology behind the viral chatbot ChatGPT is set to radically speed up game development, reduce costs and help players interact with characters in new ways. Generative artificial-intelligence tools have gained prominence in recent months for their ability to create essays, images and even music in response to simple text prompts. Game studios are already using the technology for all sorts of tasks, from helping craft virtual scenes and scripts to designing weapons and characters.

A new joint report entitled A New National Purpose, which explores how the UK can harness innovative technologies to meet future challenges, has recently been published by Tony Blair and William Hague. The "cross-party" report argues that we are currently undergoing a new form of Industrial Revolution "as developments in artificial intelligence (AI), biotech, climate tech and other fields begin to change our economic and social systems". It calls for policymakers to mitigate the consequent threats whilst embracing opportunities. Several of its proposals touch upon the convergence of law and technology, and we will consider some of these aspects below. Perhaps unsurprisingly, given Tony Blair's foiled aspirations to introduce digital ID during his premiership, much of the press attention has focused on the report's call for the government to "provide a secure, private, decentralised digital-ID system for the benefit of both citizens and businesses".

Artificial intelligence scientist Ben Goertzel said that generative AI models and tools like ChatGPT could replace many tasks performed by humans. Most people's jobs do not require'incredible' creativity or innovation, added Goertzel, who is also the co-developer of Sophia, the humanoid robot. He said that AI could be used for tasks like copy editing, writing sport score summaries, and weather reports, as reported by ZDNet. He further said with these tools automating major portions of people's jobs would lead to industry reshuffling and reassigning job duties. He, however, said that AI will not be able to replace jobs that rely on human interaction and the ones that require creativity. Generative AI replacing or automating tasks has been a hot topic of discussion.

The new video-generation systems could speed the work of moviemakers and other digital artists, while becoming a new and quick way to create hard-to-detect online misinformation, making it even harder to tell what's real on the internet. The systems are examples of what is known as generative A.I., which can instantly create text, images and sounds. Another example is ChatGPT, the online chatbot made by a San Francisco start-up, OpenAI, that stunned the tech industry with its abilities late last year. Google and Meta, Facebook's parent company, unveiled the first video-generation systems last year, but did not share them with the public because they were worried that the systems could eventually be used to spread disinformation with newfound speed and efficiency. But Runway's chief executive, Cristóbal Valenzuela, said he believed the technology was too important to keep in a research lab, despite its risks.

Are you scared yet, human? Artificial intelligence (AI) has proliferated with transformative effects in recent years, in sectors from autonomous vehicles to personalised shopping. But the latest deployment of AI to generate content such as text, images or audio has caused quite a stir. ChatGPT, a particularly superior language model, even passed the US medical speciality exam. That's not to say there haven't been some bloopers.

Keeping up with an industry as fast-moving as AI is a tall order. So until an AI can do it for you, here's a handy roundup of the last week's stories in the world of machine learning, along with notable research and experiments we didn't cover on their own. The biggest news of the last week (we politely withdraw our Anthropic story from consideration) was the announcement of Bedrock, Amazon's service that provides a way to build generative AI apps via pretrained models from startups including AI21 Labs, Anthropic and Stability AI. Currently available in "limited preview," Bedrock also offers access to Titan FMs (foundation models), a family of AI models trained in-house by Amazon. It makes perfect sense that Amazon would want to have a horse in the generative AI race.

It's inspired by the daily TechCrunch column where it gets its name. This week, I am diving deeper into what generative AI means, or doesn't mean, for enterprise buyers. I also have some notes on why your company may want to be like Figma, and how the investing side of the market is adjusting to down rounds being the new normal. When The Exchange looked into Battery Ventures' state of cloud software spending report, we started by focusing on what the title promised: fresh data on cloud software spend. And it turned out to be more encouraging than we expected.

Federated learning allows for the training of machine learning models on multiple decentralized local datasets without requiring explicit data exchange. However, data pre-processing, including strategies for handling missing data, remains a major bottleneck in real-world federated learning deployment, and is typically performed locally. This approach may be biased, since the subpopulations locally observed at each center may not be representative of the overall one. To address this issue, this paper first proposes a more consistent approach to data standardization through a federated model. Additionally, we propose Fed-MIWAE, a federated version of the state-of-the-art imputation method MIWAE, a deep latent variable model for missing data imputation based on variational autoencoders. MIWAE has the great advantage of being easily trainable with classical federated aggregators. Furthermore, it is able to deal with MAR (Missing At Random) data, a more challenging missing-data mechanism than MCAR (Missing Completely At Random), where the missingness of a variable can depend on the observed ones. We evaluate our method on multi-modal medical imaging data and clinical scores from a simulated federated scenario with the ADNI dataset. We compare Fed-MIWAE with respect to classical imputation methods, either performed locally or in a centralized fashion. Fed-MIWAE allows to achieve imputation accuracy comparable with the best centralized method, even when local data distributions are highly heterogeneous. In addition, thanks to the variational nature of Fed-MIWAE, our method is designed to perform multiple imputation, allowing for the quantification of the imputation uncertainty in the federated scenario.

Data storytelling plays an important role in data workers' daily jobs since it boosts team collaboration and public communication. However, to make an appealing data story, data workers spend tremendous efforts on various tasks, including outlining and styling the story. Recently, a growing research trend has been exploring how to assist data storytelling with advanced artificial intelligence (AI). However, existing studies may focus on individual tasks in the workflow of data storytelling and do not reveal a complete picture of humans' preference for collaborating with AI. To better understand real-world needs, we interviewed eighteen data workers from both industry and academia to learn where and how they would like to collaborate with AI. Surprisingly, though the participants showed excitement about collaborating with AI, many of them also expressed reluctance and pointed out nuanced reasons. Based on their responses, we first characterize stages and tasks in the practical data storytelling workflows and the desired roles of AI. Then the preferred collaboration patterns in different tasks are identified. Next, we summarize the interviewees' reasons why and why not they would like to collaborate with AI. Finally, we provide suggestions for human-AI collaborative data storytelling to hopefully shed light on future related research.