'The Five' co-hosts discuss Elon Musk's warning to Tucker Carlson about artificial intelligence's potential to destroy civilization. A California lawmaker introduced legislation that would criminalize using artificial intelligence to create pornography while using a person's likeness without consent. Assembly member Tri Ta, a Republican representing Westminster, California, introduced the legislation in February that aims to punish people up to $1,000, or a year in jail, if they distribute "deepfake" porn depicting an individual without their consent. "This bill would make it a crime for a person to knowingly, and without the consent of the depicted individual, distribute to, exhibit to, or exchange with others, or offer to distribute to, exhibit to, or exchange with others audio or visual media that falsely depicts an individual engaging in sexual conduct that would appear to a reasonable observer to be an authentic record of the conduct. By creating a new crime, this bill would impose a state-mandated local program," a legislative council's digest of the bill states.

Argenti also likened the advent of powerful generative artificial intelligence systems such as ChatGPT to the invention of the printing press, and predicted the technology will transform how businesses store and organize institutional knowledge, according to the email. He also raised the question of whether A.I. could make rising inequality worse. Goldman Sachs declined to comment on Argenti's message. In the email, Argenti said that while others have said generative A.I. will be more impactful than the discovery of fire, the debut of the internet, or the move to cloud computing, he believed that a better analogy is the invention of the printing press, which had the effect of both democratizing access to knowledge as well as massively accelerating the codification of knowledge. Argenti said that while "efficiency gains are capturing a lot of the mindshare" he believed "LLMs are a breakthrough in knowledge more than they are in productivity."

Card et al.'s classic paper "The Design Space of Input Devices" [4] established the value of design spaces as a tool for HCI analysis and invention. We posit that developing design spaces for emerging pre-trained, generative AI models is necessary for supporting their integration into human-centered systems and practices. We explore what it means to develop an AI model design space by proposing two design spaces relating to generative AI models: the first considers how HCI can impact generative models (i.e., interfaces for models) and the second considers how generative models can impact HCI (i.e., models as an HCI prototyping material).

Score-based generative models (SGMs) are a family of generative models which achieve state-of-the-art performance for generating audio and image data [Soh+15; HJA20; DN21; Kin+21; Son+21a; Son+21b; VKK21]; see, e.g., the recent surveys [Cao+22; Cro+22; Yan+22]. One notable example of an SGM are denoising diffusion probabilistic models (DDPMs) [Soh+15; HJA20], which are a key component in largescale generative models such as DALL E 2 [Ram+22]. As the importance of SGMs continues to grow due to newfound applications in commercial domains, it is a pressing question of both practical and theoretical concern to understand the mathematical underpinnings which explain their startling empirical successes. As we explain in more detail in Section 2, at their mathematical core, SGMs consist of two stochastic processes, which we call the forward process and the reverse process. The forward process transforms samples from a data distribution q (e.g., natural images) into pure noise, whereas the reverse process transforms pure noise into samples from q, hence performing generative modeling. Implementation of the reverse process requires estimation of the score function of the law of the forward process, which is typically accomplished by training neural networks on a score matching objective [Hyv05; Vin11; SE19]. Providing precise guarantees for estimation of the score function is difficult, as it requires an understanding of the non-convex training dynamics of neural network optimization that is currently out of reach. However, given the empirical success of neural networks on the score estimation task, a natural and important question is whether or not accurate score estimation implies that SGMs provably converge to the true data distribution in realistic settings.

Super-resolving the coarse outputs of global climate simulations, termed downscaling, is crucial in making political and social decisions on systems requiring long-term climate change projections. Existing fast super-resolution techniques, however, have yet to preserve the spatially correlated nature of climatological data, which is particularly important when we address systems with spatial expanse, such as the development of transportation infrastructure. Herein, we show an adversarial network-based machine learning enables us to correctly reconstruct the inter-regional spatial correlations in downscaling with high magnification of up to fifty while maintaining pixel-wise statistical consistency. Direct comparison with the measured meteorological data of temperature and precipitation distributions reveals that integrating climatologically important physical information improves the downscaling performance, which prompts us to call this approach $\pi$SRGAN (Physics Informed Super-Resolution Generative Adversarial Network). The proposed method has a potential application to the inter-regionally consistent assessment of the climate change impact. Additionally, we present the outcomes of another variant of the deep generative model-based downscaling approach in which the low-resolution precipitation field is substituted with the pressure field, referred to as $\psi$SRGAN (Precipitation Source Inaccessible SRGAN). Remarkably, this method demonstrates unexpectedly good downscaling performance for the precipitation field.

Tesla CEO Elon Musk is planning to launch an artificial intelligence startup that would go head-to-head with OpenAI, the Financial Times reported Friday. Musk -- the CEO of Tesla, SpaceX and Twitter -- has been building a team of researchers and engineers and has been in conversation with multiple investors, the Financial Times reported, citing sources familiar with the matter. He has also reportedly been recruiting from other top AI firms, including Alphabet-owned DeepMind. "It's real and they are excited about it," a source familiar with the matter told the Financial Times. Musk has secured thousands of Nvidia GPU processors, according to the report.

You've probably heard a lot about both Bing Chat and ChatGPT in recent weeks. Generative AI is here to stay and even if it's not something you can see yourself using in the long term, it's certainly worth trying these tools out and educating yourself a little more on them. Bing Chat is in some ways quite like ChatGPT. Microsoft is a big investor in OpenAI, the company behind ChatGPT, and uses the latest GPT-4 model in the backend of Bing Chat. So, there are definite similarities in the way the two operate.

Billionaire Elon Musk is working on launching an artificial intelligence start-up that will rival ChatGPT-maker OpenAI, the Financial Times reported on Friday, citing people familiar with his plans. Twitter-owner Musk is assembling a team of AI researchers and engineers, according to the FT report, and is also in discussions with some investors in SpaceX and Tesla about putting money into his new venture. Musk's plan for the firm comes weeks after a group of AI researchers and executives, including himself, called for a six-month pause in developing systems more powerful than OpenAI's GPT-4, citing potential risks to society. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

In last weeks, many of us have been experimenting with the powerful Azure OpenAI APIs, either in the playground or via REST API or Python SDK. However, those APIs alone are not sufficient to build powerful applications.

A new kind of AI job is emerging--and it pays six-figure salaries and doesn't require a degree in computer engineering, or even advanced coding skills. With the rise in generative artificial intelligence, a host of companies are now looking to hire "prompt engineers" who are tasked with training the emerging crop of AI tools to deliver more accurate and relevant responses to the questions real people are likely to pose. Some of these jobs can even pay up to $335,000 a year. Anna Bernstein, a 29-year-old prompt engineer at generative AI firm Copy.ai in New York, is one of the few people already working in this new field. Her role involves writing text-based prompts that she feeds into the back end of AI tools so they can do things such as generate a blog post or sales email with the proper tone and accurate information.