There's an old saying that the surest path to profit in a gold rush is to bet on the companies supplying the pickaxes -- and that idea is now igniting the toolmakers and wholesalers of today's generative AI boom. Why it matters: It's comparatively easy to see a broad tech trend on the horizon, but often much harder to home in on who will win and over what timeframe. Netscape and BlackBerry serve as cautionary tales. Software veteran Tom Siebel, who now runs enterprise AI firm C3.ai, sees a wide swath of the tech industry benefiting from the AI boom. Here are three categories of companies that serve as the modern-day axe vendors for AI.

April 14 (Reuters) - 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 Inc (TSLA.O) 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. Companies from Microsoft Corp (MSFT.O) to Alphabet Inc (GOOGL.O) are pushing to incorporate Generative AI, the technology behind chatbot sensation ChatGPT, into their offerings. However, ChatGPT is facing pushback as regulators call for well-defined rules ahead of its mass adoption.

We are in the early days of generative AI, and there's a gold rush to gain position and prominence in the sector as it takes off. But with this rush to implementation across a bewildering range of use cases come associated risks. Get artificial intelligence (AI) right, and it can be an incredibly creative, labor-saving, and efficiency-improving solution. Employ it badly, and you risk social, financial, and even legal consequences. First, there's the difficulty of predicting their value or likely success, given the unpredictability of AI outputs.

Generative AI will change the nature of how we interact with all software, and given how many brands have significant software components in how they interact with customers, generative AI will drive and distinguish how more brands compete. In our last HBR piece, "Customer Experience in the Age of AI," we discussed how the use of one's customer information is already differentiating branded experiences. Now with generative AI, personalization will go even further, tailoring all aspects of digital interaction to how the customer wants it to flow, not how product designers envision cramming in more menus and features. And then as the software follows the customer, it will go to places that range beyond the tight boundaries of a brand's product. It will need to offer solutions to things the customer wants to do.

The publication of this series was inspired by the release ChatGPT, which is a generative artificial intelligence (AI) chatbox developed by Open AI. ChatGPT uses machine learning and natural language processing to provide relatively sophisticated and human-like responses to almost any question. Unlike traditional AI systems, ChatGPT is a generative AI platform, which means that the content it creates is "new," rather than a reiteration of something that already exists. As ChatGPT demonstrates, content can be produced through generative AI in a matter of seconds and may be composed of images, videos, audio, text or even code. The reality is that generative AI is well on the way to becoming not just faster and cheaper, but better in some cases than what humans create by hand.

Twitter and Tesla CEO Elon Musk weighs in on the dangers of artificial intelligence, the future of Twitter and more in an exclusive'Tucker Carlson Tonight' interview. OpenAI CEO Sam Altman says that a letter signed by Twitter CEO Elon Musk and others in the technology community calling for a pause on "giant AI experiments" wasn't the right way to address the issue. Musk, Steve Wozniak, and other tech leaders signed the letter in March, which asked AI developers to "immediately pause for at least 6 months the training of AI systems more powerful than GPT-4." During a virtual appearance at the Massachusetts Institute of Technology on Thursday, Altman addressed the letter. "There's parts of the thrust that I really agree with," Altman said, adding that his team spent more than six months after completing the training of ChatGPT 4 to study safety components before it was released.

'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.