This is my public notebook for keeping track of my reading on the three competing forces at play: the offer of innovative products for virtual experimentation (mostly Generative AI these days with other types as well), the regulatory activity around this innovation, and the changes in the behavior of R&D professionals as they go about their daily business. Innovation, regulation and adoption - the latter being the final word on the validity of the first and the efficacy of the second component.
AI - based products work alongside the human in the R&D loop
ABB partners wth Microsoft on Gen AI, UpCode deployed and AI assistant to help search North American building codes.
A robot conducts experiments in the semiconductor industry- a potentially more efficient and more sustainable approach.
Will AI regulation force the genie back in the bottle?
As part of a recent increase in the AI - related activity, strategy was announced for US AI policymaking, following individual agencies like the FTC, the Department of Commerce and the US Copyright Office. Protecting innovation and democratic values while supporting the Silicone Valley and competing with China is high on the agenda. Section 230 of the internet law giving tech companies a free pass for content published on their platforms is up for debate in the fall.
Internal and external security testing of AI systems before their release/ sharing information across the industry and with governments, civil society and academia on managing AI risks/investing in cybersecurity and insider threat safeguards, specifically to protect model weight, impacting bias/encouraging third- party discovery and reporting of vulnerabilities/publicly reporting all AI systems' capabilities, limitation and areas of appropriate and inappropriate use/prioritizing research on bias and privacy/helping to use AI for beneficial purposes such as cancer research/developing robust technical mechanisms for watermarking - mark text, audio and visual content as machine-generated.
The announcement of the Frontier Model Forum came on the heels of the above pledge to self-regulate. So far, Big Tech is following the traditional path of high-risk industries of forming self-regulation mechanisms to collaborate and mitigate the inevitable regulation by the state. The same experience tells us that a third - party will be necessary for assurance, even to the members of the forum.
Adoption friction. Running up against the physics of the real world. Meanwhile, R&D experts and other humans ...
Reporting on 10 shifts in the state of organizations 2023: most are talent-related. Hybrid work is here to stay, new ways of recruitment and retention are shaping up, and most organizations who announce new functionality do not have the capability to support it - they are bootstrapping it to remain competitive, applying additional pressure on their workforce.
"The verdict on when—even if—that will happen with generative AI remains uncertain. “Once we figure out what good writing at scale allows industries to do differently, or—in the context of Dall-E—what graphic design at scale allows us to do differently, that’s when we’re going to experience the big productivity boost,” Avi Goldfarb (economist, UofT) says. “But if that is next week or next year or ten years from now, I have no idea". It is not a given that we as a society have to take the direction set by Big Tech "there is at least some momentum behind open-source projects in generative AI, which could break Big Tech’s grip on the models. Notably, last year more than a thousand international researchers collaborated on a large language model called Bloom that can create text in languages such as French, Spanish, and Arabic. And if Coyle and others are right, increased public funding for AI research could help change the course of future breakthroughs."
while this looks like a regular strike for more pay, its consequences will have a spillover effect into Ai regulation and other industries adopting Gen AI - which is most industries. The is a conflict between the creative class and the technology poised to replace or devalue it.
Big-name investors sank hundreds of millions into Netflix’s new vision. As it began producing original content in 2013, it applied a distinctly next-wave Silicon Valley ethos. It would make massive upfront investments, bankrolling huge productions.
As with Uber and Lyft, whose bottomless chests of venture capital allowed them to conquer new markets once dominated by stodgy old competitors — price was no object. Then they ran our of money and decided to squeeze the layout. and they did not want to disclose the data behind the re-runs. “Netflix lied to the public and Wall Street,” he says, telling them, “‘you can watch every show ever made in perpetuity, with no ads, for $15.99 a month forever.’ “That’s ludicrous.”
Ludicrous if you want to pay the people who actually create those shows for you, anyway.
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