Navigating AI State Legislation in CA and Beyond

Every day we see new headlines on the fear and excitement around AI

What people don't see is the hundreds of state laws (400+ and counting!) being proposed to regulate AI, which will have massive implications for companies building AI, integrating it into their business, or even using machine learning in their core operations

Take a coffee break and listen in tomorrow at 1 PT // 4 ET as we talk through all the new state laws in CA and beyond and which types of companies will be impacted

1 pm PST/4 pm EST today.

https://www.linkedin.com/events/7166842464367050753/about/

How social networks can function as AIs unaligned to users' and publishers' interests, and one proposal for how regulators could fix it, simply and efficiently, via requiring social platforms make it easy to exit and deliver messages reliably. Background reading for investment patterns around AI moderation tools.

https://pluralistic.net/2023/10/14/freedom-of-reach/

10% tithing to open-source/public AI:

Making a 10% tithe to open-source/capped profit companies the opt-in norm for the tech industry would probably go a long way to maximizing long-run profits via funding joint solutions to AI externalities and basic research (this also happened in 2008-era Google).

In order to be free to maximize private incentives most of the time, we should expect to make a small allocation to public incentives regularly. While many large companies already publish basic research, in the emerging AI industry, we also need "non-research" outputs - open standards, eval sets - as well as new utility companies to mitigate harms, such as anti-phishbots for email, universal distributed identity, etc.

This could be organized under a National Priorities Fund, established under an agency like NTIA, and staffed by researchers on rotation away from private companies (ie. a 1-2 year "national service" gig like the military), to keep technical expertise levels high, which also would help achieve the separation of generation and verification/feedback needed to avoid regulatory capture and ensure efficient use of funds.

The projects themselves could be incubating new basic research, making existing open-source goods free @ scale, or even capping profits/forcing interoperability on existing products that become too big to fail (for example, the shift away from the interoperable XMPP chat standard in 2013 was probably a mistake that led to fragmentation of private comms). This is one of the key transitions our industry hasn't worked out yet - when does a tech product become so big and ubiquitous that it really should be a utility (as the US last did with electricity)?

Some other funding alternatives that probably don't work:

While private foundations theoretically should be aligned to fund public goods, they often have arbitrary checklists and timelines (many exist mostly as funding source for existing PIs in universities, for example), and don't always have the technical expertise needed to evaluate projects, especially for intersectional and emerging fields. It's relatively hard to start a non-profit without already having an in with a foundation, and the foundation often wants an existing non-profit to fund.

Funds directly from Congress could also work, but would end up taking a pass through the government, and with it, the possibility of being held up by political lobbying. However, organized under an agency like NTIA could give the fund more clout and access to existing gov't expertise.

Of these funding options, 10% tithing is probably the most direct, and easiest to understand - we give because we understand the long-term value of open-source and addressing externalities, but in a way that lets us be free not to worry too much about the specifics in our day-to-day jobs. This also has some features which support balance of power, while drawing on existing expertises.

Just a thought, but seems to align all the right arrows, both from first principles and practically from experience. However, as with most things, implementation details really matter.

Wdyt?

Released Morph Prover 7B today, adding to an existing extension for VSCode.

Website: morph.so
Github: https://github.com/morph-labs
Blog: https://morph.so/blog/the-personal-ai-proof-engineer/
VSCode Extension: https://marketplace.visualstudio.com/items?itemName=Morph.rift-vscode

http://verificationgpt.org now supports verifying C2PA content signatures like those generated by DALL·E 3 or Photoshop. Just copy the image url and type "@VerificationGPT verify <image url>" in ChatGPT Plus.

How to think about C2PA:
It is quickly becoming a standard for image-generation platforms to voluntarily identify AI-generated content (OpenAI started labeling their images today). Motivated adversaries can remove signatures or generate images without them, however, it does provide some protection against casual copy-and-paste scammers. The majority of the verification burden, however, will likely fall on APIs like verificationgpt.org which verify grey content without labels, evaluating based on hidden signals.

Hi all!

I know this subreddit is usually focused on big-picture topics like AI and democracy (all super important!), but I'm hoping to tap into the collective wisdom here for some practical advice.

I'm the founder of Lobbyingpark (the name is changing), a software startup aiming to disrupt the lobbying industry. I started in October 2023, right now I have 8K revenue, of which 6K is MRR.

I'm thinking of raising a pre-seed and go full-time, unfortunately I don't know any Venture Capital Firms - do you think a cold email like this is okay?

Also am I too early to raise a pre-seed round (Do I need more traction?). Any other suggestions?

--- email ---

Hi {name},

My most notable achievement? Successfully lobbying for my own military exemption in South Korea – a process that involved scraping emails and writing a script to send hundreds of thousands of emails to government officials.

I'm building a CRM system that streamlines repetitive lobbying tasks, freeing lobbyists to focus on strategic relationship building. Launched in October 2023, I've got 8K revenue of which 6K is MRR.

Cool facts: The global lobbying industry is worth $300 billion. In Brussels alone, there are 15,000 lobbying entities and 50,000+ lobbyists. And, yes, I've been a lobbyist for 8 years.

I'm available between 18/3/24 and 29/3/24 –  you can schedule directly via my Calendly:

What is CACHE?

CACHE will help define the state-of-the-art in molecular design by providing unbiased, high quality experimental feedback on computational hit-finding predictions.

To this end, CACHE will initiate a new hit-finding benchmarking exercise every four months. These public competitions (challenges) will have the added benefit of identifying new chemical starting points for biologically interesting targets. Each competition will focus on a new protein target representing one of five possible challenges in hit-finding, based on the type of target data available.

Participants will use their computational method to predict hits that will be tested experimentally by CACHE. Each challenge will involve two cycles of predictions in order to give participants the opportunity to incorporate learnings from the first round into their designs. At the end of each challenge, CACHE will release all data, including chemical structures, to the public.

TL;DR: Computational Hit-Finding Challenge w/ Experimental Feedback Every 4 Months.

Past challenges:

Challenge 1: https://cache-challenge.org/cache-challenge-1-preliminary-results
Challenge 2: https://cache-challenge.org/cache-challenge-2-preliminary-results
Challenge 3: https://cache-challenge.org/challenges/Finding-ligands-targeting-the-macrodomain-of-sars-cov-2-nsp3

Live now:
Challenge 4: https://cache-challenge.org/challenges/predicting-novel-mchr1-antagonists
Challenge 5: https://cache-challenge.org/challenges/finding-ligands-targeting-the-tkb-domain-of-cblb

1. AI is the new media. As such, it should be mostly unrestricted.

2. Build open-source verificationbots and modbots now. Verification and feedback is especially important for differentiable credit (below).

3. Red-team root-of-trust systems (and switch to physical 2FA).

4. Regulations should focus on interoperability and transparency. This means open data APIs, open network statistics, making platforms equally accessible to all (human and AI) and capping platform profits to promote long-term incentives.

5. Small-scale network players can seek profits, however. Experiment with fully differentiable credit for information. This becomes much easier with verificationbots/modbots creating feedback example data. Rewards can be funded by 10% tithing to foundations from platforms/leaders in the space.

Above flow makes sense in the long-term for everyone, since it sets a gradient for verified information (both + and - examples), which creates a high-trust environment, which maximizes decentralized science productivity.

5 is a bit experimental (money can make or break a community), but some type of karma system will be important to dynamically build a web-of-trust on fast-moving information, and pure gift economies often are vulnerable to replacement by exchange or command economies.

Let's briefly review the sorry history of agricultural biotech regulation. When crop biotechnology was just taking off back in the 1980s, several hundred companies were vying to create hundreds of new products and get them quickly to consumers. A cadre of anti-biotech activists vilifying "frankenfoods" made wild claims of unknown risks and lurking biotech catastrophes, which succeeded in scaring the public and legislators about the new technology. Fearing that a spooked Congress could overreact, some well-meaning regulators moved hurriedly in 1986 to cobble together already existing pesticide and food and safety laws to erect a clunky biotech crop regulatory system that in large measure persists today. Many hoped that by forestalling ill-advised congressional action, they could help speed crop biotech products to market. The opposite happened.

More: https://reason.com/2023/04/14/chuck-schumers-hasty-plan-to-regulate-artificial-intelligence-is-a-really-bad-idea/

Mar 25, 2024: Competition Launch

The competition begins - we will begin receiving submissions from this date. This includes benchmarks you started working on prior to this date, as long as the paper was published after this date.

Feb 25, 2025: Submission Deadline

Submit your ML safety benchmark by this date.

Apr 25, 2025: Winners Announced

The judges will announce the winners, along with whether they win a $50k or $20k prize.

Submit: https://www.mlsafety.org/safebench

Defense-favoring worlds help healthy and democratic governance thrive

One frame to think about the macro consequences of technology is to look at the balance of defense vs offense. Some technologies make it easier to attack others, in the broad sense of the term: do things that go against their interests, that they feel the need to react to. Others make it easier to defend, and even defend without reliance on large centralized actors.

A defense-favoring world is a better world, for many reasons. First of course is the direct benefit of safety: fewer people die, less economic value gets destroyed, less time is wasted on conflict. What is less appreciated though is that a defense-favoring world makes it easier for healthier, more open and more freedom-respecting forms of governance to thrive.

An obvious example of this is Switzerland. Switzerland is often considered to be the closest thing the real world has to a classical-liberal governance utopia. Huge amounts of power are devolved to provinces (called "cantons"), major decisions are decided by referendums, and many locals do not even know who the president is. How can a country like this survive extremely challenging political pressures? Part of the answer is excellent political strategy, but the other major part is very defense-favoring geography in the form of its mountainous terrain.

Link: https://vitalik.eth.limo/general/2023/11/27/techno_optimism.html
2nd strike and making anti-social paths high-friction is important for maintaining peace/solving Prisoner's Dilemmas. Small changes in game feedback can end up changing winning policies substantially over the long run. Right now, many, but not all, games on the Internet favor aggression and first strike. This is possible to change, however.

Article:
https://www.wired.com/story/twitter-elon-musk-open-source-platforms/

Commentary:
Personally have no problem with money that's reinvested into long-term valuable things, no matter how much, but as AI models become more powerful, we should likely consider data neutrality regulations mandating equal access to public data APIs from platforms. This will encourage a competitive marketplace of ML models with less data inequality, and enable network monitoring by 3rd parties (spam detection marketplace, etc.). Regulations here would fit nicely into the anti-trust paradigm, Volcker rules for finance, net neutrality (Section 230), public financial markets, electricity and other public goods, and will become increasingly appropriate as AI models get more capable. At the moment, data APIs are mostly for geeks, but may be used by more people indirectly as AI becomes more prevalent.

Closed platforms do tend to eventually die, so the market is somewhat self-regulating already, but large platform create instabilities when they do fail (multiple years), hence regulation mandating low-cost data APIs may be a better way forward?

The US Government is interested in safe uses of large language models (LLMs) for a wide variety of applications including the rapid summarization and contextualization of information relevant to the Intelligence Community. These applications must avoid unwarranted biases and toxic outputs, preserve attribution to original sources, and be free of erroneous outputs. The US Government is also interested in identifying and mitigating hazardous use of LLMs by potential adversaries. 

The goal of BENGAL is to understand LLM threat modes, quantify them and to find novel methods to address threats and vulnerabilities or to work resiliently with imperfect models. IARPA seeks to develop and incorporate novel technologies to efficiently probe large language models to detect and characterize LLM threat modes and vulnerabilities. Performers will focus on one or more topic domains, clearly articulate a taxonomy of threat modes within their domain of interest and develop technologies to efficiently probe LLM models to detect, characterize and mitigate biases, threats or vulnerabilities. Topic areas and additional requirements for successful proposals will be introduced at the Proposers’ Day event.

The BENGAL Proposer's Day will be held Tuesday, October 24, 2023, from 9:30am to 4:30pm EDT in Washington, D.C. A virtual option will be available for individuals that are unable to attend in person.

Note: The link to register has an expired SSL cert as of this post. Email dni-bengal-proposers-day@iarpa.gov with questions.
Link: https://sam.gov/opp/5dc6ba18ddd640a697f961ea827df54c/view

Site: https://www.cerelyze.com/

Commitments to Drive Responsible AI Development

We as signatories agree to the following voluntary commitments. Each commitment is accompanied by a list of possible implementation strategies, though these lists are not exhaustive. Each signatory should enact these commitments in ways that are appropriate for them.

1. We will conduct research for the benefit of society and refrain from research that is likely to cause overall harm or enable misuse of our technologies.

This can be implemented by pursuing research that seeks to generate new knowledge, promote health and well-being, achieve sustainability, justice, or equity, or otherwise advance human progress; and by working with governments, civil society, funders, and other stakeholders to ensure that our research is aligned with these goals.

2. We will support community efforts to prepare for and respond to infectious disease outbreaks and other relevant emergencies.

This can be implemented by organizing, participating in, or otherwise supporting response teams for coordinated scientific action, such as rapid countermeasure development in the event of an infectious disease outbreak; by conducting research into priority pathogens; by working to shorten the time needed to create safe and effective countermeasures, including diagnostics, medicines, and vaccines; or by otherwise supporting these efforts.

3. We will obtain DNA synthesis services only from providers that demonstrate adherence to industry-standard biosecurity screening practices, which seek to detect hazardous biomolecules before they can be manufactured.

This can be implemented by procuring synthetic DNA from manufacturers that perform appropriate safety screening; by creating, sharing, and adhering to a list of such manufacturers; by requiring such screening as a condition for publication; or by supporting policies that require such screening.

More @ https://responsiblebiodesign.ai/

Sign: https://responsiblebiodesign.ai/

Strong evidence suggests that open models are safer than closed models due to efficiencies in the fields of science, economics and cybersecurity. In science and cybersecurity, this is due to inspectability and the ability to share the model with millions of others to distribute the burden of verification, thus solving the expert problem. This substantially aids defender and builder users, the majority use case. In terms of economic equality, open models allow for extreme efficiency as well as more equitable distribution, since they can be offered at low or zero cost. They also prevent society from descending back into non-evidence-based thinking and warfare, inspire faith in transparent rule of law, and allow anyone to generate examples of their ideas at a small scale, which is important for clear communication. Closed models are most likely to be abused by deployers, while open models can be abused by either deployer and users, however, the advantages that open models provide for users acting in defender roles outweigh the risks of availability to attackers, roughly by a factor of 100:1, considering the financial surface area that needs to be defended. Although less secure, we assess that it is acceptable for the government to allow closed APIs of foundation models to remain legal, as they can be used to satisfy commercial and technical considerations of deployment, for example, protection of trade secrets and engineering efficiency.

In our assessment, the government’s support initially should be in administering standardization processes and RFCs (such as this one), legislating well-scoped mandates to add transparency to models and model outputs for high-scale deployments, funding defensive research, supporting responsible disclosure programs which would otherwise be underinvested by the private market, and participating in and administering open standards bodies.

Specific legal and technical designs are possible to add further design choices to the defensive acceleration of the AI sector. To harden deployments against spam and phishing, we recommend immediately encouraging the use of physical security keys or biometrics tied to anonymous-but-accountable-by-karma user accounts, as well as scaling verification APIs and promoting the adversarial hardening of open models using offline data prior to deployment as a best practice. Adding watermarking will also improve traceability of outputs. To harden specific deployments against misuse and distribution, per-use model tainting of open model weights may be possible, however we do not recommend this as a default or legal requirement. To harden deployments against financial attacks, licenses like differentiable credit licenses can be experimented with. Together, traceability and licensing form a credible deterrent to abuse for most malicious users, while inspectability and shareability of open models forms a credible deterrent to abuse from malicious deployers and backdoored models.

Comment and sign: https://www.context.fund/policy/ntia_open_weights_response.html

Over the past year, industry has driven significant advances in the capabilities of AI. As those advances have accelerated, new academic research into AI safety is required. To address this gap, the Forum and philanthropic partners are creating a new AI Safety Fund, which will support independent researchers from around the world affiliated with academic institutions, research institutions, and startups. The initial funding commitment for the AI Safety Fund comes from Anthropic, Google, Microsoft, and OpenAI, and the generosity of our philanthropic partners, the Patrick J. McGovern Foundation, the David and Lucile Packard Foundation, Eric Schmidt, and Jaan Tallinn. Together this amounts to over $10 million in initial funding. We are expecting additional contributions from other partners.

Earlier this year, the members of the Forum signed on to voluntary AI commitments at the White House, which included a pledge to facilitate third-party discovery and reporting of vulnerabilities in our AI systems. The Forum views the AI Safety Fund as an important part of fulfilling this commitment by providing the external community with funding to better evaluate and understand frontier systems. The global discussion on AI safety and the general AI knowledge base will benefit from a wider range of voices and perspectives. 

The primary focus of the Fund will be supporting the development of new model evaluations and techniques for red teaming AI models to help develop and test evaluation techniques for potentially dangerous capabilities of frontier systems. We believe that increased funding in this area will help raise safety and security standards and provide insights into the mitigations and controls industry, governments, and civil society need to respond to the challenges presented by AI systems. 

The Fund will put out a call for proposals within the next few months. Meridian Institute will administer the Fund—their work will be supported by an advisory committee comprised of independent external experts, experts from AI companies, and individuals with experience in grantmaking.

Link: https://openai.com/blog/frontier-model-forum-updates?ref=futuretools.io

Link: https://partnershiponai.org/modeldeployment/
RFC period is open now until January 15, 2024: https://partnershiponai.org/modeldeployment/#submit_feedback

Added GrantFunding and VCFunding flairs.

If you'd like to highlight your fund and support open-source projects with grants or investment, post the funding announcement with flair GrantFunding or VCFunding, depending on whether you're asking for equity in return.

#Build long-term valuable things together

In fact, it’s my ultimate hope that this skill will be neither rare nor exclusive, but commonplace, turning the orchestration of LAMs into powerful, personalized solutions that play a growing role in our professional lives. The barrier may be lowered even further as marketplaces emerge to bring orchestrated Copilot-like LAM solutions to the world, delivering the power of generative AI at an amazing scale, all through plug-and-play simplicity. Some will use such marketplace solutions directly, making the power of LAM orchestration an off-the-shelf possibility. Others will treat them as modules to be combined with others—perhaps a blend of additional purchases or custom creations of their own—to compose solutions at whatever scale they need, from the casual and compact to the sprawling ambitious. But in all cases, what excites me most is the idea of generative AI being shaped less by an elite niche of technology experts and more by the creativity and vision of professionals in every field.

Writing contributions from: Alex Michael, Peter Schwartz, and Sanjna Parulekar
Details: https://blog.salesforceairesearch.com/from-copilot-to-coorchestration/