AI for Science & Safety Nodes RFP:
III. AI-first science: Bio, Neuro, Nano
Foresight has long funded the science of the future, from cryonics and longevity to molecular nanotechnology, and we want to continue to do so. If the transition to superintelligence goes well, science may begin to move extremely fast, and the question quickly becomes what we do with vastly greater scientific capability. Across all three areas below we especially want AI-first approaches, and work that has thought about: what important science is stuck, what is it actually stuck on, and can we build the thing that removes that constraint?
Application deadline: 31st October 23:59 PDT
5. Automated nanotechnology
If possible, advanced molecular nanotechnology has a hard-to-conceive range of applications, from advanced manufacturing, materials, medicines, sensors, energy systems, electronics and instruments – hardly any area of life that is defined by how atoms and molecules are arranged would be left untouched.
Foresight was founded in 1986 on Eric Drexler’s Engines of Creation, which paired nanotechnology, the engine of creation, with AI, the engine of design. Forty years later the design engine is here. The engine of creation has been lagging behind but recently progress is accelerating, leading to “Nanotechnology’s Spring” due to advancements in existing approaches such as STM/AFM tech, protein-engineering, DNA origami, and advances in simulation tools.
The building blocks for nanotech are emerging but they exist largely on separate tracks. Nanotech is still stuck in the valley of technological death where it is too transdisciplinary for academia and too early for venture funding. The connective tissue that combines novel approaches beyond incremental gains into functioning molecular machines that can produce real work is still missing. This is the gap we want to fill and thanks to AI, various aspects like simulation to design should now be within reach.
What we’re looking for: Examples of things we would like to fund include AI methods for nanoscale design, simulation, and assembly and closed-loop fabrication and characterization tools. Other work might highlight credible technical application pathways from today’s toy problems to the real-world issues in medicine, environment and bottom-up production. We also welcome more work on defensive applications and safety frameworks for advanced nanotechnology.
Why it matters: The implications of general purpose, high-throughput atomically precise manufacturing would be hard to exaggerate: the potential for inexpensive, non-polluting assemblers/nanofactories could create a world of unprecedented abundance, thereby transforming the world economy. It is important to recognize the risks in realizing this technology and work to safeguard its beneficial use.
Example projects and deliverables:
- AI design and simulation tools for realistic, functioning nanomachines.
- Demonstrated assembly or fabrication steps.
- Prototypes or roadmaps for defensive and safety-relevant applications.
6. Whole-brain emulation
Once considered a distant science fiction concept, WBE has gained traction due to recent advancements in neuroscience, machine learning, AI and similar enabling tools. A simulation of the brain is valuable for several reasons: it may yield critical near-term insights for neuroscience and medicine, could facilitate digital longevity, cognitive enhancement, and space exploration or may help answer fundamental questions about sentience and human nature. While these reasons alone justify enthusiasm for WBE development, recent progress in AGI has added urgency to the field. WBE presents a promising alternative to misaligned AI: a human-like general intelligence that could be more interpretable, easier to align with our values, and capable of integrating with or competing against AGI.
What we’re looking for: We are looking for direct work in whole-brain emulation, as well as enabling work that removes concrete scientific bottlenecks. Examples of projects could be things like cheaper and faster brain mapping, or making neurobiological data easier for models to learn from. We are interested in both high-fidelity approaches, which build toward increasingly realistic digital representations of the brain, and lo-fi emulation approaches, which seek to predict behavior of an organism from behavior and neural data, as a faster and more cost-effective route.
Example projects and deliverables:
- Better, cheaper brain preservation
- Bringing down the cost of connectomics through better imaging techniques, cheaper proofreading or other cost-saving techniques
- Proving lo-fi emulation pathways including scalable behavior and neural data collection methods, successful modeling, and related approaches
- New ways to shorten experimental feedback loops in neurobiology and related frontier fields.
7. Replacement, Enhancement, Biostasis
If the transition to highly capable AI goes well, solving longevity might not be that difficult. Right now, however, it is. We want to fund work on longevity, replacement, and biostasis that helps people alive today make it to that future. We are not only interested in work that brings human functionality back to baseline but encourage work that improves function beyond current baseline limitations.
What we’re looking for: We are looking for direct work in longevity, human enhancement, and related areas, as well as enabling work that removes concrete scientific bottlenecks. Beyond slowing or reversing aging itself, we are especially interested in replacement, which bypasses the complexity of aging by swapping old tissue for young, from small-scale cell and tissue replacement through whole organs or even parts of the brain; and biostasis, saving lives with reversible biostasis for organs and full humans, buying time for medicine to catch up.
Example projects and deliverables:
- Replacement: bypassing aging complexity by swapping old for young tissue, including tissue engineering, organ synthesis, 3D bioprinting, and progressive replacement strategies.
- Biostasis: reversible stasis for organs and full humans, including improved preservation and revival protocols, organ banking, and validation methods.
- New ways to shorten experimental feedback loops in longevity and related frontier fields.
- Datasets that let us independently test genetic predictors, gene editing, and better measures of aging.
FAQ
Proposals are first reviewed in-house for fit and quality. Strong submissions are sent to technical advisors for further evaluation. If your proposal advances, we may follow up with written questions or a short call. If you choose to opt into Lightcone Commons sharing, Foresight may also share selected applications with Lightcone Commons for evaluation, and possible additional funding consideration.
Unfortunately, due to the number of applications we receive, we are unable to provide individual feedback to unsuccessful applicants.
- Alignment with the specific RFP focus area: the degree to which the project addresses the selected focus area as outlined in the RFP
- Impact on reducing existential risks from AI: the extent to which the project can reduce existential risks associated with AI, focusing on achieving significant advancements within short timelines.
- Feasibility within short AGI timelines: the project’s ability to achieve meaningful progress within the anticipated short timeframes for AGI development. We prioritize projects that can demonstrate concrete milestones and deliverables in the next 1-3 years.
- AI-first work: Instead of large team budgets, this means smart allocation of resources and compute to automate workflows.
- Capability to execute: the qualifications, experience, and resources of the applicant(s) to successfully carry out the proposed work. Strong teams with proven expertise in the field will be prioritized.
- High-risk, high-reward potential: the level of risk involved in the project, balanced with the potential for substantial, transformative impact on the future of AI safety. We encourage speculative, high-risk projects with the potential to drive significant change if successful.
- Requirement for open source: We require the work product (code, data, and outputs) of the funding to be open-sourced.
- We fund both short-term and longer projects. Grants are typically paid in one lump sum. However, for larger projects spanning multiple years, payments may be made in tranches, with each subsequent tranche contingent upon the successful completion and reporting of previous milestones.
- We can fund overhead costs up to 10% of direct research costs, where these directly support the funded work.
- Successful applicants must pass our due diligence process, which includes confirming your connections to Foresight Institute, sharing any ongoing criminal proceedings, bankruptcy, tax documents etc., and sharing an itemized budget, project plan and organizational documents.
- By accepting funding, grantees agree that we may list their project on our website and share the project title and project lead on for example social media. If you prefer for your project to remain private, please inform us.
- Grants are subject to basic reporting requirements. Grantees are expected to submit brief progress updates at regular intervals, describing use of funds and progress against agreed milestones.
- Tax obligations vary by country and organization type. Applicants are responsible for understanding and complying with any applicable tax requirements.