Many current imaging techniques lack the resolution to image materials on an atomic scale, limiting our understanding of material properties at the most fundamental level.
Our overall knowledge of the chemical reaction space, including the catalysts that drive these reactions, is still rudimentary. We also lack detailed the large materials synthesis and processing datasets needed to enable highly predictive models.
While long-chain nucleic acid synthesis is advancing rapidly, the programmable synthesis of other polymers remains underdeveloped, limiting our capacity to design and produce diverse synthetic polymers.
We currently lack a comprehensive model explaining how biological systems decode and classify chemical signals through olfaction. Understanding this process is critical for applications ranging from flavor science to disease diagnostics to understanding and harnessing animal communication.
Chemical synthesis remains largely manual, limiting throughput and reproducibility. The field requires robust automation to accelerate discovery and production of new molecules.
Protein engineering has largely focused on designing static structures that closely mimic natural proteins. This narrow approach limits the creation of truly novel or highly functional enzymes.
Scientists are constrained to a small number of microbial hosts for bioproduction, limiting the diversity and efficiency of engineered biological systems. Expanding the repertoire of microbial hosts could unlock novel biochemical pathways, enabling the production of a wider array of biomolecules and improving the efficiency of biosynthetic processes. It is important to address any biosafety and biosecurity risks associated with developing such technologies.
Current genetic tools primarily enable modification of simple organisms. Programming more complex organisms and orchestrating entire developmental pathways remains a major challenge.
Applied synthetic biology is underutilized in applications such as building sustainable food systems and repairing the environmental damage caused by conventional agriculture and industry. Despite advances in tools and chassis engineering, there are few robust platforms that translate synthetic biology into scalable, field-ready solutions. This includes not only the production of low-impact proteins and agricultural inputs but also bioremediation technologies for legacy pollutants—such as pestic…
Current bioreactor designs are inefficient when scaling up production processes, limiting the ability to produce bioproducts at industrial scales.
We currently perform synthetic biology using naturally evolved (“kludgy”) cells rather than truly bottom-up engineered cells. This bottleneck limits our ability to design fully customizable biological systems.
Our current methods do not allow precise control over the positional placement of atoms or groups during chemical synthesis, limiting our ability to build molecules with atomic precision. A general-purpose approach to atomically precise fabrication was envisioned by Drexler in the 1980s and Feynman in the late 1950s. DNA origami made a leap in 2006, but DNA is in some key ways a much less precise and versatile nanoscale building material than proteins/peptides. A promising path would extend “DNA…
Modern chip fabs are enormous, multi-billion-dollar facilities with limited versatility in what they can produce. This bottleneck restricts the ability to create assemblies with diverse molecular components on a small scale.
“New materials create fundamentally new human capabilities. And yet…new materials-enabled human capabilities have been rare in the past 50 years.” The core challenge lies in our inability to reliably design and manufacture materials that meet specific engineering requirements–and to do so at an industrial scale and reasonable cost.
Identifying promising new materials is hampered by the slow pace of exploration. The integration of machine learning, physics-based property prediction, and self-dr…
Crystallization is crucial for determining molecular structure, yet many molecules resist forming crystals. Improved computational models of crystal growth are needed to guide experimental efforts.
While many promising materials have been discovered in the lab, current synthesis methods are often too expensive to produce these materials in sufficient quantities. Some examples of novel materials that would be highly enabling include:
• Low activation, thermally conductive materials that are resistant to radiation damage are needed to enable fusion reactors (the first wall material is currently a limitation), spacecraft, etc.
• Materials that emit at the transparency window of the atmosphe…
The cost of materials is often dominated by the cost to obtain their constituent elements. What presents commercially as the “critical minerals problem” masks a larger scientific bottleneck on how we acquire, concentrate, and substitute chemical elements.
Modern manufacturing system design remains complex, with traditional methods relying on outdated processes. AI-based design approaches have the potential to reimagine these systems without relying on the legacy of humanoid robots.
Architectural design and construction planning are complex and labor-intensive. Advanced computational design and AI-driven optimization have the potential to revolutionize how buildings and construction plans are generated.
Despite advances in automation, many bioengineering processes remain highly manual, limiting throughput and reproducibility in laboratory settings.
The simulation and modeling of complex mechanical systems is challenging due to the intricate interplay of multiple physical phenomena. Improved computational models can enhance design and optimization.
Robots have the potential to revolutionize manufacturing, logistics, and many other industries—but only if they are both affordable and capable of high performance. Today’s robotic hardware is often prohibitively expensive and built using legacy designs that do not prioritize cost reduction, modularity, or scalability. Moreover, many robots struggle with dexterity and tactile sensing, and current design practices decouple hardware and software, preventing a co-evolution that could unlock new per…
Modern manufacturing systems largely rely on paradigms developed in the last century where large machines produce components smaller than themselves. This approach is increasingly limited by scaling challenges and cost inefficiencies. To meet future demands, we need to reimagine manufacturing by developing universal robotic construction systems and low-capital, high-energy manufacturing solutions that leverage emerging technologies such as advanced robotics, precision machining, and renewable en…
Bridging the gap between simulated robot behavior and real-world performance remains a significant challenge, particularly for tactile interactions and complex environments.
Modern deep learning and general computation demand enormous energy, limiting scalability and sustainability. Addressing energy efficiency is critical for the next generation of computing platforms, though it also supports potential proliferation of advanced AI and should be advanced alongside AI safety and governance considerations.
Both human mathematicians and current AI systems struggle with proving complex math theorems. Enhancing theorem proving through interactive and automated methods could push the boundaries of mathematical reasoning.
The potential for AI systems to behave unpredictably or dangerously (“go rogue”) is a critical concern. Ensuring safe and controllable AI architectures is essential for reliable operation.
See also:
• https://www.lesswrong.com/posts/fAW6RXLKTLHC3WXkS/shallow-review-of-technical-ai-safety-2024
• https://deepmind.google/discover/blog/taking-a-responsible-path-to-agi/
The risk of AI being misused—whether through malicious intent or unintended consequences—necessitates robust safeguards and countermeasures.
Insecure software can lead to vulnerabilities that undermine the reliability and safety of computational systems. Formal methods and rigorous verification are needed to synthesize secure software.
Current AI systems exhibit narrow reasoning and planning capabilities compared to human cognition. Broadening AI training methods to include holistic, brain-inspired architectures and cognitive frameworks can advance general intelligence (flagging that there is an AI safety risk here).
Biological systems are the sole example we have of complex, evolved computation. Replicating this level of complexity in digital systems could unlock entirely new computational paradigms.
Current models struggle to accurately predict climate tipping points due to the intricate interplay of diverse climatic factors, hindering proactive intervention efforts. Additionally, designing optimal climate control strategies is challenging because of the nonlinear and multifaceted interactions among economic, technological, and social factors.
We have limited capacity to predict key disruptive events, such as solar flares that threaten power grids and communications, alongside an incomplete understanding of natural processes (atmospheric, ocean, etc.) that underpin climate models. We need better monitoring tools for characterizing phenomena that impact climate dynamics, such as aerosol-cloud interactions, and assessing potential interventions such as marine cloud brightening. These issues underscore the need for enhanced observational…
There is a critical need for more precise, rapid, and localized climate intervention strategies. Current approaches lack the fine-grained models and rapid response mechanisms required to adapt to diverse climate impacts, such as heatwaves, which demand swift and effective action. The ability to control local weather phenomena—including cloud formation and hurricanes—could help mitigate climate risks.
There are currently no direct interventions to address climate tipping points such as glacier melt, leaving some critical processes unmitigated. The fundamental science and engineering principles behind emergency climate interventions remain largely untested at relevant scales, limiting our preparedness for rapid climate change.
See: https://www.outli
