World Solve

Robotics and AI may reduce labor demand in agriculture while improving productivity. The challenge is designing ownership, retraining, land, and revenue models that prevent rural communities from losing economic agency. The solution must work with limited capital, intermittent services, and local maintenance capacity. The research opportunity is to define measurable success criteria, test the approach in realistic settings, identify failure modes, and create a pathway from prototype to accountable deployment.

Growing desalination could produce concentrated brine and chemical waste at scales that threaten coastal ecosystems. The challenge is improving recovery and discharge methods while accounting for cumulative regional impacts. Decisions must account for people who will inherit long-lived infrastructure, environmental changes, and institutional commitments. The research opportunity is to define measurable success criteria, test the approach in realistic settings, identify failure modes, and create a pathway from prototype to accountable deployment.

Growing desalination could produce concentrated brine and chemical waste at scales that threaten coastal ecosystems. The challenge is improving recovery and discharge methods while accounting for cumulative regional impacts. The solution must produce useful coordination and accountability while minimizing collection of sensitive personal and behavioral data. The research opportunity is to define measurable success criteria, test the approach in realistic settings, identify failure modes, and create a pathway from prototype to accountable deployment.

Growing desalination could produce concentrated brine and chemical waste at scales that threaten coastal ecosystems. The challenge is improving recovery and discharge methods while accounting for cumulative regional impacts. The design must remain functional when standards, trade routes, data access, and diplomatic cooperation are unreliable. The research opportunity is to define measurable success criteria, test the approach in realistic settings, identify failure modes, and create a pathway from prototype to accountable deployment.

Growing desalination could produce concentrated brine and chemical waste at scales that threaten coastal ecosystems. The challenge is improving recovery and discharge methods while accounting for cumulative regional impacts. The solution must work with limited capital, intermittent services, and local maintenance capacity. The research opportunity is to define measurable success criteria, test the approach in realistic settings, identify failure modes, and create a pathway from prototype to accountable deployment.

Companies can use AI to generate legal language that's technically accurate but deliberately obscures consumers' actual rights, making it harder to understand what protections truly apply. Improving genuine clarity in these documents remains unresolved.

Fake audio clips of political figures making statements they never actually said can spread quickly, and current detection tools don't always catch high-quality fakes before real damage to public understanding occurs. Improving detection speed and reliability remains urgent and unresolved.

AI can now craft phishing emails with perfect grammar and highly personalized context that closely mimic real business correspondence, making them far harder for both people and filters to catch. Staying ahead of this improving deception remains an unresolved challenge.

Voice assistants can still struggle to accurately understand commands in households with background noise, multiple speakers, or mixed languages, leading to frustrating and sometimes unsafe misunderstandings. Improving this reliability remains an ongoing technical challenge.

Voice and text-based interfaces for controlling software are improving, but ensuring they interpret complex commands accurately and safely, without unintended consequences, remains unresolved. This limits how much control can be safely handed over to natural language alone.

Without standardized measurement, it's difficult to compare the true environmental cost of different AI systems or hold companies accountable for improving efficiency. A consistent, transparent measurement standard remains undeveloped.

Specialized AI hardware is improving, but there's still a significant tradeoff between processing power and energy efficiency that hasn't been fully solved. Breaking this tradeoff could dramatically reduce AI's growing environmental footprint.

Weak enforcement and high demand for ivory and rhino horn sustain illegal wildlife trade networks. AI-powered camera traps and ranger-support drone systems are improving anti-poaching efforts but need wider deployment.

Testicular cancer is highly treatable when caught early, but a lack of widespread screening awareness means some cases are diagnosed later than they should be. Improving early detection and awareness remains an underaddressed public health gap.

When judges' careers, safety, or institutional funding depend on staying in the government's good graces, their ability to rule impartially against powerful officials can be compromised. Protecting judicial independence from this kind of pressure remains an ongoing struggle.

We have very good approximations, but no exact formula exists that perfectly predicts the count of primes below a chosen number. Improving this approximation remains an active and important area of research.

Personalized medicine is improving, but doctors still can't always predict in advance which patients will respond to a given treatment. Solving this could avoid painful trial-and-error treatment periods for patients.

We tend to believe humanity has become more moral over time, abolishing slavery and expanding rights, but philosophers debate whether this represents real progress or just changing preferences. Settling this affects how confident we can be about future ethical judgments.

Automated phone systems using AI can sometimes fail to correctly route urgent or unusual complaints, leaving customers stuck in unhelpful loops during time-sensitive situations. Improving reliability in these systems for edge cases remains an unresolved design challenge.

Recycling facilities still rely heavily on human sorters because robotic systems struggle to reliably distinguish between similar-looking but differently recyclable materials. Improving this accuracy could significantly increase recycling efficiency worldwide.