World Solve

Solar panels contain valuable and sometimes hazardous materials that current recycling processes struggle to recover efficiently and cheaply. As the first generation of panels ages out, this waste problem is becoming more urgent.

Early brain-computer interfaces show promise for helping paralyzed patients control devices with thought alone, but long-term safety, reliability, and signal accuracy remain unresolved. Wider medical adoption depends on solving these fundamental issues first.

Many users experience nausea, eye strain, or disorientation after even moderate use of virtual reality headsets, a problem that hasn't been fully solved despite years of hardware improvements. This limits how widely VR can be adopted for work or daily use.

Once a photo or video is edited or generated, there's often no reliable way to trace it back to prove whether the original was authentic. Building this verification into cameras and platforms from the start remains an unsolved industry challenge.

Content that provokes strong emotional reactions tends to get algorithmically boosted because it keeps people engaged longer, regardless of whether it's accurate or healthy. Redesigning these systems without destroying platform business models remains unresolved.

AI models often can't accurately tell when a question falls outside what they actually know, leading them to guess confidently rather than admit uncertainty. Teaching AI to recognize its own limits remains an unresolved research problem.

The way solid-state drives store data internally makes secure, complete deletion technically harder than with traditional spinning hard drives. This creates lingering privacy and security risks when these drives are discarded or resold.

AI systems can generate false information with the same fluent, confident tone as accurate information, making it hard for users to tell the difference without independently verifying every claim. Reducing this so-called hallucination problem remains a central AI research challenge.

Today's most capable AI models are trained on vast datasets scraped from the internet, often including copyrighted works and personal information without clear consent. Finding effective training methods that avoid this legal and ethical gray area remains unsolved.

Many smart home and industrial devices ship with weak security because manufacturers prioritize low cost over protection, creating easy targets for hackers. Fixing this at scale requires an industry-wide shift that hasn't happened yet.

Fast charging speeds up daily convenience but tends to accelerate battery wear, forcing a tradeoff between speed and battery lifespan. Solving this without expensive materials remains an unresolved engineering challenge.

Most software vulnerabilities trace back to common coding mistakes that better language design could prevent from the start. Despite decades of research, mainstream programming still makes these mistakes easy to make.

AI can now generate fake news articles, complete with realistic writing style and structure, faster than fact-checkers can identify and flag them. This growing speed gap threatens public trust in written information generally.

Zero-day flaws are unknown weaknesses in software that attackers can exploit before a fix exists, and current detection methods still miss many of them until after damage is done. Finding these flaws proactively remains one of cybersecurity's hardest problems.

Even advanced AI systems can lose track of important details from earlier in a long conversation or document, leading to inconsistent or contradictory answers. True long-term memory and context tracking remains an open technical challenge.

A few seconds of someone's voice is now enough to generate convincing fake audio of them saying anything, and current detection tools often fail to catch high-quality fakes in real time. This is already being used for financial scams targeting families and businesses.

People currently juggle dozens of separate logins and identity systems, each with different security and privacy standards. A single trusted, privacy-respecting identity system remains unbuilt at global scale.

Data centers powering cloud services and AI models consume enormous and fast-growing amounts of electricity and water for cooling. Making this growth sustainable rather than simply larger remains a critical unsolved engineering challenge.

Researchers keep finding creative ways to bypass safety filters built into AI systems, and defenses often lag behind the newest bypass techniques. This cat-and-mouse dynamic remains unresolved despite constant patching.

Passwords remain widely used despite being easy to steal or forget, and alternatives like biometrics or hardware keys haven't achieved universal, trusted adoption. A truly seamless and secure replacement remains unbuilt at global scale.