World Solve

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.

Many critical systems, including in banking and government, still run on decades-old code that few current developers understand, and automated modernization tools remain unreliable for anything but simple cases. This leaves critical infrastructure dependent on shrinking expertise.

AI hiring systems trained on historical hiring data can inadvertently learn and repeat past patterns of discrimination, even when explicitly designed to avoid it. Fully eliminating this bias while maintaining useful screening capability remains unresolved.

As AI-generated faces and voices become more realistic, some biometric security systems can potentially be fooled by convincing synthetic versions of a real person's face or voice. Strengthening these systems against this emerging threat remains an active challenge.

Companies increasingly build products using AI models developed by others, but there's no consistent, scalable way to verify those models are safe and reliable before integration. This creates hidden risk throughout the growing AI supply chain.

Even with extensive automated testing, software products regularly ship with bugs that only appear once real users start interacting with them in unpredictable ways. Comprehensive, fully reliable bug detection before release remains an unsolved engineering challenge.

Recommendation algorithms optimize for engagement, which can unintentionally narrow the range of content users are exposed to over time, reinforcing existing beliefs rather than broadening perspective. Designing systems that balance engagement with diversity of exposure remains unresolved.

Cloud storage relies heavily on trusting individual companies to remain operational and reliable long-term, and if a provider fails or shuts down, users can lose access to their data. A truly provider-independent guarantee remains unbuilt.

Automated trading systems can execute manipulative strategies at speeds far beyond human traders, and regulators still struggle to detect this kind of abuse in real time. Closing this detection gap remains an urgent unresolved challenge for financial oversight.

Fake reviews generated at scale by AI can be nearly indistinguishable from genuine customer feedback, undermining trust in online shopping decisions. Detection tools are struggling to keep pace with how convincing these fakes have become.

It's often unclear exactly what data went into training a given AI model, making it difficult to audit for bias, copyright issues, or harmful content. Greater transparency into training data remains technically and legally unresolved.

Digital voting could make elections faster and more accessible, but security concerns and lack of public trust have kept most large-scale elections reliant on paper-based or hybrid systems. A truly trusted digital alternative remains unbuilt.

Researchers have shown that inaudible or disguised audio signals can sometimes trigger voice assistants to take unintended actions without the user's knowledge. Fully closing this security gap remains unresolved.

Fake or substandard electronic parts sometimes slip into supply chains for critical equipment, including medical devices and vehicles, and current detection methods are neither fast nor cheap enough to catch every case. This creates hidden safety risks across many industries.

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.

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.

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.

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.

Water planning based on historical norms is becoming unreliable. Scenario-based planning and storage investments are needed.

Rural and low-income areas lag behind cities, limiting digital opportunity. Community networks and universal service policies can help.