ai quality trend
2022 was a watershed year in AI, as quality-related issues came to the forefront. Regulation marched along in the EU, Asia, and the U.S., and we saw companies like Zillow hobbled by AI model quality snafus.
Based on my recent discussions with dozens of Fortune 500 data science teams, I expect to see a continued spotlight on AI model quality in 2023. Here are six areas I’m keeping an eye on.
Similar to software development 20 years ago, it wasn’t until testing and monitoring became common that enterprise software use really took off. AI is at a similar inflection point. AI and Machine Learning technologies are being adopted at a rapid pace, but quality varies. Often, the data scientists developing the models are also the ones manually testing them, and that can lead to blind spots. Testing is manual and slow. Monitoring is nascent and ad hoc. And AI model quality is highly variable, becoming a gating factor for the successful adoption of AI. Automated testing and monitoring provide quality assurance and lowers uncertainty and risk.
Sécurité des mots de passe : bonnes pratiques pour éviter les failles La sécurité des…
Ransomware : comment prévenir et réagir face à une attaque Le ransomware est l’une des…
Cybersécurité et e-commerce : protéger vos clients et vos ventes En 2025, les sites e-commerce…
Les ransomwares : comprendre et se défendre contre cette menace En 2025, les ransomwares représentent…
RGPD et cybersécurité : comment rester conforme en 2025 Depuis sa mise en application en…
VPN : un outil indispensable pour protéger vos données Le VPN, ou « Virtual Private…
This website uses cookies.