Forrester: Why digitisation needs strong data engineering skills | Computer WeeklyOrganizations must evolve their data strategies to enhance adaptability and resilience against constant change.Less than 20% of enterprise data is utilized for actionable insights.
U.S. policymakers should close the AI education gender divideThe U.S. education system must adapt to address the challenges of AI and prepare students with practical skills for the future.
Why Most Machine Learning Projects Fail to Reach Production and How to Beat the OddsMachine learning projects often fail to reach production, with recent studies showing only 32% succeed.Failure in machine learning is a shared experience among practitioners, highlighting systemic challenges.
QCon SF 2024 - Why ML Projects Fail to Reach ProductionMachine learning projects face severe challenges, with an 85% failure rate primarily due to misalignment with business needs and poor data management.
Why Most Machine Learning Projects Fail to Reach Production and How to Beat the OddsMachine learning projects often fail to reach production, with recent studies showing only 32% succeed.Failure in machine learning is a shared experience among practitioners, highlighting systemic challenges.
QCon SF 2024 - Why ML Projects Fail to Reach ProductionMachine learning projects face severe challenges, with an 85% failure rate primarily due to misalignment with business needs and poor data management.
Eat a rock a day, put glue on your pizza: how Google's AI is losing touch with realityGoogle's AI Overviews feature uses generative AI to summarize search results, yet faces challenges in discerning accuracy and relevance.
Unlocking the Potential of Generative AI for Lawyers: Red Flags and Best PracticesGenerative AI offers efficiency but also poses ethical challenges for lawyers.
Meta blames hallucinations after its AI said Trump rally shooting didn't happenMeta's AI made incorrect statements regarding the attempted assassination of Donald Trump, reflecting broader challenges in generative AI technology.
AI in Homework: Tool for Learning or Shortcut for Cheating?Increased generative AI use among students presents both opportunities for learning and challenges that require responsible usage to avoid dependence.
The Evolution of Retrieval Systems in AIHybrid retrieval systems combining vector embeddings and keyword-based searches address limitations of purely vector-based systems.
Eat a rock a day, put glue on your pizza: how Google's AI is losing touch with realityGoogle's AI Overviews feature uses generative AI to summarize search results, yet faces challenges in discerning accuracy and relevance.
Unlocking the Potential of Generative AI for Lawyers: Red Flags and Best PracticesGenerative AI offers efficiency but also poses ethical challenges for lawyers.
Meta blames hallucinations after its AI said Trump rally shooting didn't happenMeta's AI made incorrect statements regarding the attempted assassination of Donald Trump, reflecting broader challenges in generative AI technology.
AI in Homework: Tool for Learning or Shortcut for Cheating?Increased generative AI use among students presents both opportunities for learning and challenges that require responsible usage to avoid dependence.
The Evolution of Retrieval Systems in AIHybrid retrieval systems combining vector embeddings and keyword-based searches address limitations of purely vector-based systems.
A Framework for Building Micro Metrics for LLM System EvaluationEach AI problem has unique challenges requiring careful tracking and observability.Build LLM metrics aligned with business goals and user needs.Adopt an incremental approach to develop systems and metrics.
Before Apple's AI Went Haywire and Started Making Up Fake News, Its Engineers Warned of Deep Flaws With the TechApple's AI initiative, Apple Intelligence, has faced major setbacks, particularly in news summarization, leading to a pause for improvements.
The Morning After: Tech's biggest losers in 20242025 begins with tech facing challenges, including social media struggles, a TikTok ban debate, and unreliable AI tools.
Data quality still lags behind, leaving AI promise unfulfilledHigh-quality data is critical for AI success, yet many IT managers neglect necessary quality assurance measures.
How sustainable compute infrastructure supports business growth & opens up an AI advantageAging IT infrastructure significantly hampers enterprise growth and competitiveness, particularly in the context of AI and modern business challenges.
Data quality still lags behind, leaving AI promise unfulfilledHigh-quality data is critical for AI success, yet many IT managers neglect necessary quality assurance measures.
How sustainable compute infrastructure supports business growth & opens up an AI advantageAging IT infrastructure significantly hampers enterprise growth and competitiveness, particularly in the context of AI and modern business challenges.
Reddit debuts AI-powered discussion search-but will users like it?Reddit's partnerships with tech firms raise concerns over misinformation and AI accuracy, as users attempt to manipulate public data.
GitHub Copilot code quality claims challengedThe validity of GitHub's claims about Copilot's code quality is challenged due to methodological concerns raised by developer Dan Cîmpianu.
Author Post: Earth to Google: Your Business Tools Need to Do BetterTechnology often feels like a barrier when it fails to provide effective solutions for business problems.
One of China's best GPU prospects admits it's in troubleXiangdixian Computing Technology has acknowledged failed GPU development targets, leading to staff layoffs and restructuring efforts amidst market pressures.
AI-Fakes Detection Is Failing Voters in the Global SouthDetection models struggle with low-quality media from less represented regions, leading to inaccuracies and potential policy missteps.
The TechBeat: Here's How we Made a Real-time Phishing Website Detector for MacOS (9/2/2024) | HackerNoonReal-time phishing detection for macOS enhances privacy through on-device alerts without needing cloud solutions.
New RAND Research - Why do AI Projects Fail? - insideAI NewsMany AI projects fail due to miscommunication, inadequate data, infrastructure issues, and overly ambitious problem-solving.Successful AI implementation requires a focus on solving real user problems rather than just deploying new technologies.
American tech workers want AI regulation - but they might have to wait a whileAmerican tech workers support strong regulations for better technology use.
Level AI applies algorithms to contact center pain points | TechCrunchAI-powered tools by Level AI improve productivity in contact centers by automating tasks and providing insights for both managers and agents.
Generative AI Is Totally Shameless. I Want to Be ItAI has notable challenges like lack of proper attribution, biased outputs, and an obsession with creating a future AI god. Despite issues, its allure remains.
DatologyAI is building tech to automatically curate AI training data sets | TechCrunchBiases can emerge from massive data sets, hindering AI models.Data preparation challenges, including cleaning, are significant obstacles for AI initiatives.