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News articleMIT Technology Review

Making AI operational in constrained public sector environments

View original at technologyreview.com
MIT Technology Review - Ai Research Title: Making AI operational in constrained public sector environments Date: 2026-04-16 13:00 Source: https://www.technologyreview.com/2026/04/16/1135216/making-ai-operational-in-constrained-public-sector-environments/ <p>The AI boom has hit across industries, and public sector organ…
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  • Large language models generate text based on training data with a cut-off date, causing hallucinations for newer information, which can be solved by forcing models to work from verified sources

    60% confidence
  • By 2027, organizations will use small, task-specific AI models three times more than general-purpose large language models

    60% confidence
  • Government organizations don't often purchase GPUs and are not used to managing GPU infrastructure, making accessing GPUs a bottleneck

    60% confidence
  • Government agencies must be very restricted about what kind of data they send to the network, which sets boundaries on how they manage their data

    60% confidence
  • It is easy to use ChatGPT for proofreading but very difficult to run large language models smoothly in environments with no network access

    60% confidence
  • Do not start with a chatbot; start with search, as much of AI intelligence is about finding the right information

    60% confidence
  • Today's AI can provide a completely new view of how to harness data

    60% confidence
  • 79 percent of public sector executives globally are wary about AI's data security

    60% confidence
  • When people in the public sector hear AI, they probably think about ChatGPT, but we can be much more ambitious as AI can revolutionize how government searches and manages large amounts of data

    60% confidence
  • Many people undervalue the operating challenge of AI, and the public sector needs AI to perform reliably on all kinds of data and grow without breaking

    60% confidence
  • The public sector has a lot of data and doesn't always know how to use it or what the possibilities are

    60% confidence

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Q4 2026 Clinical Catalyst Season: Big-Pharma Data Flow, Regulatory Milestones, and a Thin AI-Drug Fringe
October 2026 conferences (EASD, IGCS, NACFC, HFSA) are producing a dense run of late-stage readouts from Novo Nordisk, Lilly, Roche, Bayer, Vertex and Galderma. Regulatory milestones are scheduled behind them: the ivonescimab BLA PDUFA date on 2026-11-14, FDA acceptance of Roche's vamikibart BLA, and Scholar Rock's EMA resubmission. The AI-and-finance angle is peripheral. It consists of the AI-designed drug Rentosertib and QAIAx, a speculative microcap whose trial claims (the QIII pilot, anticipated 2027-01-01, and a patent update) are forecasts and filings rather than data, so they warrant skepticism.
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EPKINLY Regulatory-Clinical Success Cascade
High probability of expanded label indications, additional combination approvals, and competitive positioning strength in follicular lymphoma market. Predicts positive commercial uptake and potential accelerated review for related indications.
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ING Group
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