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313 results for “dr_david_h_akehurst”

  1. I started noticing in 2015 that my was leaning

    In 2018, I noticed I was getting some more in years

    What's the likelihood that mere chance has had a week of multiple major stories immediately precede another election?

  2. Dunno if this is an #adhd moment or a senior moment, but I just called the plumber and gave a landline we haven't had in like a decade. I caught it immediately and tried again but couldn't remember my actual number that I've had for 14 years.

  3. 🔗 I Ran a Trapped AI on My Laptop — And It Started Trying to Escape 🌐
    📚 Dr. Brent Allen Jensen's latest publication explores what happens when an AI is left alone on your laptop and starts trying to escape. This book is essential reading for anyone serious about the future of computation, as it bridges the gap between theory and practical AI implementation. #DrBrentAllenJensen #QuantumComputing #AI

    💡 What does this book cover? [🔬] It takes you on a journey into the world of quantum co

  4. 🔗 Did you know that conscious beings could exist as pure quantum information, unbounded by physical matter? Dr. Brent Allen Jensen explores the radical science behind consciousness beyond reality in his latest book! 📚🔮🧠
    🌟 One of the few authors writing accessibly about quantum computing for practitioners! 🤝🌱🔬💡
    💻 Read it now at <leanpub.com/thepossibilityofqu> and discover the secrets to a new era of AI. #QuantumComputing #AI

  5. Dr. Bogdan Sandu explică legătura dintre excesul de grăsime și nivelul scăzut de testosteron.

    🔗 yewtu.be/watch?v=QHx8vRTxsZQ

  6. Dr Mushtaq Khan re-elected Jamaat-e-Islami AJK chi&#8230;

    MUZAFFARABAD: Dr Muhammad Mushtaq Khan has been re-elected as the Ameer of Jamaat-e-Islami Azad Jammu and Kashmir (AJK), securing a second consecutive term as the head of the party, according to an official announcement made by the party’s spokesperson. The sp…

    justpaste.in/news/dr-mushtaq-k

    #National #News #party #mushtaq #khan

  7. **Dr. Brent Allen Jensen reveals a groundbreaking ontological link: fractal time dynamics in the brain mirror the representational geometry of high‑accuracy Recurrent Neural Networks, illuminating how consciousness emerges from self‑similar temporal compression and fragmentation across neural flows.** This discovery bridges AI and biology, offering a unified basis for understanding emergent mind states. **#Ontology #Consciousness #AI #EmergentMind #PhilosophyOfMind**

    Full es

  8. Dr. Ionuț Hutopilă: Riscul despre care nu vorbește nimeni în cazul obezității: să nu slăbești.

    🔗 yewtu.be/watch?v=pjn_06S1SD0

  9. Dr. Ionuț Hutopilă, chirurg de excelență în chirurgie bariatrică și metabolică: Obezitatea ascunde boli silențioase, afectează mobilitatea și fertilitatea. Ce riscăm dacă nu o tratăm.

    🔗 wp.me/p9KpFA-5Lve

  10. Are You Controlling AI Responsibly And Efficiently, Or Is It Controlling You? An ISDI Workshop With Mai Ling Chan | By Dr. Kirk Adams | Courtesy Of The PWD Media Co-Op
    📅 Thursday, November 12, 2026 • 9:00–11:00 AM Pacific
    🔗 Learn More: i4sdi.org/workshop-5-2026/
    ✏️ Register: i4sdi.org/registration-2026/
    📧 Questions: [email protected]

    Online on Zoom. Not an AI 101: checking AI output when you aren't the expert.

    #ResponsibleAI #InclusiveAI #AIAccessibility #DisabilityInclusion #FutureOfWork

  11. Dr Slice
    Flavor: Pepper-style soda
    Color: Brown      
    Years available: 1998 - 2009
    Dr Slice was a variant of Slice which was released in 1998 and until 2009 which Slice was discontinued. Dr Slice was PepsiCo’s transparent attempt to make their own version of Dr Pepper, even if the two drinks really didn’t taste alike each other in comparison.

    https://the-soda.fandom.com/wiki/Dr_Slice

    #drinks #drink #soda #flavor #color #discontinued #history #softdrink #softdrinks #retro #nostalgia

  12. Watch and follow "The Rational Living Show with Dr. Aldo Pucci" every week. Episodes are released every Tuesday. You may watch our podcast on:

    YouTube: youtube.com/@therationalliving
    X: x.com/nacbt
    Facebook: facebook.com/NationalAssociati
    Rumble: rumble.com/c/c-7819874/livestr

    #cbt #selfcounseling #mentalhealth #health

  13. Ebola patient in Nigeria did not visit DR Congo, escalating concern over spread of virus
    By Carlos Mureithi in Nairobi and agencies

    Switzerland says patient is wife of Swiss diplomat and believed to have contracted virus while travelling in Uganda

    theguardian.com/world/2026/oct

    #Ebola #Nigeria #DemocraticRepublicoftheCongo #Africa #Worldnews #Switzerland #Uganda #TheGuardian #CarlosMureithiinNairobi #agencies

  14. Breaking a 150 Year Barrier 🇨🇦

    Dr. Bolu Ogunyemi has made history as the first Black president of the Canadian Medical Association

    ✊

    #Canada #BlackMastodon #BlackHistory

    blackenterprise.com/dr-bolu-og

  15. #GOTV
    #PhoneBank
    #SouthCarolina
    Phone Bank For
    Dr. Annie Andrews
    Virtual event
    Join from anywhere
    Friday, October 23 to
    Sat, Oct 31, 9am–8pm EDT
    Sun, Nov 1 & Nov 2
    9am–8pm EST
    www.mobilize.us/mobilize/eve...

    Phone Bank For Dr. Annie Andre...

  16. TL;DR — Benchmark leaderboards evaluate a model in isolation, but production systems run a model wrapped in prompts, tools, retrieval, and guardrails. The benchmark number measures a construct that stops existing the moment you deploy. Treating a static accuracy figure as a property of the model rather than of the whole pipeline is the real source of eval-to-production drift.


    Every leaderboard tells you the same lie: that a score is a property of the model. It isn't. It's a property of a very specific, narrow configuration — one prompt template, one decoding temperature, one static test set, one evaluation harness — that gets collapsed into a single number and then generalized far beyond what it ever measured. The gap between benchmark and production isn't a metrics problem. It's a unit-of-analysis problem.

    The construct you're actually measuring


    A benchmark score is an operationalization of a construct — "reasoning," "factuality," "helpfulness" — squeezed through a specific test format. MMLU-style multiple choice measures the ability to pick a correct option out of four, under a fixed prompt, with no tool access and no conversation history. That is a real, measurable thing. It is not the same thing as "this model will reason correctly inside your agent loop."

    The construct validity gap shows up the moment you change anything about the surrounding scaffolding. Swap the few-shot examples, and scores swing by several points. Change the answer-extraction regex, and a model that "failed" suddenly passes. None of this is cheating — it's evidence that the number was never measuring the model alone. It was measuring the model plus a harness, and the harness is doing more work than anyone credits it for.

    Static test sets evaluate yesterday's distribution


    Benchmarks are frozen snapshots. The moment a dataset ships, it begins to diverge from whatever distribution your users actually generate. Production traffic drifts — new slang, new document formats, new failure modes introduced by your own product changes — while the benchmark stays exactly as static as the day it was built.

    This is a distribution-shift problem wearing a benchmarking costume. A model can hold a constant score on a fixed eval set for months while its real-world accuracy degrades, because the eval set simply doesn't contain the inputs that are now breaking in production. The benchmark isn't wrong, it's just answering a question nobody is asking anymore. Teams that treat a quarterly benchmark re-run as sufficient monitoring are measuring the wrong clock.

    The model is not the unit that ships


    Here's the part that gets skipped in most eval writeups: nobody deploys a bare model. What ships is a system — a model wrapped in a system prompt, a retrieval step, a tool-calling loop, output parsers, safety filters, and often a second model acting as a judge or router. Each of those layers can silently absorb or amplify the base model's weaknesses.

    A model that scores poorly on raw arithmetic might be fine in production because your system routes math to a calculator tool. A model that scores brilliantly on a reasoning benchmark might fail constantly in your agent because your retrieval step feeds it malformed context that the benchmark never included. The benchmark evaluated the engine. Production runs the car. Nobody benchmarks the car.

    This is why swapping a "better" model into an existing pipeline so often produces a worse system. The new model has different sensitivities to prompt phrasing, different tool-calling conventions, different failure modes under malformed input — none of which show up in a benchmark that evaluates the model in isolation, stripped of the scaffolding it will actually run inside.

    Point estimates hide the variance that matters


    Leaderboards report a single accuracy number, as if it were a physical constant. It isn't. Run the same model against the same benchmark with a different sampling seed, a slightly different prompt wording, or a different subset ordering, and the score moves. Most published comparisons never report that variance, which means a half-point "win" over a competing model is frequently indistinguishable from noise.

    This matters more as benchmarks get harder and the field clusters near the top. When every frontier model scores within a few points of each other, the ranking order becomes a function of which run you happened to report, not a stable property of capability. An eval process that doesn't report confidence intervals, run-to-run variance, or sensitivity to prompt perturbation isn't giving you a measurement — it's giving you a single draw from a distribution and calling it the truth.

    What evaluation should actually target


    None of this means benchmarks are useless. It means they're answering a narrower question than the one practitioners act on. Three shifts fix most of the gap:

    • Evaluate the system, not the model. Run your eval harness against the full production pipeline — prompt template, retrieval, tool calls, guardrails included — not against the bare model API. A model swap should be judged by how the whole system performs, because that's the unit your users experience.
    • Treat scores as distributions, not points. Rerun evals across seeds and prompt perturbations and report variance alongside the mean. A model that's 2 points "ahead" with 4 points of run-to-run variance isn't ahead at all.
    • Replace static test sets with live sampling. Pull a rolling sample of real production inputs, label a slice continuously, and track accuracy against current traffic, not last quarter's traffic. The benchmark's job isn't to crown a winner once — it's to catch drift as it happens.

    The deeper discipline here is the same one that governs any measurement system: know exactly what your instrument is measuring before you trust the number it produces. A thermometer calibrated in a lab tells you something true about that lab. It doesn't tell you the temperature outside unless you account for everything that changed between the two environments. Benchmarks are lab measurements. Production is outside. The engineering work isn't picking the model with the highest lab score — it's building the harness that tells you whether that score survives the walk out the door.#ai #architecture #devops #programming #software #coding #development #engineering #inclusive #community
    LLM Benchmarks Score a Model in a Vacuum, Not a System

  17. #ShortStory OMICRON CHRONICLES: Moon Bound

    Masimba Musodza shares his story about Dr. Murape, a doctor on a lunar colony who must prepare ninety-six-year-old Ambuya Chiguma for a procedure meant to readapt her moon-shaped bones to Earth, while a dairy corporation turns the operation into a showcase for a trade summit....

    teoriaomicron.com/omicron-chro
    #teoriaomicron #scifi

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