Dr. Argie Kasprzik (@SemAntiKast) ZBW (@ZBW_MediaTalk) fesselte die Studies mit seinem Vortrag "Mit KI auf Spatzen? Der Fußabdruck von Big Tech auf Klima, Gesellschaft und Informationslandschaft und wie wir es besser machen können" Es folgt eine sehr angeregte Diskussion!
Mehr Infos und das Line Up der Vorlesungsreihe: https://www.fh-potsdam.de/aktuelles-medien/termine/ringvorlesung-advanced-topics-den-informationswissenschaften-0
#informationswissenschaft #digitalhumanities #fedicampus
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274 results for “dr_oli_jones”
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Dr. Eirliani Abdul Rahmans Forschungsprojekt TRAPP gehört zu den 100 Finalisten des Prototypes for Humanity Summit. Mithilfe von #KI will die Forscherin Kinderhandel in Indien frühzeitig erkennen und bekämpfen.
Abdul Rahman forscht am #MZES der #UniMannheim und wird von der #BadenWürttembergStiftung unterstützt.
📸 The Women and Child Development Department, Odisha, India
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Dr. Lara Andronescu: Unele femei pot obține o sarcină în mod natural după tratament.
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Dr Strangelove tells us to be ready
https://www.counterfire.org/article/dr-strangelove-tells-us-to-be-ready-weekly-briefing/
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🔧 "In the age of AI-driven phishing, Dr. Brent Allen Jensen unveils the latest techniques and provides the knowledge needed to defend against them. This book is essential reading for anyone serious about the future of computation, bridging the gap between theory and practical AI implementation. Read on! 🔧🗣️ #AI #QuantumComputing"
Author: Dr Brent Allen Jensen | Author Links: https://medium.com/@ambitionmagician/mastering-the-art-of-ai-driven-phishing-a-comprehensive-guide-for-modern-c
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🤖 Dr. Brent Allen Jensen's Transcendence unveils the hidden dimensions of consciousness! 🧘💡 Unlocking new realms of awareness with his latest groundbreaking framework. Dive into the quantum revolution. #AI #QuantumComputing #ConsciousnessExpansion
https://www.amazon.com/dp/B0DVH5TZ8T -
🧠 Revolutionizing Nitrogen Fixation: A Game Changer for Global Agriculture 🔧💡 Dr. Jensen's de Novo protein-scaffold catalyst could transform industrial fertilizer production and feed the world! 🌱🌾 #DrBrentAllenJensen #QuantumComputing #AI
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📚 "Unlock the mysteries of consciousness with Dr. Brent Allen Jensen's groundbreaking book, The Possibility of Quantum Beings: Consciousness Beyond the Fabric of Reality. Delve into the radical science that questions our understanding of reality and what it means to be human." #QuantumComputing #Consciousness #DrBrentAllenJensen
https://leanpub.com/thepossibilityofquantumbeings -
🔗 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
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🔗 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 <https://leanpub.com/thepossibilityofquantumbeings> and discover the secrets to a new era of AI. #QuantumComputing #AI -
🤖 "Unlocking Quantum Gates: The Uncharted Territory of Simulation and Field Manipulation 📚📦
Dr. Brent Allen Jensen delves into the secret code of reality, exploring quantum gates that could revolutionize our understanding of simulation and field manipulation. Discover how these unseen forces shape our world 🌍 #QuantumComputing #ArtificialIntelligence"
https://www.amazon.com/dp/B0DW7TYYW2 -
Dr. Dre, Snoop Dogg & Too $hort Electrify Jay-Z30 Tour’s Final Stop
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Dr. Bogdan Sandu explică legătura dintre excesul de grăsime și nivelul scăzut de testosteron.
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Dr Mushtaq Khan re-elected Jamaat-e-Islami AJK chi…
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…
https://justpaste.in/news/dr-mushtaq-khan-re-elected-jamaat-e-islami-ajk-chief/
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**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
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Dr. Ionuț Hutopilă: Riscul despre care nu vorbește nimeni în cazul obezității: să nu slăbești.
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『ドゥームズデイ』Dr.ドゥームがZD TOYS「Xシリーズ」よりフィギュア化！炎エフェクトパーツや武器パーツも付属！楽天ブックスで予約受付中！
https://hobby.dengeki.com/news/3206538/ -
this is what Dr. Kel gets up to each night after he completes the daily task #votv #voicesofthevoid #meme #drkel
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TL;DR: Over-relying on AI for decision-making and writing strips away human critical thinking, judgment, and essential learning. #AI should be used as a tool, not as a replacement for the thinking process itself.
Nobody is Thinking Anymore https://yusufaytas.com/nobody-is-thinking-anymore #zeitgeist
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TL;DR On September 23, 2026, Microsoft announced the integrated security operations...#security #ai #devops #programming #software #coding #development #engineering #inclusive #community
ISOC Is Microsoft's Answer to Agent-Speed Attacks. The Loop Still Needs Human Judgment -
TL;DR: For a marketplace chatbot service, this AI comparison puts OpenAI and ChatGPT alongside Claude...#ai #saas #marketplace #software #coding #development #engineering #inclusive #community
Chatbot AI Service Comparison Features Explained for Marketplace CRM Quality Latency -
TL;DR: For a real-time, user-facing image request, the lowest-latency API is only one part of the...#ai #imagegeneration #latency #gaming #software #coding #development #engineering #inclusive #community
Lowest-Latency Image Generation API for Real-Time Game Support Response -
TL;DR: to select a text summarization API model in Node.js, route moderation reports through a small,...#ai #healthtech #moderation #node #software #coding #development #engineering #inclusive #community
How to Select a Text Summarization API Model in Node.js — Healthtech Review Queues -
Uganda orders 21-day Ebola quarantine for citizens returning from DR Congo https://www.aljazeera.com/news/2026/10/25/uganda-orders-21-day-ebola-quarantine-for-citizens-returning-from-dr-congo?traffic_source=rss
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TL;DR: The best embedding model for chatbot RAG semantic search is the one that wins on your labeled...#ai #rag #python #software #coding #development #engineering #inclusive #community
Embedding Model Selection for Chatbot RAG Semantic Search: CRM Action Gates -
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 -
TL;DR: Chrome DevTools MCP is the Chrome DevTools team's open-source MCP server. It lets Claude Code,...#mcp #ai #chrome #webdev #software #coding #development #engineering #inclusive #community
Chrome DevTools MCP in Claude Code (setup, attach mode, vs Playwright MCP)
