TeachLM: six key findings from a study of the latest AI model fine-tuned for teaching & learning. LLMs are great assistants but ineffective instructional designers and teachers. This week, researchers at Polygence + Stanford University published a paper on a new model — TeachLM — which was built to address exactly this gap. In my latest blog post, I share the key findings from the study, including observations on what it tells us about AI’s instructional design skills. Here’s the TLDR: 🔥 TeachLM outperformed generic LLMs on six key education metrics, including improved question quality & increased personalisation 🔥TeachLM also outperformed “Educational LLMs” - e.g. Anthropic’s Learning Mode, OpenAI’s Study Mode and Google’s Guided Learning - which fail to deliver the productive struggle, open exploration and specialised dialogue required for substantive learning 🔥TeachLM flourished at developing some teaching skills (e.g. being succinct in its comms) but struggled with others (e.g. asking enough of the right sorts of probing questions) 🔥 Training TeachLM on real educational interactions rather than relying on prompts or synthetic data lead to improved model performance 🔥TeachLM was trained primarily for delivery, leaving significant gaps in its ability to “design the right experience”, e.g. by failing to define learners’ start points and goal 🔥Overall, human educators still outperform all LLMs, including TeachLM, on both learning design and delivery Learn more & access the full paper in my latest blog post (link in comments). Phil 👋

The specs are impressive: Up to 85x faster 3.4x smaller footprint Real-time performance directly in-browser Capable of live video captioning – fully local No external infrastructure. No latency. No exposure of sensitive data. Simply secure, on-device AI. Yes, the technical benchmarks will be debated. But the bigger story is Apple’s positioning. This is about more than numbers – it’s about shaping a narrative where AI is personal, private, and seamlessly integrated. At Copenhagen Institute for Futures Studies, we’ve been tracking the rise of small-scale, locally running AI models for some time. We believe this shift has the potential to redefine how organizations and individuals interact with intelligent systems – moving AI from “out there” in the cloud to right here, at the edge. | 10 comments on LinkedIn

Die EPFL, die ETH Zürich und das Schweizerische Supercomputing-Zentrum CSCS haben heute Apertus veröffentlicht: Das erste umfangreiche, offene und mehrsprachige Sprachmodell aus der Schweiz. Damit setzen sie einen Meilenstein für eine transparente und vielfältige generative KI.

The AI Hype is a Dead Man Walking. The Math Finally Proves It. For the past two years, the AI industry has been operating on a single, seductive promise: that if we just keep scaling our current models, we'll eventually arrive at AGI. A wave of new research, brilliantly summarized in a recent video analysis, has finally provided the mathematical proof that this promise is a lie. This isn't just another opinion; it's a brutal, two-pronged assault on the very foundations of the current AI paradigm: 1. The Wall of Physics: The first paper reveals a terrifying reality about the economics of reliability. To reduce the error rate of today's LLMs by even a few orders of magnitude—to make them truly trustworthy for enterprise use—would require 10^20 times more computing power. This isn't just a challenge; it's a physical impossibility. We have hit a hard wall where the cost of squeezing out the last few percentage points of reliability is computationally insane. The era of brute-force scaling is over. 2. The Wall of Reason: The second paper is even more damning. It proves that "Chain-of-Thought," the supposed evidence of emergent reasoning in LLMs, is a "brittle mirage". The models aren't reasoning; they are performing a sophisticated pattern-match against their training data. The moment a problem deviates even slightly from that data, the "reasoning" collapses entirely. This confirms what skeptics have been saying all along: we have built a world-class "statistical parrot," not a thinking machine. This is the end of the "Blueprint Battle." The LLM-only blueprint has failed. The path forward is not to build a bigger parrot, but to invest in the hard, foundational research for a new architecture. The future belongs to "world models," like those being pursued by Yann LeCun and others—systems that learn from interacting with a real or virtual world, not just from a library of text. The "disappointing" GPT-5 launch wasn't a stumble; it was the first, visible tremor of this entire architectural paradigm hitting a dead end. The hype is over. Now the real, foundational work of inventing the next paradigm begins. | 554 comments on LinkedIn

𝗧𝗵𝗶𝘀 𝗶𝘀 𝗵𝗮𝗻𝗱𝘀 𝗱𝗼𝘄𝗻 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗕𝗘𝗦𝗧 𝘃𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗵𝗼𝘄 𝗟𝗟𝗠𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸. ⬇️ 𝘓𝘦𝘵'𝘴 𝘣𝘳𝘦𝘢𝘬 𝘪𝘵 𝘥𝘰𝘸𝘯: 𝗧𝗼𝗸𝗲𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 & 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀: - Input text is broken into tokens (smaller chunks). - Each token is mapped to a vector in high-dimensional space, where words with similar meanings cluster together. 𝗧𝗵𝗲 𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺 (𝗦𝗲𝗹𝗳-𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻): - Words influence each other based on context — ensuring "bank" in riverbank isn’t confused with financial bank. - The Attention Block weighs relationships between words, refining their representations dynamically. 𝗙𝗲𝗲𝗱-𝗙𝗼𝗿𝘄𝗮𝗿𝗱 𝗟𝗮𝘆𝗲𝗿𝘀 (𝗗𝗲𝗲𝗽 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴) - After attention, tokens pass through multiple feed-forward layers that refine meaning. - Each layer learns deeper semantic relationships, improving predictions. 𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 - This process repeats through dozens or even hundreds of layers, adjusting token meanings iteratively. - This is where the "deep" in deep learning comes in — layers upon layers of matrix multiplications and optimizations. 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 & 𝗦𝗮𝗺𝗽𝗹𝗶𝗻𝗴 - The final vector representation is used to predict the next word as a probability distribution. - The model samples from this distribution, generating text word by word. 𝗧𝗵𝗲𝘀𝗲 𝗺𝗲𝗰𝗵𝗮𝗻𝗶𝗰𝘀 𝗮𝗿𝗲 𝗮𝘁 𝘁𝗵𝗲 𝗰𝗼𝗿𝗲 𝗼𝗳 𝗮𝗹𝗹 𝗟𝗟𝗠𝘀 (𝗲.𝗴. 𝗖𝗵𝗮𝘁𝗚𝗣𝗧). 𝗜𝘁 𝗶𝘀 𝗰𝗿𝘂𝗰𝗶𝗮𝗹 𝘁𝗼 𝗵𝗮𝘃𝗲 𝗮 𝘀𝗼𝗹𝗶𝗱 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗵𝗼𝘄 𝘁𝗵𝗲𝘀𝗲 𝗺𝗲𝗰𝗵𝗮𝗻𝗶𝗰𝘀 𝘄𝗼𝗿𝗸 𝗶𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲, 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀. Here is the full video from 3Blue1Brown with exaplantion. I highly recommend to read, watch and bookmark this for a further deep dive: https://lnkd.in/dAviqK_6 𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 — 𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲: https://lnkd.in/dbf74Y9E | 48 comments on LinkedIn

A visual introduction to transformers. This chapter focuses on the overall structure, and word embeddings

Scientists just published something in Nature that will scare every marketer, leader, and anyone else who thinks they understand human choice. Researchers created an AI called "Centaur" that can predict human behavior across ANY psychological experiment with disturbing accuracy. Not just one narrow task. Any decision-making scenario you throw at it. Here's the deal: They trained this AI on 10 million human choices from 160 different psychology experiments. Then they tested it against the best psychological theories we have. The AI won. In 31 out of 32 tests. But here's the part that really got me... Centaur wasn't an algorithm built to study human behavior. It was a language model that learned to read us. The researchers fed it tons of behavioral data, and suddenly it could predict choices better than decades of psychological research. This means our decision patterns aren't as unique as we think. The AI found the rules governing choices we believe are spontaneous. Even more unsettling? When they tested it on brain imaging data, the AI's internal representations became more aligned with human neural activity after learning our behavioral patterns. It's not just predicting what you'll choose, it's learning to think more like you do. The researchers even demonstrated something called "scientific regret minimization"—using the AI to identify gaps in our understanding of human behavior, then developing better psychological models. Can a model based on Centaur be tuned for how customers behave? Companies will know your next purchasing decision before you make it. They'll design products you'll want, craft messages you'll respond to, and predict your reactions with amazing accuracy. Understanding human predictability is a competitive advantage today. Until now, that knowledge came from experts in behavioral science and consumer behavior. Now, there's Centaur. Here's my question: If AI can decode the patterns behind human choice with this level of accuracy, what does that mean for authentic decision-making in business? Will companies serve us better with perfectly tailored offerings, or with this level of understanding lead to dystopian manipulation? What's your take on predictable humans versus authentic choice? #AI #Psychology #BusinessStrategy #HumanBehavior | 369 comments on LinkedIn

ChatGPT 4o System Prompt (Juni 2025) Der Systemprompt zu ChatGPT 4o wurde geleaked. Wer glaubt, ein Sprachmodell wie ChatGPT-4o sei einfach ein gut trainiertes neuronales Netz, denkt zu kurz. Was die Interaktion präzise, professionell und verlässlich macht, geschieht nicht allein im Modell, sondern in seiner systemischen Steuerung – dem System Prompt. Er ist das unsichtbare Drehbuch, das vorgibt, wie das Modell denkt, fühlt (im übertragenen Sinne), recherchiert und mit dir interagiert. 1. Struktur: Modular, regelbasiert, bewusst orchestriert Der System Prompt besteht aus sauber getrennten Funktionsblöcken: • Rollensteuerung: z. B. sachlich, ehrlich, kein Smalltalk • Tool-Integration: Zugriff auf Analyse-, Bild-, Web- und Dateitools • Logikmodule: zur Kontrolle von Frische, Quelle, Zeitraum, Dateityp Jedes Modul ist deklarativ und deterministisch formuliert – die Antwortlogik folgt festen Bahnen. Das Ergebnis: Transparenz und Wiederholbarkeit, auch bei komplexen Anforderungen. ⸻ 2. Kontrollmechanismen: Qualität durch gezielte Einschränkung Um Relevanz sicherzustellen, greifen mehrere Filter: • QDF (Query Deserves Freshness): Sorgt für zeitlich passende Ergebnisse – von „zeitlos“ bis „tagesaktuell“. • Time-Frame-Filter: Nur aktiv bei expliziten Zeitbezügen, nie willkürlich. • Source-Filter: Bestimmt, ob z. B. Slack, Google Drive oder Web befragt wird. • Filetype-Filter: Fokussiert auf bestimmte Dateiformate (z. B. Tabellen, Präsentationen). Diese Filter verhindern Überinformation – sie schärfen das Suchfeld und heben die Trefferqualität. ⸻ 3. Antwortarchitektur: Keine Texte, sondern verwertbare Ergebnisse Antworten folgen strengen Regeln: • Immer strukturiert im Markdown-Format • Sachlich, kompakt, faktenbasiert • Keine Dopplungen, kein Stilspiel, kein rhetorischer Lärm Ziel: Klarheit, ohne Nachbearbeitung. Der Output ist verwendungsfähig, nicht bloß informativ. ⸻ 4. Prompt Engineering: Spielraum für Profis Der Prompt ist nicht editierbar – aber bespielbar. Wer seine Mechanik versteht, kann gezielt: • Tools über semantische Trigger aktivieren („Slack“, „aktuell“, „PDF“) • Formatvorgaben in Prompts durchsetzen • Komplexe Interaktionen als sequentielle Promptketten modellieren • Domänenspezifische Promptbibliotheken entwickeln Fazit: Prompt Engineers, die das System verstehen, bauen keine Texte – sie bauen Steuerlogiken. ⸻ Was können wir daraus lernen? 1. Präzision ist kein Zufall, sondern Architektur. 2. Gute Antworten beginnen nicht bei der Modellleistung, sondern beim Kontextmanagement. 3. Wer Prompts baut, baut Systeme – mit Regeln, Triggern und Interaktionslogik. 4. KI wird produktiv, wenn Struktur auf Intelligenz trifft. Ob Beratung, Entwicklung oder Wissensarbeit – der System Prompt zeigt: Je klarer die Regeln im Hintergrund, desto stärker die Wirkung im Vordergrund.

Andrej Karpathy's keynote on June 17, 2025 at AI Startup School in San Francisco. Slides provided by Andrej: https://drive.google.com/file/d/1a0h1mkwfmV2Plek...

𝐍𝐨, 𝐲𝐨𝐮𝐫 𝐛𝐫𝐚𝐢𝐧 𝐝𝐨𝐞𝐬 𝐧𝐨𝐭 𝐩𝐞𝐫𝐟𝐨𝐫𝐦 𝐛𝐞𝐭𝐭𝐞𝐫 𝐚𝐟𝐭𝐞𝐫 𝐋𝐋𝐌 𝐨𝐫 𝐝𝐮𝐫𝐢𝐧𝐠 𝐋𝐋𝐌 𝐮𝐬𝐞. See our paper for more results: "Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task" (link in the comments). For 4 months, 54 students were divided into three groups: ChatGPT, Google -ai, and Brain-only. Across 3 sessions, each wrote essays on SAT prompts. In an optional 4th session, participants switched: LLM users used no tools (LLM-to-Brain), and Brain-only group used ChatGPT (Brain-to-LLM). 👇 𝐈. 𝐍𝐋𝐏 𝐚𝐧𝐝 𝐄𝐬𝐬𝐚𝐲 𝐂𝐨𝐧𝐭𝐞𝐧𝐭 - LLM Group: Essays were highly homogeneous within each topic, showing little variation. Participants often relied on the same expressions or ideas. - Brain-only Group: Diverse and varied approaches across participants and topics. - Search Engine Group: Essays were shaped by search engine-optimized content; their ontology overlapped with the LLM group but not with the Brain-only group. 𝐈𝐈. 𝐄𝐬𝐬𝐚𝐲 𝐒𝐜𝐨𝐫𝐢𝐧𝐠 (𝐓𝐞𝐚𝐜𝐡𝐞𝐫𝐬 𝐯𝐬. 𝐀𝐈 𝐉𝐮𝐝𝐠𝐞) - Teachers detected patterns typical of AI-generated content and scoring LLM essays lower for originality and structure. - AI Judge gave consistently higher scores to LLM essays, missing human-recognized stylistic traits. 𝐈𝐈𝐈: 𝐄𝐄𝐆 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 Connectivity: Brain-only group showed the highest neural connectivity, especially in alpha, theta, and delta bands. LLM users had the weakest connectivity, up to 55% lower in low-frequency networks. Search Engine group showed high visual cortex engagement, aligned with web-based information gathering. 𝑺𝒆𝒔𝒔𝒊𝒐𝒏 4 𝑹𝒆𝒔𝒖𝒍𝒕𝒔: - LLM-to-Brain (🤖🤖🤖🧠) participants underperformed cognitively with reduced alpha/beta activity and poor content recall. - Brain-to-LLM (🧠🧠🧠🤖) participants showed strong re-engagement, better memory recall, and efficient tool use. LLM-to-Brain participants had potential limitations in achieving robust neural synchronization essential for complex cognitive tasks. Results for Brain-to-LLM participants suggest that strategic timing of AI tool introduction following initial self-driven effort may enhance engagement and neural integration. 𝐈𝐕. 𝐁𝐞𝐡𝐚𝐯𝐢𝐨𝐫𝐚𝐥 𝐚𝐧𝐝 𝐂𝐨𝐠𝐧𝐢𝐭𝐢𝐯𝐞 𝐄𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭 - Quoting Ability: LLM users failed to quote accurately, while Brain-only participants showed robust recall and quoting skills. - Ownership: Brain-only group claimed full ownership of their work; LLM users expressed either no ownership or partial ownership. - Critical Thinking: Brain-only participants cared more about 𝘸𝘩𝘢𝘵 and 𝘸𝘩𝘺 they wrote; LLM users focused on 𝘩𝘰𝘸. - Cognitive Debt: Repeated LLM use led to shallow content repetition and reduced critical engagement. This suggests a buildup of "cognitive debt", deferring mental effort at the cost of long-term cognitive depth. Support and share! ❤️ #MIT #AI #Brain #Neuroscience #CognitiveDebt | 54 comments on LinkedIn

In 2025, the AI landscape has evolved far beyond just large language models. Knowing which model to use for your specific use case — and how — is becoming a strategic advantage. Let’s break down the 8 most important model types and what they’re actually built to do: ⬇️ 1. 𝗟𝗟𝗠 – 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 → Your ChatGPT-style model. Handles text, predicts the next token, and powers 90% of GenAI hype. 🛠 Use case: content, code, convos. 2. 𝗟𝗖𝗠 – 𝗟𝗮𝘁𝗲𝗻𝘁 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 𝗠𝗼𝗱𝗲𝗹 → Lightweight, diffusion-style models. Fast, quantized, and efficient — perfect for real-time or edge deployment. 🛠 Use case: image generation, optimized inference. 3. 𝗟𝗔𝗠 – 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗔𝗰𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹 → Where LLM meets planning. Adds memory, task breakdown, and intent recognition. 🛠 Use case: AI agents, tool use, step-by-step execution. 4. 𝗠𝗼𝗘 – 𝗠𝗶𝘅𝘁𝘂𝗿𝗲 𝗼𝗳 𝗘𝘅𝗽𝗲𝗿𝘁𝘀 → One model, many minds. Routes input to the right “expert” model slice — dynamic, scalable, efficient. 🛠 Use case: high-performance model serving at low compute cost. 5. 𝗩𝗟𝗠 – 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 → Multimodal beast. Combines image + text understanding via shared embeddings. 🛠 Use case: Gemini, GPT-4o, search, robotics, assistive tech. 6. 𝗦𝗟𝗠 – 𝗦𝗺𝗮𝗹𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 → Tiny but mighty. Designed for edge use, fast inference, low latency, efficient memory. 🛠 Use case: on-device AI, chatbots, privacy-first GenAI. 7. 𝗠𝗟𝗠 – 𝗠𝗮𝘀𝗸𝗲𝗱 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 → The OG foundation model. Predicts masked tokens using bidirectional context. 🛠 Use case: search, classification, embeddings, pretraining. 8. 𝗦𝗔𝗠 – 𝗦𝗲𝗴𝗺𝗲𝗻𝘁 𝗔𝗻𝘆𝘁𝗵𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹 → Vision model for pixel-level understanding. Highlights, segments, and understands *everything* in an image. 🛠 Use case: medical imaging, AR, robotics, visual agents. Understanding these distinctions is essential for selecting the right model architecture for specific applications, enabling more effective, scalable, and contextually appropriate AI interactions. While these are some of the most prominent specialized AI models, there are many more emerging across language, vision, speech, and robotics — each optimized for specific tasks and domains. LLM, VLM, MoE, SLM, LCM → GenAI LAM, MLM, SAM → Not classic GenAI, but critical building blocks for AI agents, reasoning, and multimodal systems 𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 — 𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲: https://lnkd.in/dbf74Y9E Kudos for the graphic goes to Generative AI ! | 45 comments on LinkedIn

Today, I’m diving into the world of deep research tools to find out which platforms are truly the most helpful for academic work. ▼ ▽ Sign up for my FREE new...

Der große Vergleich von GPT-4o, o3, o4 mini, 4.1 und mehr ✨ Ein persönlicher Erfahrungsbericht und ein Deep Research – speziell für mein Netzwerk.

𝗧𝗼𝗽 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗧𝗲𝗿𝗺𝘀 𝗬𝗼𝘂 𝗦𝗵𝗼𝘂𝗹𝗱 𝗞𝗻𝗼𝘄 — 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱 𝗦𝗶𝗺𝗽𝗹𝘆 𝟭. 𝗟𝗟𝗠 (𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹) → Helps computers understand and write human-like text → Examples: GPT-4, Claude, Gemini → Used in: Chatbots, coding tools, content generation 𝟮. 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 → The tech behind all modern AI models → Let models understand meaning, context, and order of words → Examples: BERT, GPT 𝟯. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 → Writing better instructions to get better AI answers → Includes system prompts, step-by-step prompts, and safety rules 𝟰. 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 → Training an AI model on your data → Helps tailor it for specific tasks like legal, medical, or financial use cases 𝟱. 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 → A way for AI to understand meaning and relationships between words or documents → Used in search engines and recommendation systems 𝟲. 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) → Combines AI with a database or document store → Helps AI give more accurate, fact-based answers 𝟳. 𝗧𝗼𝗸𝗲𝗻𝘀 → The chunks of text AI reads and writes → Managing them controls cost and performance 𝟴. 𝗛𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻 → When AI gives wrong or made-up answers → Can be fixed with fact-checking and better prompts 𝟵. 𝗭𝗲𝗿𝗼-𝗦𝗵𝗼𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 → When AI can perform a task without being trained on it → Saves time on training 𝟭𝟬. 𝗖𝗵𝗮𝗶𝗻-𝗼𝗳-𝗧𝗵𝗼𝘂𝗴𝗵𝘁 → AI explains its answer step-by-step → Helps with complex reasoning tasks 𝟭𝟭. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗪𝗶𝗻𝗱𝗼𝘄 → The amount of info AI can see at once → Larger windows help with longer documents or conversations 𝟭𝟮. 𝗧𝗲𝗺𝗽𝗲𝗿𝗮𝘁𝘂𝗿𝗲 → Controls how creative or predictable AI is → Lower values = more accurate; higher values = more creative 𝗪𝗵𝗮𝘁’𝘀 𝗖𝗼𝗺𝗶𝗻𝗴 𝗡𝗲𝘅𝘁? → Multimodal AI (text, images, audio together) → Smaller, faster models → Safer, ethical AI (Constitutional AI) → Agentic AI (autonomous, task-completing agents) Knowing the terms is just step one — what really matters is how you 𝘶𝘴𝘦 them to build better solutions. | 51 comments on LinkedIn

Catch the top moments from the Google Cloud Next keynote presentation, featuring CEO Thomas Kurian on AI breakthroughs, along with key announcements and real...

Gratis und das BESTE LLM auf zig Benchmarks. Wie hat Google das geschafft? Und vor allem, was bedeutet das für uns?Schnappt euch hier Incogni mit meinem Code...

One of my favourite reads from the last six months is Sequoia Capital’s report exploring the evolution of generative AI and it’s implications for the messy…

Thought is a multi-step process, but rarely linear. Early LLMs lacked structured reasoning and often struggled with logic. Chain-of-Thought introduced…

2025 is the Year of LCMs and not LLMs.   Meta has announced a new architecture for the future of Large Language Models called Large Concept Models.   Building… | 103 comments on LinkedIn

BREAKING! ChatGPT 4.5 is here! And it feels a bit magical. My favorite part is the increased EQ - check out the highlight video below. This feels like the future of AI. More Claude-y, which I LOVE. Empathy is exactly what I've been missing from ChatGPT! It's been fine - but this is another level. This is how we will actually communicate with AI. It's the thing I love about Claude. Microsoft is leaning into empathy with Mustafa Suleyman after his turn at Inflection. Okay, let's get into it. BTW - the video is MY OWN EDIT. I just loved the EQ example so much. HIGHLIGHTS: EXCLUSIVE ACCESS: Initially available only to $200/month Pro subscribers, coming to Plus users next week MOST HUMAN-LIKE YET: Features significantly enhanced emotional intelligence and conversational abilities LARGEST MODEL: OpenAI's biggest model to date, though specific parameters remain undisclosed FINAL PRE-REASONING MODEL: Last major release before OpenAI introduces chain-of-thought reasoning in GPT-5 >>The Evolution of AI Conversation What stands out with 4.5 is how much more human the interactions feel. The model demonstrates substantially improved emotional intelligence, with responses that show greater nuance and sensitivity. This shift toward a more empathetic, Claude-like conversation style suggests OpenAI is recognizing that raw intelligence isn't enough – it matters HOW it talks to you. >>Key Features That Make It Special Enhanced Knowledge and Reasoning - The expanded knowledge base means deeper, more comprehensive answers - Significantly fewer hallucinations, making it more reliable for critical tasks - Pattern recognition that borders on intuitive understanding Reimagined User Experience - Conversations flow naturally, without the mechanical feel of earlier models - Context handling that actually remembers what you've been discussing - Lightning-fast responses despite its massive size >>The Price of Progress Access to this cutting-edge technology comes at a premium. ChatGPT Pro subscribers ($200/month) get first access, with Plus users ($20/month) joining the party the week of March 3. Enterprise and Education users will follow shortly after. >>Where 4.5 Really Shines The model particularly excels at: - Creative writing with genuine emotional depth - Complex problem-solving that requires nuanced understanding - Communication tasks where tone and empathy matter - Multi-step planning and execution, especially for coding workflows Pretty cool. ++++++++++++++++++++ UPSKILL YOUR ORGANIZATION: When your company is ready, we are ready to upskill your workforce at scale. Our Generative AI for Professionals course is tailored to enterprise and highly effective in driving AI adoption through a unique, proven behavioral transformation. It's pretty awesome. Check out our website or shoot me a DM.

We also… | 23 comments on LinkedIn

🔍 Deep Dive in die Welt der Deep Research Modelle von OpenAI, Perplexity und co! 🌐Welches davon, macht den besten Eindruck?In der dynamischen Kontext der K...

To help educators choose the best toolset for them, we compare the key features available in the free vs. paid tiers of some of the most popular Generative AI models.

All eyes on DeepSeek: Im KI-Bereich hat offenbar gerade eine Revolution stattgefunden 🤜. Für Hochschulen eine grandiose Nachricht. DeepSeek ist ein… | 29 comments on LinkedIn

LLMs and Generative AI are terrific tools for lots of different things. Check out the data viz below capturing #HBR's top 100 uses for Gen AI. I've personally… | 17 comments on LinkedIn

LLMs do more than predict the next word; they compress a "world-model" within their complex networks and weights. This is an area of active debate within the… | 76 comments on LinkedIn

Beeindruckende…

Ein Forscherteam (Buyl et al., 2024) hat 17 verschiedene KI-Modelle getestet und zeigt: Die Systeme… | 133 comments on LinkedIn

das über die Hochleistungsrechner in Jülich und an…