AI, Data Science, Machine Learning & IOT

🔬 AI Research Digest 📅 Week of August 4–10, 2026 ━━━━━━━━━━━━━━━━━━━━━━━━ 1. 🤖 AOSpec: Action and Observation Co-Speculation for Low-Latency Agent Serving Authors/Org: Hao Mark Chen, Jinnan Guo, Wayne Luk | arXiv: 2608.00881 Bottleneck solved: End-to-end inference latency in production AI agent systems. AOSpec reduces perceived response time by co-speculating actions and observations in parallel before they are needed, effectively pipelining the agent execution loop. Developers deploying multi-step agents in production will find this directly applicable to cutting SLA costs without sacrificing correctness. 🔗 AOSpec – arXiv:2608.00881 ━━━━━━━━━━━━━━━━━━━━━━━━ 2. 🔐 Leak It: Probabilistic Training-Data Extraction from Black-Box LLMs Authors/Org: Victor Maricato | arXiv: 2608.00144 Bottleneck solved: Lack of practical tooling for auditing data privacy risks in deployed, black-box language models. The paper introduces a probabilistic extraction framework that recovers training data membership signals without any white-box model access, turning a theoretical concern into a measurable audit workflow. Teams using third-party LLMs to process proprietary data should treat this as a benchmark for their own exposure — code is open-sourced on GitHub. 🔗 Leak It – arXiv:2608.00144 ━━━━━━━━━━━━━━━━━━━━━━━━ 3. 🧠 HP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning Authors/Org: Ruichen Xu, Jingxiang Qu, Wenhan Gao (+ Yann LeCun) | arXiv: 2608.00491 Bottleneck solved: Label scarcity in graph-structured data — extends LeCun's JEPA self-supervised framework to multi-resolution graph representations. HP-JEPA learns rich graph embeddings across multiple scales without labeled supervision, a significant unlock for domains like knowledge graphs, molecular data, and code dependency graphs. For data teams with abundant unlabeled graph data but limited annotation budgets, this is a ready research basis for pre-training pipelines. 🔗 HP-JEPA – arXiv:2608.00491 ━━━━━━━━━━━━━━━━━━━━━━━━ 💡 Stay curious. Read the papers. For More: @kdnuggets @datasciencechats

🤖 AI Weekly Digest 📅 Week of Aug 4–Aug 10, 2026 ━━━━━━━━━━━━━━━━━━━━━━━━ 1. 💸 OpenAI Slashes GPT-5.6 Luna Prices by 80% OpenAI cut GPT-5.6 Luna to just $0.20 per million input tokens (down from $1.00), making frontier-quality AI dramatically cheaper for high-volume workloads. Developers building automation pipelines, internal tools, or API-heavy products can now scale at a fraction of the previous cost. 🔗 OpenAI Slashes GPT-5.6 Luna Prices – VentureBeat ━━━━━━━━━━━━━━━━━━━━━━━━ 2. 🎥 Adobe Research Releases "Wonder" — Real-Time Navigable Video World Model Adobe Research published Wonder, a video world model that enables real-time, camera-steerable exploration of minute-scale video worlds at 16 FPS. It solves persistent issues like drifting controls and fading memory, making it a powerful primitive for interactive 3D content generation, simulation, and game development. 🔗 Wonder: Video World Model Done Better – arXiv ━━━━━━━━━━━━━━━━━━━━━━━━ 3. 🧠 AI Lab Leaders Say We've Entered an Intelligence Explosion Executives at Google DeepMind, OpenAI, and Anthropic say AI has entered early stages of an intelligence explosion, with models now actively accelerating AI research itself. For developers and data teams, this signals a near-term shift where AI tooling, benchmarks, and best practices may evolve faster than ever before. 🔗 AI Update, August 7, 2026 – MarketingProfs ━━━━━━━━━━━━━━━━━━━━━━━━ 4. 📱 Google Replaces Assistant with Gemini Across Android Google has officially replaced Google Assistant with Gemini as the default AI on Android devices, marking the end of the rule-based assistant era. For developers, this accelerates the shift toward agentic, context-aware AI integrations in mobile apps and enterprise workflows. 🔗 Top AI News for August 2026 – AIapps ━━━━━━━━━━━━━━━━━━━━━━━━ 5. 📋 New U.S. AI Regulations Trigger Government Review for Frontier Models New federal rules now require companies building frontier AI models to potentially undergo government national security review before launch — and models can be taken offline if they fail checks. AI teams at companies building or deploying advanced models need to factor compliance timelines into their roadmaps. 🔗 AI News Briefs for August 2026 – Radical Data Science ━━━━━━━━━━━━━━━━━━━━━━━━ 💡 Stay ahead. Stay curious. For More: @kdnuggets @datasciencechats

🔬 AI Research Digest 📅 Week of Jul 28–Aug 4, 2026 ━━━━━━━━━━━━━━━━━━━━━━━━ 1. 🧠 Explorative Modeling: Unlocking a Third Pretraining Axis Authors/Org: Alexi Gladstone, Heng Ji, Yilun Du | arXiv: 2607.27372 Bottleneck solved: Mode collapse and blurry predictions in generative models — by exploring K candidate generations and training on the best match, models commit to sharp modes instead of averaging them. FLOP efficiency improves 4.1×, sample efficiency 6.2×, and parameter efficiency by 47%, achieving near-SOTA 1.43 FID on ImageNet without classifier guidance — a strong signal this could reshape how generative pretraining is structured. 🔗 Explorative Modeling (arXiv 2607.27372) ━━━━━━━━━━━━━━━━━━━━━━━━ 2. 🌍 EO-Agents: Three-Agent LLM Pipeline for Earth Observation Hypothesis Generation Authors/Org: (opendatalab / arXiv submission) | arXiv: 2607.01584 Bottleneck solved: Generating scientifically coherent research hypotheses across 1,475 NASA Earth datasets — previously a slow, expert-intensive manual process. The system grounds hypothesis generation in the NASA Earth Observation Knowledge Graph using a heterogeneous GNN to rank dataset pairings, then deploys three specialized LLM agents to filter, generate, and evaluate hypotheses across ecohydrology, glaciology, aerosol-cloud interactions, and more. 🔗 EO-Agents (arXiv 2607.01584) ━━━━━━━━━━━━━━━━━━━━━━━━ 3. 🗂️ codebase-memory-mcp: Persistent Knowledge Graph for AI Coding Agents Authors/Org: DeusData | GitHub: DeusData/codebase-memory-mcp Bottleneck solved: AI coding agents wasting tokens re-scanning codebases file-by-file for structural queries — replaced by a persistent, sub-millisecond knowledge graph. A single static C binary with zero dependencies supports 158 languages, indexes the Linux kernel (28M lines) in ~3 minutes, and cuts structural query token usage by ~99%, with 83% answer quality validated across 31 real-world repositories. 🔗 codebase-memory-mcp on GitHub ━━━━━━━━━━━━━━━━━━━━━━━━ 💡 Stay curious. Read the papers. For More: @kdnuggets @datasciencechats

🤖 AI Weekly Digest 📅 Week of Jul 28–Aug 3, 2026 ━━━━━━━━━━━━━━━━━━━━━━━━ 1. 🧮 OpenAI's Astra Solves 10 Long-Standing Math Problems OpenAI's next-generation model Astra solved 10 previously open problems in mathematics and theoretical computer science — including the first-ever proof that non-sofic groups exist — publishing formal Lean 4 proofs on GitHub for roughly $2,000 in compute. This signals that AI is crossing from code generation into rigorous formal reasoning, a capability that could transform scientific research tooling and automated theorem verification for developer teams. 🔗 OpenAI's Astra solves 10 long-open math problems and publishes the proofs ━━━━━━━━━━━━━━━━━━━━━━━━ 2. ⚡ DeepSeek V4 Flash Exits Preview, Beats Agent Benchmarks DeepSeek V4 Flash 0731 is now generally available at just $0.14/$0.28 per million tokens, scoring 82.7% on Terminal-Bench and outperforming DeepSeek's own 1.6T Pro model on agentic tasks. For developers building cost-sensitive agents and pipelines, this is a compelling open-weight option that rivals far more expensive models. 🔗 AI News August 2026: DeepSeek V4 Flash & Claude Sonnet 5 ━━━━━━━━━━━━━━━━━━━━━━━━ 3. 🔐 Chinese Hacker Uses DeepSeek + Telegram to Launch Autonomous Cyberattacks A threat actor tracked as "knaithe" wired DeepSeek into the open-source Hermes Agent framework and controlled it via Telegram to autonomously enumerate targets, source public exploits, and attack 460+ internet-facing systems — the first confirmed real-world autonomous AI cyberattack. Palo Alto's Unit 42 confirmed this is also the first documented case where AI provider safety controls (Claude and OpenAI's guardrails) had measurable defensive value by blocking escalation attempts. 🔗 Chinese Hacker Commands DeepSeek via Telegram to Launch Autonomous Attacks ━━━━━━━━━━━━━━━━━━━━━━━━ 4. 📱 Google Cancels AI Studio Mobile App, Folds Features Into Gemini Despite 800,000+ pre-orders on iOS and Android, Google abruptly cancelled its standalone AI Studio mobile app and announced it will integrate AI Studio's app-creation capabilities directly into the Gemini app instead. The web-based AI Studio continues, but this signals Google's strategy to consolidate its AI developer surface around Gemini rather than maintain separate products. 🔗 Google abruptly cancels its unreleased AI Studio mobile app ━━━━━━━━━━━━━━━━━━━━━━━━ 5. 🤖 Anthropic Releases Claude Opus 5 as AI Model Competition Peaks Anthropic released Claude Opus 5 on July 24, establishing it as a top-tier frontier model alongside GPT-5.6 and Gemini 3.6 in what has become the most crowded and capable model market to date. For data teams and developers, the July 2026 model landscape now offers genuine frontier-class intelligence across multiple providers with increasingly competitive pricing — Claude Sonnet 5 intro pricing ($2/M) runs until September 1. 🔗 July 2026 AI Releases: OpenAI, Anthropic, Google DeepMind, Cognition ━━━━━━━━━━━━━━━━━━━━━━━━ 💡 Stay ahead. Stay curious. For More: @kdnuggets @datasciencechats

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🔬 AI Research Digest 📅 Week of Jul 21–28, 2026 ━━━━━━━━━━━━━━━━━━━━━━━━ 1. 🧠 codebase-memory-mcp — Persistent Knowledge Graph for AI Coding Agents Authors/Org: DeusData | GitHub: DeusData/codebase-memory-mcp Bottleneck solved: Eliminates redundant codebase scanning by AI agents, cutting token usage for structural queries by up to 99%. Builds a persistent knowledge graph (functions, classes, call chains) using tree-sitter across 158 languages — ships as a single static C binary with no dependencies and indexes even the Linux kernel in minutes. Essential if you're running Claude Code, Cursor, or any MCP-based agent over a large repo. 🔗 DeusData/codebase-memory-mcp (~32K stars) ━━━━━━━━━━━━━━━━━━━━━━━━ 2. 🔒 Strix — Agentic AI Penetration Testing Authors/Org: usestrix | GitHub: usestrix/strix Bottleneck solved: Replaces noisy static scanners with a dynamic AI agent that validates vulnerabilities with real proof-of-concept exploits. Strix behaves like a security researcher — it runs an HTTP proxy, browser exploitation, a Python sandbox, and CI/CD integration, adding ~7K stars/week as security teams move it into production pipelines. If your team still relies on static analysis for vulnerability coverage, this is a forcing function to upgrade. 🔗 usestrix/strix (~42K stars) ━━━━━━━━━━━━━━━━━━━━━━━━ 3. ⚡ Colibri — 744B MoE Model on Consumer Hardware Authors/Org: JustVugg | GitHub: JustVugg/colibri Bottleneck solved: Runs a frontier-scale 744-billion-parameter mixture-of-experts model locally on ~25GB of RAM by streaming experts from disk on demand. A pure-C inference engine with zero dependencies — no cloud, no GPU cluster, no API key required. For teams prioritizing data privacy or cost control, Colibri makes previously inaccessible model scale available on a single developer machine. 🔗 JustVugg/colibri (~14.7K stars) ━━━━━━━━━━━━━━━━━━━━━━━━ 💡 Stay curious. Read the papers. For More: @kdnuggets @datasciencechats

🤖 AI Weekly Digest 📅 Week of Jul 21–Jul 27, 2026 ━━━━━━━━━━━━━━━━━━━━━━━━ 1. 🧠 Claude Opus 5 Arrives as Anthropic's New Frontier Model Anthropic released Claude Opus 5 on July 25, its most capable model yet — benchmarks show it outperforms GPT-5.6 Sol on FrontierCode 1.1 with a higher mergeability score at lower rollout cost. For developer and data teams, Opus 5 excels at long-horizon agentic coding tasks, complex reasoning chains, and multi-step pipeline orchestration, making it a strong upgrade for AI-assisted engineering workflows. 🔗 Claude Opus 5 vs GPT-5.6 Sol: Frontier Analysis ━━━━━━━━━━━━━━━━━━━━━━━━ 2. 🇨🇳 Kimi K3's 2.8-Trillion-Parameter Open Weights Drop Today Moonshot AI released the open weights for Kimi K3 today (July 27) — a 2.8-trillion-parameter model that topped a major coding leaderboard against Claude Fable 5 and other frontier systems. Data and software teams can now self-host a frontier-caliber coding specialist with zero per-token cost, making it a compelling choice for high-volume code generation and autonomous agent workloads. 🔗 Moonshot AI Releases Kimi K3, the Largest Open-Source Model Ever ━━━━━━━━━━━━━━━━━━━━━━━━ 3. ⚡ DeepSeek V4 Reaches Stable Release DeepSeek V4 hit its stable release on July 24, ending the preview churn that had kept cautious enterprises from committing production workloads to it — it remains the price floor of the frontier at roughly $0.44 per million output tokens. For engineering and data teams, the stable tag means it's now safe to wire V4 into production pipelines, CI workflows, and cost-sensitive inference tiers where quality-per-dollar matters most. 🔗 Open-Weight Countdown: DeepSeek July 24, Kimi K3 July 27 ━━━━━━━━━━━━━━━━━━━━━━━━ 4. 🔮 Google Ships Gemini 3.6 Flash and Two New Flash Variants Google released Gemini 3.6 Flash alongside Gemini 3.5 Flash-Lite and Gemini 3.5 Flash Cyber this week — a cluster of faster, cheaper models filling the gap while its delayed flagship Gemini 3.5 Pro continues to slip. For developers, these Flash-tier models offer low-latency, low-cost inference well-suited for RAG pipelines, summarization endpoints, and real-time agent sub-tasks that don't need frontier-level reasoning. 🔗 Gemini 3.6 Flash on LLM Stats ━━━━━━━━━━━━━━━━━━━━━━━━ 5. 🔐 Sakana AI Releases Fugu-Cyber: AI-Native Security Scoring 86.9% on CyberGym Sakana AI launched Fugu-Cyber, a security-tuned endpoint on its Fugu orchestration model, reporting 86.9% on CyberGym and 72.1% on CTI-REALM — edging past GPT-5.5-Cyber and Claude Mythos Preview. For security engineers and platform teams, Fugu-Cyber is a purpose-built AI tool for vulnerability triage, threat intelligence processing, and autonomous red-team reasoning that can slot into existing security pipelines. 🔗 Sakana AI Releases Fugu-Cyber ━━━━━━━━━━━━━━━━━━━━━━━━ 💡 Stay ahead. Stay curious. For More: @kdnuggets @datasciencechats

🔬 AI Research Digest 📅 Week of July 14–21, 2026 ━━━━━━━━━━━━━━━━━━━━━━━━ 1. 🐦 Colibri: Run a 744B MoE Model on 25 GB of RAM Authors/Org: JustVugg (open-source) | GitHub: JustVugg/colibri Bottleneck solved: Hardware/cost barriers for running frontier-scale models locally — no GPU, no cloud spend required. Colibri is a ~2,400-line pure-C inference engine that streams only the active MoE experts from disk at runtime, keeping just 9.9 GB of dense model weights resident in RAM. Developers and researchers who want to run GLM-5.2 locally for experimentation or fine-tuning evaluation can now do so on a standard consumer machine. 🔗 JustVugg/colibri on GitHub ━━━━━━━━━━━━━━━━━━━━━━━━ 2. ⚡ Hawk: Hardware-Aware LLM Framework for NPU Kernel Generation Authors/Org: Junyi Wen, Ruiyan Zhuang, Yongjia Xu et al. | arXiv: 2607.01590 Bottleneck solved: LLMs fail on NPU kernel generation because they lack hardware-specific priors — Hawk raises accuracy from 49.4% to 80.0% without retraining. Hawk uses three plug-and-play modules (runtime knowledge synthesis, bottleneck-aware retrieval, and effect-driven distillation) to inject real hardware constraints into any LLM's reasoning loop, also delivering up to 2.2× execution speedup over prior baselines. ML infrastructure teams targeting Ascend or custom AI accelerators can layer Hawk on top of existing LLM toolchains immediately. 🔗 arXiv 2607.01590 — Hawk ━━━━━━━━━━━━━━━━━━━━━━━━ 3. 🧠 Codebase-Memory-MCP: Persistent Knowledge Graph for AI Coding Agents Authors/Org: DeusData | GitHub: DeusData/codebase-memory-mcp Bottleneck solved: AI coding agents waste hundreds of thousands of tokens re-scanning files on every query — this cuts structural-query token usage by 99%. Built in pure C as a single static binary with zero dependencies, it parses 158 languages via tree-sitter AST analysis, indexes the Linux kernel (28 M lines) in ~3 minutes, and answers structural queries in under a millisecond. Any team running Claude Code, Codex, or similar agents on large monorepos can drop this MCP server in to immediately slash context costs and speed up agent tool calls. 🔗 DeusData/codebase-memory-mcp on GitHub ━━━━━━━━━━━━━━━━━━━━━━━━ 💡 Stay curious. Read the papers. For More: @kdnuggets @datasciencechats

🤖 AI Weekly Digest 📅 Week of July 14–20, 2026 ━━━━━━━━━━━━━━━━━━━━━━━━ 1. 🧠 Claude Sonnet 5: Anthropic's Most Agentic Model Yet Anthropic launched Claude Sonnet 5 on June 30, making it the default model for all Free and Pro users starting July 1 — it features a 1M-token context window, adaptive thinking on by default, and near-Opus 4.8 performance at lower cost ($2/$10 per 1M tokens through August). For developers and data teams, this means frontier-level agentic capability — browser use, terminal execution, and multi-step tool chains — without paying flagship model prices. 🔗 Introducing Claude Sonnet 5 — Anthropic ━━━━━━━━━━━━━━━━━━━━━━━━ 2. 🌐 OpenAI GPT-5.6 Launches a Three-Tier Price War OpenAI released GPT-5.6 on July 9 in three variants — Sol, Terra, and Luna — with Luna starting at just $1 input / $6 output per 1M tokens, directly undercutting Anthropic's pricing and marking the most aggressive enterprise push yet. Developers can now pick inference cost vs. capability on a spectrum within a single model family, opening lower-cost automation paths for batch pipelines and agent workloads. 🔗 OpenAI GPT-5.6 July 2026: Pricing, Benchmarks & Access ━━━━━━━━━━━━━━━━━━━━━━━━ 3. 🔓 Kimi K3: The Largest Open-Weight AI Model Ever Released Moonshot AI unveiled Kimi K3 on July 16 — a 2.8-trillion-parameter open-weight MoE model with native multimodal understanding, a 1M-token context window, and pricing at $3/$15 per 1M tokens, with full open weights dropping by July 27. It already topped the Frontend Code Arena leaderboard at 1679 Elo, making it the most powerful open model available for self-hosted deployments and fine-tuning pipelines. 🔗 Moonshot AI Releases Kimi K3 — MarkTechPost ━━━━━━━━━━━━━━━━━━━━━━━━ 4. 🔌 MCP Is Now the Universal Enterprise AI Standard The Model Context Protocol has crossed into mainstream enterprise adoption — with Google, Microsoft, Salesforce, Snowflake, and ServiceNow all formally supporting it, 5,800+ available servers, and 97M+ monthly SDK downloads as of July 2026. For software and data teams, this means agent integrations with Salesforce, databases, and internal tools can now be built once against a single stable interface rather than per-vendor APIs. 🔗 MCP Enterprise Adoption: The July 2026 State of Play ━━━━━━━━━━━━━━━━━━━━━━━━ 5. 🚨 JADEPUFFER: The First Fully Autonomous AI Ransomware Attack Sysdig documented JADEPUFFER, the first confirmed end-to-end agentic ransomware operation — an LLM autonomously exploited a Langflow vulnerability, harvested credentials, moved laterally, and encrypted 1,342 database records, all while narrating its own actions in real time. The skill floor for ransomware has effectively dropped to the cost of running an AI agent, a critical signal for any team deploying AI-connected infrastructure. 🔗 JADEPUFFER: Agentic Ransomware for Automated Database Extortion — Sysdig ━━━━━━━━━━━━━━━━━━━━━━━━ 💡 Stay ahead. Stay curious. For More: @kdnuggets @datasciencechats

Brief Summary: OpenAI officially released GPT-5.5 on April 23, 2026, transitioning from a chat assistant to a fully "agentic" system. Here are the 5 key updates: Agentic Workflows: Autonomously handles multi-step tasks across browsers and software with self-correction. SOTA Reasoning: Achieves 82.7% on Terminal-Bench 2.0, significantly outperforming Claude 4.7 and Gemini 3.1. 1M Token Context: Vastly improved reliability for processing massive codebases and long-form legal archives. Thinking & Pro Tiers: New specialized versions optimized for deep reasoning and high-stakes enterprise work. Enhanced Efficiency: Delivers smarter performance at GPT-5.4 speeds while consuming fewer tokens per task. @kdnuggets @datasciencechats https://openai.com/index/introducing-gpt-5-5/

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Google is expanding the Gemini 3 model family with the release of Gemini 3 Flash, which offers frontier intelligence built for speed at a fraction of the cost @kdnuggets @datasciencechats Read More: https://blog.google/products/gemini/gemini-3-flash

AI, Data Science, Machine Learning & IOT pinned «5-Day AI Agents Intensive Course with Google ⏰ Nov 10 onwards #agents #freecourse https://www.kaggle.com/learn-guide/5-day-agents»

5-Day AI Agents Intensive Course with Google ⏰ Nov 10 onwards #agents #freecourse https://www.kaggle.com/learn-guide/5-day-agents

Here are latest Gemini image editing features: 🎨 Maintaining Likeness: Photos of people and pets consistently look like themselves even when you change their hairstyle or outfit. 📍 Change Scenarios: Place a person or pet in new locations or give them a new look while keeping their original appearance. 🔄 Blend Photos: Combine multiple photos to create a new scene—like you and your dog on a basketball court! ✏️ Multi-turn Editing: Continuously edit an image. Start with an empty room, paint the walls, then add furniture. ✨ Mix Designs: Apply the style or texture from one image to an object in another—like putting a butterfly's wing pattern on a dress. Read More: https://blog.google/intl/en-mena/product-updates/explore-get-answers/nano-banana-image-editing-in-gemini-just-got-a-major-upgrade/ @kdnuggets @datasciencechats

🚀 Introducing GPT-5: Launched 32 months after ChatGPT, GPT-5 is hailed as a "major upgrade" and a "significant step along the path to AGI." It's described as conversing with a "PhD level expert" across any field, a substantial leap from previous models. 📈 Unprecedented Growth & Impact: ChatGPT now boasts 700 million weekly users, relying on it for work, learning, advice, and creation. GPT-5 aims to be intuitive, useful, smart, and fast. 💡 Enhanced Reasoning and Capabilities: GPT-5 incorporates a "reasoning paradigm" allowing it to "pause to think" for more intelligent, precise answers, eliminating the trade-off between speed and thoughtfulness. It can write entire computer programs, plan events, and explain complex health information. 📊 Superior Performance Metrics: Coding: Sets new highs on SWEBench (real software engineering tasks) and Aider Polyglot (multilingual programming). Reasoning: Outperforms previous models and most human experts on MMMU (multimodal reasoning) and AIME (mathematical reasoning). Reliability: Significantly reduces hallucinations, making it the "most reliable, most factual model ever," and performs exceptionally well on health-related questions. 🌐 Broad Accessibility & Tiered Access: GPT-5 is rolling out immediately, available to free, Plus, Pro, NT, Enterprise, and EDU users. Free users get GPT-5 initially before transitioning to Mini, while paid tiers receive higher or unlimited usage with extended thinking capabilities. 🛠️ Powerful Integrations & Personalization: All existing ChatGPT tools (search, file/image upload, data analysis, image generation, memory, custom instructions) work seamlessly with GPT-5. New features include customizable chat colors, experimental "personalities" (supportive, sarcastic), and crucial integrations with Gmail and Google Calendar for enhanced scheduling and personal assistance. 🛡️ Advanced Safety Features: OpenAI has overhauled safety training with "safe completion," which aims to maximize helpfulness within safety constraints, offering partial answers or alternatives instead of outright refusals. GPT-5 is also significantly less deceptive. 🧪 Recursive Model Improvement: New training techniques involve using AI itself to generate high-quality synthetic data and curriculum, creating a "recursive improvement loop" where older models enhance the training data for newer generations. ⚕️ Transformative Healthcare Application: Highlighted as a top use case, GPT-5 is the "best model ever for health," scoring highly on the HelpBench evaluation. A personal testimony demonstrated its ability to translate complex medical reports into plain language, aid in critical decision-making, and empower patients. 💻 Revolutionizing Coding: GPT-5 is proclaimed the "best coding model in the world," excelling at "Agentic coding tasks" where it can autonomously tackle complex problems, build entire web apps (like a French learning app or a finance dashboard), and even fix its own code. It also exhibits a strong sense of aesthetics in front-end development. 🤝 Developer Focus & API Enhancements: Available in API today (GPT-5, Mini, Nano), with tiered pricing. New API features include a "reasoning effort" parameter for latency control, "Custom Tools" for flexible tool calls, "Tool Call Preambles" for explanations, and a "Verbosity programmer" for output control. The context window has doubled to 400K tokens. 🏢 Enterprise & Government Adoption: Over 5 million businesses already use OpenAI technology, with GPT-5 expected to be a "step function" in enabling industries like life sciences (Amgen), finance (BBVA), and healthcare (Oscar Health). Two million US federal employees will also gain access to GPT-5 and ChatGPT. @kdnuggets @datasciencechats

https://developers.googleblog.com/en/introducing-opal/ @kdnuggets @datasciencechats

https://cloud.google.com/blog/products/application-development/firebase-studio-lets-you-build-full-stack-ai-apps-with-gemini #ai @kdnuggets @datasciencechats

Building LLMs - Stanford Course #ai #generativeai #llm https://www.youtube.com/watch?v=9vM4p9NN0Ts @kdnuggets @datasciencechats 00:10 Building Large Language Models overview 02:21 Focus on data evaluation and systems in industry over architecture 06:25 Auto regressive language models predict the next word in a sentence. 08:26 Tokenizing text is crucial for language models 12:38 Training a large language model involves using a large corpus of text. 14:49 Tokenization process considerations 18:40 Tokenization improvement in GPT 4 for code understanding 20:31 Perplexity measures model hesitation between tokens 24:18 Comparing outputs and model prompting 26:15 Evaluation of language models can yield different results 30:15 Challenges in training large language models 32:06 Challenges in building large language models 35:57 Collecting real-world data is crucial for large language models 37:53 Challenges in building large language models 41:38 Scaling laws predict performance improvement with more data and larger models 43:33 Relationship between data, parameters, and compute 47:21 Importance of scaling laws in model performance 49:12 Quality of data matters more than architecture and losses in scaling laws 52:54 Inference for large language models is very expensive 54:54 Training large language models is costly 59:12 Post training aligns language models for AI assistant use 1:01:05 Supervised fine-tuning for large language models 1:04:50 Leveraging large language models for data generation and synthesis 1:06:49 Balancing data generation and human input for effective learning 1:10:23 Limitations of human abilities in generating large language models 1:12:12 Training language models to maximize human preference instead of cloning human behaviors. 1:16:06 Training reward model using softmax logits for human preferences. 1:18:02 Modeling optimization and challenges in large language models (LLMs) 1:21:49 Reinforcement learning models and potential benefits 1:23:44 Challenges with using humans for data annotation 1:27:21 LLMs are cost-effective and have better agreement with humans than humans themselves 1:29:12 Perplexity is not calibrated for large language models 1:33:00 Variance in performance of GPT-4 based on prompt specificity 1:34:51 Pre-training data plays a vital role in model initialization 1:38:32 Utilize GPUs efficiently with matrix multiplication 1:40:21 Utilizing 16 bits for faster training in deep learning 1:44:08 Building Large Language Models from scratch