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Incredible news from Anthropic 🎉 🎊 !!! It has just announced significant upgrades to its AI portfolio, including the enhanced Claude 3.5 Sonnet and the upcoming Claude 3.5 Haiku. There's also a remarkable new "computer control" feature now in public beta. 🤖💡 🔧 The upgraded Claude 3.5 Sonnet has set a new benchmark in AI-powered coding, achieving a stunning 49.0% on the SWE-bench verified benchmark. This surpasses all publicly available models, including those from OpenAI and specialized coding systems. GitLab has reported up to a 10% boost in reasoning across various use cases without any additional latency. 🏆 🖥️ Pioneering computer interaction capabilities, Claude 3.5 Sonnet can now view screens, control cursors, click, and type—mirroring human actions. This makes it the first AI model offering such human-like computer control. Initial benchmarks are promising, with an impressive 14.9% on screenshot-only OSWorld tests, nearly doubling the performance of the next-best system! 📈 And let's not forget about the upcoming Claude 3.5 Haiku, set for release later this month. It promises to match the performance of Claude 3 Opus while maintaining cost-effectiveness and speed, achieving a noteworthy 40.6% on SWE-bench verified—outperforming the original Claude 3.5 Sonnet and even GPT-4o. Anthropic remains committed to safety and responsible scaling, having conducted rigorous evaluations with both US and UK AI Safety Institutes, and adhering to the ASL-2 Standard. #AI #Coding #TechInnovation #Anthropic #MachineLearning #FutureOfWork #AIResearch #TechNews

Unlocking the Math Genie: A New Benchmark for Artificial Reasoning 🚀 Want to know how AI really thinks about math? 🤯 Existing benchmarks for evaluating AI's mathematical abilities are limited. They often focus on specific problems with predefined rules, which don't truly capture the nuances of reasoning and adaptability. That's where the UTMath Benchmark comes in! 💡 This new benchmark offers a comprehensive evaluation of AI's mathematical reasoning, using: ☑️ Extensive Unit Tests: 1,053 problems across 9 domains, with over 68 test cases per problem. ☑️ Robust Evaluation: Inspired by software development, it prioritizes both accuracy and reliability of results. ☑️ Reasoning-to-Coding of Thoughts (RCoT): This novel approach encourages AI to explicitly reason before generating code, leading to more sophisticated solutions and improved performance. The UTMath Benchmark is a game-changer for advancing Artificial General Intelligence. By pushing the boundaries of AI's mathematical reasoning, we can unlock its full potential. 💪 Check out the paper: https://lnkd.in/gyZhrJ4M #math, #research, #paper, #unittesting, #reasoning, #coding, #AI, #machinelearning, #deeplearning, #UTMath, #ltimindtree, #genai, #generativeai, #aiml, #trends

This is mind-blowing !! 😳 @Anthropic just launched “Computer Use,” enabling an LLM to take control of your computer screen based on a simple prompt and execute tasks for you This is going to revolutionize the AI agent landscape. Some potential applications: ➔ Coding with Computer Use ➔ Computer Use for Creative Exploration ➔ Task Orchestration with Computer Use ➔ Automating Complex Workflows And the best part? Anthropic also rolled out the upgraded Claude 3.5 Sonnet, now the world’s top AI model for coding. We’re stepping into an era where anyone can build whatever they imagine. AI is taking over the world. PS - Let me know what you want to build?

[𝗔𝗜 𝗦𝘁𝗼𝗿𝗶𝗲𝘀] 📚✨ 𝗧𝗵𝗲 𝗚𝗼𝗹𝗱𝗲𝗻 𝗔𝗴𝗲 𝗼𝗳 𝗔𝗜 - In 1936, Turing invented the "computer" (universal Turing machines) by resolving Hilbert’s 𝗘𝗻𝘁𝘀𝗰𝗵𝗲𝗶𝗱𝘂𝗻𝗴𝘀𝗽𝗿𝗼𝗯𝗹𝗲𝗺.  - In 1950, the concept of the 𝗧𝘂𝗿𝗶𝗻𝗴 𝘁𝗲𝘀𝘁 (https://lnkd.in/eaWNfuDi) was first described.  - In 1951, the first commercial computer, the 𝗙𝗲𝗿𝗿𝗮𝗻𝘁𝗶 𝗠𝗮𝗿𝗸 𝟭 produced by the British electrical engineering firm Ferranti Ltd, was introduced to the market. This laid the material foundation for the further development of software. 𝗧𝗵𝗲 𝗴𝗼𝗹𝗱𝗲𝗻 𝗮𝗴𝗲 𝗼𝗳 𝗔𝗜 began here, from around 𝟭𝟵𝟱𝟲 𝘁𝗼 𝟭𝟵𝟳𝟰. - In the mid-1950s, McCarthy invented 𝗟𝗜𝗦𝗣, a programming language still taught and used seventy years later.  - In 1956, the term "𝗮𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲" (https://lnkd.in/eiqXhJyM) was coined by John McCarthy.  - In the mid-1960s, Joseph Weizenbaum invented 𝗘𝗟𝗜𝗭𝗔 (https://lnkd.in/eYVJg5az), the first chatbot. Although based on keyword-based canned scripts, it made a significant impact at the time.  - In 1971, Terry Winograd invented 𝗦𝗛𝗥𝗗𝗟𝗨 (https://lnkd.in/e4P_hYhN), a simulated robot, demonstrating one of the most acclaimed achievements in AI, showcasing problem-solving and natural language understanding capabilities.  - Between 1966 and 1972, 𝗦𝗛𝗔𝗞𝗘𝗬 (https://lnkd.in/e8D-_9fT), the first mobile intelligent robot, was developed at the Stanford Research Institute (SRI), serving as a model for future robots. Starting from 1972, the primary founders of AI research began to experience funding cuts across the UK, with the trend following in the United States. The decade from the early 1970s to the early 1980s became known as the first 𝗔𝗜 𝘄𝗶𝗻𝘁𝗲𝗿. What caused this winter? What brought the Golden Age of AI to an end? In my next post, I will explore the significant AI challenges of that era. #machinelearning #artificialintelligence #datascience #ml #ai #aihistory

Anthropic has just unveiled a groundbreaking feature in AI technology: Claude's 'Computer Use'. This innovation allows AI to control your computer screen and take actions on your behalf, opening up a world of possibilities in agentic coding, automated debugging, customer support, and education. Here's how it works: ☑ Real-Time Interaction ↳ Claude takes static screenshots and sends them back to the API in real time, allowing it to move your cursor, click, and type text. ☑ First of Its Kind ↳ Claude 3.5 Sonnet is the first frontier model to offer 'Computer Use' to the public. Limitations to Note: Training Restrictions ↳ You can't train computer use on internal data (yet). Time and Context Limitations ↳ Actions are limited to ~15 minutes with context window constraints. Developer Access Only ↳ Available via API, requiring developer access to explore this feature. Anthropic believes we're in the early days of a new era, akin to the GPT-3 days for computer use-type systems. We're on the brink of the autonomous agent age, where agents work for us even while we sleep. 🔍 What are your thoughts on this revolutionary feature? Let's discuss in the comments below! #AI #ArtificialIntelligence #Claude #Anthropic #TechInnovation #AutonomousAgents ♻️ Repost this if you learned something. PS: If you want to fight your FOMO harder... 1. Scroll to the top. 2. Click on "Subscribe to newsletter". 3. Follow ⚡️Nitin Sachdeva 🚀 to never miss a post.

🌟 Fun Fact Friday: The Father of Artificial Intelligence 🌟 One of the greatest innovators in the field of AI was John McCarthy, widely recognized as the father of Artificial Intelligence. Born in 1927, McCarthy made ground breaking contributions to computer science and AI that have paved the way for the incredible advancements we see today. In 1956, he coined the term "Artificial Intelligence" during the Dartmouth Conference, which he organized. This event is considered the birth of AI as a field of study. McCarthy's work led to the development of the Lisp programming language, which became a fundamental tool for AI research due to its excellent support for symbolic reasoning and recursive functions. McCarthy also developed the concept of time-sharing, which allowed multiple users to interact with a computer simultaneously, revolutionizing computing accessibility and efficiency. His vision and innovations laid the foundation for modern AI, influencing everything from machine learning algorithms to intelligent systems used in various industries today. John McCarthy's legacy continues to inspire researchers and technologists around the world, driving forward the possibilities of artificial intelligence. SEPIA INNOVATIONS Hanny Patil Rahul Pawar Pratiksha Jadhav Nidhi Choudhary Shweta Garg Dr. Vinayak Shinde #TechHistory #ArtificialIntelligence #JohnMcCarthy #Innovation #FunFactFriday #TechTrivia #ComputerScience #AI #MachineLearning #TechPioneers #MachineLearning #AIResearch #TechInnovation

#IJCAIworkshop Workshop on No-Code Co-Pilots 👉 https://lnkd.in/dK4PkY2U 📅 Abstract Submission Deadline: 10 May The goal of this workshop is to bring together researchers from various disciplines, including programming languages, natural language processing, computer vision, knowledge representation, planning, human-computer interaction, and business process management, to chart a cross-disciplinary research agenda that will guide future work in the field of no-code copilots and the revolution that AI brings to it.

Artificial Intelligence is an exciting and promising field in computing, which has been delivering important contributions since the 1950s. It will continue to thrive forward, even after the bubble bursts, and "generative AI" gets to where it belongs best. But there is no substitute for true knowledge, principled science and reliable engineering. Modern society and businesses depends on software with quantifiable trustworthiness.

Fine-tuning a large language model (LLM) is incredibly enjoyable, especially when you're refining it to avoid negative prompts and instead provide default, self-learned answers. However, these models often inadvertently reveal that they are not supposed to discuss certain topics, which is fascinating! 😅 I have started exploring AI space, and the more I am diving deeper, I can clearly sense that AI with custom data is the way forward for the businesses down the lane, RAG (Retrieval-Augmented Generation), is certainly gonna help. But what about finetuning? Imagine the future: in the next 5 years, prompt engineering could become a major focus for many professionals. The real challenge lies in fine-tuning these models, despite their parallel self-training capabilities. Ensuring that your model doesn’t respond to irrelevant queries is tough, as prompts can vary widely. This is where smart negative prompt engineering and thorough verification come into play, potentially opening up significant opportunities for engineers in this emerging field. Thoughts are welcome, this post is my personal POV. #ailearningdays #llms #finetuning

Alan Turing's concept of AI was focused on the theoretical aspects of machine intelligence and the question of whether machines could exhibit human-like intelligence. Turing's #AI was primarily symbolic, the emphasis was on programming computers to follow explicit instructions or rules to solve problems. Modern-day AI, adopted by Converta, encompasses a wide range of technologies and approaches that have evolved significantly since Turing's time. Our focus right now is on developing our machine learning tools. CONVERTA’s tech stack enables us to learn from vast amounts of data, recognise patterns, make decisions, and improve performance over time. We make the complex simple. Talk to us about how we can define your target audience, nurture relationships and active a sales growth strategy - all with machine learning and human interaction at its heart. #DataIntelligence #MachineLearning #leadgeneration #LeadManagment