TWI Hellas’ Post

The 1956 Dartmouth Summer Research Project on Artificial Intelligence, often remembered as the Dartmouth Conference was the brainchild of four American computer scientists – John McCarthy, Marvin Minsky, Nathaniel Rochester and Claude Shannon – and brought together some of the brightest minds in computer science, mathematics and cognitive psychology at the time. These scientists, along with some of the 47 people they invited, set out to tackle an ambitious goal: to make intelligent machines. As McCarthy put it in the conference proposal, they aimed to find out “how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans”. #ai #artificialintelligence

Today, Google DeepMind published #AlphaGeometry - an AI model that can compete with the world's brightest students. Named after the Olympics of ancient Greece, the Olympiad is an arena where the world's best students compete in mathematics. #AlphaGeometry achieved gold-medalist scores at the Olympiad, answering 25 of the 30 benchmark problems. This blew the previous state-of-the-art model's score of 10 out of the water. #AlphaGeometry combines two models. One is a neural language model that brings human-like intuitive reasoning by spotting patterns and finding ways forward. The other is a symbolic deduction engine that uses hard rule-based logic to solve problems. This combination boasts the best qualities of human problem solving - logic and intuition. The next step will be super-human scores, and I'm sure it won't take long.

Meanwhile in London, DeepMind's AlphaGeometry has correctly answered 25 of 30 questions from the high school International Mathematical Olympiad, according to a paper published in Nature. Thinking Fast and Slow, the neuro-symbolic system is very good at Maths. My colleagues say this a big step towards artificial general intelligence. https://lnkd.in/ezqmj-VW #aiadvancements #agi #maths #uk #ai 

🚀 "Give Your LLMs a Left Brain" - Join Stephen Chin at #GIDS on April 24th and discover how to enhance generative AI with the analytical prowess of knowledge graphs. 🧠 Explore: 💡How the human brain's hemispheres inspire better AI functionality. 💡Integrating Large Language Models (LLMs) with updated, factual knowledge graphs. 💡Using Retrieval Augmentation Generation (RAG) for more reliable AI outputs. Ideal for developers, AI researchers, and tech innovators, this session will guide you through the techniques to ground AI's creative capabilities with solid, logical data processing. Learn More & Register: https://lnkd.in/dUckUz3m #GenerativeAI #KnowledgeGraphs #TechLeadership #GIDS2024 #Neo4j Neo4j

DeepMind hits milestone in solving maths problems 🤖 I'm thrilled to see AlphaProof mastering questions from this year's Mathematical Olympiad! ✨ This is a significant step towards creating substantial proofs with artificial intelligence. As we edge closer to this milestone, I predict that AI will transform the way we approach complex mathematical problems. In the next 5 years, we can expect to see AI-assisted proofs becoming a norm, revolutionizing the way mathematicians work. Key takeaways: • AI is rapidly advancing in its ability to assist mathematicians in solving complex problems • The potential for AI-assisted proofs to revolutionize the field of mathematics is vast • This milestone is a testament to the power of collaboration between humans and AI Subscribe to my AI Newsletter [Subscribe here](https://lnkd.in/gRgPMgKc) to stay updated on the latest AI news and trends! 📧

"So far as the theories if mathmematics are about reality, they are not certain; so far as they are certain, they are not about reality." – Albert Einstein As I immerse myself in mathematics for AI and Data Science, I’ve been particularly captivated by the paradox of the derivative in calculus. It’s fascinating how calculus helps us understand the smallest changes and yet, when we dive deep, it reveals unexpected complexities. The derivative paradox highlights that math, while certain, often unveils the profound mysteries of reality. Calculus is at the core of AI—whether it’s optimizing algorithms or understanding neural networks, its applications are endless. I’m excited to continue exploring how these concepts translate into solving real-world problems! #AI #DataScience #Mathematics #Calculus #Einstein #LearningJourney #MathForAI #Derivatives #STEM

Google's AlphaGeometry: can #AI discover new mathematics? Google DeepMind's AI system, AlphaGeometry, has demonstrated impressive problem-solving skills in geometry, nearly matching those of top students. In a recent study published in Nature, AlphaGeometry correctly answered 25 out of 30 questions from the International Mathematical Olympiad. This neuro-symbolic system combines language learning and deductive reasoning, offering a balanced approach to problem-solving. While this development is significant, there is still a greater challenge ahead: determining whether AI can discover new #mathematics to solve previously unanswered questions. https://lnkd.in/eWQ-YF6j #Loquat #banks #creditunions #baas

Insights on causal inference, machine learning and the future of AI. Thank you Dr. Forney for your time, a great listen for anyone interested in AI

Deep Learning is inherently empirical. Unlike disciplines such as physics, mathematics, rationalism, and logic, we can't grasp its workings from first principles alone. Instead, we rely on tinkering, experimenting, exploring, and discovering. Historically, this notion has been remarkably radical. #DeepLearning #EmpiricalApproach #Innovation #Exploration #DataScience #AI #MachineLearning

What are some strengths of generative AI? I was involved in a SWOT analysis of generative AI at #templeuniversity last week. It was my first conference for 2024, and a very productive one. With a team of amazing educators from several disciplines, it was a joy to not only see it through my language diversity lens but professors who teach Math, Sciences, Psychology, Engineering and more. #ai #professionaldevelopment #templeuniversity #mcc