Vijay Morampudi’s Post

Feels good to be back with my posts after long vacation! 🚀 This time we have an exciting update from Open AI! 🚀 OpenAI has launched the o1 series, bringing advanced reasoning to complex problems! 💡 Here’s what you need to know: 🔑 Key Highlights: 💻 o1-mini: Fast, cost-effective coding model (80% cheaper!). 🧠 Smarter Reasoning for science, math, and coding. 🛡️ Stronger Safety with improved jailbreak resistance. 🌐 Available Now via ChatGPT Plus and API. AI just getting a lot more powerful everyday! 🚀 #AI #OpenAI #TechInnovation #FutureOfAI #MachineLearning #Coding

"Strawberry" is here! 🍓 The new model from OpenAI can reason independently to solve more complex tasks. It's a new scaling paradigm that, instead of creating larger models, focuses on inference compute to question itself "behind the scenes" to produce better results.

For PlatorAI followers - has anyone with Plus or API use used this yet? There was a slight lag of between announcement and availability - it’s now available on our App* (except advanced voice - see other post!) I would be interested to find out users initial views. We will be exploring its use cases on both Plus App and API over the coming weeks. #PlatorAI #EthicalAi #OpenAi

OpenAI o1 marks a crucial advancement in AI, enhancing its ability to reason through complex tasks. This development highlights the importance of ethics in using AI to tackle future challenges in science and technology.

Exciting times in AI! We’re seeing a monumental shift as models evolve from fast, intuitive responses to deep, deliberate reasoning—transitioning from System-1 to System-2 thinking. This leap enables AI to tackle complex problems in science, coding, and mathematics that were once out of reach. By embracing chain-of-thought reasoning, AI now spends more time thinking through problems before responding, refining strategies, and recognizing mistakes—much like we do. This not only enhances accuracy but also reduces errors and misunderstandings. The rapid pace of AI development is shortening the shelf-life of internal tools, making adaptability more crucial than ever. Can’t wait to see where this evolution takes us!

AI wrote the following comment for me: “Great!”

Finding the limits of GPTs over the last 18 months has been an evolving passion in #promptEngineering but not distinctly different from a form of algorithmic or #programaticDevelopment in itself. Despite the time saving benefits, 5-6 levels of abstraction depth encounters many #LLMModel #InterpretiveChallenges even in coding from templated applications. More capable complex models with Agents will be a defining moment in where AI will take us in Applied use in enterprise and solving bigger problems. #excitingGPTDevelopments

This is really going viral. OpenAI released its new model #o1 with reasoning. Now that the storm 🌬 has calmed down a little overnight from a European perspective and people have started to think for themselves, here's what I think: - OpenAI o1 is not ‘the’ #AGI that many expected. It is much more a further development to correct known weaknesses. - The main performance improvements are, as OpenAI confirms, due to the reasoning part. A good drawing from Tom Yeh of how this is done after the actual #LLM can be found here: https://lnkd.in/eFhSxqJR - I think OpenAI o1 is proof that LLMs do not get better through scaling, but rather through an approach that is comparable to that of automatic agents. However, it remains to be seen whether the reasoning core, insofar as it is integrated into the model, will perform better in the long term. My prediction: #OpenSource LLMs will close the existing gap with something like automatic agents #AutoAgents in a short time. My conclusion: It will be increasingly important to execute LLMs quickly in order to be able to iteratively improve a result in a reasonable amount of time. In the future, we will consume more tokens for a request in order to achieve better results. What do you think? 🤔

💡 Reflection of the Day 💡 Very impressive, AI models like GPT-o can process massive amounts of information, but if no human can manage that volume, who ensures its answers are accurate, especially in fields like genetics? This raises an important point about the role of humans in guiding and assessing AI. AI models are powerful tools that can help us work faster, but to ensure both speed and quality, we need professionals who can lead, evaluate, and improve these tools continuously. Growth as professionals is essential as AI becomes more integrated into our work. #AI #Genetics #ProfessionalGrowth #AIEthics #TechnologyLeadership

I cannot wait to see where the world of medicine & healthcare goes with the implementation of AI.