Ky-Nam 🧑‍🚀’s Post

We're excited to announce that Epsilla (YC S23) now supports GPT-4o-mini, the cheaper, faster, and better LLM by OpenAI that’s set to obsolete GPT-3.5-turbo! 🚀🤖 But what about its performance against GPT-4o? Here, we use GPT-4o-mini and GPT-4o to create two financial analysts and let them compete side by side in analyzing Meta’s 10-K report. The report contains many tables and charts, and we leveraged a secret sauce technology to extract the information (to be announced tomorrow, stay tuned!). The question involves a deep understanding of the numbers and math calculations. Do you think GPT-4o-mini does a similarly good job as GPT-4o? Watch the video and join the conversation! For more in-depth insights, check out the detailed comparisons here: - GPT-4o-mini: https://lnkd.in/eBf7Z6_U  - GPT-4o: https://lnkd.in/eZ8uFwY7 PS: A less mentioned advancement is that GPT-4o-mini supports 16k output tokens, meaning it can generate 4 times more content with each completion request than previous GPT-4 models (and almost all other SOTA LLMs). In my honest opinion, this is much bigger than the so-called million-token long context window advance, which focuses on increasing input token length. Think about this: now you can let the LLM do more things with fewer completion requests, without needing to repeatedly provide the same context. This means 4 times less token passing, on top of the per-token cost reduction of GPT-4o-mini. I am really excited to see the huge potential with RAG plus this more balanced input-output token limit setting. #Epsilla #RAG #GPT4omini #GPT4o #AI #ML #LLM

AI is now smarter than the average human!! And its name is 'o1' (the reason for the odd name is below!) OpenAI’s latest release, the o1 model series, is set to shake up the AI landscape. But what exactly makes these models, including o1-preview and o1-mini, so groundbreaking? Let's take a closer look under the hood of these AI chain-of-thought powerhouses. What makes o1 special? Supersized Context: With a staggering 128,000 token context window, o1 can grasp and process information like never before from OpenAI models. Reasoning Superpowers: o1 uses innovative "reasoning tokens" to break down complex problems step-by-step, emulating human-like thinking. Unparalleled Performance: In tests, o1 has already blown past previous models in areas like math, coding, and analytical reasoning. A peek behind the curtain! o1's secret sauce is a combination of cutting-edge reinforcement learning (learning through rewards and penalties) and those game-changing reasoning tokens. As o1 tackles a problem, it generates hidden reasoning tokens that steer its thought process. It's a bit like having a superhuman chess master, able to think multiple moves ahead, anticipate every possible scenario, and devise brilliant strategies on the fly. That's the kind of intellectual prowess o1 brings to the table, but across a vast range of complex problems, not just on the chessboard. The future of problem-solving.. o1 represents a leap in AI's ability to reason, plan, and tackle intricate challenges. It has the potential to revolutionise fields like finance, healthcare, and beyond, where complex decision-making is paramount. Thoughts.. o1's approach to problem-solving is incredibly exciting. Its ability to reason and plan could have far-reaching implications across industries. But what do you think? How do you see the inner workings of o1 being leveraged in your field? What aspects of the model intrigue you the most? Oh, and the reason for the name - by resetting the naming back to ‘1’, OpenAI aims to emphasise the distinction between this new series and previous GPT models, like GPT-4, while highlighting its unique advancements in problem-solving and safety features.

What will GPT-5 bring?... Tried GPT-4 and wondering what GPT-5 will be like? ‘Agentic’ seems to be the next big thing. 📬 Old post by Andrej Karpathy (a lead AI researcher & co-founder of OpenAI): "A more complete picture is emerging of LLMs not as a chatbot, but the kernel process of a new Operating System." Consider agents as an LLM taking over your computer. Does the idea of an LLM taking over your computer seem far-fetched? We are experimenting with agents on some very specific (for now) use cases, such as desk research and optimizing work prioritization sheets. The key ingredients: - 💻 Open source locally hosted LLM (Llama 3), since the agents are VERY ‘api-call’ hungry - 🤖 Crewai for building and running the agents: defining roles, delegating tasks, and managing their performance." The outcome: “A mix of hits and misses, with low consistency from the agents leading us to integrate rule-based task management, somewhat defeating the original purpose of full autonomy." Curious what you think! Will ‘Agentic’ really be this big? Or is the ‘AI bubble’ just overly excited?

February & March were wild for AI Builders. All of a sudden OpenAI isn't the only game in town anymore. There was much to go through and digest. Below I've put together some basic practical details on using multiple LLMs in your generative AI application. Some highlights: 1. GPT-4-turbo is a workhorse for complex logic. 2. Claude 3 is willing to create whatever you ask for and has a better professional writing style. 3. Claude 3 has some great new upper-mid range models to compete with GPT-3.5-turbo. 4. Open source Mixtral and Gemma models on Groq are BLAZING FAST and INCREDIBLY CHEAP. I hope this helps. Let me know what you think!

A new LLM recently showed up on lmsys.org called gpt2-chatbot. It was only up for a short time before being taken down, but showed exceptional performance, especially in reasoning. Was this a preview of gpt-4.5 turbo or gpt-5? I have a sneaking suspicion, given the name, that this model was actually gpt-2 using the secretive Q* reasoning engine. I feel like open AI might have been demonstrating how much better a model performs and reasons when using Q*. Perhaps using another model to summarize and reflect back the model's output dozens of times to arrive at a more thoughtful answer? Is this why gpt2-chatbot was slow to respond, yet stunningly thoughtful and accurate in its answers? If so, imagine Q* with gpt-4 or gpt-5. 🤯 what do you think?

I hadn't gotten a chance to run an evaluation on GPT-4o mini until today, and I was blown away. At cost, it's the best performance on the market hands down. Any model with comparable performance would be far more expensive via commercial api or self-hosting. Great job from the OpenAI team. Meanwhile, Meta has successfully focused on performance and open AI development. Llama 3.1 405B purportedly surpasses GPT-4 on key benchmarks. Takeaway: Both companies have made it easier to leverage high-performance models, but the most cost-effective performance is still coming from OpenAI.

🚀 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗶𝗻𝗴 𝗔𝗜 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻: 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗚𝗣𝗧-4𝗼 The future of interaction between humans and machines is here. Today, we're witnessing a monumental shift in the way we collaborate with AI technology. With the launch of GPT-4o, the newest flagship model from OpenAI, we're entering an era of unprecedented ease of use, sophistication, and accessibility. 𝗗𝗲𝗺𝗼𝗰𝗿𝗮𝘁𝗶𝘇𝗶𝗻𝗴 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗔𝗜 𝗧𝗼𝗼𝗹𝘀 At the heart of OpenAI's mission is the goal of making advanced AI tools available to everyone, regardless of their background or expertise. With GPT-4o, this vision is becoming a reality. For the first time, free users will have access to the same level of intelligence and capabilities as paid users, bridging the gap between those who have access to cutting-edge technology and those who don't. 𝗚𝗣𝗧-4𝗼: 𝗙𝗮𝘀𝘁𝗲𝗿, 𝗦𝗺𝗮𝗿𝘁𝗲𝗿, 𝗮𝗻𝗱 𝗠𝗼𝗿𝗲 𝗔𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗹𝗲 So, what makes GPT-4o so special? This latest model is faster, more efficient, and more capable than its predecessors. It can process text, vision, and audio input in a more seamless and integrated way, making it feel more natural and human-like. With GPT-4o, users can upload screenshots, photos, and documents, and initiate conversations about the content. The model also comes with advanced features like memory, browse, and data analysis, making it an indispensable tool for creatives, professionals, and anyone looking to augment their abilities with AI. 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗦𝗽𝗲𝗲𝗰𝗵 One of the most impressive aspects of GPT-4o is its real-time conversational speech capability. This feature enables users to engage in spontaneous, natural-sounding conversations with the AI model, just like they would with a human. The implications of this technology are far-reaching, from helping people overcome language barriers to revolutionizing the way we interact with machines. 𝗠𝗮𝘁𝗵, 𝗖𝗼𝗱𝗲, 𝗮𝗻𝗱 𝗘𝗺𝗼𝘁𝗶𝗼𝗻𝘀: 𝗡𝗼 𝗟𝗶𝗺𝗶𝘁𝘀 𝘁𝗼 𝗔𝗜'𝘀 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 The live demos showcased the incredible range of GPT-4o's capabilities. From solving linear equations and understanding code to translating languages and detecting emotions from facial expressions, this AI model is truly a marvel of modern technology. 𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗔𝗜 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 As we move forward, OpenAI is committed to pushing the boundaries of what's possible with AI. With GPT-4o, we're taking a significant step towards creating a future where humans and machines collaborate seamlessly, effortlessly, and intuitively. The possibilities are endless, and we can Here is the full video: https://lnkd.in/d-387_kb #AIinnovation #FutureTech #GPT4o #OpenAI #TechRevolution

Many are saying how great Llama3 is, so we decided to test if it could help reduce hallucination seen in our DSPy RAG pipeline by pairing LLama3: 70B with different embedding models, while continuing gpt3.5 + ada-002 as our comparison baseline. Combinations tested in the video are (with Elon Musk's wikipedia as knowledge source): 1. gpt3.5 + ada-002 vs. llama3: 70b + ada-002 2. gpt3.5 + nomic embedding + llama3: 70b + nomic embedding In the first test, llama3 did provide more factually correct answers and was more logically consistent with its answers compared to gpt3.5, based on the questions we asked. However, when both language models were paired with nomic embedding, llama3 started acting weird. This question as an example: "Who are the other founders Elon Musk co-founded SpaceX with?" Previously with ada-002, llama3 correctly answered: "None", but when paired with nomic embedding, it said: "Paypal" (see 4:41 in the video). I mean, it not only did it wrong but also thinks that "Paypal" is a FOUNDER? Definitely still looking into how to troubleshoot, but at least one of the takeaways from this experience is: the choices of embedding mode, language model or even prompt frameworks (in our case, DSPy is used) are all important variables that can cause varying results. So, next time when you see some claims such as "X model is matching Y model's capabilities in many regards and it's going to change the AI landscape!" It's probably a good idea to further investigate like "what type of data/questions were tested? and how was the pipeline set up and any baseline being compared against?", as other people's good or bad experience with these models doesn't necessarily mean it will be the same for you. And a single language model itself probably doesn't make all the magic happen nor make everything worse - the other partners in the LLM pipeline also matter a lot too. #llama3 #rag

Curious to hear from small and medium-sized organizations using large language models (LLMs): Do you feel like you are financially constrained by: Hosting your own LLMs, or Purchasing tokens/API calls from providers like OpenAI? I'm trying to get a sense of the real-world costs for organizations leveraging LLMs. Please share your experiences in the comments. Let's help each other navigate the economics of AI! #LLM #GenerativeAI #GenAI