Filip Vítek

Breaking Language Barriers: The GPT40 App vs. Moshi 🚀 Just tested the real-time translation capabilities of The GPT40 app, and WOW, it's a game-changer! 🌍✨ 🔍 Compared to Moshi, which I tested recently, here's the scoop: Response Time: Moshi is quicker, but... Accuracy: GPT40 wins hands down. It's like having a human translator in your pocket! 🧠📱 Bidirectional Translation: GPT40 doesn't just translate; it does so BOTH ways seamlessly. Perfect for real-world conversations with non-English speakers! 🗣️↔️🗣️ Imagine traveling or working internationally and being able to communicate effortlessly. No more awkward misunderstandings or lost-in-translation moments. 💬🔄 If you're in need of reliable translation, whether for business or personal use, give The GPT40 app a try. It’s transforming the way we connect globally! #Innovation #Tech #Translation #GlobalCommunication #GPT40 #LanguageBarriers #RealTimeTranslation #AI #TechTrends #BusinessTravel #InternationalRelations #DigitalTransformation

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Important: OpenAI is (I think fairly) excluding o1's reasoning from its API output, which has significant implications for synthetic data generation and generative AI market dynamics. Right now, people use APIs from OpenAI and other companies to generate training data for smaller, faster, cheaper specialized models. For example, if someone wanted to train a model to extract contact information from a website into JSON, they could have GPT-4o generate 10,000 examples of doing this, then use that to train a small model that they owned. This can be useful for situations such as when a big, general-purpose model is overkill for a very focused use case. Or, perhaps because you want to run a model in a pipeline and not rely on a third-party vendor's uptime. However, it also contributes to rapid commoditization, because people can also use such output to train or fine-tune competing models, even though terms of use often prohibit this. For example, if you wanted an open-source model to be as good as GPT-4o at an economically useful task, you could fine tune it on GPT-4o output for that task. With o1, OpenAI is changing this. While the o1 API will output the model's final answer, it won't output all of the reasoning that went into that answer. This will make it impossible to mirror o1's ability to reason without creating a similar training process to what OpenAI uses, which as I understand it is a combination of chain-of-thought reasoning, reinforcement learning, and training on resulting synthetic data. This could have a SIGNIFICANT impact on market dynamics. Currently, GPT-4-level capability is a commodity. Many models are at a GPT-4 level, and companies are battling on things like price and speed. But I don't think this will be as much the case for o1-level reasoning models. We may see models at this level from the biggest labs—OpenAI, Google, Anthropic, xAI, Meta—over time. But I think we'll see way fewer companies able to compete and a slower pace to the level of commoditization we now have at the top model level.

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I was impressed by Lllama-3's performance at 8 billion parameters. Well, Microsoft just released a model half its size with comparable performance. The model is Phi-3, and the smallest version is 3.8 billion parameters, yet achieves comparable performance to GPT-3.5 across many (but admittedly not all) benchmarks. The Phi series of models use highly curated data, including a lot of synthetic data generated by GPT-4, to achieve these great results. What this means for the world: Powerful AI that can run locally on devices including phones, and be very tailored via retrieval-augmented generation or fine-tuning to individuals, companies, and tasks. I have to update my understanding of the world to comprehend how you can compress an internet's worth of information into 3.8 billion parameters. Paper is here: https://lnkd.in/gYNswkcs

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🤖 AI pioneer Yann LeCun to next-gen AI builders: ‘Don’t focus on LLMs’ Instead, Meta's Yann LeCun says, work on next-gen AI systems that lift the limitations of LLMs. But what are those next-gen systems? 🤔 Ie, Multimodal AI, Reasoning and general intelligence, Embodied AI and robotics, Unsupervised and self-supervised learning, Artificial general intelligence (AGI) etc. #artificialintelligence #generativeai #llm

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This is one of the advantages of using Knowledge Graph based RAG over using a Vector RAG. This shift from chunk-based retrieval to a relationship-focused (Entities and Edges) approach reduces dramatically the amount of tokens. Instead of feeding the LLM model with K chunks of text, it's feed with a list of much more facts with much less tokens.

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The 3-Step Guide to Making Your Company AI-Forward 🚀: 1. Provide Access to ChatGPT Plus / Claude Pro($20/user/month) 2. Empower People to Experiment 💡 3. Encourage Sharing 📢 Sure, it sounds simple—maybe too simple—but many executives are overthinking it, bogged down by "CEO guides." Every day, people are discovering nuanced AI workflows that collectively save weeks of time across the company. This isn't a far-off dream; it’s happening now. Why wait until 2040? The AI tools you need are available today. And guess what? Early adopters are eager to experiment. The excitement around AI tools is still very much alive. Let your team experiment. Give them a platform to share their insights. It's that simple! Transform your business with AI today! 🚀 #ArtificialIntelligence #Startups #Technology #AIFuture #Innovation

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How could you use Prospective to quickly get a sense of how many of your trucks might be affected by the severe weather events affecting parts of the country? This demo does not answer that question entirely, but you can see how you could get there. #ObsessivelyEnthusiasticInvestor at REFASHIOND Ventures: The Industrial Transformation Fund. #DataVizualiation #DecisionAnalytics Ps: Eric Schmalzbauer, I think you'd enjoy chatting with Ivo Böhm.

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Is the age of #PowerPoint ending? For your next presentation, consider using #Quarto. See below for some tips from Sophie Lee, PhD, FHEA 👇👇👇

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