Deepgram’s Post

I used Caude on a project last week to compare to GPT. The takeaway was that for specific well-defined taskes within the strengths of Claude, the results were acceptable. I had to bring the data for Claude, although it wrote a good summary. From what I’ve experienced across this generative model landscape, each model seems to have strengths, meaning you need to know which to use for best task outcomes. And, since this is a highly dynamic space with new advancements almost daily, do not become coupled with one solution. For example, I’ve been increasingly dissatisfied with CoPilot results, seeming to become less useful or unable to produce meaningful results with time, so I then go to GPT directly. I plan to do a little more work with Claude across the next few months. https://flip.it/9QThHe

Anthropic releases Claude 2.1, with a 200k tokens context window and x2 hallucinations reduction 😮 🔍 The new 200K token context window allows users to send lengthy documentation like codebases or books to Claude. This upgrade satisfies user requests for larger context windows and improves accuracy with extensive documents. 📈 Hallucination rates have seen a 2x reduction, enhancing trust and reliability in the system. —————————————————— Want to stay at the forefront of Generative AI developments? Follow NLPlanet for daily insights into the most relevant news, guides, and research! 🚀

I developed a chatbot using Google Gemini and an API key. Here is the backend Integration for this AI application

Just solved another Leet Code problem (easier one) with my own and it is 99% correct and 100% correct in terms of time complexity but some issues are there for space complexity as i uses list which takes more space solution is I must take variable instead of list to solve the problem. More effective solution is given by ai but i submit my own solution

The AI arms race is heating up, with a flurry of major updates from key players this week. Read more in this cheat sheet on the latest tech updates

A few months ago I gave a talk on using #OpenTelemetry to monitor #GenerativeAI. ICYMI, the high level was that there were three levels of monitoring that OpenTelemetry can help with: 1. Is the system behaving well? 2. Are the AI components/infra behaving well? 3. Is the output good (definition left intentionally vague...)? A good AI observability system has to do all 3 -- while (1) and (2) are table-stakes for OpenTelemetry, (3) is a lot more complicated more complicated. I'm really excited to say that #Burr now fully supports OpenTelemetry traces, and can help you answer all three about the applications you build! In this blog post we talk about multiple points of integration -- how Burr ingest OpenTelemetry traces, and how you can log Burr traces to any provider. Big thanks to Traceloop for building a powerful AI observability platform as well as the OpenLLMetry library -- they're the primary .provider example for the post!

🔥 my webinar on multimodal search will be happening in under 2 hrs come learn about the future of searching through multiple modalities! cc: Holt S. Paige Bailey Google Cloud

Guilfoyle: "Based on the amount of work left to do and the number of hours left before the festival, I decided to task 'Son of Anton' to use machine learning to debug some of our code." Richard: "Are you [expletive] kidding me? You gave your AI permission to overwrite code in the internal file system? Were you going to tell me about this?" Guilfoyle: "No, I thought that was the company policy these days." Richard: "Okay, well your AI just failed epically." Guilfoyle: "That's unclear. It's possible 'Son of Anton' decided that the most efficient way to get rid of all the bugs was to get rid of all the software, which is technically and statistically correct. But artificial neural nets are sort of a black box, so we'll never know for sure." https://lnkd.in/gE48cHa3

This is the future of LLMs. A new technique named "StreamingLLM" can handle infinite text input without any drop in accuracy. It works finding key tokens that guide the model's decisions and caching recent tokens. The result: It delivers up to 22x faster inference than vanilla LLMs. Link in comments. ♻️ Repost this if you found it useful. ↓ Are you technical? Check out https://AlphaSignal.ai to get a daily summary of breakthrough models, repos and papers in AI. Read by 200,000+ devs.

Breakthrough in AI: Infinite Text Processing 🚀 StreamingLLM is revolutionizing how language models handle text input. This innovative technique: • Processes unlimited text without accuracy loss • Identifies crucial decision-guiding tokens • Caches recent information efficiently • Achieves up to 22x faster inference than traditional LLMs This advancement opens doors for real-time language processing in unprecedented ways. Imagine chatbots that never lose context or AI assistants that can analyze entire books instantly. What applications do you envision for this technology? Share your ideas below! 👇 #genAI #MachineLearning #FutureOfTech