Artificial Intelligence (AI) is no longer a futuristic concept confined to science fiction—it's here, it's real, and it's rapidly transforming how we live, work, and think. From personalized recommendations on streaming platforms to real-time fraud detection in banking, AI is increasingly integrated into the core of modern life. Over the past few years, the field has seen explosive growth, thanks to breakthroughs in deep learning, natural language processing (NLP), and generative models. AI refers to the simulation of human intelligence in machines that are programmed to think and learn. It spans a broad range of technologies, including: Machine Learning (ML): Systems that learn from data to make decisions. Natural Language Processing (NLP): Enables machines to understand, interpret, and respond to human language. Computer Vision: Helps machines interpret and make decisions based on visual input. Robotics: Combines AI with hardware to perform physical tasks

After going through Dave’s course, would you recommend obtaining any AI industry certifications from Microsoft or Google? Has anyone found if that helps when job hunting? I figured actual projects will be important too and to get some stuff on our GitHub?

Limited by the context window size of Large Language Models(LLMs), handling various tasks with input tokens exceeding the upper limit has been challenging, whether it is a simple direct retrieval task or a complex multi-hop reasoning task. Although various methods have been proposed to enhance the long-context processing capabilities of LLMs, they either incur substantial post-training costs, or require additional tool modules(e.g.,RAG), or have not shown significant improvement in realistic tasks. Our work observes the correlation between the attention distribution and generated answers across each layer, and establishes the attention allocation aligns with retrieval-augmented capabilities through experiments. Drawing on the above insights, we propose a novel method InfiniRetri that leverages the LLMs's own attention information to enable accurate retrieval across inputs of infinitely length. Our evaluations indicate that InfiniRetri achieves 100% accuracy in the Needle-In-a-Haystack(NIH) test over 1M tokens using a 0.5B parameter model, surpassing other method or larger models and setting a new state-of-the-art(SOTA). Moreover, our method achieves significant performance improvements on real-world benchmarks, with a maximum 288% improvement. In addition, InfiniRetri can be applied to any Transformer-based LLMs without additional training and substantially reduces inference latency and compute overhead in long texts. In summary, our comprehensive studies show InfiniRetri's potential for practical applications and creates a paradigm for retrievaling information using LLMs own capabilities under infinite-length tokens. https://arxiv.org/abs/2502.12962

Speed-to-lead is everything. When someone fills out a form — for a quote, a demo, a booking — the moment they hit submit is when they’re most interested. ⏱️ Wait too long? They get distracted. Move on. Forget. "The fortune is in the follow-up… but the timing is what seals the deal." That’s why I’ve been building a voice agent that: -Automatically calls new leads right after form submission -Book appointment -Qualifies them in real-time with custom questions -Supports multiple languages -And sends all their responses right back to you, structured and ready This isn’t just a demo it’s a system designed to replace slow follow-ups, lost leads, and ghosted DMs. 🎥 i've uploaded the demo if you want to see it in action! If you run ads or generate leads through any kind of form this might change your game. Drop a comment or message if you’re curious. Happy to walk you through how this can plug into your business

The more I learn about AI the more trees I see in what seemed just a bunch of them. Yesterday, studying the module "Building Applications with LLMs" I came across different concepts I didn't know it existed and while reading about the article I posted before there were some others. There are LLMs, BERTs, LRMs, GPTs, PTs and many more models that cover a different type or aspect of the AI ecosystem. What it seemed to me a complex but small group of subjects (trees) it's becoming bigger and denser as I dive in. Trees, lianas and lost of small plants that make the landscape a jungle. Do you feel the same way? Did you, when you started? Should I feel overwhelmed or it's just the starting feeling?