How to building LLMs from Scratch

Curious about the technology behind Large Language Models (LLMs)? Check out "Building LLMs from the Group Up" by Sebastian Raschka on YouTube! This tutorial is perfect for anyone eager to understand LLMs’ building blocks and code them from the ground up using PyTorch.

🎥 What’s Inside?
Sebastian covers everything you need, from understanding LLM input data to building a simple GPT-like architecture, pretraining, and even fine-tuning with open-source libraries. Here’s a quick look:

• Intro to LLMs (2:17) – Learn about LLM milestones and applications.

• Input Data and Tokenizers (10:48) – Dive into data processing essentials.

• Coding LLM Architecture (41:03) – Structure the model with PyTorch.

• Pretraining & Finetuning (1:07:11) – Train and optimize your model.

• Evaluating Performance (2:26:45) – Measure and benchmark results.

🔗 Watch the full tutorial on YouTube and start coding your LLM today! 

OpenAI search is not a Google Killer

OpenAI’s new ChatGPT Search shows potential as an AI-powered search tool but falls short of being a "Google killer" for several reasons. While it excels at handling complex, open-ended questions that require synthesis from multiple sources, it struggles with the short, navigational queries that make up most of Google’s traffic. Users often search for specific information with just a few words, like "library hours" or "weather," where Google excels in quickly delivering relevant results. However, ChatGPT Search, relying on Microsoft Bing and AI-generated responses, sometimes provides incorrect or outdated information, creates hallucinated links, and lacks the precision for these shorter, everyday queries.

In one test “Nuggets score” was type to check how a live NBA game between the Denver Nuggets and the Minnesota Timberwolves was going. ChatGPT told me the Nuggets were winning even though they were actually losing, and showed a Timberwolves score that was 10 points lower than it really was, according to a Google result at the same time.

Additionally, ChatGPT Search’s reliance on longer, natural questions reflects a different user experience, which might suit deeper research but not daily web navigation. OpenAI acknowledges these limitations and aims to improve, but for now, the tool primarily fills a niche rather than replacing Google’s essential role in directing users to specific web pages and information instantly.

Link to full article

Director of Terminator James Cameron comment on AI

James Cameron, the director of "The Terminator," shared his views on artificial intelligence (AI) and artificial general intelligence (AGI) at an AI and robotics summit. Cameron said he is "bullish on AI but not so keen on AGI" because he worries that AGI technology will be in the hands of private corporations rather than government.

As quoted in the article, Cameron stated: "It will emerge from one of the tech giants currently funding this multibillion-dollar research. Then you'll be living in a world that you didn't agree to, didn't vote for, that you are co-inhabiting with a super-intelligent alien species that answers to the goals and rules of a corporation." He is concerned that this could lead to "digital totalitarianism" and a world where "people are putting more faith in machines and less into their sense of purpose."

Despite his reservations, Cameron has continued to be involved in the AI and technology industry, recently joining the board of directors for the generative AI company Stability AI.

Link to full article

Overcoming the Reliability Barrier for Large Language Models

As generative AI becomes more prevalent, organizations are faced with a major challenge - the occasional tendency of large language models (LLMs) to produce unreliable or "hallucinated" outputs. This has prevented wider enterprise adoption of these powerful AI tools.

Cleanlab's new Trustworthy Language Model (TLM) aims to change that. By providing a trustworthiness score with every LLM response, TLM helps organizations automatically identify and filter out inaccurate outputs.

Rigorous benchmarking shows that TLM's trustworthiness scores are more effective at detecting errors than existing approaches like self-evaluation or probability-based confidence estimates. Remarkably, TLM can also leverage these trust scores to produce responses that are more accurate than the underlying LLM itself.

Unlocking New Use Cases

With trustworthy outputs, LLMs can now be deployed for mission-critical applications. TLM enables new use cases like chatbots that can escalate to human agents when they are unsure of a response.

TLM can also enhance existing LLM applications, such as automating data labeling or information extraction tasks. By relying on high-trust responses and routing low-trust ones for human review, organizations can achieve significant cost and time savings.

Link to full article

Today’s Amazing AI Open Source Project - Screenshot to Code

Ever wondered if you could take a screenshot of a UI design and instantly get the HTML/CSS code? With the Screenshot-to-Code tool, that’s possible!

🌟 What it does:
This project leverages machine learning to convert screenshots of web interfaces into HTML/CSS code, speeding up front-end development workflows and reducing manual coding efforts. It’s especially useful for:

• Designers and Developers who want to quickly translate designs into code prototypes.

• Teams looking to automate repetitive code generation tasks for standardized layouts.

• Hackathons and Rapid Prototyping when time is limited, and an initial code structure is needed fast.

🛠️ How it works:
Simply upload a screenshot, and this tool will generate a structured HTML and CSS output based on the visual elements detected. Think of it as a bridge from UI design to actual implementation.

💻 Check it out here: GitHub Repository