The rise of Big Data I want to take you back to 2012 . In those early years, our focus was big data, and everyone else was looking at the cloud. We were pioneering, but in a world that was still beginning to understand the value of data.
Executive Summary ML6 went to the AI Engineer summit in Paris. We are confident that agents are here to stay, and MCP will be at the forefront of this trend. While MCP adoption is skyrocketing, its potential remains heavily underutilized. And while many agent projects are still explorative, those that find a suitable use case can radically transform their processes through iterative engineering.
Things are moving fast in LLM and generative AI space. A lot of things moved forward with the speed of light. Is this a temporary momentum we experience or are we at day 1 of an ever expanding universe of possibilities?
Transform Enterprise Operations with Google’s Latest AI Stack: Smarter Agents, Multimodal Interfaces, Real Business Impact The annual Google Cloud Next event took place in Las Vegas in April, where Google presented its latest innovations for Google Cloud Platform (GCP).
What does the day in the life of a medical specialist who encounters a patient with an unclear diagnosis look like? Its combing through tens or maybe hundreds of scientific papers to find a gene, cell therapy or something else that may be the key to saving their patient’s life. As you can imagine, this can be a lengthy and time-consuming process. But what if there was a tool this specialist could use to get this information through simple queries, cutting down the amount of time it takes to find the needed information?
One of the projects we are working on involves generating code for a custom dialect of a programming language using a large language model (LLM). With a dataset of instructions and their corresponding implementations, we aim to fine-tune a model to automate this process. Given the rapid advancements in AI, fine-tuning LLMs can significantly enhance their performance for specific tasks, offering tailored solutions that generic models might not provide.
Remember J.A.R.V.I.S. from Iron Man? That intelligent assistant that seemed to have a solution for everything? While we’re not quite there yet, the rapid evolution of Large Language Models (LLMs) like GPT-4, Claude, and Gemini is bringing us closer than ever. Today’s LLMs are impressive. They can generate content, translate languages, and even write code. But let’s be real — they’re still pretty much glorified text processors.
In recent years, Large Language Models (LLMs) have revolutionised natural language processing by enabling machines to understand and generate human-like text with unprecedented accuracy and coherence. Their applications span across diverse fields such as chatbots and content creation, driving significant advancements in automation and AI-driven solutions. As a result, LLMs have become crucial tools in both academic research and commercial innovation, pushing the boundaries of what AI can achieve. Though, I’m sure you already knew this.
For avid followers of the Protein design space, you’ll likely have come across our earlier blog detailing the ins and outs of the current state of affairs. Well, time inevitably marches on, and if there are few certainties in this universe, you can count on one being a continual development in Machine Learning. So given the recent advancements, enhancements and brute-forced micro-improvements, ML6 is back once again to give you the scoop on what exactly is “up” in the crazy, little intersection of Protein Design and ML. Though this time there’s a twist… We’ll specifically be focusing on updates in sequence generation, with some other notable mentions where appropriate.
An introduction to ESM3 Protein structure prediction and design play pivotal roles across many scientific and industrial fields, impacting drug discovery, enzyme engineering, and biotechnology. Traditionally, these endeavours have been hindered by the complexities of accurately predicting how amino acid sequences fold into functional three-dimensional structures. This challenge stems from the vast conformational space proteins can adopt and the subtle interactions governing their stability and function. ESM-3 is a new approach that leverages advanced ML techniques to unify sequence, structure and function prediction. If these concepts are unfamiliar to you and you’d like further clarity, there’s a good overview here . Models in the past have only been capable of achieving this to a limited extent, and have instead had to specialise within a domain to achieve the best performance. ESM-3 not only enhances our understanding of protein biology but also holds promise for accelerating the discovery and design of novel proteins with tailored functions.