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 The field of Artificial Intelligence is in a constant state of evolution. For years, the focus was on building a single, powerful model capable of tackling any task thrown its way. But as the complexity of our problems grows, it's becoming clear that a lone genius, no matter how brilliant, can be outmatched by a well-coordinated team of specialists. This is the paradigm shift that brings us to multi-agent AI systems. A Multi-Agent System (MAS) is a team of specialized AI agents that collaborate to tackle complex problems. Use a multi-agent system when your challenge involves multiple tasks, dynamic environments, or specialized expertise that require agent coordination and agent communication. For simpler or well-defined workflows, a single intelligent agent or structured agentic workflow is often a better, faster, and more cost-efficient choice. The golden rule is to use the simplest setup that effectively solves your problem.
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?
Generative AI is ubiquitous these days, and organizations are rapidly integrating GenAI into their business processes. However, building GenAI applications comes with its own set of specific challenges. The models are often large, meaning inference costs for running these models can quickly get out of hand, and model selection often requires balancing performance against costs and latency. Other common challenges include the misuse of generative models or data leakage. While implementing measures such as rate limiting, monitoring, and guardrailing in your GenAI applications can help overcome these problems, doing so for every individual project brings significant overhead for your engineering teams. It also becomes easy to lose track of global usage of generative AI within your organization and leads to many cases of reinventing the wheel as teams solve the same problems over and over again.
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.
ESM Metagenomic Atlas This blogpost is aimed at those who want to understand how artificial intelligence is being implemented in the field of biology, specifically with regard to proteins. We give a brief overview of what proteins are, their characteristics and the applications of protein engineering. The potentials of AI in this area are explored by giving an overview of current state-of-the-art models that are involved in solving various protein-related problems.
When it comes to artificial intelligence and machine learning, we often think of factory halls where sensors and data can be used to optimize processes. Yet, other sectors can also benefit from AI systems. In the creative industry, for example, designers can work in radically different ways.
Detecting the lines of a tennis court
Why care about dynamic pricing?