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.
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.
When Christmas is nearing, everybody is looking forward to the Christmas tree, maybe snow, presents, Santa Claus and the new year. For us at ML6, there is something more! We get some time off our regular projects and get to spend time exploring new horizons for ML6: new tech, new applications, new areas of interest. How cool is that?
ML6 is often contacted by clients having a problem that looks like it can be solved with some cameras and a clever machine learning solution. Great, so where do we start? There is one more thing we need: labeled data. Currently, the most practical and robust way to implement a real-world computer vision algorithm is to teach it what patterns it needs to be able to recognize. This means creating a training dataset, a collection of labeled example data which the model parameters can be tuned on to learn to solve the problem.
Why care about dynamic pricing?
![AI for the green transition - Applications and use cases [Part 2]](https://5386477.fs1.hubspotusercontent-na1.net/hubfs/5386477/organic%20farm%20in%20house%20with%20working%20robot.png)
AI has great potential to accelerate efforts to protect our environment, such as reducing emissions or making more efficient use of scarce resources. Let’s dive into some examples of use cases - with no ambition to be exhaustive - where AI can help tackle important challenges.
Google Search is so magical that you sometimes forget how much technology is involved. Your prompt immediately yields the results, no matter how cryptic you describe what you're looking for!
Introduction When designing real-world NLP applications, you are often confronted with limited (labeled) data, latency requirements, cost restrictions, etc. that hinder unlocking the full potential of your solution.
No matter how well you curate your data and tweak your machine learning model, even the best models are not capable of giving perfect answers all the time. In any sufficiently challenging setting, being limited to a finite amount of training data, regardless of quality, and contending with inherent real world randomness prevent them from achieving perfect modeling and prediction.
In this blog post series, we are looking at the importance of data for building performant ML models. The focus of our first blog post (check it out here if you haven’t already) was on how to unlock the full potential of data, looking especially at data labelling, data quality and data augmentation.