Mar 22, 2023|Life Sciences, Technology

Introduction

So, you have data and want to get started with machine learning. You’ve heard that machine learning will help you make sense of that data; that it will help you find the hidden gold within.

Deep learning. By this point, we’ve all heard of it. It’s the magic silver bullet that can fix any complex problem. It’s the special ingredient that can take any bland or rudimentary analysis and create an immense five course meal of actionable insights. But, what is at the core of this machine learning technique? Is it truly something that all companies need to invest in? Will implementing it bring immediate business value and create forever-increasing ROI? Or, is this some type of elusive marriage masked by marketing hype? Before even considering these questions, we need to take a step back and accurately define what deep learning is.

Sorting Out .sort() and sorted()

Sometimes sorting a Python list can make it mysteriously disappear.  This happens even to experienced Python programmers who use .sort() when they should have used sorted() instead. The differences between these two ways of sorting a list are presented in this blog.

SEMICON West 2021 lived up to its status as the signature conference for the extended microelectronics supply chain. Business and technology leaders, researchers, and analysts from across the semiconductor industry connected in-person and virtually for a 360 view of technological and market trends.

At this year’s SEMI Strategic Materials Conference, leaders in the semiconductor industry across the supply chain came together to discuss the big challenges and opportunities that are likely to emerge over the next 5 years. 

In our recent C&EN Webinar: Accelerating Consumer Products Reformulation with Machine Learning, we demonstrated how to leverage digital tools and technology to bring new products to market faster. The webinar was well attended by scientists, engineers, and business leaders across the product development spectrum eager to learn how these concepts can be applied to their work. We received many good questions during the event, and wanted to make our detailed responses available to the wider community. If you have any follow up questions, please reach out to us. Enthought is here to help your business navigate the challenges in R&D digital transformation and affect real change and business value generation.

Digital skills personas for success in digital transformation

The digital skills mix varies widely across companies, from those just starting to invest in digital transformation initiatives, to ones well into their journey. Building a community of people who think digitally and are able to innovate and quickly prototype ideas is key to delivering results. 

Scaling innovations in R&D can be considered challenging in two dimensions: from lab to lab; and from the lab to the end customer. A 100x or 1000x advance in R&D efficiency challenges an organization to adapt, from R&D to engineering, manufacturing, and to the marketplace, where there is a significant business opportunity.

The ultimate project goal of EnergizAIR Infrastructure was to raise individual awareness of the contribution of renewable energy sources, and ultimately change behaviors. Now ten years later, with orders of magnitude more data, AI/machine learning, cloud, and smartphones in the hands of individuals, this is an idea whose time has come.