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Time Capsule: 1964

The Beatles stormed America, China detonated its first atomic bomb, and New York City hosted a World’s Fair for the ages

3 min read
Dartmouth professors, John Kemeny and Thomas Kurtz
Photo-Illustration: Gluekit

Fifty years ago, as IEEE Spectrum was publishing its first issue, the world of technology was abuzz with activity. Here, we fondly recall some of the seminal firsts from that extraordinary era.

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GPT Protein Models Speak Fluent Biology

Deep learning language models design artificial proteins for tricky chemical reactions

3 min read
Two protein structures labelled ProGen Generated and 25% Mutation.

By learning the "language" of functional proteins, the AI learned to prioritize its most structurally important segments.

SalesForce

Artificial intelligence has already shaved years off research into protein engineering. Now, for the first time, scientists have synthesized proteins predicted by an AI model in the lab, and found them to work just as well as their natural counterparts.

The research used a deep learning language model for protein engineering called ProGen, which was developed by the company Salesforce AI Research in 2020. ProGen was trained, on 280 million raw protein sequences from publicly available databases of sequenced natural proteins, to generate artificial protein sequences from scratch.

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Learn How Global Configuration Management and IBM CLM Work Together

In this presentation we will build the case for component-based requirements management

2 min read

This is a sponsored article brought to you by 321 Gang.

To fully support Requirements Management (RM) best practices, a tool needs to support traceability, versioning, reuse, and Product Line Engineering (PLE). This is especially true when designing large complex systems or systems that follow standards and regulations. Most modern requirement tools do a decent job of capturing requirements and related metadata. Some tools also support rudimentary mechanisms for baselining and traceability capabilities (“linking” requirements). The earlier versions of IBM DOORS Next supported a rich configurable traceability and even a rudimentary form of reuse. DOORS Next became a complete solution for managing requirements a few years ago when IBM invented and implemented Global Configuration Management (GCM) as part of its Engineering Lifecycle Management (ELM, formerly known as Collaborative Lifecycle Management or simply CLM) suite of integrated tools. On the surface, it seems that GCM just provides versioning capability, but it is so much more than that. GCM arms product/system development organizations with support for advanced requirement reuse, traceability that supports versioning, release management and variant management. It is also possible to manage collections of related Application Lifecycle Management (ALM) and Systems Engineering artifacts in a single configuration.

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