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Helping Computers Help Themselves

The IT world's heavy hitters--IBM, Sun, Microsoft, and HP--want computers to solve their own problems

12 min read
Helping Computers Help Themselves

This is part of IEEE Spectrum's special R&D report: They Might Be Giants: Seeds of a Tech Turnaround.

If you're being chased by a big snarling dog, you don't have to worry about adjusting your heart rate or releasing a precise amount of adrenaline. Your body automatically does it all, thanks to the autonomic nervous system, the master-control for involuntary functions from breathing and blood flow to salivation and digestion.

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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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