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Generative AI – The End of Empty Textboxes

TechEmpower

On a different project, we’d just used a Large Language Model (LLM) - in this case OpenAI’s GPT - to provide users with pre-filled text boxes, with content based on choices they’d previously made. This gives Mark more control over the process, without requiring him to write much, and gives the LLM more to work with.

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Yardi Debuts Machine Learning Software

socalTECH

Santa Barbara-based property management software developer Yardi said today that it has launched a new product, which uses machine learning for prescriptive analytics.

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Qulab Applies Artificial Intelligence To Drug Design

socalTECH

Los Angeles-based Qulab , a biopharmaceuticals startup developing artifical intelligence software aimed at small molecule drug design, said on Wednesday that it has launched its first product, Quleap. He also attended USC's Viterbi School of Engineering as a Ph.D.

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Sixgill Rolls Out Machine Learning Tool

socalTECH

Santa Monica-based software developer Sixgill has launched a new product this morning, saying it has unveiled a new application called HyperLabel, which helps developers, engineers, and data scientists implement machine learning. a month or $99 a year, as well as other "Pro" and "Enterprise" editions.

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How ZestFinance Is Using Machine Learning to Reshape Lending, with Jay Budzik

socalTECH

One of the hottest sectors of the technology startup market today is the use of artificial intelligence, big data, and machine learning across many different sectors including finance. We worked with them to create a machine learning model for that line of business, which significantly outperforms traditional methods.

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CrowdStrike Adds Endpoint Machine Learning Features

socalTECH

Irvine-based cybersecurity software developer CrowdStrike says it has added machine learning capabilities to its endpoint protection products, to help provide protection against malware and "malware-free" attacks.

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5 Steps To Strategy Tuning Through Machine Learning

Startup Professionals Musings

Conventional thinking in business has long been that strategy decisions are made by humans, while the focus of automation and machine learning should be on execution. Only smart software can plow through the volume of live data, recognizing trends, customers, and match offerings to reality. The efficiency gain is tremendous.