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Sesamm bags $37M to give corporates ESG insights using natural language processing

TechCrunch

Companies can access Sesamm’s flagship product, TextReveal , via several conduits, including an API that brings Sesamm’s NLP engine into their own systems. Elsewhere, private equity firms can use Sesamm for due diligence on potential acquisition or investment targets.

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AI & the enterprise: protect your data, protect your enterprise value

CIO

In 2018, I wrote an article asking, “Will your company be valued by its price-to-data ratio?” This will require the adoption of new processes and products, many of which will be dependent on well-trained artificial intelligence-based technologies. Stolen datasets can now be used to train competitor AI models.

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How to Use Generative AI and LLMs to Improve Search

TechEmpower CTO

While traditional search systems are bound by the constraints of keywords, fields, and specific taxonomies, this AI-powered tool embraces the concept of fuzzy searching. One of the most compelling features of LLM-driven search is its ability to perform "fuzzy" searches as opposed to the rigid keyword match approach of traditional systems.

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What is NLP? Natural language processing explained

CIO

Natural language processing definition Natural language processing (NLP) is the branch of artificial intelligence (AI) that deals with training computers to understand, process, and generate language. While the term originally referred to a system’s ability to read, it’s since become a colloquialism for all computational linguistics.

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7 famous analytics and AI disasters

CIO

MIT Technology Review has chronicled a number of failures, most of which stem from errors in the way the tools were trained or tested. A similar example includes an algorithm trained with a data set that included scans of the chests of healthy children. Dataset trained Microsoft chatbot to spew racist tweets.

Analytics 218
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How ML System Design helps us to make better ML products

Xebia

With the industry moving towards end-to-end ML teams to enable them to implement MLOPs practices, it is paramount to look past the model and view the entire system around your machine learning model. The classic article on Hidden Technical Debt in Machine Learning Systems explains how small the model is compared to the system it operates in.

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3 factors to consider when adding remote visual assistance software to your tech stack

TechCrunch

One such technology proved an especially vital addition to businesses that had to quickly implement tools to meet customer service and training needs while maintaining essential safety protocols. Workflow reviews. Be prepared to review your workflows in any area where you’re planning to incorporate remote visual assistance software.