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7 famous analytics and AI disasters

CIO

In 2017, The Economist declared that data, rather than oil, had become the world’s most valuable resource. And 20% of IT leaders say machine learning/artificial intelligence will drive the most IT investment. The algorithm learned to identify children, not high-risk patients. The refrain has been repeated ever since.

Analytics 360
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10 most in-demand generative AI skills

CIO

Most relevant roles for making use of NLP include data scientist , machine learning engineer, software engineer, data analyst , and software developer. TensorFlow Developed by Google as an open-source machine learning framework, TensorFlow is most used to build and train machine learning models and neural networks.

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Machine Learning: Unlocking the Next for Insurers

Coforge

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Bust the Burglars – Machine Learning with TensorFlow and Apache Kafka

Confluent

And that the FBI clearance figures indicate that only 13% of all burglaries in 2017 were cleared due to lack of witnesses and/or physical evidence? I will show how to implement this use case in this blog post. In such a situation, one approach would be to disarm the system if it knows that you are at home.

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Why Data Governance Is Crucial for All Enterprise-Level Businesses

Cloudera

It’s believed the source of the breach was Marriott’s Starwood subsidiary and Marriott might not have done due diligence when merging its newly acquired subsidiary’s data into its own databases. In 2017, Anthem reported a data breach that exposed thousands of its Medicare members. From Bad to Worse.

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Generative AI: A paradigm shift in enterprise and startup opportunities

CIO

For the first time, because of generative AI models, we have systems that understand natural language at a near-human level and can generate and synthesize output in various media, including text and images. Deep learning emerged in academia in the early 2000s, with broader industry adoption starting around 2010.

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Price Forecasting: Applying Machine Learning Approaches to Electricity, Flights, Hotels, Real Estate, and Stock Pricing

Altexsoft

Predictive analytics requires numerous statistical techniques, such as data mining (identification of patterns in data) and machine learning. The goal of machine learning is to build systems capable of finding patterns in data, learning from it without human intervention and explicit reprogramming.