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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? This post will dive deeper into the nuances of each field.

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Generative AI use cases for the enterprise

IBM Big Data Hub

Tools such as Midjourney and ChatGPT are gaining attention for their capabilities in generating realistic images, video and sophisticated, human-like text, extending the limits of AI’s creative potential. This data is fed into generational models, and there are a few to choose from, each developed to excel at a specific task.

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Collecting multiple community viewpoints after a crime

IBM Big Data Hub

IARS is a mobile app, so thinking through the capabilities of a phone, we needed to allow facts to be captured after a crime via text, pictures, video and audio (voice). Originally there were 12 people working on this, covering the frontend development, setup of the backend and the data science and AI needs.

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Embeddable AI saves time building powerful AI applications

IBM Big Data Hub

With embeddable AI, you get a set of flexible, fit-for-purpose AI models that developers can use to provide enhanced end-user experiences—like, automatically transcribing voice messages and video conferences to text. Users can then mine the data using simple keyword searches to find the information they need.

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ChatGPT: A Brave New World for Cybersecurity

eSecurity Planet

Unlike most other AI research projects, ChatGPT has captivated the interest of ordinary people who do not have PhDs in data science. As innovations like ChatGPT get more powerful, there will need to be a way to distinguish between human and AI content – whether text, voice or videos. The answers are often succinct. The Future.

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10 everyday machine learning use cases

IBM Big Data Hub

Marketers use ML for lead generation, data analytics, online searches and search engine optimization (SEO). ML algorithms and data science are how recommendation engines at sites like Amazon, Netflix and StitchFix make recommendations based on a user’s taste, browsing and shopping cart history.