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5 ways IBM helps manufacturers maximize the benefits of generative AI

IBM Big Data Hub

While still in its early stages, generative AI can provide powerful optimization capabilities to manufacturers in the areas that matter most to them: productivity, product quality, efficiency, worker safety and regulatory compliance. Generative AI can improve the fidelity of images that are then reviewed for quality assurance.

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Getting ready for artificial general intelligence with examples

IBM Big Data Hub

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. However, these systems lack genuine understanding and can’t adapt to situations outside their training. Regardless, these are examples of narrow AI.

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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. Generative adversarial networks (GANs) or variational autoencoders (VAEs) are used for images, videos, 3D models and music.

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The most valuable AI use cases for business

IBM Big Data Hub

But right now, pure AI can be programmed for many tasks that require thought and intelligence , as long as that intelligence can be gathered digitally and used to train an AI system. For example, Amazon reminds customers to reorder their most often-purchased products, and shows them related products or suggestions.

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Elections 2024, artificial intelligence could upset world balances

Security Affairs

An attacker could employ generative AI to forge realistic content, including images, videos, and audio, which can sway public opinion in myriad ways. Fabricated audio and videos concocted swiftly and disseminated ahead of elections could jeopardize a political party or candidate’s reputation.

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

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10 everyday machine learning use cases

IBM Big Data Hub

For example, many use it to contact users who leave products in their cart or exit their website. At Slack, ML powers video processing, transcription and live captioning that’s easily searchable by keyword and even helps predict potential employee turnover. Many stock market transactions use ML.