The Power and Potential of Generative AI: A Comprehensive Exploration Author: Tony Ojeda The Dawn of Generative AI In the ever-evolving world of technology, Generative AI, a branch of artificial intelligence, has been making significant strides. This innovative technology has the unique ability to create new content from scratch, be it a piece of music, […]
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Revolutionizing AI: The Power of Large Language Models Author: Konstantin Galperin Large Language Models (LLMs) have revolutionized the field of artificial intelligence (AI) by demonstrating remarkable capabilities in understanding and generating human language. These models, such as OpenAI’s GPT-4, are built upon deep learning techniques and vast amounts of training data, enabling them to perform
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Ethics in the Absence of Artificial Intelligence: What don’t we talk about when we talk about the ethics of AI? Author: Evie Fowler When we talk about ethics in AI, we tend to talk about its failures. We repeat embarrassing stories and gotcha moments, evidence that the wonders of the technological age are just as
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From business managers, to data scientists, to UX developers — anyone who works with data knows anomalies can be a chore to find and an even bigger chore to resolve. Incorrect or faulty data can cause a business to miss revenue opportunities or potentially make poor business decisions based on erroneous analysis. For organizations whose
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Artificial intelligence, or “AI”, is a buzzword that has been around for decades… but what does itreallymean? For most people, it can be hard to differentiate between the futuristic sounding AI and its less glamorous sounding counterpart, machine learning, sometimes called “ML”. Even Fulcrum’s Data Scientists agree, that while the average person interacts with some
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Business decision makers rarely crave more reams of data. They really want well-defined, actionable insights from data that they can evaluate and consider for future actions. They also want confidence that they aren’t missing something critical in the data, either because the data is not being analyzed thoroughly or correctly, or worse… because the data
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