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Here’s How You May Start Using Google’s Gemini?

Analytics Vidhya

In this article, we’ll talk about how you may start using Gemini AI right now, within Google’s Bard chatbot, unlocking new dimensions of interactive and refined chat experiences. The Gemini Google’s recent […] The post Here’s How You May Start Using Google’s Gemini?

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ChatGPT, Author of The Quixote

O'Reilly on Data

We have many current and future copyright challenges: training may not infringe copyright, but legal doesn’t mean legitimate—we consider the analogy of MegaFace where surveillance models have been trained on photos of minors, for example, without informed consent. This is the basis of The New York Times lawsuit against OpenAI.

Modeling 275
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To understand the risks posed by AI, follow the money

O'Reilly on Data

Time and again, leading scientists, technologists, and philosophers have made spectacularly terrible guesses about the direction of innovation. It’s difficult to argue with David Collingridge’s influential thesis that attempting to predict the risks posed by new technologies is a fool’s errand.

Risk 221
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Rising Tide Rents and Robber Baron Rents

O'Reilly on Data

They are a price that we pay for a rising tide of innovation. During the expansive period of a new technology cycle, market leaders emerge because they solve new problems and create new value not only for consumers but also for a rich ecosystem of suppliers, intermediaries, and even competitors. What Is Economic Rent?

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Monetizing Analytics Features: Why Data Visualizations Will Never Be Enough

Think your customers will pay more for data visualizations in your application? Five years ago they may have. But today, dashboards and visualizations have become table stakes. Discover which features will differentiate your application and maximize the ROI of your embedded analytics. Brought to you by Logi Analytics.

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The Real Problem with Software Development

O'Reilly on Data

Lots of applications and tools start simple. We also see this phenomenon as one application replaces another. We also see this phenomenon as one application replaces another. One advantage of computers, of course, is that they don’t care about complexity. They do 80% of the job well, maybe 90%. I’m not the one to say.)

Software 306
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Quality Assurance, Errors, and AI

O'Reilly on Data

Generative AI will be used to create more and more software; AI makes mistakes and it’s difficult to foresee a future in which it doesn’t; therefore, if we want software that works, Quality Assurance teams will rise in importance. We quickly run into an extension of Kernighan’s Law : debugging is twice as hard as writing code.

Testing 192