Remove what-we-do run
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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. What does “testing” mean when the test suite itself may have bugs? But what about AI?

Testing 191
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You Can’t Regulate What You Don’t Understand

O'Reilly on Data

Most notably, The Future of Life Institute published an open letter calling for an immediate pause in advanced AI research , asking: “Should we let machines flood our information channels with propaganda and untruth? Should we automate away all the jobs, including the fulfilling ones? What we need to know is what they are being told.

Metrics 283
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The Real Problem with Software Development

O'Reilly on Data

That statement nicely summarizes what makes software development difficult. They do 80% of the job well, maybe 90%. We also see this phenomenon as one application replaces another. We don’t want a future that consists of code too clever to be debugged by humans—at least not until the AIs are ready to do that debugging for us.

Software 304
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Rising Tide Rents and Robber Baron Rents

O'Reilly on Data

Why is it that Google, a company once known for its distinctive “Do no evil” guideline, is now facing the same charges of “surveillance capitalism” as Facebook, a company that never made such claims? What Is Economic Rent? They are a price that we pay for a rising tide of innovation. But not all rents represent abuse of power.

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Getting the Right Answer from ChatGPT

O'Reilly on Data

A couple of days ago, I was thinking about what you needed to know to use ChatGPT (or Bing/Sydney, or any similar service). It’s easy to ask it questions, but we all know that these large language models frequently generate false answers. The number it outputs also isn’t 29. smaller than the square root of the number under test.

Testing 260
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Seekr finds the AI computing power it needs in Intel’s cloud

CIO Business Intelligence

For IT leaders, the question of where to run AI workloads and how to do so affordably are fast becoming top of mind — especially at scale. We really began last year looking at what it would really take in terms of hardware to scale our business,” Clark says. “We Clark says.

IT 117
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The Quality of Auto-Generated Code

O'Reilly on Data

First, we wondered about code quality. There are lots of ways to solve a given programming problem; but most of us have some ideas about what makes code “good” or “bad.” We know how to test whether or not code is correct (at least up to a certain limit). Do we care? Things like that.

Testing 300