GPT-LLM

artificial intelligence as GPT and LLM

“the future cracked open”

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Race Bannon sees AI (or really machine learning) changing many jobs, such as technical writing, in the near future.

“I believe within 5-10 years much of technical documentation will be written by AI. Certainly, the basic procedural stuff (Step 1, Step 2, and so on) will be written by AI, but even the contextual stuff surrounding the procedural documentation (use cases, examples, and implementation tips) will be written by AI eventually too.” —The Future of Technical Writing

In The Atlantic, Derek Thompson thinks that creativity will not save our jobs from AI.

We may be in a “golden age” of AI, as many have claimed. But we are also in a golden age of grifters and Potemkin inventions and aphoristic nincompoops posing as techno-oracles. The dawn of generative AI that I envision will not necessarily come to pass. So far, this technology hasn’t replaced any journalists, or created any best-selling books or video games, or designed some sparkling-water advertisement, much less invented a horrible new form of cancer. But you don’t need a wild imagination to see that the future cracked open by these technologies is full of awful and awesome possibilities. —The Atlantic 2022-12-01

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learning about machine learning

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Why is machine learning [ML] important for your business? If you work at Nokia, your Chairman can explain it to you in a one hour presentation he developed over six months of research. Risto Siilasmaa helped make his network smarter. Everyone needs to know if ML can help with their business problems, but first they have to understand the basics, says Siilasmaa.

  • Digitization has created an explosion of information
  • ML is based on models like logistic regression, which can be fairly easy to understand
  • ML is fitting the model to the data
  • ML is neural networks learning from data sets
  • The more high quality data, and computing power, the fewer mistakes ML will make
  • In a large neural network you can have 100 million parameters in a single layer
  • Flawed outputs can happen if human oversight confirms incorrect ML conclusions (human oversight becomes very important)
  • A neural network first learns from a data set (time consuming) and then can be tested against other data sets
  • The important work is done by systems of ML systems
  • Machines are still getting faster and more tools are being developed
  • The data we are helping create (e.g. through use of speech recognition) is feeding AI corporations
  • ML can be tricked if you know the underlying algorithms
  • Remember: Garbage-in, Garbage-out
  • Big question: What data will we need in the future to make better decisions?
  • Business and human work is moving to — Low Predictability + High Complexity
  • ML can help to experiment faster and better in order to deal with Low Predictability + High Complexity
  • The future of work: First experiment … then develop a strategy

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