How to Use Massive AI Models (Like GPT-3) in Your Startup - Deepstash

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The journey to this point began a little more than 9 years ago with a 2012 submission, called AlexNet, to the annual ImageNet LS

The journey to this point began a little more than 9 years ago with a 2012 submission, called AlexNet, to the annual ImageNet LS

  • Within a year, startups began springing up to replicate AlexNet
  • Fast forward to 2022: Neural networks have changed every aspect of machine intelligence in software systems we all use daily, from recognizing our speech to recommending what's in our news feed
  • Today's systems still employ neural networks - but at a vastly different scale
  • Recent systems for understanding and generating human language, such as OpenAI's GPT-3, were trained on supercomputer-scale resources
  • These new systems require tens of millions of dollars in computation

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Pre-trained neural networks give smaller teams a leg up

  • A neural network is first trained on a large general-purpose dataset using significant amounts of computational resources, and then fine-tuned for the task at hand using a much smaller amount of data and compute resources.
  • The use of pre-trained networks has exploded in recent years as the industrialization of machine learning has taken over many fields and data has increased.

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The risks of foundation models: size, cost, and outsourced innovation

  • One of the risks associated with foundation models is their ever-increasing scale.
  • Pre-training on a large general-purpose dataset is no guarantee that the network will be able to perform a new task on proprietary data.
  • Dataset alignment and recency of training data can matter immensely depending on the use case.

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Cloud APIs are easier, but outsourcing isn't free

  • The cost of API calls, data storage, and cloud instances will scale along with usage.
  • Many companies that have used cloud APIs for machine learning today may eventually attempt to transition to self-hosted or self-trained models to gain more control over their machine learning pipelines.

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Be strategic and keep an eye on the big AI labs

  • Cloud APIs bring their own problems long-term. It's important to have a strategic exit plan so these APIs do not control your product destiny.
  • Keep tabs on what is coming out of the big corporate AI labs.

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