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Understanding machine learning models
Improving data analysis and decision-making
How Google uses logic in machine learning
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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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 ...
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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 pipeli...
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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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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 st...
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