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

Unlock the potential of massive AI models for your startup. Explore the transformative power of technologies like GPT-3, and learn how to harness their capabilities to drive innovation, streamline operations, and create unparalleled customer experiences.

The journey to this point began a little more than 9 years ago

In 2012, a team called AlexNet won the ImageNet LSVRC competition using a convolutional neural network

• Within a year, startups began springing up to replicate AlexNet
• 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
• The emergence of these massive-scale, high-cost foundation models brings opportunities and risks for startups

Cloud APIs are easier, but outsourcing isn’t free

Companies such as OpenAI, Microsoft, and Nvidia have seen the scale challenges and are responding with cloud APIs that enable running inference and fine-tuning of large-scale models on their hosted infrastructure.

• This can provide a limited pressure relief valve to startups, researchers, and even individual hobbyists by offloading the compute and infrastructure challenges to a larger company.

Be strategic and keep an eye on the big AI labs

Cloud APIs accelerate a company’s path to product-market fit, but often 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

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.

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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