A tipping point for Generative AI

A tipping point for Generative AI

Part I

Machines are just starting to get good at creating sensical and beautiful things – generating something new rather than analysing something that already exists

A powerful new class of large language models is making it possible for machines to write, code, draw and create with credible and sometimes superhuman results. Humans write poetry, design products, make games and crank out code. Up until recently, machines had no chance of competing with humans at creative work – they were relegated to analysis and rote cognitive labour. But machines are just starting to get good at creating sensical and beautiful things. This new category is called “Generative AI,” meaning the machine is generating something new rather than analysing something that already exists.

Global AI investment surged from $12.75 million in 2015 to $93.5 billion in 2021, and the market is projected to reach $422.37 billion by 2028. Already over $2B has been invested in Generative AI, up 425% since 2020, according to the Financial Times.

Just as mobile unleashed new types of applications through new capabilities like GPS, cameras and on-the-go connectivity, we expect these large models to motivate a new wave of generative AI applications.

The three waves

  • Wave 1: Small models reign supreme (Pre-2015)

5+ years ago, small models are considered “state of the art” for understanding language. These small models excel at analytical tasks and become deployed for jobs from delivery time prediction to fraud classification. However, they are not expressive enough for general-purpose generative tasks. Generating human-level writing or code remains a pipe dream. 

  • Wave 2: The race to scale (2015-Today) 

A landmark paper by Google Research (Attention is All You Need) describes a new neural network architecture for natural language understanding called transformers that can generate superior quality language models while being more parallelizable and requiring significantly less time to train. These models are few-shot learners and can be customized to specific domains relatively easily.

Sure enough, as the models get bigger and bigger, they begin to deliver human-level, and then superhuman results. Between 2015 and 2020, the compute used to train these models increases by 6 orders of magnitude and their results surpass human performance benchmarks in handwriting, speech, and image recognition, reading comprehension and language understanding. OpenAI’s GPT-3 stands out: the model’s performance is a giant leap over GPT-2 and delivers tantalizing Twitter demos on tasks from code generation to snarky joke writing.  Despite all the fundamental research progress, these models are not widespread. They are large and difficult to run (requiring GPU orchestration), not broadly accessible (unavailable or closed beta only), and expensive to use as a cloud service. Despite these limitations, the earliest Generative AI applications begin to enter the fray. 

Image: As AI models got progressively larger, they begun to surpass major human performance benchmarks; Sources:The Economist, London, 11 June 2022;www.science.org
  • Wave 3: Better, faster, cheaper (2022+) 

Compute gets cheaper. New techniques, like diffusion models, shrink down the costs required to train and run inference. The research community continues to develop better algorithms and larger models. Developer access expands from closed beta to open beta, or in some cases, open source.

For developers who had been starved of access to LLMs, the floodgates are now open for exploration and application development. Applications begin to bloom.

[To be continued]

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