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Joaquim Cardoso MSc
Health Transformation — institute for research and strategy
October 7, 2022
In just a few weeks, technology has advanced to a level where it could have wide-ranging applications
Financial Times
Richard Waters
October 7, 2022
For all its huge potential, the field of artificial intelligence has been something of a backwater in the investment world.
There are companies that have ridden the AI wave in important ways:
- Google claims to have refined many of its services with the help of AI,
- machine learning has boosted sales of Nvidia’s graphics processing units and
- TikTok’s algorithm is reputedly a big part of what keeps users coming back to its short videos.
But it’s hard to find a pure AI company that has risen on the back of the technology, or to identify a big new market that has been created.
That picture may be about to change, and in a big way.
According to Pat Grady, a partner at Sequoia Capital, something significant has happened in AI in recent weeks.
Generative systems — ones that automatically produce text and images from simple text prompts — have advanced to a level where they could have wide-ranging business uses.
A partner at another leading Silicon Valley venture capital firm, who describes the recent history of AI as a graveyard for start-up investors, also reports that the race is on to find breakthrough applications for this new technology.
Generative systems — ones that automatically produce text and images from simple text prompts — have advanced to a level where they could have wide-ranging business uses.
… the race is on to find breakthrough applications for this new technology.
Since the launch of OpenAI’s GPT3 text-writing system two years ago, generative models like this have been all the rage in AI.
The ethical issues they raise are profound, ranging from any biases they could imbibe from the data they are trained on, to the risk that they could be used to spew out misinformation.
But that hasn’t prevented the hunt for practical uses.
Since the launch of OpenAI’s GPT3 text-writing system two years ago, generative models like this have been all the rage in AI.
Three things have changed to turn these systems from clever party tricks into potentially useful tools.
- 1.One is that the AI systems have moved beyond text
- 2.The second big change comes from the rapidly falling cost of training giant AI models
- 3.The third change has been the availability of the technology.
1.One is that the AI systems have moved beyond text.
Last week, Meta unveiled the first system capable of producing a video from a text or image prompt. That breakthrough had been thought to be two years or more away.
Not to be outdone, Google responded with not one but two AI video systems of its own.
This year’s biggest AI breakthrough has come in image generation, thanks to systems such as OpenAI’s Dall-E 2, Google’s Imagen and the start-up Midjourney.
Emad Mostaque, the London hedge fund manager behind Stable Diffusion, the latest image-generating system to take the AI world by storm, claims pictures will be the “killer app” for this new form of AI. For the generation that grew up with TikTok and Snapchat, this new creative tool could be profound, he says.
It also presents an obvious threat to anyone whose livelihood rests on creating images in other ways.
It also presents an obvious threat to anyone whose livelihood rests on creating images in other ways.
2.The second big change comes from the rapidly falling cost of training giant AI models.
Microsoft’s $1bn backing of OpenAI three years ago highlighted the prohibitive expense of this for ever-larger models.
New techniques that make it possible to achieve high-quality results by training neural networks with fewer layers of artificial neurons are changing the picture.
The computing resources used to train Stable Diffusion would have cost only $600,000 at market prices, according to Mostaque.
Microsoft’s $1bn backing of OpenAI three years ago highlighted the prohibitive expense of this for ever-larger models.
The computing resources used to train Stable Diffusion would have cost only $600,000 at market prices, according to Mostaque.
3.The third change has been the availability of the technology.
Google and OpenAI have been wary about making their technology widely available, partly out of concern about possible misuse.
By contrast, Midjourney’s image system is available to all users through a freemium pricing model.
Stable Diffusion has gone further, open-sourcing its software and releasing details of how it trained its system. That makes it possible for other organisations to train an image model on their own data sets.
Stable Diffusion has gone further, open-sourcing its software and releasing details of how it trained its system. That makes it possible for other organisations to train an image model on their own data sets.
The risks that stem from such generative systems have received much attention.
They churn out fresh images or text based on the millions of examples they have learnt from, with no understanding of the underlying material.
That can lead to nonsensical results, as well as deliberate misinformation.
But in a business setting, at least some of these shortcomings could be controlled.
The trick will be to find ways to embed the technology in existing work processes, creating tools that can suggest new ideas or speed up creative production, with human workers filtering the output.
The idea is already being used to generate computer code.
The big question now, says one investor, is:
will the existing giants of industries such as marketing, media and entertainment be the first to make use of these powerful new creative tools?
Or will they be disrupted by a new generation of upstarts with their roots in AI?
The big question now, says one investor, is: will the existing giants of industries such as marketing, media and entertainment be the first to make use of these powerful new creative tools?
Or will they be disrupted by a new generation of upstarts with their roots in AI?
Originally published at https://www.ft.com on October 6, 2022.
