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Google’s AI’s are learning how to build new AI’s

Logically speaking getting AI's to build AI's makes sense, but as AI's continue to proliferate and be woven into the digital fabric of our society it opens up a dangerous pandoras box

Key takeaways

  • Late last year, for example, Facebook announced its AI's were already building new AI's, and it's the start of a growing trend.
  • The team's idea is that if they can get AI's to build other AI's then not only will it speed up and simplify the process of making new AI's but that it will also make it much, much cheaper.
  • For now, Google says its AI maker is not advanced enough yet to compete with human engineers.
Cite or link to this article

Griffin, M. (2017) 'Google’s AI’s are learning how to build new AI’s', 311 Institute, 20 January. Available at: https://www.311institute.com/googles-ais-are-going-to-be-building-more-ais/ (Accessed: 25 October 2026).

As many companies, large and small will tell you, creating a good artificial intelligence (AI) is difficult, really difficult, and for companies such as Alibaba, Facebook, Google, IBM and Microsoft where AI could literally make or break their futures it's a battle they have to win. And now the team at Google's AI research lab, Google Brain, think they have the answer, get AI's to design and build new AI's, but they're not alone, not by a long shot. Late last year, for example, Facebook announced its AI's were already building new AI's, and it's the start of a growing trend.

What could possibly go wrong!?

If you were one of the people who said "nothing" and "that sounds like a great idea" then you might want to go and stand in the corner with everyone else who said that, and it's a lonely corner.

The team's idea is that if they can get AI's to build other AI's then not only will it speed up and simplify the process of making new AI's but that it will also make it much, much cheaper.

At the moment if you want to make your own AI you have to train it using machine learning techniques, hire experts and have the right tools, so the teams new hope is that in the future if a company wants to make their own AI then they can simply rent an AI builder instead of going out to market and trying to hire a team of experts. The upshot of all of this of course is that AI's would proliferate.

Today most AI's can take anywhere between a few weeks to a few months to build and require huge amounts of computing power to be trained, an AI builder could streamline everything, optimise and even find new ways for the next generation of AI's to learn.

For now the Google Brain team are focusing on automating the machine learning process, and in recent months several other groups have also reported progress on getting machine learning programs to create other machine learning programs so they aren't alone in their quest. They include researchers at the nonprofit research institute OpenAI, owned by Elon Musk, MIT, the University of California Berkeley, and Google’s other artificial intelligence research group, DeepMind.

“Currently the way to solve AI problems is to have human expertise, data and computation,” said Jeff Dean, who leads the Google Brain research group, "the question we want to answer is can we eliminate the need for a lot of machine learning expertise?”

Put another way, is there a way they can automate the machine learning process to free up data scientists time to do "other stuff," and while the answer is inevitably going to be yes it's inevitably going to raise other questions, such as what happens when you have what many regard already to be a "black box technology," whose own creators aren't even sure how it really works, creating other black boxes?

Around the world there are already companies, including Google and MIT, who are trying to assuage people's potential concerns, whether that's by creating AI "kill switches" for rogue AI's or developing new technologies that help to crack open AI's so we humans can really understand what's going on inside the black box.

For now, Google says its AI maker is not advanced enough yet to compete with human engineers. However, given the rapid pace of AI development, it may only be a few years before that is no longer true. Hopefully it doesn't happen before we are ready.

311 Intelligence

What this means for your organisation

Expertise stops being the barrier.

Today building an AI needs scarce experts, data and heavy compute. An AI builder you can rent would let far more firms make their own models. Advantage then shifts to owning good data and clear use cases.

Black boxes may build black boxes.

Many models are already hard for their creators to explain. Machine-built models add another layer that people did not design. Leaders should require explainability and kill switches before deploying them.

Teams will mix people and AI.

Data scientists freed from routine model building can focus on problems that matter. Managers will run teams where AI does much of the technical work. Plan the skills and roles this needs now.

Three questions for your next leadership meeting

  1. Which AI projects stall today for lack of expert staff?
  2. How would we explain decisions made by models no person designed?
  3. What data do we own that an AI builder could turn into value?

The trends and technologies behind this story

From the 311 Starburst, the 311 Strategic Intelligence Unit’s map of the technologies and trends shaping the next 50 years.

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Analysis by the 311 Strategic Intelligence Unit, from Matthew Griffin’s article and 311 data. First published on Fanatical Futurist.

FAQ

Why does this matter?

Logically speaking getting AI's to build AI's makes sense, but as AI's continue to proliferate and be woven into the digital fabric of our society it opens up a dangerous pandoras box

Matthew Griffin

About the author

Matthew Griffin Founder, President and Futurist, 311 Institute

Matthew Griffin is a multi-award winning Futurist and expert in Disruption and Innovation, Geopolitics, Leadership, and Technology, who NASA have described as a "walking encyclopaedia of the future" and a "futurist Polymath."

Read full bio

Matthew Griffin is a multi-award winning Futurist and expert in Disruption and Innovation, Geopolitics, Leadership, and Technology, who NASA have described as a "walking encyclopaedia of the future" and a "futurist Polymath." 17-time best selling author of the "Codex of the Future" series, Matthew is the Founder, President and Futurist of the 311 Institute, a global Futures and Deep Futures advisory firm working with royal households, world leaders, G7, G20, and G77 governments, NGOs, and multi-national mid and mega cap firms to help them explore, shape, and lead the next 50 years of business and society.

An award-winning YouTube creator with over a million followers, with an unrivalled global reach and impact, Matthew is a highly sought-after international keynote speaker, lecturer, and mentor who collaborates with global leaders through the United Nations Alliance of Civilizations (UNAOC) and United Nations General Assembly (UNGA) to shape pivotal initiatives such as the UN’s AI for Humanity programme, the United Nations Conference of the Parties (UN COP), and the World Economic Forum in Davos.

As the former Global Head of Cloud, National Security, and Enterprise Sales for companies including Atos, Dell-EMC, and IBM, Matthew has a proven track record of building multi-billion dollar business units and turning failing divisions into market leaders. His ability to identify, analyse, and communicate the implications of hundreds of emerging technologies and trends is unparalleled, and his insights are trusted by many of the world’s most respected organisations, including ABB, Accenture, Adidas, AON, ARM, BCG, Centrica, Citi, Coca-Cola, Dentons, Deloitte, Dow Jones, EY, Google, KPMG, Lego, Legal & General, LinkedIn, Microsoft, PepsiCo, Qualcomm, RWE, Samsung, Siemens AG and Siemens Energy, T-Mobile, UBS, VISA, Walmart, Workday, Worldpay and many others.

Regularly featured in the global media including the AP, BBC, Bloomberg, CNBC, Discovery, Forbes, Khaleej Times, Telegraph, TIME, ViacomCBS, WIRED, and the WSJ, Matthews mission is to help organisations create a fair and sustainable future whose benefits are shared by everyone irrespective of their ability, background, or circumstances.

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Sources and further reading

  1. nonprofit research institute OpenAI arxiv.org
  2. 1611.02167 arxiv.org
  3. 1606.01885 arxiv.org
  4. 1611.05763 arxiv.org
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