https://www.answer.ai/posts/2023-12-12-launch.html

Answer.AI

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On this page

  * tl;dr
  * A new R&D lab
  * An iterative path to harnessing AI
  * Our research platform
  * From fast.ai to Answer.AI
  * We don't really know what we're doing

A new old kind of R&D lab

Answer.AI is a new kind of AI R&D lab which creates practical
end-user products based on foundational research breakthroughs.
Author

Jeremy Howard

Published

December 12, 2023

tl;dr

Jeremy Howard (founding CEO, previously co-founder of Kaggle and
fast.ai) and Eric Ries (founding director, previously creator of Lean
Startup and the Long-Term Stock Exchange) today launched Answer.AI, a
new kind of AI R&D lab which creates practical end-user products
based on foundational research breakthroughs. The creation of
Answer.AI is supported by an investment of USD10m from Decibel VC.
Answer.AI will be a fully-remote team of deep-tech generalists--the
world's very best, regardless of where they live, what school they
went to, or any other meaningless surface feature.

A new R&D lab

In 1831 Michael Faraday showed the world how to harness electricity.
Suddenly there was, quite literally, a new source of power in the
world. He later found the basis of the unification of light and
magnetism, and knew he was onto something big:

    "I happen to have discovered a direct relation between magnetism
    and light, also electricity and light, and the field it opens is
    so large and I think rich." Michael Faraday; letter to Christian
    Schoenbein

But it wasn't quite clear how to harness this power. What kinds of
products and services could now be created that couldn't before? What
could now be made far cheaper, more efficient, and more accessible?
One man set out to understand this, and in 1876 he put together a new
kind of R&D lab, which he called the "Invention Lab": a lab that
would figure out the fundamental research needed to tame electricity,
and the applied development needed to make it useful in practice.

You might have heard of the man: his name was Thomas Edison. And the
organization he created turned into a company you would know: General
Electric.

Today, we find ourselves in a similar situation. There's a new source
of power in the world--artificial intelligence. And, like before, it's
not quite clear how to harness this power. Where are all the
AI-powered products and services that make our lives and work
dramatically easier and more pleasant?

To create these AI-powered products and services, we've created a new
R&D lab, called Answer.AI. Answer.AI will figure out the fundamental
research needed to tame AI, and the development path needed to make
it useful in practice.

An iterative path to harnessing AI

Harnessing AI requires not just low-level computer science and
mathematical research, but also deep thinking about what practical
applications can take advantage of this new power. The "D" in "R&D"
is critical: it's only by considering the development of practical
applications that the correct research directions can be targeted.

That's why Answer.AI is built on the work of experts in both research
and development. Co-founders Jeremy Howard (that's me!) and Eric Ries
have created pioneering ideas in each of these areas. I co-founded
fast.ai, where I have worked for the last 7 years on research into
how to best make AI more accessible, particularly through transfer
learning and fine tuning. I've been working with machine learning for
over 30 years, including creating the ULMFiT method of fine-tuning
large language models which is used as the basis of all popular
language models today, including OpenAI's ChatGPT and Google's
Gemini. I have developed the longest running online courses on Deep
Learning in the world, in which I show students how to start with
simple models and then iteratively improve them all the way to the
state of the art.

I've known Eric for years, and there's no-one I trust or respect
more, which is why I asked him to serve as the founding director of
Answer.AI. Eric has dedicated the last 10 years of his life to
improving how companies operate, serve customers, and are governed.
He is the creator of the Lean Startup movement, which is the basis of
how most startups build products and scale their organizations. His
work focuses on development: how can organizations go from an idea to
a sustainable, mission-driven, and profitable product in practice.
One of his key insights was to create and then iteratively improve a
Minimal Viable Product (MVP).

I asked Eric for his thoughts on Answer.AI's unique approach to R&D,
and he summarised better than I ever could, so I'll just quote his
reply here directly:

    "People think that the order is research-development, and that
    therefore an R&D lab does "R" and then "D". That is, the research
    informs the development, and so being practical means having
    researchers and developers. But this is wrong, and leads to a lot
    of bad research, because development should inform research and
    vice-versa. So having development goals is a way to do more
    effective research, if you set that out as your north star."

Eric is also an expert on governance and how companies should be led
in order to align profit and increased human flourishing. He created
the Long-Term Stock Exchange (LTSE), the first fundamentally new US
Stock Exchange in over 50 years. LTSE mandates that listed companies
and likeminded investors work towards long-term value, rather than
just short-term profit maximization. Eric serves as the Chairman of
LTSE, meaning he is not only up to date on the right long-term
governance frameworks, but on the cutting edge of inventing new
systems.

It will take years for Answer.AI to harness AI's full potential,
which requires the kind of strategic foresight and long-term tenacity
which is hard to maintain in today's business environment. Eric has
been writing a book on exactly this topic, and his view is that the
key foundation is to have the right corporate governance in place.
He's helped me ensure that Answer.AI will always reflect my vision
and strategy for harnessing AI. We're doing this by by setting up a
for-profit organization that focuses on long-term impact. After all,
over a long-enough timeframe, maximizing shareholder value and
maximizing societal benefits are entirely aligned.

Whilst Eric and I bring very different (and complementary) skills and
experiences to the table, we bring the same basic idea of how to
solve really hard problems: solve smaller easier problems in simple
ways first, and create a ladder where each rung is a useful step of
itself, whilst also getting a little closer to the end goal.

Our research platform

Companies like OpenAI and Anthropic have been working on developing
Artificial General Intelligence (AGI). And they've done an
astonishing job of that -- we're now at the point where experts in the
field are claiming that "Artificial General Intelligence Is Already
Here".

At Answer.AI we are not working on building AGI. Instead, our
interest is in effectively using the models that already exist.
Figuring out what practically useful applications can be built on top
of the foundation models that already exist is a huge undertaking,
and I believe it is receiving insufficient attention.

My view is that the right way to build Answer.AI's R&D capabilities
is by bringing together a very small number of curious, enthusiastic,
technically brilliant generalists. Having huge teams of specialists
creates an enormous amount of organizational friction and complexity.
But with the help of modern AI tools I've seen that it's possible for
a single generalist with a strong understanding of the foundations to
create effective solutions to challenging problems, using unfamiliar
languages, tools, and libraries (indeed I've done this myself many
times!) I think people will be very surprised to discover what a
small team of nimble, creative, open-minded people can accomplish.

At Answer.AI we will be doing genuinely original research into
questions such as how to best fine-tune smaller models to make them
as practical as possible, and how to reduce the constraints that
currently hold back people from using AI more widely. We're
interested in solving things that may be too small for the big labs
to care about---but our view is that it's the collection of these
small things matter a great deal in practice.

This informs how we think about safety. Whilst AI is becoming more
and more capable, the dangers to society from poor algorithmic
decision making have been with us for years. We believe in learning
from these years of experience, and thinking deeply about how to
align the applications of models with the needs of people today. At
fast.ai three years ago we created a pioneering course on Practical
Data Ethics, as well as dedicating a chapter of our book to these
issues. We are committed to continuing to work towards ethical and
beneficial applications of AI.

From fast.ai to Answer.AI

Rachel Thomas and I realised over seven years ago that deep learning
and neural networks were on their way to becoming one of the most
important technologies in history, but they were also on their way to
being controlled and understood by a tiny exclusive sliver of
society. We were worried about centralization and control of
something so critical, so we founded fast.ai with the mission of
making AI more accessible.

We succeeded beyond our wildest dreams, and today fast.ai's AI
courses are the longest-running, and perhaps most loved, in the
world. We built the first library to make PyTorch easier to use and
more powerful (fastai), built the fastest image model training system
in the world (according to the Dawnbench competition), and created
the 3-step training methodology now used by all major LLMs (ULMFiT).
Everything we have created for the last 7 years was free--fast.ai was
an entirely altruistic endeavour in which everything we built was
gifted to everybody.

I'm now of the opinion that this is the time for rejuvenation and
renewal of our mission. Indeed, the mission of Answer.AI is the same
as fast.ai: to make AI more accessible. But the method is different.
Answer.AI's method will be to use AI to create all kinds of products
and services that are really valuable and useful in practice. We want
to research new ways of building AI products that serve customers
that can't be served by current approaches.

This will allow us to make money, which we can use to expand into
more and bigger opportunities, and use to drive down costs through
better efficiency, creating a positive feedback loop of more and more
value from AI. We'll be spending all our time looking at how to make
the market size bigger, rather than how to increase our share of it.
There's no moat, and we don't even care! This goes to the heart of
our key premise: creating a long-term profitable company, and making
a positive impact on society overall, can be entirely aligned goals.

We don't really know what we're doing

If you've read this far, then I'll tell you the honest truth: we
don't actually know what we're doing. Artificial intelligence is a
vast and complex topic, and I'm very skeptical of anyone that claims
they've got it all figured out. Indeed, Faraday felt the same way
about electricity--he wasn't even sure it was going to be of any
import:

    "I am busy just now again on Electro-Magnetism and think I have
    got hold of a good thing but can't say; it may be a weed instead
    of a fish that after all my labour I may at last pull up."
    Faraday 1931 letter to R. Phillips

But it's OK to be uncertain. Eric and I believe that the best way to
develop valuable stuff built on top of modern AI models is to try
lots of things, see what works out, and then gradually improve bit by
bit from there.

As Faraday said, "A man who is certain he is right is almost sure to
be wrong." Answer.AI is an R&D lab for people who aren't certain
they're right, but they'll work damn hard to get it right eventually.

This isn't really a new kind of R&D lab. Edison did it before, nearly
150 years ago. So I guess the best we can do is to say it's a new old
kind of R&D lab. And if we do as well as GE, then I guess that'll be
pretty good.