Arthur C Clarke famously said that
any sufficiently advanced technology is indistinguishable from magic
and perhaps the first time that you played with Generative AI
it did evoke a sense of magic.
Suddenly, for the first time in our history,
we have a technology that can speak our languages, understand
our requests and produce entirely novel output.
AI can write poetry and draw otherworldly images.
It can write code.
It can surprise and delight us with an original joke or musical composition.
It can create and an act of creation that often inspires wonder.
But AI is not magic.
It's math and science.
And it wasn't sudden.
These experiences have been decades in the making.
AI is going to touch every aspect of our lives.
It will change the world.
But how it will change the world is up to us.
To all of us.
Welcome to the AI Academy.
My name is Darío Gil.
I'm an electrical engineer and computer scientist by trade and the head of IBM
Research, but also a business leader and a senior vice president at IBM.
In this series, we are going to demystify
AI. We’ll show you how we got here, how generative AI works, and explore
some of the ways that it will transform business and society.
So let's start at the beginning.
People have been speculating about the possibility
that machines would someday think since the late 1800s,
but the idea really took root with Alan
Turing seminal paper in 1950.
Historians called Turing the father of AI.
He theorized that we could create computers that could play chess,
that they would surpass human players,
that we could make them proficient in natural language.
He theorized that machines would eventually think,
thanks to my career in IBM research.
I have seen and been part of achieving many of the milestones
that Turing identified on the way to a thinking machine, including chess
with deep blue jeopardy and debating systems.
But Turing was just a beginning.
If Turing's 1950 paper was the spark, just six years later,
we had the Big Bang, the Dartmouth Workshop.
A couple of young academics got together with a couple of senior scientists
from Bell Labs and IBM, and proposed an extended summer workshop
with just a small handful of top people in adjacent fields
to intensively consider artificial intelligence.
That is how the phrase artificial intelligence was coined,
and in marks the point at which AI was established as a field of research.
They laid out in extensive detail many of the challenges
that we've been working all these years to solve and develop machines
that could potentially think neural networks, self-directed
learning, creativity and more. All still relevant today.
For perspective,
this was 1956, the same year
the invention of the transistor won the Nobel Prize.
Now we can have over 100 billion transistors
on a GPU and banks and banks of interconnected GPUs to provide
the compute power to create and execute generative AI functions.
All these years, the AI theories, techniques and ideas
have been developed in parallel with progress in hardware
that result in dramatic reductions in compute and storage costs,
all converging now to make generative AI real and practical.
But I want to make this critical point.
It's not just about powerful hardware and clever algorithms.
The third, and maybe the most important ingredient,
particularly when it comes to your business, is data.
You can't talk about generative AI without talking about data.
It's the third leg of the AI stool
model architecture, plus compute
plus data.
You hear about large language
models or LLMs that are powering generative AI.
So what are they?
At a basic level, they are a new way of representing language
in a high dimensional space with a large number of parameters,
a representation that you create by training of massive quantities of text.
From that perspective, much of the history of computing has been
about coming up with new ways to represent data and extract value from it.
We put data in tables, rows of employees
or customers, and columns of attributes in a database.
This is great for things like transaction processing
or writing checks for payments to individuals.
Then we started representing data with graphs.
We start to see relationships between data points.
This person or this business or this place is connected
to these other people or businesses and places.
Data represented this way starts to reveal patterns.
And we can map a social network or spot
anomalous purchases for credit card fraud detection.
Now, with large language models,
we are talking lots of data and representing it
in neural networks that simulate an abstract version of brain cells.
Layers and layers of connections with tens of billions
or hundreds of billions, even trillions of parameters.
And suddenly you can start to do some fascinating things.
You can discover patterns that are so detailed that you can
predict relationships with a lot of confidence.
You can predict that this word is most likely connected to this next word.
These two words are most likely followed by a specific third word
building up, reassessing and predicting again and again
until something new is written or something
new is created or generated.
That's what generative AI is: the ability to look at data
and discover relationships
and predict the likelihood of sequences with enough confidence
to create or generate something that didn't exist before.
Text, images, sounds,
whatever data can be represented in the model.
We could do a limited version of this before with deep learning,
which was an AI milestone in its own right.
With deep learning, we started representing a massive amount
of data using very large neural networks with many layers.
But until recently, a lot of the training happened using annotated data.
This is data that humans would label manually.
We call this supervised learning, and it's expensive and time consuming.
So only large institutions were doing that work and it was done for specific tasks.
But around 2017, we saw a new approach,
power by an architecture called transformers
to do a form of learning called Self-supervised Learning.
In this approach, a model is trained on a large amount
of unlabeled data by masking certain sections of the text, words,
sentences, etc., and asking the model
to fill in those masked words.
This amazing process, when done at scale results
in a powerful representation that we call a large language model
instead of narrow use cases and areas of expertise.
You could start to have something broader.
Basically, these LLMs could be trained on huge volumes of internet data
and acquire a human like set of natural language capabilities.
Self supervision at scale combined with massive data and compute,
Give us representations that are generalizable and adaptable.
These are called foundation models, large scale neural networks that are trained
using self supervision and then adapt it to a
wide range of downstream tasks.
This means that you can take a large pre-trained model,
ideally trained with trustworthy industry specific data,
and that your institutional knowledge to tune the model
to excel at your specific use cases.
You end up with something that is tailored for you,
but also quite efficient and much faster to deploy.
The current thinking is usually
that you can apply this to language, but that sparks a question.
What is a language?
Signals in a piece of industrial equipment are talking to you.
The clicks of a user navigating a website, software
code, chemistry and the diagrammatic representations of chemicals.
If you squint, everything starts looking like a language
that can be deciphered and understood.
AI can
be specialized to do all kinds of things
that boost productivity in any of those languages.
That means that AI can stretch horizontally
across your business to H.R. processes.
Customer service and self-service cybersecurity, code writing,
application modernization, and so many other things.
With all the advances achieved in the last few years.
The ambition of the 1950s has come full circle.
Today's models don't constitute true general intelligence,
but some of them can pass the Turing test.
So what does it mean for all of us?
Some people encounter generative AI and think we're at the dawn of a bright
utopian age, while others think this is the prelude to dystopian misery.
As a scientist, I take a moderate view.
Both the optimism and the anxiety are valid,
and we've asked the same questions at every major
innovation milestone from the Industrial Revolution onward.
AI isn't just about the digital world.
It's also about the physical world. Applied properly,
imagine what AI can do for the pace of discovery and innovation,
what it can do for discovering new materials,
for medicine, for energy, for climate,
and so many of the pressing challenges that we face as a species.
Ultimately, our success depends on how we approach AI
I want you to think back
to the first time you heard about generative AI.
It's a phrase that really became part of the public conversation
in maybe November or December of 2022.
We have seen new models, evolved models
and an explosion of open models.
Generative
AI has gone from being a fascinating novelty, to a new business imperative
in less than a year and every day there is news of a new use case or application.
There's such
rapid growth that I can't predict exactly where
we'll be ten years from now or even ten months from now.
But I do know that you're going to
want to be actively engaged in shaping that journey.
The future of the AI is not one or two
amazing models to do everything for everyone.
It's multimodal.
It needs to be democratized, leveraging the energy
and the transparency of open science and open source AI so that we all have
a voice in what AI is, what it does,
how it's used, and how it impacts society.
Where you get to decide what AI can do
and how it integrates with your business.
It's time to start making plans for how you can effectively,
safely and responsibly put AI to work.
And then to leave you with four main pieces of advice.
Number one, you want to protect your data.
Your data and the representations of that data,
which, as I just explained, are what AI models are,
will be your competitive advantage.
Don't outsource that. Protect it.
Number two,
you have to make sure that you are embracing principles
of transparency and trust so that you can understand and explain
as much as possible of the decisions or recommendations made by AI.
Number three, you want to make sure that your AI is implemented
ethically, that your models are trained on legally accessed quality data.
That data should be accurate and relevant,
but also control for bias, hate speech, and other toxic elements.
A number four, don't be a passenger.
You need to empower yourself
with platforms and processes to control your AI destiny.
You don't need to become an AI expert,
but every business leader, every politician, every regulator,
everyone should have a foundation from which to make informed decisions
about where, when, and how we apply this new technology.
We will cover all of these topics in more detail
in the rest of the AI Academy series.
Every video will feature a subject matter
expert with a specific point of view on these key topics.
I hope you will join us for the next episode
where I will be hosting again to talk about why it's imperative to take control
of your journey to go beyond just an AI user
and become an AI value creator.