When oxygen, heat and fuel combine, we get fire.
It's basic.
It's primal.
And is the key that unlocked human progress.
Fire provided light and heat and protection. With fire
our ancestors could move to new climates and eat new foods.
Pottery, metallurgy, chemistry, rapid transportation
and many other technologies all start with fire.
But imagine if fire had been proprietary.
What if that knowledge hadn't been shared?
What if there had been just a few keepers of the fire?
What would we be?
My name is Dario Gil
and I'm senior vice president at IBM and a director of IBM Research.
We are in a landmark moment now with generative AI
that will shape our society for generations to come.
In this episode, I'm going to show you why you need to be your own
AI firestarter, how to take control of your AI destiny,
to go from being just an AI user to become an AI value creator.
Chances are you're already an AI user, even if you don't have a formal
AI initiative or an AI team in place, it's already in use across your business.
It might be baked into your off the shelf applications.
It may show up in chat bots, in H.R.
self-service portals, and transcription services.
These are what we might call traditional AI.
The focus is on executing discrete tasks, and usually each instance is trained
individually on its own data that you have to compile and label.
Now, what makes Generative AI different is that it is enormously flexible
and not limited to narrow tasks.
Think of it as instead of filling specific blanks,
it can write the whole document from scratch to the point of being able
to create or generate something entirely new.
What makes this possible,
are foundation models.
Foundation models aren’t trained the same way as traditional AI.
They're trained using self-supervised learning.
You don't have to manually annotate a massive amount of data.
You tell them to go read enormous amounts of text, and they do.
And you end up with a large but versatile model
with more humanlike language capabilities.
Algorithms use mathematical models to represent
the relationships between the words they ingest.
If you give the model a few words in a prompt,
you can mathematically predict the likelihood of words in the response.
Instead of needing to build one A.I.
model for each specific task, you can train one model
and adapt it to many varied downstream tasks.
So we went from one task, one model to one model,
many tasks.
Your chat bot and your H.R. self-service
can be built on the same model as the new app
that will write your marketing emails and summarize legal documents.
So that's the first critical point.
Ideally, a model is in the final form of AI.
It's just a foundation you build on.
How you use it is up to you.
When it comes to using AI,
there are basically three modes of consumption.
The first is embedded AI,
which I already mentioned, is baked in to off the shelf software.
The software vendor creates the AI
and you put it to use in your business.
Whether it's a writing a system that can help you strike the right
tone in your email or image editing software
that can automatically process your images,
you get access to some great functionality
that can make you more productive, which we always want.
But the caveat is that what you can buy
so can everybody else. Those capabilities
and productivity opportunities don't become differentiators.
They set a new higher baseline for everyone.
The second mode of consumption is through API calls.
As you develop custom applications for your business,
they can call out to another company’s AI service
using that company's models and processes and then return the results.
This is also a valuable way of consuming AI,
depending on how cleverly you use the APIs and the diversity
of AI service providers you use.
You can start to differentiate how you put AI to work relative to your competitors.
But there are caveats here too.
The first is that, like with software,
the models and services you tap into are available to everyone.
The second is that when that API call goes out,
it's connecting to what looks like a black box.
You don't necessarily know what's happening on the other end
or the provenance and governance of the data use,
which can make people nervous because your business
is still accountable for the final outcomes.
And a second word of caution when using someone else's
AI, has to do with the creation and accrual of value over the long term.
In the past, we've seen a lot of value extractive business models.
Another company would offer you a service like this API call
and you get value from that.
But the other company is also extracting value
from your usage and from your data accumulating more and more.
How much faster is their value growing than yours?
Well, you can see it in the stock prices over time.
There is an imbalance in the relationship,
that can have long term consequences both for your specific business
and for the overall economy and progress of technology.
This goes back to the metaphor I used earlier.
Philosophically, do we as a society really want just a few keepers of the fire
upon which we are all dependent?
Is that what's best for your individual business, for your shareholders?
The third model of A.I.
consumption is the platform model.
This is the most comprehensive.
This is how you become your own AI firestarter.
And no, it doesn't mean doing it alone and reinventing the AI from scratch
and spending years and millions of dollars to build your own models.
With a platform you have all the elements and ingredients
in place to build your own AI solutions.
You have foundation models.
You have tools to improve and customize models,
and you have processes to build your own tailored AI solutions.
And importantly, you create
and accrue value that is unique to your business.
Ultimately, I believe that most businesses
should end up with a mix of all three.
You'll use third party software with AI embedded.
Sometimes you'll be totally appropriate to use someone else's AI,
but to fully realize the value of AI and to differentiate yourself from competitors,
you’ll want a platform approach to create and own your AI.
Let me just go a bit deeper
on AI value creation, starting with foundation models.
Foundation models are large scale deep neural networks trained with
lots of unlabeled data and subsequently adapted to many downstream tasks.
It may be a broad general model, or it may be a narrower, deeper model,
but the key is that it is pre-trained with the expectation
that you can further enhance it with your own proprietary data.
Like when a new employee joins your business,
they come in with some general skills as a foundation and the ability to learn.
The more they learn about your business, the more they add
institutional knowledge and expertise, the more value they deliver.
Well, the same is basically true of our foundation model.
You use your AI platform to tune it with your specific business data,
your proprietary knowledge and expertise,
and it becomes more expert and more valuable for your business over time.
And because you're in control of the platform and the processes
and the data, you accrue ever larger amount of value over time.
With some of the consumer AI on the market,
we've already seen some of what happens when you surrender that control.
You can get bad data that leads to bad outcomes.
You can get hallucinations, basically an
AI generating very confident and very incorrect answers.
You can get into some trouble for inadvertently
using someone else's rights managed content.
We've even seen proprietary or sensitive data
being inadvertently leaked back into the public spaces.
That's why you need to know how your model is built
and what data was included.
And it's why tight control of your sensitive data should be prioritized.
Strong AI governance is absolutely critical.
Yes, now it is the time to jump into AI,
but please look before you leap in and ensure
that you're investing in a smart, safe, and sustainable approach
where your business and your clients and customers
are the primary beneficiaries.
There is, I think, a myth about AI right now,
a basic misunderstanding.
For the general public generative AI has seemingly come out of nowhere.
A lot of people think that there is
a handful of consumer oriented AI experiences out there
and that one model is going to win, or just a few leaders.
I don't think that that's how it's going to play out.
The future of AI is not about one model,
it's multi-model.
Your business will be using multiple fine tuned models
to achieve the best results when applied to specific use cases.
That's why that platform approach is so important.
And realistically, the future of AI is not only proprietary.
It will also be powered by open science and open source.
Proprietary models will play a part.
But so much of what is going to happen in the future will not happen behind closed doors.
It will play out in plain view with full transparency and accountability
that open source provides.
The energy in the open source community is phenomenal.
They are distributed projects, university projects, corporate efforts, all driving
innovation, producing foundation models that you can tune and deploy
for your use cases. Hugging Face is like GitHub for foundation models.
And there are over 325,000 open
source models available and thousands more being added.
And this is exactly how it should be for the good of society in the long term.
We don't want just one or a few winners, a few companies
that can define what AI is and dictate how it's used.
But that's not going to happen.
What we're going to see is the democratization of AI.
Okay,
so I've talked about a lot of different topics, and I basically told you
that you have a lot of work to do to be great at AI,
and maybe that feels overwhelming, but really that's not my intention.
My goal is to take down barriers to participation.
Not put them up.
There is a sense of urgency and fear about waiting
too long and missing the moment.
That's okay.
I assure you that everybody is in the same situation, feeling the same emotions.
But I promise that if instead of rushing into fast
and easy options to check the box and say, yes, I put AI in the business,
if instead you are thoughtful and deliberate and strategic
about an AI platform and data management, and AI governance,
if you become an AI value creator.
You're going to be in a position to succeed over the long term
and you won't have to start over every time the wind changes direction.
You may not have all the products and processes you need today.
You may not have all the skills in place.
That's fine.
If you're not ready to start alone,
find a partner that you can trust and get to work
so that you can take advantage of the extraordinary
opportunities that generative AI will unlock for your business.
Personally, I'm
very excited about this chapter in technology.
We, all of us together are going to use the AI to reshape
not just our digital world, but our physical world.
We are going to use it to help tackle some of our toughest
social, medical, environmental problems, and more.
We'll do it through science, but also by empowering businesses
like yours and mine to do more, faster and more responsibly.
Whatever things you do, you're going to get
a power full new tool to help you do them better.
And its the AI value creators,
that will be the ones who make the biggest impact.
It is the value creators who will take the amazing foundational technology
that is generative AI and use it to build entirely new solutions.
That's why it is our goal to make AI accessible to everyone.
To put it in your hands
and I can't
wait to see what you will do with it.
Thank you for watching this episode of AI Academy.
I hope you will join us again as our IBM experts take you on deeper dives
into everything that a business leader needs to know about AI.