Rocket Launch Analogy for AI Training
Key Points
- Training large language models is likened to launching a rocket: it demands massive compute resources, months of effort, and meticulous planning because once training starts, design changes aren’t possible.
- Kate Soule, acting as “mission control” at IBM, emphasizes that her business‑strategy background drives a focus on ensuring LLM research delivers real, tangible value for clients rather than just technical breakthroughs.
- Generative AI extends traditional AI by not only analyzing data but also creating new content, enabling use cases such as automated customer service, code generation, and complex document extraction that boost productivity and cut costs.
- Foundation models are large, general‑purpose systems trained on vast unlabeled data via unsupervised learning, which can then be fine‑tuned for a wide variety of applications, making them more versatile than task‑specific traditional ML models.
- Building internal expertise and dedicated teams now is crucial, as generative AI is becoming a key business differentiator and organizations need the capability to innovate and shape the evolving AI landscape.
Sections
- LLM Training as Rocket Launch - The speaker likens large language model training to a resource‑intensive rocket launch, emphasizing meticulous planning, the inability to tweak once training begins, and a business‑focused mission to deliver real, tangible value.
- Framework for Responsible GenAI Adoption - The speaker outlines a step‑by‑step approach—building a skilled team, piloting a low‑risk use case, defining value and compliance requirements, ensuring transparent and trustworthy operations, and selecting proper evaluation metrics—to successfully integrate generative AI into business.
- Continuous Model Retraining & Governance - The speaker stresses IBM’s practice of regularly retraining foundation models to incorporate new data, regulatory updates, and risk‑management best practices, emphasizing the need for a robust AI platform with governance tools that can guide organizations from experimentation to self‑managed deployment.
Full Transcript
# Rocket Launch Analogy for AI Training **Source:** [https://www.youtube.com/watch?v=1JzMSbcInxc](https://www.youtube.com/watch?v=1JzMSbcInxc) **Duration:** 00:08:14 ## Summary - Training large language models is likened to launching a rocket: it demands massive compute resources, months of effort, and meticulous planning because once training starts, design changes aren’t possible. - Kate Soule, acting as “mission control” at IBM, emphasizes that her business‑strategy background drives a focus on ensuring LLM research delivers real, tangible value for clients rather than just technical breakthroughs. - Generative AI extends traditional AI by not only analyzing data but also creating new content, enabling use cases such as automated customer service, code generation, and complex document extraction that boost productivity and cut costs. - Foundation models are large, general‑purpose systems trained on vast unlabeled data via unsupervised learning, which can then be fine‑tuned for a wide variety of applications, making them more versatile than task‑specific traditional ML models. - Building internal expertise and dedicated teams now is crucial, as generative AI is becoming a key business differentiator and organizations need the capability to innovate and shape the evolving AI landscape. ## Sections - [00:00:00](https://www.youtube.com/watch?v=1JzMSbcInxc&t=0s) **LLM Training as Rocket Launch** - The speaker likens large language model training to a resource‑intensive rocket launch, emphasizing meticulous planning, the inability to tweak once training begins, and a business‑focused mission to deliver real, tangible value. - [00:03:17](https://www.youtube.com/watch?v=1JzMSbcInxc&t=197s) **Framework for Responsible GenAI Adoption** - The speaker outlines a step‑by‑step approach—building a skilled team, piloting a low‑risk use case, defining value and compliance requirements, ensuring transparent and trustworthy operations, and selecting proper evaluation metrics—to successfully integrate generative AI into business. - [00:06:24](https://www.youtube.com/watch?v=1JzMSbcInxc&t=384s) **Continuous Model Retraining & Governance** - The speaker stresses IBM’s practice of regularly retraining foundation models to incorporate new data, regulatory updates, and risk‑management best practices, emphasizing the need for a robust AI platform with governance tools that can guide organizations from experimentation to self‑managed deployment. ## Full Transcript
Training.
A new large language model is a bit like launching a rocket.
Five.
It's exciting.
Four.
It's resource intensive.
Three.
It requires an enormous amount of compute power.
Two.
And the training process takes months.
One.
So you need intensive planning and preparation to make sure
you've got the latest and best technologies in place.
Because once you press go and the GPUs fire
up and start training, the rocket has liftoff.
You can no longer tweak the design.
Any new innovation has to wait until the next launch.
And just like rocket launches change the frontier of science,
large language models and the broader class of generative
AI that they belong to called foundation models represent
a paradigm shift in how the world is going to leverage AI.
Zero.
All engines running.
Welcome to AI Academy.
I'm Kate Soule, Senior Manager of Business Strategy
at IBM Research and the MIT-IBM Watson AI Lab.
And in that analogy, I work at mission control.
My job is to oversee at a program level the training and development
of all the large language models for IBM's AI and data platform.
And I come at that role
from a business and consulting background rather than a pure technical one.
But it means that I approach my job and the work that we do
with a focus on trying to make sure that our research has impact on the world,
that what we're doing is solving real business problems
and generating real, tangible value for our clients.
And in terms of real value, the opportunities with generative
AI are extraordinary.
While traditional AI can analyze data and tell you what it sees,
generative AI can use that same data to create something new.
And that's a vital tool for businesses to have because that same power
can be applied to customer service and support, code generation for developers,
extracting key information from complex documents.
More use cases are being developed every day.
Companies can increase
productivity, reduce costs and open up new lines of business, while
traditional machine learning is narrowly focused and purpose-built
for a specific task and takes a lot of human intervention.
Foundation models are bigger, broader general purpose models
that benefit from unsupervised learning,
which means they can be trained on large, unlabeled data sets.
And then afterwards, this general purpose model
can be further tailored for an array of applications.
The types of things these models can do is evolving incredibly quickly.
So now is the time to start building your expertise.
As generative AI becomes a business differentiator,
you're going to want the ability to innovate so that you're not just
following what other companies have done, and you're going to want to be part
of the broader conversation about what AI
is and where the field is going in building that muscle mass
in your organization for how to build and experiment with generative AI.
When looking to get
started, building expertise is critical.
First, you need to establish a team of people
who can become comfortable and fluent working with foundation models
so that they can experiment, testing out new models as they become available,
prototyping on example use cases and so on.
The second step is to pick an internal low
risk use case that you can use as a testing ground.
You could build a prototype and test out deployment.
Then use what you learn as your team gains more experience.
Third, you need to have an in-depth conversation about what
you require to get those real value drivers and revenue drivers.
That generative AI can help you unlock.
For example, you need to determine what requirements around trustworthiness
and other regulatory issues your models need to meet to be deployed in production.
And all those questions only become more relevant as you leave the experimentation
phase and get into the actual building of a model
for real on an application that can drive business impact.
And finally, you need to be able to operate
with a level of responsibility and transparency.
You've got to be transparent regarding data collection, showing
what is and isn't in your data and how it all gets filtered and managed.
You need to be
able to explain how your AI is making decisions.
You want it to be fair and trustworthy
and ready for compliance with upcoming regulations.
The number one success factor in each of these steps
is choosing the right evaluation metrics
that reflect your business tasks and measure the model's robustness,
fairness, scalability and cost for deployment across your business.
And even though that evaluation can be quite difficult for generative AI,
when the right answer could be subjective, when you evaluate across
all these dimensions, you may find that some use cases don't
justify the cost or risk of leveraging a huge model on the cloud.
That's why one model doesn't have to rule them all.
Within IBM research, we are seeing that smaller specialized models.
Now, when I say smaller, I'm
still talking billions, not trillions of parameters and size
can be as proficient as those giant trillion
plus language models when they are evaluated on specialized tasks.
These smaller models are significantly more cost efficient
and can be run more easily on prem to reduce your deployment
risk.
When you're getting started
on your journey with generative AI and looking at all the options available.
My recommendation is to start simple, start with a pre-trained model
and try to do light customizations with your own data
through a process called tuning.
This way you can tailor the model for your specific use cases
while taking advantage of the large
general purpose capabilities that other providers have developed.
It's important, though, to update those pre-trained models every couple of months.
Going back to the rocket ship analogy.
IBM Research has a regular launch cadence,
retraining all of our foundation models multiple times a year
as more information is made available in the world continues to progress.
We want our models to be able to reflect changes.
We also want to make sure that our models consider the latest regulatory
guidance and risk management best practices.
The field and the regulatory guidance around it is constantly evolving.
So models that aren't regularly retrained
with the latest best practices will quickly become stale.
That's why the right AI and data platform is so important.
You should look for a platform that has proven expertise in foundation
models, the governance tools in place
to help you address potential ethical concerns
and can help you transition from experimentation to deployment.
Then, as you get better and more confident over time and training
and owning the models, you'll eventually be able to maintain
and build them out on your own.
There's a lot of complexity to AI in foundation models,
but working through all that complexity
truly is worth it for where it's going to take us,
both in terms of our business successes and our progress as a society.
Think about those NASA scientists and engineers.
Doing something new is never easy, but because they did the work,
we've set foot on the moon and sent probes beyond our solar system.
We can now explore our universe.
Generative AI may not literally be a rocket, but it will help us do more
to travel farther and faster to unlock new possibilities and explore new frontiers.
And I'm so excited to see where it will take us.
Thank you for watching.
Please join us again for more episodes of AI Academy
as we explore some of the most important topics in AI for business.