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Learning Software Engineering During the Era of AI | Raymond Fu | TEDxCSTU
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[Music]
At the turn of this century when I
started to learn software engineering my
one of my professors told us that in the
future every job is a programming job.
That's in 2001
and he said that we're holding a golden
ticket to job security.
Just last month, the CEO of GitHub said
that the future of programming is
natural language.
It looks like the prediction of my
professor at the turn of this century is
going to become true, but probably not
in the way that he had imagined.
Artificial intelligence is capable of
writing code for you through a natural
language prompt. GitHub C-Pilot can
complete code for you and fix bugs for
you. and chat GBT can create an entire
project for you within seconds and all
these tools available to anyone.
So I find myself wondering, have we lost
our golden tickets to job security?
And as a CST professor and a father to a
daughter who studies computer science,
there's a bigger question for me.
If AI is going to do programming, does
it still worth it for us to learn
software engineering anymore?
Today, I would like to explore this
question with all you guys. Let's talk
about what AI can do and more
importantly, how we can how our students
of software engineering uh prepare for
the future roles of a real software
engineer. So, let's dive in. First,
let's talk about what AI is good at in
when in terms of programming. AI is
really good at generating thousands of
lines of code. It translate between
programming languages. It can create
user uh interfaces and fix bugs for you
and it excels at repetitive tasks and
you know pattern recognition.
You know, once I asked ChatGBT to create
a project for me, uh, a dating app like
Tinder in Python, and within seconds, it
actually created a a complete
application with user profiles, the
swiping logic, and even a sample
database. The only thing didn't it
But AI has a lot of limitations. We have
to accept that. you know, it still
doesn't understand the why behind all
the tasks we asked them to do. Um, it's
it's um it needs you human input for
real world context and scenarios. It may
not work well prioritizing long-term
business goals and assessing trade-offs.
And last but not least, it's not reliable.
reliable.
It hallucinates and sometimes give us
the wrong answer.
The statistics say that 55% of the
developers today are actually starting
to use co-pilot but only 30% of them are
accepting the outcome without any
changes. So if you are a developer and
you are not in the first 55%
that means you're not using AI you're in trouble.
trouble.
But if you are in the 30% that means you
trust AI too much. You may be in bigger trouble.
All right. So, all the leading AIs today
are built on top of large language
models and it's trained on the text of
human knowledge. It's impressive. If you
give a clear prompt, it'll give you very
good results. But all the strategic
thinking are still us. It's the the
human. And you can think of AI as a
brilliant junior developer that you hire
to your team and they can do a lot of
jobs very quickly and efficiently. But
it's up to us human to define the vision
to validate the results and ensure what
we're building is good for the society.
So there's another thing that I want to
talk about that um AI is is struggling
on. It's struggling to communicate and
collaborate with human beings. Well,
maybe you will say this is more of a
human problem, right? We humans
sometimes deal with the same problem
too. But this is something, you know, we
will have to work out. Let AI do what AI
is good at and we humans can take care
of of the boring jobs such as handling
office politics.
All right. So, talked about the the
capabilities and limitations of AI. Now
we can take a look at the software
engineering roles.
So software engineering roles is not
just about writing code. It actually
talks about you know we need to
understand what the user needs. We need
to uh collaborate across roles and also
making tough decisions with empathy and
responsibility. This is what a soft
engineer should be doing, right? We're
not just tax executors. uh the the best
engineers are not the ones who code the
fastest but the ones who think the deepest.
deepest.
So a good engineer will take messy
problems, ambiguous problems and guide
machines towards a structured and
meaningful outcomes.
So there are system architects who
design the best solutions and they
should be the AI collaborators who use
AI to implement those solutions and then
they need to be ethical technologist to
make sure the solutions that we're
building are b truly benefiting human
being. So AI is actually democratizing a
lot of complicated technical pro tasks
like today a designer can mock up an
application and then you know it it's
just with a prompt and also marketers
they can they don't need data engineers
they can just run data analytics with
some you know without writing any code.
Does that mean soft engineers are losing
our advantages? The answer is no.
actually you know it it it still remains
essential for software engineers and the
reason is as follows.
First we understand AI better. We not
only know how to prompt and we also know
what's under the hood, the models, the
data pipelines, the limitations and
risks and the understanding of these are
very important because AI is integrated
into every product we're using and we're
building in the future. Second, we can
make better use of AI when building
software. So nowadays anybody can you
know prototype a demo or or create a
simple application of features but
softwares think of the bigger picture.
We are actually using AI to build a
production ready software that is
scalable reliable with long-term maintainability.
maintainability.
Finally we are making AI better. We
fine-tune models. We optimize the
performance and improve usability. We
make AI available and useful for
everybody else. The next generation of
AI are still built by software
engineers. Do you guys remember this
quote from CEO of GitHub? This is not in
reality yet. It's still up to the
software engineers to improve AI and
make this happen.
So software engineers, we're not losing
the golden ticket to job security. As a
matter of fact, we're collecting even
more because we're no longer just
building software. We're actually
building the future intelligence itself.
And what we're how we train, direct, and
supervise AI today will define the kind
of systems, technology, and society that
we're building tomorrow.
AI is raising the floor, but software
engineers were raising the ceiling. And
I want to share this not just with you.
You can applaud. That's okay. I want to
share this with not just system
engineers. This is for everyone. All
right? We have AI that's rooting us up
from the floor, but it's human that we
have to reach to the ceiling and raise
up the ceiling.
All right? So after all these now we can
talk about software engineering
education, right? So you know in the
past coding is very important piece of
uh software engineering education but
software engineering education is not
just about writing code. It's also about
you know teaching you how to break
complex problems into steps think
logically and critically and harness the
digital tools to build solutions that
really matters.
So in the time when everybody AI is
everybody's assistant engineers becomes
the orchestrators we remove remove
barriers and open doors
and in order for us to uh be a
successful software engineer the
students should go beyond learning code
as quickly as possible and get into the
following things
you know so in order to become a
successful engineer in the future we
should focus on master to the
foundations, the data structure, the
algorithm, the programming concepts,
they're still very important. Spend
enough time to learn on these and make
make become an expert on those because
they're the very important basics.
Next, think about system like architect
because you know uh aim higher, meet the
expectation of a senior engineer as soon
as possible and think about designing
systems that can that is reliable and scalable.
scalable.
go beyond u go full stack across
disciplines. The days when a soft
engineer can uh can focus on either the
front end or the back end or the
database is gone. The future software
engineers all full stack engineers and
there's more. You need to also get into
the other disciplines like design,
product, data, project management and be
prepared to wear multiple hats.
practice communication and
collaborations. Learn to work with
people um you know through team projects
because you know in the future the way
you if you can explain and connect it it
it'll become increasingly important and
it will set you apart.
Use AI as a creative partner. Embrace
UI, don't hate it, and learn LLM,
generative AI, you know, model
fine-tuning and uh rack, etc. You
discuss your project with AI and
delegate your work to AI as if it's one
of your teammates.
Last but not least, stay adaptable.
Tools change, principles last. So, you
should always focus on learning how to learn.
learn.
So in the future when everyone uh can
code a little the ones who can master
the craft will build the path for
everyone and becomes the leader. So in
the era of AI software engineering is
I've talked a lot about programming but
perhaps programmer is no longer the
right term we should be using to refer
to software engineers. The software
engineers of the AI era should be
visionaries who can define meaningful
result uh meaningful problems. A
bridgeuer who can connect tools, teams
and disciplines and leaders who not only
lead human beings but also lead AI. So
the future doesn't belong to those who
code the fast fastest but it also it
should belong to the one who think
deeply adapt quickly and collaborate
efficiently. They are the ones who don't
just predict the future we build the future.
future.
Thank you. [Applause]
[Applause] [Music]
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