1.
Anjan Karmakar
The Rise of AI Programmers
How Machine Learning Models are learning to code,
and what it means for you.
SFSCon 2022
NOI Tech Park Bozen/Bolzano

2.
The Rise of AI Programmers
What AI systems are not doing?
What they are actually doing?
How AI coding systems work?
Deep dive, into the AI model!
What it means for you and me?
Overview

3.
What AI systems are not doing?

4.
Killing humans!
At least, not yet!
Let’s talk if you think AI robots
will rule the world!*

5.
Running the matrix!
At least, not yet!
Let’s talk if you think we are
living in a simulation!*

6.
What AI systems are actually
doing?

7.
Disease diagnosis
in the healthcare industry

8.
Detecting fraud
in the finance industry

9.
Sorting produce
in food industry

10.
Not just that …

11.
AI learns to write code
🎉 🎉 🎉

12.
Demo?
Time for a

13.
How does it work?

14.
“But wait … what about?”

15.
AI robots
programming
themselves?
Nope!

16.
Not there yet!
🚫
AI robots
programming
themselves?

17.
“The AI models still need a
valid input prompt from the user”
It cannot think for itself!

18.
Deep dive into more details!

19.
Note: This is a representative illustration, simplified for the explanation, the AI programming models have more complex architectures.

20.
User Prompts (commands)
and their meanings
are encoded in a latent space
of vectors.
e.g.
Print [13, 7 …] print()
times [9, 21, ..] use loop

21.
User Prompts (commands)
and their meanings
are encoded in a latent space
of vectors.
e.g.
Print [13, 7 …] print()
times [9, 21, ..] use loop

22.
The output source code
is decoded based on the
intermediate vectors
in the hidden layers.
e.g. when “times” is used in
the user command, the model
must understand it must use a
loop for the print operations.

23.
The output source code
is decoded based on the
intermediate vectors
in the hidden layers.
e.g. when “times” is used in
the user command, the model
must understand it must use a
loop for the print operations.

24.
1. Dividing a problem
into smaller sub-parts
2. Encoding their (individual
and combined) meaning in
the latent space of vectors
3. Decoding the code output
based on the input context
provided to the model

25.
Some examples

26.
Should you be worried/afraid?

27.
beaten on Hackerrank?
Yes, definitely, yes!
Afraid of being
“Go” home loser!

28.
your developer job?
No, not at all!
Afraid of losing
I hired an AI to do your job!
I built that AI to do my job,
while I am dealing with your shit!

29.
your developer job?
No, not at all!
Afraid of losing
I hired an AI to do your job!
I built that AI to do my job,
while I am dealing with your shit!

30.
“The AI models still need a …
programmer
to correctly prompt the models,
check the validity of the code,
explore unknown domains.”

31.
Free-to-use Open Source AI Programmers:
* Salesforce CodeGen
* GPT-CC
* CodeGeeX
Paid AI Programmers:
* OpenAI Codex
* DeepMind AlphaCode
* Amazon CodeWhisperer
Try it yourself!

32.
Thank you!
Questions?

33.
The Rise of AI Programmers
SFSCon 2022 Anjan Karmakar
SFSCon 2022
Slides are available at:
https://bit.ly/sfscon-ai