Should you still learn to code? (ft. Devin)
Summary
TLDRThe video delves into the future of jobs amidst advancements in AI, spotlighting 'Devon,' a cutting-edge autonomous bot designed to tackle programming tasks and data analysis with ease. Despite its impressive capabilities, the video explores whether learning to code remains essential. It contrasts Devon's performance with other AI models, highlighting its proficiency yet emphasizing the continuous need for human guidance and specification in complex tasks. The video also touches on Coursera's educational offerings, other AI advancements, and concludes with an exploration of 'Claude,' another AI model, showcasing its potential in economic analysis. Ultimately, it suggests a future where coding skills, coupled with AI tools, could unlock new problem-solving paradigms.
Takeaways
- 💻 The CEO of a major company suggests that the future of jobs, particularly in programming and computer science, may not align with the common belief that everyone should learn to code.
- 🤖 Devon, introduced as a fully autonomous AI bot with capabilities in coding and browsing, represents a significant step towards automation in software engineering and data analysis.
- 📊 The AI, Devon, impresses with its ability to troubleshoot code, find and fix bugs iteratively, and even perform data analytics, showcasing a broad range of potential impacts on professional tasks.
- 📖 Education platforms like Coursera remain relevant for learning essential skills in data analytics, with special deals and comprehensive courses recommended for beginners.
- 🔎 Comparative tests reveal Devon's proficiency in resolving real-world GitHub issues, significantly outperforming other models, though still far from perfect, indicating a trajectory towards improvement.
- 🛠 Despite Devon's advanced capabilities, complex tasks require detailed human prompts, suggesting that human input remains crucial in guiding AI to solve specific problems effectively.
- 🧐 The introduction of tools by GitHub to automatically fix code without developer intervention hints at diminishing the need for human involvement in certain coding tasks.
- 📈 Anthropics' Claude, positioned as a competitor to OpenAI's GPT-4, demonstrates advanced economic analysis capabilities, including GDP prediction and transcribing accuracy, suggesting diverse applications for AI beyond software engineering.
- 📱 The script highlights the importance of integrating coding skills, particularly Python, with AI technologies to tackle more complex analytical problems, indicating a future where coding remains a valuable skill.
- ✨ The ongoing development and hype around AI tools like Devon and Claude reflect the dynamic nature of AI advancements, driven by funding and market interest, while stressing the critical role of human-AI collaboration.
Q & A
What is the CEO of the third most valuable company's claim about the future of jobs and learning computer science?
-The CEO claimed that contrary to the popular belief that learning computer science is vital for future jobs, the reality might be almost exactly the opposite.
What is Devon, and what capabilities does it have?
-Devon is described as a fully autonomous bot equipped with a coding environment and browser, capable of taking over tasks with just a simple prompt, conducting internet research, coding, and providing analyses that mimic human job functions.
How did Devon approach fixing a bug in a code according to the demonstration?
-Devon used an iterative approach to debug, adding print statements to analyze the inputs and outputs of the failing test, identifying the incorrect case, and then updating the code to fix the identified bug.
Can Devon perform data analytics tasks?
-Yes, besides troubleshooting code bases, Devon demonstrated the ability to perform data analysis, showcasing its versatility beyond just software engineering tasks.
What did the demonstration reveal about Devon's ability to train AI models?
-The demonstration highlighted Devon's capability to download code and fine-tune a model, although it did not conclude the effectiveness of the training process.
What unique task did Devon accomplish related to making money?
-Devon was tasked with identifying potholes on a road using computer vision, updating necessary packages, fixing a bug, running the model, and providing a detailed report along with screenshot examples of its work.
How does Devon's problem-solving process work according to the examples?
-Devon's process involves receiving a problem from a human, finding solutions through iterative approaches, pulling necessary GitHub repositories, and reporting back, highlighting the ongoing need for human interaction in defining problems.
What new tool did GitHub introduce, and how does it challenge the need for human intervention?
-GitHub introduced a new tool that automatically fixes code, potentially remediating more than two-thirds of vulnerabilities found, often without developers needing to edit any code, suggesting less need for human intervention.
How does Devon compare to other common models in resolving real-world GitHub issues?
-Devon achieved a 14% success rate in resolving real-world GitHub issues, which is significantly higher than the best market model at around 2%, indicating a positive trajectory for AI in coding tasks.
What is Claude, and how does it demonstrate its capabilities in economic analysis?
-Claude is a model introduced by the team at Anthropic, demonstrating capabilities in economic analysis by transcribing GDP graph data, evaluating accuracy, predicting GDP using Monte Carlo simulations, and performing international economic analysis, showcasing advanced use of coding and large language models in analytics.
Outlines
🤖 The Future of Jobs and AI's Role
This paragraph discusses the changing landscape of jobs due to advancements in AI technology. It highlights the capabilities of Devon, an autonomous bot that can perform tasks like coding and data analysis, potentially impacting job security. The speaker explores the implications of AI in software engineering and data science, emphasizing the importance of human involvement in guiding AI tools. The paragraph also touches on the limitations of AI and the need for continuous learning to stay relevant in the evolving job market.
📈 Comparative Analysis of AI Models
The second paragraph focuses on the performance of AI models, particularly Devon, in solving real-world problems. It compares Devon's success rate in resolving GitHub issues with the best models available in the market, highlighting the progress and potential of AI. The speaker also addresses the overhyped nature of AI technologies and the role of funding in their development. The paragraph concludes with a discussion about another AI model, Claude, which demonstrates the potential of combining AI with data analysis tools like Python for more complex problem-solving in the future.
Mindmap
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Keywords
💡AI software engineer
💡Computer science education
💡Devon
💡GitHub repository
💡Data analytics
💡迭代方法 (Iterative approach)
💡自动化
💡编程
💡经济分析
💡Python
💡Coursera
Highlights
The introduction of Devon, a fully autonomous bot capable of coding and browsing the internet.
Devon's ability to break down tasks and solve problems by iterating and debugging code.
The demonstration of Devon's data analysis capabilities beyond just software troubleshooting.
Devon's use of an iterative approach to identify and fix bugs in code.
The showcasing of Devon's potential in data science through AI training.
Devon's capability to make money by solving real-world problems, such as identifying potholes using computer vision.
The importance of human involvement in guiding AI like Devon to solve the right problems.
GitHub's introduction of a tool to automatically fix code vulnerabilities without developer intervention.
The sponsor mention of Coursera and its Google Data Analytics certificate for aspiring data analysts.
Comparative testing of Devon against other models in resolving real-world GitHub issues.
The current trajectory of AI technology suggests a potential future where it could solve all issues, but not in the immediate future.
The necessity of detailed prompting for complex tasks, indicating the current limitations of AI in autonomy.
The discussion on the overhyping of AI technologies and the influence of funding on their promotion.
The introduction of Claude, an AI model working as an economic analyst.
Claude's use of machine learning to predict GDP trends and its accuracy in transcribing data from graphs.
The impressive parallel processing capabilities of Claude in analyzing global economies.
The potential future of analytics combining coding with large language models to solve complex problems.
The tutorial recommendation for learning SQL as a starting point in data analytics.
Transcripts
that nerds our jobs in the future are
going to be a lot different the CEO of
the third most valuable company made
this bold claim um almost everybody who
sits on a stage like this would tell you
it is vital that your children learn
computer
science um everybody should learn how to
program and in fact it's almost exactly
the opposite so is it even worth your
time learning how to program well in
this I'm going to be exploring a few new
technologies that just came out in order
to answer that question last week the
world was introduced to Devon a fully
autonomous bot equipped with tools like
a coding environment and browser with
this it can basically take over the
world providing it with only a simple
prompt it gets to work putting together
a plan of action breaking up what it
needs to do into simple tasks it starts
by browsing the internet to make you
feel more confident that its answers
aren't going to be hallucinations then
jumps into some light coding to make you
even feel less secure about your
long-term job security and then after it
fixes a quick bug it provides this
groundbreaking analysis that you can
provide to your boss as your own work so
let's break Devon down it's advertised
as the first AI software engineer
there's a lot of problems with that
we'll get to that but let's actually
look at some of the use cases they've
used it for now all these demonstrations
were done by employees of cognition so
to be fair there haven't been a lot of
outside tests of this tool anyway in
this case the engineer wants to fix a
bug in the code he provides some pretty
detailed instructions and Devon gets to
work now one of the impressive things
from this demo was it used as an
iterative approach so Deon here actually
wrote uh actually added a print
statement to debug the outputs uh and
the uh inputs to the failing test reran
the tests and actually found which case
was wrong which is actually a second bug
that Devon found and it then went and
updated the code to fix this second bug
and with that demo you may be like Luke
I'm a data nerd not a software engineer
this thing's just troubleshooting code
bases and not actually performing data
analytics so I have nothing to worry
about with my job well if you recall
from that first example that I showed
Devon did do data analysis additionally
they had a demo showcasing well as they
stated today I'm going to show you an AI
training in AI which is not only meta
but also shows that this is not just
geared towards software Engineers but
also has the potential defect Us in data
science now this followed a similar
approach as we've seen before of
downloading the code and in this case
going through and fine-tuning a model
which after about an hour it's only
about 4% done with training and
conveniently there's no conclusion on
what happened with the training now one
of the most impressive exercises that
demon demonstrated was the ability to
actually make money it was provided with
a problem from upward which side note
how are you going to calculate hourly
rates whenever AIS work almost
instantaneous anyway this thing was
looking at making inferences with a
computer vision model that's fancy talk
in this case as all it really wanted to
do was label potholes on a Road Devon
got to work and the first thing I noted
was some of the packages were out of day
so it updated it which then it found a
bug in the code which wasn't supposed to
be there and once again it used that
print statement approach in order to
find it I'll be honest I don't know why
it's not using a debugger so finally
after this it gets into running the
model and providing a detailed report on
it and even provides some screenshot
examples of it working in action along
with this final write up in a text file
that overviews the work and also the
conclusions that it came to not going to
lie if I received this on upwork I'd be
pretty impressed so there's a Core theme
that Devon is following that I found in
all these examples some human is unhappy
because it doesn't know how to solve a
problem so it all flows that to Devon
who gets to work Devon then in all these
cases went and pulled this GitHub
repository after it found a solution
working in an iterative approach and
reported back to the human I love you
Devon which should no longer be unhappy
and this demonstrates an important point
you still need a human in the mix in
order to guide Devon onto what problems
it needs to be solving oh wait what's
this GitHub introduces a new tool in
order to automatically fix code this new
feature promises that this new system
can remediate more than 2third of the
vulnerabilities that it finds often
without the developers having to edit
any code
themselves okay scratch that on human
intervention all right before we go
further we need to pay some bills and
give a shout out to the sponsor of this
video corsera which is having a special
deal right now now the number one course
that I recommend for aspiring data
analyst is the Google data analytics
certificate this covers not only what
it's like to be a data analyst but also
goes into all the core Technologies you
need to know including SQL programming
languages Vis tools and spreadsheets
it's where I recommend anyone new to
that analytics start and I've made a
number of videos interviewing those that
have taken this to better understand the
value of this certificate now right now
corsera is offering a heck of a deal
where you can receive $100 off your
yearly subscription to corsera plus
which works out to being less than a
dollar a day with this it not only gives
you access to the Google certificate but
also 7,000 other learning programs
including a ton of resources on my
favorite programming language now I'm
not just recommending corsera because it
was a sponsor of this video I've
actually personally paid for a corsera
plus and used it for my learnings as
shown by this receipt more recently I've
been using this to improve my knowledge
on applying AI in data analytics
specifically I've been working through a
lot of different courses and I just
completed this project based course on
using Python's Lang chain for analyzing
your own data which we're going to go
into more detail in a bit of what
Technologies I'm going to be covering
over the next year all right thanks
again to corsera for sponsoring this
video and let's get back to it so how
does Devon actually perform in a
comparative test to other common models
and these results were testing whether
it can resolve real world GitHub issues
Devon got a whopping 14% which you're
probably like Luke that's nowhere near
100% that's like 3 and 20 how the heck
is it going to actually do my job but if
you look at the best model in the market
today it's only at around 2 % so it's a
little bit better personally I think
this graph answers on whether you should
learn coding or not it's not solving all
the issues today or tomorrow but we're
on a positive trajectory to maybe one
day be at 100% but not anytime soon now
the other thing that reassures me from
this is that I don't know if you noticed
this from those videos that I showed
earlier but for complex tasks Devon
takes a fair bit of prompting and by
fair I mean a lot and this case I feel
the engineer had to go into an enormous
amount of detail in order to specify how
it wanted it to solve its problem which
with this level of specifity I think
even the free version of chat gbt could
solve it and that's where I think we are
with this technology today yeah although
they're claiming that Devon is first AI
software engineer Auto GPT which has
been around for almost a year now has
been doing a lot of the same things but
doesn't get as nearly as much virality
as Devon did which coincidentally is
happening almost in tandem when these
type of companies are raising funding
like cognition did last week I want to
be clear I'm not trying to on Devon
and say it's a bad tool in fact I think
quite opposite I think they've done
incredible advancements and we're moving
in the right direction but these type of
Technologies can be overhyped and is
driven by funding now there's another
announcement last week that I feel is
more relevant to us data nerds and it
deals with this model which is only
second to open ai's GPT 4 the team at
anthropic released this video on Claude
working as an economic analyst they
prompted it to look up GDP trends for
the US and write a markdown table of the
estimates which you got to work
transcribing this screenshot of a graph
of the GDP from there they went to
evaluate how accurate those
transcriptions were so it had the model
plot those transcribed values in this
interactive plot and then after having
provided the model the actual results it
plotted them side by side so how
accurate is Claude at using the vision
model for transcription we tried it with
a large sample of madeup GDP graphs and
its transcription accuracy was within
11% on average which not bad but
probably can be improve so then they
moved into having Claude use machine
learning in order to predict GDP in this
case using a Monte Carlo simulation and
just like most people thinks the US
economy is going to be just fine for the
next few years but really none of this
was impressive until I saw this where it
asked Claude to perform in an analysis
of the world's economy looking at more
than just one country in this case
although they didn't disclose it it
looks like they were using some sort of
large language model framework in order
to implement agents which all of these
agents were working in parallel
collecting all the data they needed for
these top countries and processing it
pretty dang impressive for the final
results it provided these pie charts
comparing the two values side note I was
a little disappointed with this because
pie charts are actually really bad at
comparing values but nonetheless it not
only provided an analysis it also
provided a final summary detailing how
the major countries planed a fair over
the next few years now I thought this
was more impressive because it
demonstrated how you can actually use
coding such as python to perform an
analysis with a large language model and
frankly this is where I see analytics
going into the future personally I'm
going to be exploring more on this
channel how to use things like python in
conjunction with libraries that build
out agents for large language models to
solve more complex problems all right as
always you got value out this video
smash that like button and if you'd like
to learn more about how to start coding
in data analytics I just made this
tutorial right here on how to learn SQL
all right with that see you in the next
one
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