Should you still learn to code? (ft. Devin)

Summary

The 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

00:00

🤖 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.

05:01

📈 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

The term 'AI software engineer' refers to an artificial intelligence system designed to perform tasks typically associated with software engineering. In the context of the video, Devon is advertised as the first AI software engineer, capable of coding, debugging, and problem-solving in software development. This concept challenges the traditional notion of software engineering by suggesting that AI can autonomously handle complex tasks, raising questions about the future of jobs in the field.

💡Computer science education

Computer science education refers to the academic and professional study of computer technology, software, and programming. The video script suggests that it is widely believed that children should learn computer science to prepare for the future job market. However, the speaker challenges this notion by presenting the emergence of AI technologies like Devon, which could potentially reduce the demand for traditional programming skills.

💡Devon

Devon is a fully autonomous AI bot that has been showcased with capabilities such as coding, debugging, and data analysis. It is presented as a tool that can potentially revolutionize software engineering by taking over tasks that would normally require human expertise. The video discusses Devon's ability to understand prompts, browse the internet for validation, and perform coding tasks, which raises concerns about the future of human jobs in software engineering.

💡GitHub repository

A GitHub repository is a storage location for a project's code, where developers can collaborate, track changes, and manage the project's versions. In the context of the video, Devon is shown to pull from a GitHub repository to find solutions to coding problems, highlighting the importance of open-source collaboration in AI development and problem-solving.

💡Data analytics

Data analytics involves the process of examining data sets to draw conclusions about the information they contain. In the video, the AI's application in data analytics is discussed, with examples such as Devon performing data analysis and Claude working as an economic analyst. This highlights the expanding role of AI in not just software engineering, but also in analyzing and interpreting complex data sets.

💡迭代方法 (Iterative approach)

迭代方法是一种解决问题或开发产品的方法，它涉及重复的周期，每个周期都包括对工作的评估和改进。在视频中，Devon使用迭代方法来调试代码和修复bug，这表明AI可以采用逐步细化的方式来完善其解决方案，类似于人类软件工程师的工作方式。

💡自动化

自动化是指使用技术来控制过程和系统，以减少或消除人类干预的需求。在视频的上下文中，自动化特指Devon这样的AI工具能够自动执行编程和数据分析任务，这可能会改变未来工作的性质，尤其是在软件工程和数据科学领域。

💡编程

编程是创建计算机程序的过程，涉及编写代码来指示计算机执行特定任务。在视频中，编程是讨论的主要话题之一，特别是在探讨AI如Devon和Auto GPT等工具是否将取代人类程序员的背景下。视频提出了关于学习编程是否仍然有价值的问题，考虑到AI的发展可能会改变编程职业的未来。

💡经济分析

经济分析是研究经济数据和趋势以提供对经济状况的见解和预测的过程。在视频中，通过展示AI模型Claude作为经济分析师的工作，它能够查找GDP趋势并进行预测，这突出了AI在处理和解释复杂经济数据方面的潜力。

💡Python

Python是一种广泛使用的高级编程语言，以其清晰的语法和代码可读性而闻名。在视频中，Python被提及作为分析数据和与大型语言模型结合使用的工具，这表明Python在数据科学和AI领域中的重要性和应用潜力。

💡Coursera

Coursera是一个在线学习平台，提供来自世界各地大学和机构的课程。在视频中，Coursera被提及作为一个资源，推荐给有抱负的数据分析师，强调了其提供的Google数据分析证书课程的价值，这表明了在线教育在专业发展和技能提升中的作用。

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