SummarAIze

1. Introduction to Chachibt's data code interpreter

2. Exploratory data analysis using the AI interpreter

3. Potential impact of AI on data analyst jobs

4. Limitations of the AI interpreter in data analysis

5. Comparison to the impact of spreadsheets on accountancy jobs

6. Problems encountered in using the code interpreter for data analysis

7. AI interpreter's limitations in producing accurate analysis and visuals

8. Need for human operators to ensure accurate and effective results in data analysis

9. Using the code interpreter to calculate impedance and maximum power for an outdoor amplifier

10. Limitations of the AI in accessing internet data, file size, and security concerns.

data analysis, chachibt's data code interpreter, ai, job market, data science, spreadsheets, accountancy jobs, code interpreter, chat gpt, python, job postings, errors, human operator, alexa, spotify, outdoor speakers, amplifier, impedance, maximum power, xerong, security feature, internet access, file limit, linkedin, twitter, security issues, confidential data, midjourney, art content

Introduction

In today's data-driven world, the demand for efficient and powerful data analysis tools has grown exponentially. One such tool that has garnered attention is Chachibt's code interpreter plugin. With its ability to quickly analyze and provide insights from data sets, there is a belief that it might even replace traditional data analyst jobs. In this blog post, we will explore the capabilities of Chachibt's code interpreter plugin, its potential impact on the job market, and its limitations.

Chachibt's Code Interpreter Plugin: Unlocking the Power of Data

Chachibt's code interpreter plugin is a game-changer for data analysts and professionals alike. Its ability to explore data sets, identify key columns, and provide descriptions makes data analysis more efficient and less time-consuming. Moreover, the plugin can perform exploratory data analysis, generate visualizations, and analyze data compared to expected values. This means that professionals can gain valuable insights and make data-driven decisions with ease.

Will Data Analyst Jobs Become Obsolete?

The question on everyone's mind is whether this plugin will replace data analyst jobs altogether. However, history suggests otherwise. Tools like spreadsheets were initially thought to replace jobs but ended up creating more opportunities for professionals. By automating repetitive tasks, these tools allowed professionals to focus on higher-value tasks, ultimately increasing their productivity and the value they bring to their roles. Similarly, Chachibt's code interpreter plugin is expected to enhance professionals' capabilities rather than replace them entirely.

The Role of Human Intervention in Data Analysis

While Chachibt's code interpreter plugin is a powerful tool, it is not without its limitations. For instance, Chat GPT, an AI tool used in conjunction with the plugin, has been known to generate inaccurate or misleading visualizations or data analysis results. In one instance, a graph showing the most common skills requested in job postings had incorrect occurrences for certain keywords. This highlights the importance of human intervention and guidance to ensure accurate and reliable analysis.

Overcoming Challenges and Looking Towards the Future

To overcome the limitations and challenges of AI tools like Chat GPT, improvements are necessary. By prompting Chat GPT to print after every step in the code, the issue was resolved, and the desired visualization was produced. However, ad hoc analysis of unfamiliar subject areas may still require additional human input. Therefore, while AI tools like Chachibt's code interpreter plugin offer great potential, human expertise remains crucial to ensure accurate analysis and reliable results.

Embracing the Future of AI Tools

Despite its limitations, Chachibt's code interpreter plugin has proven its value in performing complex calculations. For example, determining the appropriate size of an amplifier for outdoor speakers is a task that can be efficiently accomplished with the help of this tool. However, it is important to note that the plugin has restrictions, such as a file size limit and an environment limit, which may limit the amount of data that can be analyzed.

Additionally, concerns about security have hindered the widespread implementation of Chachibt's code interpreter plugin in professional settings. Google, for instance, has advised its employees not to input confidential data into their own chatbot, emphasizing the seriousness of security concerns. As advancements continue to be made in AI tools, addressing these limitations and security concerns is crucial for wider adoption in the industry.

Conclusion

Chachibt's code interpreter plugin, in conjunction with AI tools like Chat GPT, offers professionals a powerful and efficient way to analyze and gain insights from data sets. While its potential impact on data analyst jobs may be a topic of concern, history suggests that it will instead enhance professionals' capabilities. By automating repetitive tasks, professionals can focus on higher-value tasks and provide greater value in their roles. However, it is important to recognize the need for human intervention to ensure accurate analysis and reliable results. With further advancements and improvements, the future of AI tools like Chachibt's code interpreter plugin looks promising, enabling professionals to perform more complex analyses and unlock greater value in their work.

🔊 Wondering how AI contributes to practical problem-solving in everyday life? Here's a fascinating case that would surely make you rethink the boundaries of AI. 💥 Let's dive in!

🎯 As a case in point, consider using AI tools like code interpreters for addressing relatively complex issues, such as determining the right amplifier for outdoor speakers.

Key Insights:

🔹 AI-based platform, Chat GBT, can assess comprehensive information, like speaker model specifications, and execute calculations to determine the required impedance and maximum power for an amplifier. Such application of AI, therefore, aids hassle-free online purchases.

🔹 Yet, it's not all rosy! Although advantageous in aspects, AI tools like Chat GPT come with certain limitations: internet access, small file limit, potential security concerns. These hinder seamless data integration and analysis and raise legitimate apprehensions about their application in data-sensitive sectors.

📊 However, considering how swiftly AI tools are evolving (e.g. the art content generating tool Midjourney), resolving these issues might not be a distant reality.

👉 Is your industry ready for the AI revolution? Remember, every tool has its strengths and weaknesses. It is crucial to view these developments in a balanced perspective and adapt accordingly.

#AI #Chatbots #SolvingRealWorldProblems #TechnologyEvolution🤖💡

🚀Artificial Intelligence in Data Analysis - A Deeper Dive into Jobs Market! 🚀

Hello all data science professionals and enthusiasts! 👋

Our recent work in the realm of AI-based data analysis brought to light some intriguing findings about the strengths AND limitations of AI Tools like OpenAI.🤖 Such exploration happening right in the heart of data science industry is of utmost interest!

Here's what we experienced during a detailed analysis of data science job postings. Let's reflect on these insights: 🎯

1. 📈 Analyzing datasets using code interpreter brought to light specific limitations that one should be aware of. Interestingly, OpenAI released a new feature for custom instructions, which we found advantageous for customizing data representation.

2. 📚 Familiarizing the AI Chat with data significantly improved the relevance of the insights generated. Acting as a human operator for AI Tools can be rewarding in terms of driving accurate data analysis.

3. 🖼 We found discrepancies in generated visuals (such as the number of occurrences exceeding total sample size). However, guiding the tool through these hiccups led to accurate visualizations, for instance on the top 20 keywords in data science job postings.

4. 😲 It's important to watch out for hallucinations in the analysis! In processes of deeper data analysis, our AI model tended to revert back to its knowledge as a large language model, rather than using the data provided.

5. ➡ Remember: These AI tools need human guidance. They're powerful, but not independent. Human intervention is required to keep them on track, especially during more intricate analysis.

Curious about more insights into the realm of AI-enhanced job market analysis and beyond? Stay tuned for our forthcoming posts. We're navigating new waters, and we invite you to join the journey! 🚀

#ArtificialIntelligence #DataAnalysis #DataScienceJobs #OpenAI

💡 **The Future of Data Analysis Jobs in the Age of AI: How will AI Impact our Roles as Data Analysts?**

In this era of rapid technological advancement, we hear a lot about how AI is changing jobs, especially ours in the data analysis industry.

✨ *REDIRECTING FEAR TO EXCITEMENT:* Many fear that advancements like Chachibt's new code interpreter plugin will replace data analysts. But the lessons of past revolution in work methods indicate hope and optimism instead of fear.

👉 *CASE STUDY:* The invention of the electronic spreadsheet. A massive game-changer of the previous era. This innovation did not end jobs but created more skilled roles. Accountants shifted from manual calculations to providing deeper analysis and higher value.

💼 *BRINGING INNOVATION INTO THE LABOUR MARKET:* Following this progression, AI tools like code interpreter, instead of replacing us, might open doors to more advanced professional roles.

Here are some insights we can glean from our past experiences:

1️⃣ *Greater efficiency:* AI can take on the monotonous labor, enabling analysts to focus on interpreting and leveraging data insights.

2️⃣ *Higher value roles:* Like accountants of the past, the focus of data analysts could shift from manual analysis to strategic decision-making based on data-driven insights.

3️⃣ *Expanded capacity for data interpretation:* With AI handling the heavy lifting, we'll have more time and mental energy to mine insights out of increasingly complex data sets.

4️⃣ *Opportunity for up-skilling:* As AI takes over basic jobs, it creates a vacuum at the top for roles requiring higher skills and deeper expertise.

🔮 The future is exciting! The same way spreadsheets did not put accountants out of jobs, AI will probably not take away data analysts' jobs but revolutionize it for the better. Are you ready to harness these advancements and skills to increase productivity and performance? Because the future of data analysis starts now!

1/10 🧑‍💻🔎 Just ran some tests on Chachibt's new code interpreter plugin! It handles advanced problem solving at a breathtaking speed, right from analyzing data sets to dispensing priceless insights within seconds. A bit of both AI and ML magic, if you ask me. #AI #DataAnalysis

2/10 🤖 But the big question on every data analyst's mind is this: Will these tech advancements steal our jobs? Well, I decided to really push this bad boy to find out the truth. Buckle up, coworkers. #FuturisticFriday #AIJobs

3/10 🕵️‍♂️ Using the EDA approach, I loaded up a CSV data set. Even without a prompt, the tool dove right in. Not only did it display the data set clearly, but it also made Python code accessible for any additional fiddling. 😎 #Python #DataScience

4/10 🧐 I pushed harder, asking the tool to describe the data set provided. Even then, it was fantastic, correctly identifying the data set as job postings for data-related roles. The tool even highlighted key columns. #MachineLearning #DataAnalysis

5/10 😯 Most impressively, it took on the task of exploratory data analysis. With well-organized steps, the tool identified data types, handled missing values, and provided summary statistics. #BigData #DataScience #AI

6/10 🤓 But it didn't stop there. It went on to visualize data distributions independently, correctly noted salary data skews, and even hand-picked columns of interest for deeper analysis. Mind. Blown. #DataVisualization #AI

7/10 📊 Then came visually appealing analysis of top job titles and companies in the dataset. And, trust me, when I say this took moments, it's an understatement. #AIOptimization

8/10 🛠 Asked it to export the python code. Does it look like the code interpreter plugin is eyeing our jobs? Erm, not quite. Here's why: We've seen this before - with spreadsheets. Stick with me for a bit of a history lesson. #AI #Coding

9/10 🏦 From physical paper to digital screen, spreadsheets transformed accounting jobs decades ago. Some thought PCs would steal jobs, but acct. jobs actually increased. The focus shifted from rote tasks to higher-value analysis. #FutureOfWork

10/10 🔮 Could the same not happen now? Instead of fearing AI, let's use it to elevate our workmanship. Allow it to handle the mundane and optimize our jobs to focus on critical assessments. AI is not stealing jobs; it's transforming them. #AILoveJobs #DataScience

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1/10 📊🚀 "Hey #DataScience folks! I've been navigating through code interpreter for my job, trying to analyze and uncover its limitations. Surprisingly, there's quite a bit to share, so let's dive in!" #AI #OpenAI

2/10 🧩 "During my exploration, OpenAI launched a new feature for custom instructions. I tweaked my graphs accordingly, so you'll notice they've taken on a fresh look! Continual learning and improvement is a fascinating part of our field, isn't it?" #dataviz

3/10 💼📚 "The dataset from our previous discussions is playing quite a pivotal role here. I compressed everything into a zip file - our previous python file, data set, and even the text summary from Chat GBT. Upon upload, Chat GBT surprisely knew where we left off!" #AI

4/10 🧮🔎 "I wanted to explore the most common skills requested in job postings. Chat GBT provided a graph that initially seemed fine, but when I delved deeper, I noticed something off..." #dataanalysis #problemsolving

5/10 🐍🔢 "The keyword 'python' appeared in the graph 56,000 times! With only 50,000 job postings, that's impossible. This data inconsistency is a stark example of AI limitations. An experienced data analyst would've picked up on this instantly." #dataaccuracy

6/10 ✅💡 "After a little bit of to-and-fro with Chat GBT and providing it with clearer instructions, I got a visualization displaying the top 20 keywords in data science job postings represented as a percentage. #improvement #growth"

7/10 🔄🎢 "This wasn't a one-shot victory though. Chat GBT at first gave me blank graphs, then started 'hallucinating' top skills within the dataset. The hallucinations originated not from the data, but from its inherent language model." #AIglitches

8/10 👨‍💻🛠️ "Re-prompting didn't fix the error right away. But as I had it print each step in the code, it corrected itself! Just like handling Excel, even powerful AI tools like these need human intervention and direction to stay on track." #HumanandAI

9/10 🤔📈 "This is indeed crucial when dealing with complex subjects. While ad hoc analysis is great, consistent human input is key to ensuring accuracy, especially in unfamiliar areas." #AIethics #BigData

10/10 💡🌐 "In conclusion, Chat GPT's coding capabilities are potent and promising, but we must remain vigilant of its limitations and ensure we're cross-checking AI outputs. After all, #DataScience is as much an art as it is a science!" #AI #DataAccuracy #DataQuality

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1. 📣 Hey, tech enthusiasts! I've been testing the limits of #ChatGPT's code interpreter, and made some fascinating discoveries. Particularly pertaining to a unique scenario involving two outdoor speakers and a quest for the perfect amplifier. 🔊 Let's dive in! 🚀 #TechThread (1/9)

2. So, my outdoor speakers, directly connected to Alexa, weren't loud enough. Apparently, they need an amplifier.😨 You'd think it's easy, but choosing the right one on Amazon was like finding a needle in a haystack. 🧐 So, I turned to #codeinterpreter. (2/9)

3. I fed the speaker specs to code interpreter. Thanks to its underlying Python code, it calculated both the impedance and max power. This led me to the perfect amplifier by Xerong. Now to see if it works as promised! 🎛️ #AI #Algorithm (3/9)

4. But it wasn't all smooth-sailing. True, code interpreter spared me the hardcore math but it has limitations. The crucial drawback is lack of internet access which means no direct access to online databases or no 'pip install' functionality. 🖥️ #DataScience #AIlimitations (4/9)

5. The other issue many of you might relate to is the file size limit. The data we crunch in our day jobs are massive, and #AI tools need to be up for that challenge. A 2GB limit didn't cut it for me. #DataAnalysis #BigData (5/9)

6. If you work with #data, you get what I mean. I downloaded my data to CSV for analysis, but only 200,000 row entries made it through. For a machine that claims to do the heavy lifting, this was underwhelming! 👇 #DataIssues #TechLimitations (6/9)

7. Then there's the elephant in the room - security. Are we comfortable with AI systems learning from our confidential data? Resources suggest this is a significant worry for most AI enthusiasts. Google's own employees are told to avoid BERT for confidential data! 🔒 (7/9)

8. With all its limitations, chat GPT has shown its potential. #AI tools continue to evolve and improve — can't wait to see what's next. Meanwhile, I've got my Xerong amplifier, which arrived today, and I'm off to enjoy some amplified music in my backyard. 🎶 (8/9)

9. Stay tuned to know how the installation goes! 😊 If you're interested in how I used the Notable plugin for data connections, check my video out. ⚡ If you enjoyed this thread, smash that like button! See you in my next adventure! #TechJourney #DataLovers (9/9)

[00:00:00] Will AI Take Data Analyst Jobs? An Exploration with Chachibt's Data Interpreter

[00:00:00] Luke Barousse: Data nerds Chachibt's new code interpreter plugin can do some pretty advanced problem solving for my job, like analyzing this data set and showing me some pretty incredible insights in a matter of seconds.

[00:00:12] Luke Barousse: And a lot of people are claiming that this is going to take away data analyst jobs.

[00:00:16] Luke Barousse: So I've been testing this bad boy non stop since its release, and I have some prompts to see if it's true.

[00:00:21] Luke Barousse: First test up is some EDA or just exploring a data set.

[00:00:25] Luke Barousse: Let's see what type of files this tool can take, and it looks like it takes a number of files.

[00:00:29] Luke Barousse: Let's go.

[00:00:29] Luke Barousse: With CSV.

[00:00:30] Luke Barousse: Providing this data set with no prompt, it takes the initiative to start diving into this and exploring what this CSV is even about.

[00:00:38] Luke Barousse: It shows a sample of the first four rows of the data set, along with some of the columns.

[00:00:42] Luke Barousse: And all of this is done with Python code, which you can easily see it if you want to, and that's not too bad.

[00:00:48] Luke Barousse: Let's move into a harder question, asking it what this data set is even about.

[00:00:53] Luke Barousse: It pretty impressively.

[00:00:54] Luke Barousse: Identifies that this is a collection of job postings related to data science roles, where each row in the data set represents a different job posting.

[00:01:01] Luke Barousse: It then goes as far to highlight some of the key columns in the data set with a description.

[00:01:05] Luke Barousse: Next, I prompt it with your knowledge of the data set, perform exploratory data analysis, and it starts by identifying five main steps that it's going to take.

[00:01:14] Luke Barousse: First, it shows the data types of this, and it looks like everyone's an object, so that's not really useful.

[00:01:19] Luke Barousse: From there, it even identifies there are missing values with a summary, clarifying which columns have a significant number missing.

[00:01:26] Luke Barousse: Next, it provides some summary statistics about the numerical columns.

[00:01:29] Luke Barousse: It even goes as far as visualizing these distributions on its own.

[00:01:33] Luke Barousse: And then providing an analysis of it points out that the salary data is skewed to the right, which is typical for salary data.

[00:01:41] Luke Barousse: That's pretty neat.

[00:01:41] Luke Barousse: That provides this analysis of how this compares to expected values.

[00:01:45] Luke Barousse: Finally, it identifies some columns of interest that it wants to dive into further.

[00:01:49] Luke Barousse: And it provides these visualizations showing what are the top ten job titles and what are the top ten companies.

[00:01:56] Luke Barousse: And it looks like data engineers and data scientists are beating out data analysts.

[00:02:00] Luke Barousse: And so if I want to export this code, all I have to do is prompt it.

[00:02:04] Luke Barousse: And this is pretty revolutionary.

[00:02:06] Luke Barousse: So I think it's settled.

[00:02:08] Luke Barousse: Code Interpreter it's taking my job.

[00:02:14] Luke Barousse: Well, not so fast.

[00:02:16] Luke Barousse: To answer that, we need to look at this.

[00:02:18] Luke Barousse: Yes, it's a blank Excel spreadsheet.

[00:02:20] Luke Barousse: But hear me out.

[00:02:21] Luke Barousse: In order to look into the future of what AI holds for our jobs, we need to look at the past, what previous tools have done to transform our jobs.

[00:02:28] Luke Barousse: Before spreadsheets migrated to computers, they started out being physical papers that accountants would use in order to calculate finances.

[00:02:36] Luke Barousse: Yeah, a physical paper.

[00:02:38] Luke Barousse: My hands hurt just thinking about this.

[00:02:40] Luke Barousse: There were entire departments with numerous accountants with the sole purpose of updating these paper spreadsheets.

[00:02:46] Luke Barousse: Then in comes the invention of the personal computer and these dudes want to revolutionize the way we work.

[00:02:52] Luke Barousse: In this video, you're going to see the future.

[00:02:54] Luke Barousse: And so the electronic spreadsheet was conceived.

[00:02:57] Luke Barousse: Marketers everywhere began to overdramatize just how powerful this tool was going to be, with sub ads claiming it would take up to 150 jobs.

[00:03:05] Luke Barousse: But you know what happened to accountants jobs after this?

[00:03:07] Luke Barousse: Well, they actually increased.

[00:03:09] Luke Barousse: Accountants could now refocus their time from tallying numbers and focus on more important things like building those mind numbing PowerPoints.

[00:03:17] Luke Barousse: But seriously, their attention now shifted into performing deeper analysis.

[00:03:21] Luke Barousse: They could now use these tools and provide higher value with their jobs.

[00:03:25] Luke Barousse: And with that history lesson, maybe we can also infer where we'll go next with these AI tools.

[00:03:31] Exploring the Limitations of Code Interpreter in Data Analysis

[00:03:31] Luke Barousse: Alright, so it's been a few days and I've been going through and using code interpreter for my job analyzing and trying to find any limitations.

[00:03:38] Luke Barousse: Surprisingly, I found quite a bit.

[00:03:40] Luke Barousse: Also during this, OpenAI released a new feature for custom instructions.

[00:03:44] Luke Barousse: So I've customize my graphs a little bit and they're going to look a little bit different.

[00:03:47] Luke Barousse: So let's dive into some of those limitations by doing a deeper dive of that data set that we were exploring before.

[00:03:53] Luke Barousse: We're going to start with a new chat since my last one timed out.

[00:03:55] Luke Barousse: Here I have a folder with a python file we exported last the data set and also a text file that I had Chat GBT output last time.

[00:04:02] Luke Barousse: That summarized all our analysis.

[00:04:04] Luke Barousse: I compress this into a zip file for upload and then prompt Chat GBT to familiarize itself with the contents of this file.

[00:04:11] Luke Barousse: And it looks like it knows where we left off.

[00:04:13] Luke Barousse: So we're going to dive into exploring the skills from these job postings.

[00:04:17] Luke Barousse: Specifically, I want to see what is the most common skill requested.

[00:04:21] Luke Barousse: And conveniently, it gives me this graph which looks pretty good at first sight.

[00:04:24] Luke Barousse: But after diving into it, I find that the highest number of occurrences of the keyword python is at 56,000, which that's not possible because there's only 50,000 job postings.

[00:04:35] Luke Barousse: And chat.

[00:04:36] Luke Barousse: GPT should know this.

[00:04:37] Luke Barousse: It was in the summer.

[00:04:37] Luke Barousse: This type of mistake is something I would expect a data analyst to pick up on, and yet Chat GBT doesn't.

[00:04:43] Luke Barousse: So I reprompt Chat GPT to fix this error and then I take it a step further by having it display these keywords as a percentage vice as occurrence.

[00:04:51] Luke Barousse: And we finally get this visualization showing the top 20 keywords in data science job postings.

[00:04:56] Luke Barousse: Now, I did this same analysis a couple of days ago and I ran into even more issues.

[00:05:01] Luke Barousse: The first time I asked for this, it just gave me blank graphs, and then it had the audacity to start hallucinating what the top skills were in this data set.

[00:05:09] Luke Barousse: It basically reverted back to what it knew as a large language model vice, actually using the data provided.

[00:05:15] Luke Barousse: I then reprompted telling it that the graphs were blank, that it needed to fix this error.

[00:05:19] Luke Barousse: And it didn't really seem to fix it.

[00:05:21] Luke Barousse: Eventually, I just prompted it to print.

[00:05:23] Luke Barousse: After every step in the code, it somehow worked itself out and ended up getting this final visualization.

[00:05:28] Luke Barousse: So, going back to that Excel history lesson, yes, this is a powerful tool, but it still takes some sort of human operator to help guide and steer this tool on where it actually needs to go and make sure that it's staying on track.

[00:05:42] Luke Barousse: This is especially true when we're diving into deeper, more complicated subject areas.

[00:05:47] Luke Barousse: But what happens if I need a quick ad hoc analysis of maybe a subject area that I'm not familiar with, like something that's not data science?

[00:05:55] Luke Barousse: Job postings.

[00:05:59] Using OpenAI's Code Interpreter for Speaker Amplification Analysis

[00:05:59] Luke Barousse: All right, so I think I have a unique use case for code interpreter and involves this Alexa play.

[00:06:07] Luke Barousse: Spotify.

[00:06:09] Luke Barousse: Sure.

[00:06:09] Luke Barousse: Here's Spotify.

[00:06:13] Luke Barousse: So, I have two outdoor speakers here, and if you can't tell, it's really not that loud.

[00:06:19] Luke Barousse: There's a big problem that I'm having.

[00:06:20] Luke Barousse: These speakers themselves are meant to be actually connected into some sort of amplifier, and right now, we just have them going right into Alexa.

[00:06:30] Luke Barousse: When I go to Amazon and look for an outdoor amplifier, I get a shit whack of results.

[00:06:35] Luke Barousse: I'm not really sure which amplifier to choose, so I searched through quite a few forums trying to find out what size amplifier I needed to get, and it looks like it's really math based.

[00:06:43] Luke Barousse: So that's why I think code interpreter is gonna be perfect for this.

[00:06:45] Luke Barousse: So I looked up the model number online and found the different specs that I think I needed, and I gave this information to it.

[00:06:51] Luke Barousse: So this is pretty crazy.

[00:06:52] Luke Barousse: Chat GPT went through and knew it needed to calculate both the impedance and maximum power, and it used some Python code to actually calculate both of those things and determine what it needs to be.

[00:07:04] Luke Barousse: So I think I found the perfect amplifier from this company called Xerong.

[00:07:08] Luke Barousse: So now we just got to wait for that amplifier to come in, and we'll test to see if Chat GBT was right.

[00:07:13] Luke Barousse: So I've tested code interpreter to its limits, and I think I've reached it.

[00:07:17] Luke Barousse: Now I have some bad news.

[00:07:19] Luke Barousse: Code interpreter probably not going to take away my job with these limitations.

[00:07:23] Luke Barousse: 1 second thought, it's actually good news.

[00:07:25] Luke Barousse: So, let's say we have some data online, like this Google Sheet full of data.

[00:07:28] Luke Barousse: In the past, I've used Python to connect to this.

[00:07:30] Luke Barousse: However, it tells me it doesn't have the ability to access the Internet, prompting it further asking if I just pip install libraries.

[00:07:36] Luke Barousse: It tells me this is prevented as a security feature designed to protect user data and privacy.

[00:07:41] Luke Barousse: Kind of get this because chatgb's previous plugins had issues when accessing the Internet.

[00:07:47] Luke Barousse: Anyway, because of all of this, I now have to take an extra step of downloading the data that I need and then uploading it to chatgbt.

[00:07:54] Luke Barousse: And my data is spread all over the place.

[00:07:56] Luke Barousse: I don't just have it in Google Sheets, but also have it in things like databases.

[00:08:00] Luke Barousse: So I downloaded one of my databases to a CSV for analysis, which at about a million rows was a pretty big data set.

[00:08:07] Luke Barousse: When I tried to upload it, it gave me this warning that it has a small ass file limit and an environment limit of only two gigs.

[00:08:14] Luke Barousse: So I can't even get all the data that I have here into it to analyze, which is like the first step of my job.

[00:08:20] Luke Barousse: I found that most I could get in 200,000 rows of data, so I was super disappointed in this.

[00:08:25] Luke Barousse: Now there are workarounds in chat GBT for connecting to an external data source like the plugin Notable, and I have a whole video on it.

[00:08:32] Luke Barousse: But comparing both of these tools, although Notable excels in some areas like data connections, I find code interpreter performs a much more thorough analysis with less prompting.

[00:08:42] Luke Barousse: Anyway, these internet issues and file limits aren't even the most detrimental issues to code interpreter.

[00:08:48] Luke Barousse: So I pulled my subscribers on LinkedIn and Twitter, I mean X, and asked them what is stopping them from implementing this tool in their jobs?

[00:08:56] Luke Barousse: And it was a resounding consensus that they had concerns with security issues.

[00:09:00] Luke Barousse: You see, these chatbots take these prompts and also data that you give it to, then be used to build on and improve these chat bots.

[00:09:07] Luke Barousse: The problem is, if it's confidential data, it could be seen by the reviewers of this chat bot, or even worse, it could be fed back into this chatbot and potentially be prompted by another user and seen by them.

[00:09:20] Luke Barousse: It's kind of a big deal.

[00:09:21] Luke Barousse: So big in fact, that Google has told its own employees not to put confidential data into their very own chatbot barred.

[00:09:28] Luke Barousse: That's like telling meta employees they can't use Facebook or Instagram.

[00:09:32] Luke Barousse: There's a pretty big red flag.

[00:09:33] Luke Barousse: However, with all these limitations, I think there's hope for the future.

[00:09:37] Luke Barousse: Take midjourney for an example, an AI tool to generate art content.

[00:09:41] Luke Barousse: Look how far this tool has come in as little as a year.

[00:09:44] Luke Barousse: So imagine we'll be in the future with these type of tools once we get through all these different limitations.

[00:09:50] Luke Barousse: Anyway, the sample arrived today and we're going to go install it.

[00:09:54] Luke Barousse: I hope it doesn't go x wrong.

[00:10:02] Luke Barousse: After unpacking this bad boy, I realized it had no instructions.

[00:10:05] Luke Barousse: So I resorted to using chat GPT to tell me how to install this and gave okay advice.

[00:10:12] Luke Barousse: Anyway, after a few steps and nearly getting shocked and losing my life, I was able to get it installed.

[00:10:18] Luke Barousse: All that's left now is to test that bad boy.

[00:10:20] Luke Barousse: Oh, if you're curious about that video that used the notable plugin, it's right here with that Lexa play.

[00:10:26] Luke Barousse: Spotify here's.

[00:10:29] Luke Barousse: Spotify, as always, got value out of this video.

[00:10:35] Luke Barousse: Smash that like button that see you in the next one.