The day ChatGPT outstripped its limitations for Me

20

October

2023

No ratings yet.

We all know ChatGPT since the whole technological frenzy that happened in 2022. This computer program was developed by OpenAI using GPT-3.5 (Generative Pre-trained Transformer) architecture. This program was trained using huge dataset and allows to create human-like text based on the prompts it receives (OpenAI, n.d.). Many have emphasized the power and the disruptive potential such emerging technology has whether it be in human enhancement by supporting market research and insights or legal document drafting and analysis for example which increases the efficiency of humans (OpenAI, n.d.).

Hype cycle for Emerging Technologies retrieved from Gartner.

However, despite its widespread adoption and the potential generative AI has, there are still many limits to it that prevent us from using it to its full potential. Examples are hallucinating facts or a high dependence on prompt quality (Alkaissi & McFarlane, 2023; Smulders, 2023). The latter issue links to the main topic of this blog post.

I have asked in the past to ChatGPT, “can you create diagrams for me?”  and this was ChatGPT’s response:

I have been using ChatGPT for all sorts of problems since its widespread adoption in 2022 and have had many different chats but always tried to have similar topics in the same chat, thinking “Maybe it needs to remember, maybe it needs to understand the whole topic for my questions to have a proper answer”. One day, I needed help with a project for work in understanding how to create a certain type of diagram since I was really lost. ChatGPT helped me understand but I still wanted concrete answers, I wanted to see the diagram with my own two eyes to make sure I knew what I needed to do. After many exchanges, I would try again and ask ChatGPT to show me, but nothing.

One day came the answer, I provided ChatGPT with all the information I had and asked again; “can you create a diagram with this information”. That is when, to my surprise, ChatGPT started creating an SQL interface, representing, one by one, each part of the diagram, with the link between them and in the end an explanation of what it did, a part of the diagram can be shown below (for work confidentiality issues, the diagram is anonymized).

It was a success for me, I made ChatGPT do the impossible, something ChatGPT said itself it could not provide for me. That day, ChatGPT outstripped its limitations for me. This is how I realized the importance of prompt quality.

This blog post shows the importance of educating the broader public and managers about technological literacy in the age of Industry 4.0 and how with the right knowledge and skills, generative AI can be used to its full potential to enhance human skills.

Have you ever managed to make ChatGPT do something it said it couldn’t with the right prompt? Comment down below.

References:

Alkaissi, H., & McFarlane, S. I. (2023). Artificial hallucinations in ChatGPT: implications in scientific writing. Cureus, 15(2).

Smulders, S. (2023, March 29). 15 rules for crafting effective GPT Chat prompts. Expandi. https://expandi.io/blog/chat-gpt-rules/

Please rate this

Generative AI in the Data Scientist’s Universe: A Friendly Companion or a Sneaky Enemy?

2

October

2023

5/5 (1)

In today’s business landscape, data is key. The widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) has sparked a compelling debate about the future of the data scientist’s role. As we progress into an era where being code literate and possessing technical knowledge are no longer barriers to entry, the accessibility of generative AI tools has transcended the boundaries of academic research and tech giants. It’s now within reach for anyone with an internet connection and an email address.

It made me wonder if this transformation means the death of data scientist-like professions or whether it signifies the birth of new roles. I will delve into this question using my personal experience as a starting point.  

The Learning Curve

About a year ago I started in a newly created role as a junior business controller. I quickly realized the importance of harnessing data to create insightful dashboards that guide data-driven decision-making. Learning to navigate the complex world of data was a challenge in itself (I mean, how do I know that I can trust my data? What data is available and what is relevant?), but the real game-changer came when I discovered the power of generative AI.

It was like having a 24/7 coding mentor at my fingertips

Starting from scratch with SQL was no walk in the park. I encountered countless errors, syntax hiccups, and moments of sheer frustration. But I followed some courses, watched YouTube videos and found out that SQL is amazing (honestly: I would highly recommend learning it)! It was during this process that I stumbled upon an invaluable ally: ChatGPT. Whenever I hit a roadblock, I would turn to ChatGPT and put my code-related questions in. Within seconds, I’d receive clear and concise explanations, troubleshooting tips, and even code snippets to resolve my issues. It was like having a 24/7 coding mentor at my fingertips.

ChatGPT can even help write queries from scratch! Source: https://blog.devart.com/how-to-use-chatgpt-to-write-sql-join-queries.html

The Role of Generative AI in Data-Related Work

Generative AI, like ChatGPT, is not just a tool for beginners like me. I’m convinced that it has the potential to revolutionize the way data scientists and coders work. But should we see it as a friendly companion or a sneaky enemy that is stealing jobs?

AI as a Productivity Booster

First and foremost, generative AI can significantly enhance the productivity of data professionals. It excels at automating repetitive tasks, such as data retrieval and cleaning as well as tackling error messages. This frees up valuable time for more critical analysis. It can also serve as a valuable learning resource, providing instant answers and explanations for coding queries. Due to this I have experienced a steep learning curve in my SQL journey.

Human data scientists and coders bring a unique skill set to the table; they possess the ability to interpret, contextualize, and make nuanced decisions based on data

The Future of Data-Related Jobs

The future of data-related jobs stands at a crossroads, where the demand for AI-ready professionals who can effectively harness tools like ChatGPT is already evident in job descriptions. The fear of AI replacing human expertise is a legitimate concern, but it’s important to remember that AI is a tool, not a replacement. While generative AI offers substantial productivity gains, it’s not immune to errors or hallucinations, reminding us of the irreplaceable value of deep domain expertise. Human data scientists and coders bring a unique skill set to the table; they possess the ability to interpret, contextualize, and make nuanced decisions based on data. AI can undoubtedly assist in the technical aspects of the job, streamlining processes and automating tasks, but it cannot replicate the human touch required for holistic and insightful data analysis.

Well, the way I see it..

In my role, generative AI has truly been a game-changer. It’s become an ally helping me out with coding challenges and making my dashboards way better and more trustworthy. It surprised me by getting results faster than I expected, and I can’t complain about that! And whenever my SQL query was actually running properly, I put it in and asked for suggestions to improve performance. Using ChatGPT for my coding-related questions has boosted my efficiency and effectiveness, and I see it as a sneak peek into the future of data work. It’s all about humans and AI teaming up, playing to each other’s strengths for some seriously awesome outcomes.

So, the next time you encounter a coding conundrum, don’t hesitate to turn to generative AI. It’s not a replacement for your expertise but a trusted partner in your data-driven journey.

Curious to hear your thoughts and experiences! Also let me know whether some of you use other GAI tools for your data- and coding-related issues! 😉

Please rate this

A comparison between SQL and NoSQL

29

September

2016

No ratings yet. A comparison between SQL and NoSQL

Huge amounts of data are collected these days, which creates the need for advanced storage technologies. Since databases were introduced in 1960’s, different types have been developed. SQL (Structured query language) is a well-known database that has emerged in the 70’s, but one of the technologies that has gained particular attention since the late 2000s is a NoSQL database. (Hadjigeorgiou, 2013). I will make a comparison between these two databases and discuss the advantages and limitations of each.

Let me start by explaining what SQL and NoSQL databases actually are: the basic concept of SQL is that it is a relational database. This means that all data is stored in relations, structured in a set of tables with columns and rows. The columns define data categories, and each row is contains a unique instance of these defined categories (Khan, 2011).

NoSQL is developed in response to the high volume data that is being created, stored and analysed by users and applications. NoSQL combines a selection of different database technologies and are non-relational databases, meaning that it does not require fixed table schemas. (Planet Cassandra, 2015). The non-relational databases can generally be divided into three categories: the document model (organizes data as a collection of documents) the graph model (data stored using nodges, edges and properties) and the Key-value Wide column models (data stored as attribute name or key with its corresponding value) (MongoDB, 2016)

A downside of a relational database might be that data has to fit in a table. The same table cannot be used to store different information, which introduces complexity issues in case of adding or restructuring data. However the table ensures a strict data storage, limiting consistency issues. (Planet Cassandra, 2015). The advantages of a NoSQL database are that it is able to process a large amount of unrelated and unstructured data, enabling developers to cope with the growing amount of data velocity, variety, volume and complexity. Because of their simpler data models, NoSQL are also able to process data faster than SQL databases. (Khan, 2011). However, NoSQL databases are less reliable compared to SQL databases because of less reliability and less data-integrity, making SQL databases preferable when data-integrity is essential (Buckler, 2015).

So which one is better, SQL or NoSQL databases? The choice should depend on the particular problem that one wants to solve. Both have their advantages and limitations and hybrid solutions may even be more suitable in some cases than eliminating one of the two.

 

References:

  • <http://www.thewindowsclub.com/difference-sql-nosql-comparision>
  • https://www.sitepoint.com/sql-vs-nosql-differences/

Please rate this