How Will Generative AI Be Used in the Future? Answer: AutoGen

21

October

2023

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The generative AI we know of today is ChatGPT, Midjourney, and DALL·E 3 and many more. This generative AI is very good and advanced, but there are some flaws, like not being able to perform long iterations. Now there is something new called AutoGen. AutoGen is an open-source project from Microsoft that was released on September 19, 2023. AutoGen at its core, is a generative AI model that works with agents; those agents work together in loops. Agents are in essence, pre-specified workers that can become anything, so there are agents that can code well and agents that can review the generated code and give feedback. Agents can be made to do anything and become experts in any field, from marketing to healthcare.

An example of what AutoGen can do is the following: if I want to write some code to get the stock price of Tesla, I could use ChatGPT, and it will output some code. Most of the time, the code that is written by chatGPT via the OpenAI website will have some errors. But with AutoGen, there are two or more agents at work: one that will output code and the second one that is able to run the code and tell the first model if something is wrong. This process of generating the code and running the code will go on until the code works and results in the correct output. This way, the user does not have to manually run the code and ask to fix the errors or other problems with AutoGen it is done automatically.

I also tried to create some code with AutoGen. I first installed all the necessary packages and got myself an API key for openAI GPT4. Then I started working on the code and decided to create the game “Snake”. Snake is an old and easy game to create, but it might be a challenge for AutoGen. I started the process of creating the snake game, and it had its first good run. I was able to create the first easy version of the game. I then came up with some iterations to improve the game. The game now also has some obstacles that, if the snake bumps into one, the game will end. This was also made by AutoGen without any problems. After palying around, I was really amazed at how powerful this AutoGen is, and I can only imagine what else can be created with AutoGen.

AutoGen is a very promising development and will be the future of professional code development or atomization tasks. If the large language models (LLMs) get more powerful, this AutoGen will also be more powerful because all the individual agents will be more powerful. It is interesting to follow this development and see if this AutoGen could create games that are not yet existing.

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The day ChatGPT outstripped its limitations for Me

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2023

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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/

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Using GenAI as a Teacher (2/2)

18

October

2023

ChatGPT is a good writer. It is a better teacher!

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In part one of this series (find it here) I have already outlined why it’s a good idea to use ChatGPT as a teacher. But how can you use ChatGPT to aid you in learning? It’s all about packaging your individual needs into prompts. So, think first about how you can learn best. As for me, I like to get bullet point lists and definitions. Here are a few examples of my favorite prompts:

If you’re completely new to a subject:

  • Imagine you are “expert in the field”. Explain “topic” to me on “high school/university/expert” level. Use bullet points.
    • Imagine you are John McAffee. Explain cybersecurity to me on high school level. Use bullet points.

If you are already familiar but lack clarity on how different things connect:

  • In “field you are learning”, explain “level of detail” of “topic you learn” via “keywords you know should be in the explanation”.
    • In software engineering, explain the basics of agile development to me via sprints, scrum, and scope. Use bullet points.

If you have similar but different words but cannot find a good explanation anywhere:

  • In “field you are learning”, what is the difference between “X, Y and Z”?
    • In statistics, what is the difference between errors, residuals, and variance?

ChatGPT is also good for reading. Imagine you have a long text to read and cannot get a glimpse on what it’s about. You can copy/paste the text into ChatGPT and tell it the following prompt:

  • Summarize the key points of the given text in ten bullet points.

Let’s say ChatGPT gives you six distinct bullet points but four are kind of vague or around the same subject. Then you repeat the prompt but make it six bullet points. If the result is six concise bullet points, you get the idea of the text. Finally, you should still read the whole text with this understanding in mind (you will likely still find valuable new information in the text).

There you have it! Now you can use ChatGPT as your personal teacher. I hope you learned something and wish you great success!

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Using GenAI as a Teacher (1/2)

18

October

2023

ChatGPT is a good writer. It is a better teacher!

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It is a common theme that students use generative AI, most prominently ChatGPT, as a short-cut to generate content for written assignments. While this straight-forward application of the given resources to “get done as soon as possible” may be useful in the short-term, of course it’s no way to actually learn something in the end (aside from also conducting plagiarism). In the long-term, digital dementia is looming, which is a phenomenon that describes people who became more forgetful because they could google what they need anytime. Now with GenAI, the ability to create original thought and overall creativity are in peril, too. Imagine where we would end up if we outsourced all our creative work and thinking processes to AI for just two years.

So, why don’t we take a step back and use ChatGPT’s ability to explain any topic exactly in the way that we need it to, to our personal advantage? It may be a great writer, but it’s a better teacher. I have been using ChatGPT in this way for a while now and I can tell you: Using ChatGPT as a teacher, rather than a substitute, elevates our own understanding and enables us to learn better, leading to added value both for ourselves and our assignments or jobs. It can increase our understanding before we write any assignment ourselves(!), with our own knowledge. This way, we can also use ChatGPT to understand and learn any topic!

There are multiple ways to use ChatGPT as a teacher and overall learning aid. The most obvious one is having it summarize a long text that you have to read or having it explain a concept to you. In any case, specific prompts will help. Both of these use cases will be discussed further in part 2 of this blog series (find it here).

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AI-Powered Learning: My Adventure with TutorAI

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October

2023

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Shaping Tomorrow’s Data Analysts: The Impact of AI in Data Analytics Education

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October

2023

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In the rapidly evolving world of data analytics, education is the cornerstone of staying relevant and effective. As the data landscape transforms, so too must the way we prepare the data analysts of the future. In my previous blogpost I assessed the question whether generative AI is a friendly companion or a sneaky enemy. I figured from some comments as well as other posts that generative AI has taken a prominent place in our educational journeys. It made me curious to explore the future of data analytics education a bit further.

The AI Revolution in Data Analytics Education

Artificial Intelligence (AI) has been a game-changer in data analytics education. It’s not just about teaching students how to analyze data; it’s about equipping them with the skills to leverage AI and machine learning for more robust and insightful analysis. Through AI-driven algorithms and platforms, students gain access to hands-on experience with real-world datasets and can explore advanced techniques that would have been impossible a decade ago. One notable example of AI in education is personalized learning. AI algorithms can analyze a student’s progress and tailor lessons to their specific needs. This adaptive learning approach ensures that students receive customized support, helping them grasp complex concepts and skills more effectively.

Emerging Data-Fuelled Curriculum

The data analytics curriculum is evolving to keep pace with industry demands. Courses now cover emerging topics such as machine learning, big data, and AI integration. The emphasis is shifting from theoretical knowledge to practical skills. Students are encouraged to work with real-world datasets and apply their knowledge to solve complex problems.

My personal experience with generative AI tools, like ChatGPT, has been instrumental in this learning journey. These tools can assist in generating complex SQL queries, automating data cleaning processes, and even providing insights from basic data is what I personally experienced. The dialogue with for example ChatGPT – inserting error messages and getting back steps to take towards a solution – really felt like I had a virtual tutor within arm’s length.

Ethics and Responsible Data Analytics

While AI brings immense power to data analytics, it also raises ethical considerations. Data analytics programs are now integrating ethics courses to prepare students for responsible data analysis. This is crucial as data analysts often deal with sensitive data that can impact individuals and society.

An example that illustrates the importance of ethics in data analytics is the Cambridge Analytica scandal. Improper use of data led to severe consequences, highlighting the need for ethical guidelines and responsible practices in the field. Data analytics education should equip students with the knowledge and tools to make ethically sound decisions.

Real-Life Impact

The impact of AI in data analytics education isn’t just theoretical. It’s creating a workforce ready to tackle real-world challenges. Consider healthcare, where AI-powered analytics can predict disease outbreaks and improve patient care. In finance, AI algorithms analyze vast datasets to detect fraudulent transactions. These are just two examples of how AI-educated data analysts are making a difference.

In conclusion, the future of data analytics education is an exciting blend of AI-driven learning, emerging curricula, and ethical considerations. As a student, I’ve experienced the transformative power of AI tools in enhancing my data analysis skills. But not just with data analysis, they have helped me write essays in my bachelor program and even assisted me – after some discussions back and forth – in writing this blog. Could you tell?

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Can AI help me get a job?

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October

2023

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I am searching for a new job. A job that I can combine with my studies and which can provide me with enough to allow my shoe-box-sized apartment. But to get there, one often needs to write long motivational letters to various organisations and go through various potential job postings. However, the new age offers many opportunities to write motivational letters automatically and adapt to each and every company.  

In this search, I tested three separate AI-powered websites, ChatGPT, Kickresume, LazyApply and Rezi.

Kickresume, LazyApply and Rezi all provide a free trial of the algorithm that formulates extensive motivation letters. What is more, they all provide an easy User experience. The latter three websites also provide the user with prompts, like ‘’paste the job description’’ and ‘’paste your CV’’ which can provide a great deal of intertwined attention to one’s abilities and the required skills. The given prompts can also be skipped or occasionally modified if deemed to be unnecessary. Therefore, a complementary document can be readily made if one has a CV.

Regarding more mainstream and wide-use AI-language models like ChatGPT, one needs to insert a significant number of self-created prompts to create even a slightly similar quality document. It can be a helpful tool for people with more background knowledge of HR. For others, it can also complicate the creative process even more since Farrohina et al. (2023) find that AI language tools, if not used right, can significantly hinder one’s productivity and creativity.

Baert and Verhaest (2019) also emphasize that overqualification in the application process does not lower one’s chances of receiving the job and even increases the chances of employment for temporary jobs. Therefore, additional effort can not be of harm.  

Overall, all platforms provide similar-level content and are an excellent tool to create a personalized motivation letter. Sadly, the lack of layout options persists but can be easily tackled, by the use of other platforms.

Last but not least, the AI language models are built upon similar documents; therefore, the originality can only reach as far. Hence, the generated letters can come out to be too generic if applied to highly sought-after positions. Therefore, as helpful as these websites can be, they cannot replace well-thought-out and personal material.

Baert, S., & Verhaest, D. (2019). Unemployment or overeducation: which is a worse signal to employers? De Economist, 167(1), 1-21.

Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2023). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 1-15.

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

2

October

2023

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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! 😉

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Could AI be contributing to the disappearance of language diversity?

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September

2023

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Almost in no time, AI-powered large language models (LLMs) such as ChatGPT, Bing AI Chat, Google Bard AI, etc., have gained popularity among the mainstream part of society. However, I have noticed increased social media attention, specifically among Latvian language speakers, about the lack of applicability and, oftentimes, even comedic outputs these language models create.

I test this observation by typing ‘write a poem’ in multiple languages in the dialogue interface of ChatGPT. English, Russian, French, Arabic, Hindi, Dutch, Latvian, Estonian and Lithuanian. Interestingly, although ChatGPT can produce, to some extent, coherent text in all prior languages, the latter three, i.e., the Baltic countries, excel with incoherent meanings and even grammar and style inconsistencies. Bang et al. (2023) argue that these are low-resource languages, i.e., languages with relatively few speakers. Not surprisingly, Latvian is spoken by 1,5 million native inhabitants (Latvian Presidency, 2023), and the AI model has not received the necessary data input to produce grammatically or style-wise coherent sentences (see picture).

So, how can this be an issue?

In Baltic countries, approximately 95% of individuals speak at least two languages (Latvian Presidency, 2015). The second language most often is Russian or English, i.e., high-resource languages.

This is a worry, as many native speakers might instead stick to high-resource languages while browsing or creating content. This, in return, reciprocates the poor usability of low-resource languages and exacerbates language polarization. The low-resource languages are, therefore, at risk unless new measures are implemented to better the LLM training, e.g., training AI with ‘small data’ as suggested by Ogueji, Zhu, Lin (2021), or feeding AI with new data resources.

Of course, the future of the language is not as one-dimensional and depends on many factors, but at times of language globalization, the mainstream AI tools have helped no further!

Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., … & Fung, P. (2023). A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity. arXiv preprint arXiv:2302.04023.

Ogueji, K., Zhu, Y., & Lin, J. (2021). Small data? no problem! exploring the viability of pretrained multilingual language models for low-resourced languages. In Proceedings of the 1st Workshop on Multilingual Representation Learning.

Latvian Presidency. (2015). Language. Latvian Presidency of the Council of the European Union. https://eu2015.lv/latvia-en/discover-latvia/language 

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Acceptance of AI through a cultural lens

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September

2023

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Artificial intelligence (AI) has become a big part of our lives, yet the perceptions on the acceptance of AI seem to differ across countries. I came across an article stating that a few campsites in the Netherlands use facial recognition to provide customers access to the swimming pool, instead of letting staff check their card or wristband. However, what’s surprising is that one campsite had 300 customers and only three of them opted for this futuristic convenience. Most people were worried about the adoption of facial recognition because of privacy concerns. On the busy streets of China, such application of AI is common practice in public places and people seem to be more accepting. The strong difference between these two countries, raises intriguing questions, such as: How does culture shape our acceptance of AI? What kind of role does it play in the way we perceive and accept AI?

The acceptance of AI could be explained through two cultural dimensions from Geert Hofstede. For example, Lee & Joshi (2020) used uncertainty avoidance (UA) and collectivism/individualism. UA refers to the way that society deals with the uncertainty of the future and to which extent they feel threatened by unknown situations. Individuals from high UA culture are more likely to adopt AI, compared to those from low UA culture. The reason is that technological solutions appeal more to individuals from high uncertainty avoidance cultures, as they can increase predictability and are more likely to invest in technologies. However, people from individualistic cultures may not be as inclined as those from collectivistic cultures when it comes to depending on AI. A reason for this could be that the use of facial recognition in collectivistic countries, are perceived to benefit the society as a whole and may prioritize efficiency and convenience, which could lead to greater acceptance of AI. 

When organizations want to increase the adoption of AI, it is worthy to consider it from a cultural perspective. Do you think that culture has an influence on the acceptance of AI? Share your thoughts in the comments! 👇

Sources:

Hulsen, S. (2023). Steeds meer campings met gezichtsherkenning: handig, maar mag dit zomaar? https://www.rtlnieuws.nl/nieuws/nederland/artikel/5394988/steeds-meer-campings-met-gezichtsherkenning-zwembad

Hofstede Insights. (2023). Country Comparison Tool. https://www.hofstede-insights.com/country-comparison-tool?countries=china%2Cnetherlands

Lee, K., & Joshi, K. (2020). Understanding the Role of Cultural Context and User interaction in artificial intelligence based systems. https://www.tandfonline.com/doi/full/10.1080/1097198X.2020.1794131

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