AI and the automation of accounting 

9

October

2024

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AI has changed the landscape in many work fields, such as in data, law, finance and even accounting. Recently I have taken an assistant-accounting job at a firm paired with my master studies. I have discovered a lot about AI and what it could do for accounting that I thought were not possible.  Having been there for 2 months, I have learned a lot about accounting itself, but also what AI did for me while I was at the job. According to an article by the open journal of business and management, accountants become more efficient in handling the bookkeeping and become more productive in their work due to the use of AI (peng et al, 2023). I found this to be true as well. 

The first week I began working the invoices in the bookkeeping system, I was very reliant on my own knowledge. I tried categorizing each invoice in a specific item for the ledger, which was confusing sometimes. Because there were a lot of different items and different kind of invoices. With the help of ai however, it showed me what type of invoice belongs to what items in the ledger.  For example, one particular invoice showed me a bill of the restaurant where a particular client of the accounting company was having dinner. At first, I was not sure where this type of cost could be booked in the ledger. I asked AI: “at which item does a restaurant bill belong to in a ledger?” The prompt I gave ChatGPT provided me with this answer:

I asked my employer, who is an RA (registered accountant) for verification is this was true, he confirmed that it was indeed true. From then on, I started using AI a lot more for bookkeeping. Especially for items I was unsure of. It helped me become more knowledgeable, but also helped me to become more efficient and productive. 

Even though there are a lot of benefits of using ai in accounting, there are also downsides. Each bookkeeping for a specific client is different. Tailoring to these clients cannot be done always done through AI. My employer explained to me that the accuracy of knowledge AI has on accounting and bookkeeping is broad, but that I should not always rely on it to keep the books. Because clients sometimes require specific wishes in what purpose their ledger serves and on what books costs are categorized. For example, some clients want to receive a larger tax return. Therefore, they would categorize some cost to a specific item in the ledger that are relevant for that outcome, and others use It to justify cost that they made for the past few months that could be lost in other items, due to inaccuracy of the AI. This type of variety is sometimes very confusing for the AI making some prompts not always accurate to what the client needs. As accepting is also a type of consulting and advisory to companies.

In my opinion, AI is beneficial for the future of financial bookkeeping, and it will probably change a lot of aspects in the financial field. I do however think that when it comes to personalized tailoring to clients with jobs such as consulting and advice, especially in the tax field. It is still of relevancy that accountants or financial advisors take responsibility in helping clients themselves to keep them satisfied and use AI to their benefit. Becoming more efficient and accurate themselves due to the clever use of it.

Peng, Y., Ahmad, S. F., Ahmad, A. Y. B., Al Shaikh, M. S., Daoud, M. K., & Alhamdi, F. M. H. (2023). Riding the waves of artificial intelligence in advancing accounting and its implications for sustainable development goals. Sustainability, 15(19), 14165

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Tech-Driven Triumphs: The New Era of Sports Performance

20

September

2024

5/5 (1)

In 2016, the National Basketball Association (NBA) tournament witnessed the best record done by a team in a regular season. The Golden States Warriors (GSW) finished the regular season with a record of 73-9 (win-loss). They broke the previous 72-10 record established in 1996 by the Michael-Jordan-led Chicago Bulls. In that 2016 season, GSW’s point guard Stephen Curry set the record for most 3-point attempts made, with 402 made 3’s. Before that, no player has made 300 three-pointers in a season. The Warriors did not win the trophy that year, but they went on to become champions in the following 2 seasons.

It was not only the talent of the players and coaching staff (which, of course, played a big role in the historic run) that propelled the success of the franchise. Behind the curtain, it was the technology and analytics that the GSW leveraged that helped them transform into such a dominant sports team. The Warriors was one of the first teams to adopt tracking technologies and analytics (Tina, 2017). From 2010, they have installed SportsVU, a camera tracking technology that utilizes computer vision, to track what’s happening on the court. The tool provides a rich dataset of the movement and coordinates of players, and the analytics team and coaching staff can use this data to drive decision-making processes. GSW was one of the best teams to utilize the technology, leading them to achieve the 2016 MIT Sloan Sports Analytics Conference award of “Best Analytics Organization” (Fahey, 2016).

The technology-immersed world of sports

Embracing the use of technology is not only just a competitive advantage for sports teams anymore; it has become a do-or-die thing, and it has touched every branch of sports. Baseball, for example, was probably the first sport to popularize the concept of sports analytics, with the publishing of Michael Lewis’s book “Moneyball: The Art of Winning an Unfair Game” (2003) and the later movie adoption “Moneyball” (2011). Nowadays, the Major League Baseball (MLB) teams use Statcast, a tracking technology to gather real-time data, including pitch velocity, exit velocity, launch angle and more (Becoming a Baseball Analytics Expert, 2023). 

Wearable technologies have also been used heavily by sports teams. In the National Football League (NFL), teams are using Catapult, a wearable tracking device that could track all performance and physical condition indicators of players (Becker, 2019). The same wearable technology is applied in football to capture player movements, actions on the field and physical exertions, along with other metrics such as player workload , movement efficiency, and game-specific physical profiles (Ambler, 2024).

Not just team-performance enhancement, technologies have been invasive in every aspect of sports. Teams have been using analytics in scouting to pinpoint how much an athlete is worth by looking at player’s longevity, heart rate, natural health condition and prediction on how these impact the player’s career (Hanchett, 2012). Artificial intelligence has been incorporated in football’s Goal-Line Technology to help referees determine goals. Virtual Reality (VR) have been used to simulate training environment for athletes without physical strain, and Augmented Reality (AR) technology has been enforcing fans’ engagement with live stadium tours or interactive gaming during live events. The list goes on and on.

The future: Super athletes, super teams, and super sporting?

The past 50 years have witnessed incredible sports feats by athletes and teams. A marathon finished under 2 hours was deemed impossible, until Eliud Kipchoge did it in 2019. Arnand Duplantis, a Swedish pole vaulter, broke his own world record for the 9th time this Olympics. Great athletes like Lebron James, Ronaldo and Novak Djokovic keep pushing the age limit for a competitive professional sports career. The GSW’s 73-9 record, Real Madrid’s three-peat of the Champions League, Man City’s 100-point season are among impossible feats achieved recently. All of these amazing sports achievements have been greatly assisted with the help of technology advancement. And now, when we step into the age of another technology breakthroughs with AI, will the world witness the making of super athletes, surpassing Lebron James and Cristiano Ronaldo?

References

Tina. (2017, November 9). How analytics drives the Golden State Warriors. Chartio. https://chartio.com/blog/how-analytics-drives-the-golden-state-warriors/

Fahey, A. (2016, March 13). Warriors earn “Best Analytics Organization” award at 2016 MIT Sloan Sports Analytics Conference. NBA.com. https://www.nba.com/warriors/news/warriors-earn-best-analytics-organization-award-2016-mit-sloan-sports-analytics-conference

Becoming a baseball analytics expert. (2023, August 15). Kore Baseball Products. https://www.korebaseball.com/blogs/blog/mastering-baseball-analytics-a-comprehensive-guide-to-analyzing-and-interpreting-game-data

Becker, J. (2019b, October 21). How analytics is changing sports. American University Online. https://programs.online.american.edu/mssam/sports-management-masters/resources/how-analytics-is-changing-sports

Ambler, W. (2024, July 3). Sports Analytics: What is it & How it Improves Performance? – Catapult. Catapult. https://www.catapult.com/blog/what-is-sports-analytics#Applications-of-Sports-Analytics

Hanchett, D. (2012). Playing Hardball With Big Data: How Analytics Is Changing The World of Sports. EMC, pp. 2. Retrieved from https://www.emc.com/collateral/article/137534-sports-analysis.pdf.

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The Transformation of Supermarkets: From Products to Digital Platforms

18

September

2024

5/5 (2)

Purchasing goods in bulk, stocking shelves, and selling to customers; this is how supermarkets used to operate as they were using a traditional product-based business model. Over time, supermarkets evolved by integrating technology, such as self-service, price scanners, and loyalty programs (Sundarabharathi & Muthulakshmi, 2023). The landscape is changing rapidly as supermarkets shift toward a digital platform business model. This shift focuses on personalized experiences, with data analytics predicting consumer behavior and adapting to innovations (Sundarabharathi & Muthulakshmi, 2023). One key player embracing this transformation is Albert Heijn, a Dutch supermarket chain leading the way in digital innovation.

Albert Heijn was the first Dutch supermarket who took the omnichannel approach where in-store meets online (Albert Heijn Launches Subscription, ‘My Albert Heijn Premium’, 2021). The introduction of digital tools, like Albert Heijn’s bonus card and mobile app “AH app”, have transformed the way customers interact with the brand. Their bonus card initially started as a loyalty card for discounts, but has since evolved into a sophisticated data-gathering tool. The combination of the bonus card and the multifunctional AH app, lets Albert Heijn track customers’ shopping habits. With the use of artificial intelligence, the supermarket can recommend personalized offers, recipes based on what you buy, and even predict future purchases. This personalized shopping experience keeps customers engaged while generating valuable data.

As these apps and digital tools grow in functionality, they are part of a broader trend where supermarkets collect an increasing amount of data. Every interaction, from the products scanned to online searches, feeds into algorithms that help supermarkets optimize inventory, suggest new products, and refine marketing strategies. This approach transitions supermarkets from merely being places to purchase goods to digital platforms that provide value-added services based on consumer behavior.

The future allows supermarkets to become even more platform oriented due to new technological innovations and upcoming trends. There are different opportunities around the corner for supermarkets to make this transformation. Examples are the replacement of barcodes with QR codes in 2027 in the Netherlands and Artificial Intelligence becoming more sophisticated (DigitalTrends, 2023)(NOS, 2024).

As supermarkets adopt these technologies, their business models will need to continue evolving towards more digitally-driven, data-powered operations. It is interesting to see how it affects the business models of supermarkets like Albert Heijn right now, and how it will develop in the future years. How far can we transform to this digital platform model and will the traditional physical supermarket as we know disappear completely in the future?

References:

Albert Heijn launches subscription, ‘My Albert Heijn Premium’. (2021, 27 oktober). https://www.aholddelhaize.com/en/news/albert-heijn-launches-subscription-my-albert-heijn-p

remium/

DigitalTrends. (2023, 19 juli). How Artificial intelligence (AI) is becoming increasingly sophisticated. Medium. https://medium.com/@digitaltrends1/how-artificial-intelligence-ai-is-becoming-increasingly-sophisticated-4b837f7e31ba

NOS. (2024, 26 juni). De streepjescode bestaat 50 jaar, maar het einde nadert. https://nos.nl/artikel/2526164-de-streepjescode-bestaat-50-jaar-maar-het-einde-nadert

SUNDARABHARATHI, M., & MUTHULAKSHMI, C. (2023). American Supermarkets – PAST, Present Vs. Future Trends and Technologies. In G. Venkataswamy Naidu College & Manonmaniam Sundaranar University, Res Militaris (Vol. 13, Nummer 2, pp. 6270–6271).

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Physical goods, information goods, & neurological goods(?)

17

September

2024

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I am fascinated by the idea of information goods – firstly introduced during the start of the 1950s, they prevail to be an incredibly profitable and state of the art product type.1 My fascination arises from the impressive difference between information and physical goods. If the gap between information goods and the next product type is equally big or even larger, which products could we think of?

Imagine, all a person knows is physical goods – the world hasn’t developed non-object (information) goods yet. This fictitious individual is used to buy products, which need to be newly produced, whenever an item is ordered. Additionally, personalizing a product takes a lot of extra work, as the product needs to be adjusted physically, by steps in a manufacturing process not being standardized. Ultimately, the product will break down sooner or later. Telling this person, that there will be another kind of product, being transformable and customizable easily, without any massive extra costs, would leave this person stunning. Further, explaining that the product could be reproduced an unlimited amount of time, without any significant additional cost as well, and that this product wouldn’t be subject to wear and tear over time, would sound unimaginable for this person. This whole process left me thinking: How could the next tremendous product development look like?

Upcoming trends show that this could be cognitive or even neurological goods. In my understanding, both enable physical or knowledge enhancements to be directly bought. On the one hand, cognitive goods would include obtaining knowledge, being entailed in a book, directly (without reading the actual book), by buying and transferring it. Neuralink, for instance, tries to develop enhanced communication between the brain and computers.2 Accelerating this communication to real-time speed, would enable a market for cognitive goods. On the other hand, neurological goods could include all types of physical capabilities. This could not only be skills, which otherwise would need to be learnt over time (e.g., playing the piano), but also those, which can not be learnt anymore, for example because of paralyzation (e.g., walking even though being paraplegic). The precision, with which neuronal activity can be surveilled, replicated, and even stimulated, becomes comprehensible, when considering experiments such as the one of Takagi and Nishimoto (2022), in which they tried to measure neuronal activity of people seeing things, in order to replicate the objects they have seen.3 The results, visualized in the graphic, should leave us stunning and maybe realizing that a market for neurological as well as cognitive goods is not too far away anymore.

  1. Timothy Williamson (2023). History of computers: A brief timeline. Retrieved from: https://www.livescience.com/20718-computer-history.html ↩︎
  2. Ben Kendal (2024). Was wollen Neuralink und Musk mit ihrem Gehirnchip erreichen – und ist er überhaupt sicher? Retrieved from: https://www.rnd.de/wissen/neuralink-was-ist-das-und-was-will-elon-musk-mit-dem-chip-im-gehirn-erreichen-ZFEHGH2WDVDZ3AGVYXP6OOWYU4.html#; https://neuralink.com ↩︎
  3. Sarah Kuta (2023). This A.I. Used Brain Scans to Recreate Images People Saw. In: Smithsonian Magazine, retrieved from: https://www.smithsonianmag.com/smart-news/this-ai-used-brain-scans-to-recreate-images-people-saw-180981768/ ↩︎

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Data Privacy and GenAI

16

September

2024

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When ChatGPT launched at the end of 2022, most data protection professionals had never heard of generative AI and were then certainly not aware of the potential dangers it could bring to data privacy (CEDPO AI Working Group, 2023). Now that AI platforms grow more sophisticated, so do the risks to our privacy, and therefore, it is important to discuss these risks and how to disarm them as effectively as possible.

GenAI systems are built on vast datasets, often including sensitive personal and organizational data. When users interact with these platforms, they unknowingly share information that could be stored, analyzed, and even potentially exposed to malicious actors (Torm, 2023). The AI itself could potentially reveal confidential information learned from previous interactions, leading to privacy breaches. This could have some major implications for the affected individuals or organizations if sensitive information is being shared without proper anonymization or consent.

Continuing on the topic of consent: Giving consent for generative AI platforms to use your data can be tricky, as most platforms provide vague and complex terms and conditions that are difficult for most users to fully understand. These agreements often include legal jargon and technological terminology, making it hard to know exactly what data is being collected, how it’s being used, or who it’s being shared with. This lack of transparency puts users at a disadvantage, as they may unknowingly grant permission for their personal information to be stored, analyzed, or even shared without fully understanding the risks involved.

To reduce the potential dangers of GenAI platforms, several key measures must be implemented. First, transparency should be prioritized by simplifying terms and conditions, making it easier for users to understand what data is being collected and how it is being be used. Clear consent mechanisms should be enforced, requiring explicit user approval for the collection and use of personal information. Additionally, data anonymization must be a standard practice to prevent sensitive information from being traced back to individuals. Furthermore, companies should limit the amount of data they collect and retain only what is necessary for the platform’s operation. Regular audits and compliance with privacy regulations like GDPR or HIPAA are also crucial to ensure that data handling practices align with legal standards (Torm, 2023). Lastly, users should be educated on best practices for protecting their data when using GenAI, starting with being cautious about what they share on AI platforms.

In conclusion, while generative AI offers transformative potential, it also presents significant risks to data privacy. By implementing transparent consent practices, anonymizing sensitive data, and adhering to strict privacy regulations, we can minimize these dangers and ensure a safer, more responsible use of AI technologies. Both organizations and users must work together to strike a balance between innovation and security, creating a future where the benefits of GenAI are harnessed without compromising personal or organizational privacy.

References:

CEDPO AI Working Group. (2023). Generative AI: the Data protection Implications. https://cedpo.eu/wp-content/uploads/generative-ai-the-data-protection-implications-16-10-2023.pdf

Torm, N. (2023, December 11). Steps to safeguarding privacy in the Gen AI era. www.cognizant.com. https://www.cognizant.com/se/en/insights/blog/articles/steps-to-safeguarding-privacy-in-the-gen-ai-era

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Netflix’s (seemingly too?) Perfect Recommendation System.

7

September

2024

5/5 (1)

Netflix is widely seen as one of the world’s most successful streaming platforms to date. Many might accredit this success to its broad library of fantastic titles and simple, yet effective, UI. However, behind the scenes a lot more is going on, which keeps users on the platform longer, and most importantly, reduces subscriber churn.

While Netflix has 277 million paid subscribers across 190 countries, no user experience is the same for any of these users. Over time, Netflix has developed its incredibly intelligent Netflix Recommendation Algorithm (NRE) to leverage data science, and create the ultimate personalized experience for every user. I think most of us are aware of some personalization algorithms, but not the extent to which they go!

The NRE is composed of multiple algorithms that filter Netflix’s content based on a user’s profile. These algorithms filter through more than 5000 different titles, divided in clusters, all based on an individual subscriber’s preferences. The NRE works by analyzing a wealth of data, including a user’s viewing history, how long they watch specific titles, and even how often they pause or fast-forward. This, in turn, results in videos with the highest likelihood of being watched by the user, being pushed to the front. Which is, according to Netflix, essential, since the company estimates that they only have around 90 seconds to grab a consumer’s attention. I think, as consumer attention drops even further (with apps like TikTok destroying our attention span), this might become even more of a problem in the future. I mean, who has the time to sit down and watch a whole movie these days??

This also ties into the concept of the Long Tail which we discussed, which refers to offering a wide variety of niche products that can appeal to smaller audience segments. Netflix can now surface lesser-known titles to the right audiences using its recommendations algorithms. While these niche titles might have never been discovered by users in the past, Netflix can now monetize the Long Tail of its Library. You must have definitely noticed that your family or friends have titles on their Homepage that you would never see on your own, and this is the NRE at work.

While this model is largely successful, it might raise concerns around content bias. For example, Netflix’s use of different promotional images for the same content based on a user’s perceived race or preferences has sparked debate. Although the intent is to tailor recommendations more effectively, it risks reinforcing stereotypes and narrowing the scope of content that users are exposed to.

Ultimately, user data is exchanged for a super personalized experience, though this experience can sometimes be flawed. What do you think about Netflix’s NRE and its effects on users? Do you think this data exchange is fine, or would you rather just see the same Homepage as everyone else?

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“Gas-as-a-Service”? Fuelling servitization through digital

4

September

2024

5/5 (2)

In the very first class, professor Ting Li mentioned an app – WeFuel – which aims to radically change the way we think about fuelling our vehicles: on-demand gas delivery.
I was striked by this innovation, enabled by digital technologies: thanks to real time geolocalization, WeFuel will reach to the customer to fill up their gas tanks, instead of having the customer going to a gas station. This could be an important solution in case of emergency needs or to overcome accessibility issues in gas stations.

source: Wefuelinc.com

Given that the value proposition, as well as the business model, changed, I started wondering how extreme this change could be. At first, I thought of the possible downsides under the customers’ perspective: this service may be more expensive than traditional gas stations, because of its on-demand nature and the variable costs connected to the way it works – even though the usual (huge) fixed costs of running a gas station aren’t faced by the company. And so I thought: since fuelling vehicles is a repetitive action and what WeFuel‘s customers look for – apparently, at least – are comfort and convenience, why not charging a fixed price, a subscription, instead of having prices per gallon/liter? After all, gas is a commodity, just like water or electricity for households are: that’s how I thought of a “Gas-as-a-Service” approach. Customers can pay a monthly subscription tailored on their needs – whether in terms of quantity, frequency or assistance they require for each “fueling session”. Users can then upgrade their subscription if needed, or pay for extra “sessions” or extra liters every once in a while. Facing a premium price to be sure not to run out of gas ever again, or to never have to spend precious time waiting in a queue at a gas station seems completely reasonable. At the same time, WeFuel could gather much more data than traditional gas stations, enabling them to put the customer at the center and to use data analytics to optimize their offers.

I must note that, while writing this article, I’ve come across a company which has a very similar business model: 4Refuel. However, it is a B2B company, while I decided to focus on B2C business models.

source: Sensorfy.ai

Nevertheless, many questions arise: who would be willing to pay for this service? How pervasive does WeFuel‘s network of tank cars need to be? But most importantly: is there a future for businesses in the gas fuel market, given the transition to EVs?
Actually, the electric revolution could boost a service like WeFuel‘s: given the wind down of traditional gas stations (or, more probably, their reinvention as electric charging stations), an on-demand, Gas-as-a-Service approach could fulfill the needs of a to-be niche market. In fact, petrol-powered car enthusiasts could still enjoy their traditional vehicles while (happily) paying a premium price, which could be also seen as a way to deal with consumption externalities. But how long could this business last?

source: The New York Times’ website

Overall, customer centricity and the ability to collect data, as well as the blend of digital and physical, position this idea as a possible digital disruption, but how feasible is it? And what’s its futurity?

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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

20

October

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!

5/5 (1)

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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