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The integration of generative AI into business models is becoming essential for companies aiming to stay competitive in today’s fast-evolving digital landscape. One such example is Slim Academy, a leading provider of academic summaries in the Netherlands. As the demand for efficient and up-to-date study materials grows, Slim Academy has the opportunity toleverage generative AI, specifically through its new tool, Course Oracle, to enhance both its operational efficiency and the value it provides to students.

Course Oracle is an advanced generative AI system designed to streamline the creation of summaries and exam preparation materials. By automating the process of summarizing content from various sources, such as lecture transcripts, video transcripts, readings, and old exams, this tool allows Slim Academy to significantly reduce the manual labour involved in producing and updating study materials. Currently, these tasks are handled by Study Heroes, student employees responsible for gathering, writing, and reviewing content. However, this process is both time-consuming and costly, limiting Slim Academy’s ability to scale its offerings to more niche study programmes.

With the integration of Course Oracle, Slim Academy can automate the time-intensive content creation process, allowing Study Heroes to focus more on refining and improving the quality of the materials. This shift not only speeds up the production of summaries but also reduces the likelihood of human errors, ensuring higher accuracy and reliability in the final products. Furthermore, Course Oracle’s ability to generate exam-like questions that closely resemble actual exams provides students with a more realistic and effective exam preparation experience, addressing a key weakness in Slim Academy’s current offerings.

The impact of this integration extends beyond just operational efficiency. By improving the quality of exam preparation materials, Slim Academy can significantly enhance its value proposition. The AI-generated practice questions offer students a more comprehensive learning tool that better prepares them for real exams. This added value is expected to increase customer satisfaction and loyalty, further enhancing Slim Academy’s position as a market leader.

Additionally, the integration of Course Oracle enables Slim Academy to expand its course coverage, entering new markets and offering materials for niche programs and master’s courses. This horizontal growth would not have been possible without the efficiency gains brought about by AI and generative AI. Moreover, by reducing the reliance on manual labour, Slim Academy can lower its operational costs, allowing the company to offer more competitive pricing and increase its market share.

In conclusion, the integration of generative AI through Course Oracle in the existing business model will take Slim Academy to the next step. By automating key activities and enhancing the quality of its products, Slim Academy can continue to deliver high-quality, up-to-date study materials to its customers while improving its operational efficiency and expanding its reach. As generative AI continues to shape the future of business, Slim Academy will bewell-positioned to capitalise on this technology to maintain its leadership position and drive future growth in the summaries market.

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Generative AI tools are increasingly becoming part of our daily lives, enhancing productivity by automating repetitive tasks and assisting with problem-solving. As someone who is switching between work responsibilities, personal life, and academic projects, I wanted to talk about how AI, mostly ChatGPT, became a central part of my life.

I started using ChatGPT in January 2023 and immediately got the paid version. I mostly used it for university. This was a whole new world. I no longer needed to search for things anymore. I could ask it anything and get very good answers. This helped me significantly get my schoolwork done more efficiently. It is also really good in explaining hard subjects in a way that I can understand it well. I did not needed to ask fellow students or the professor anymore, I had my personal trainer.

Around this time, I also began using it more for personal purposes, primarily to generate ideas. It would generate a lot of information and then I would select what I wanted. Gradually, I started using Google less, turning to ChatGPT for most of my questions. This was because it often provided more personalised and accurate answers. It understood the questions I was asking. Occasionally, I also used it for text-to-image generation, but the images it produced still looked quite fake and didn’t really capture what I meant. That’s why I stopped using it. However, recently, I tried out Midjourney, and it generates high-quality images that make it increasingly difficult to tell if they are real or fake. This got my attention and now I am generating more and more images.

At work, if I had a question about the tools I worked with, I could actually ask this to ChatGPT and get a clear explanation. I did not have to search the internet anymore, which saved a lot of time. I cannot use it for content, because of the restrictions from the company I work at.

However, I noticed that the AI wasn’t perfect. There were times when the outputs were not correct, especially when the task required a deeper understanding of the context. In one instance, when I asked the AI to generate a report based on a specific set of data, the output was wrong. I tried multiple times, and it just could not understand what I wanted. This highlighted a major gap: while AI can perform tasks quickly, it still lacks the critical thinking that comes from experience and human insight. However I have being trying out o1-preview version from ChatGTP and I can really see that it improved that critical thinking if I think about 1 year ago. This shows the rapid improvements AI is going through.

For coding, ChatGTP has a deep understanding and can really translate well what you want. I experimented with AI-generated code, I asked the AI to create a simple script to download music from YouTube. While it provided a solid foundation, I still needed to tweak it and ask the same question in different ways to get the code to align with my exact requirements. The potential here is really big, but for now, AI still relies on human oversight.

Looking forward, I think the real value of AI lies in improving its understanding of context. If generative AI could evolve to understand more subtle nuances, it would become a far more reliable problem-solving tool. Additionally, making AIs that have an understanding of everything would also increase the use case and ease of use a lot. Right now, there are too many different AIs that are good at one thing.

In the end, while I think AI is far from replacing human expertise, it already has and certainly will have a really big impact on personal life, work, and school.

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Ever wondered how we’re going to keep all that AI data safe in the future? The same tech behind cryptocurrencies might just be the thing we need to protect our AI systems. Let’s dive in and see why!

The Need for Enhanced Data Security in AI

AI systems rely on large datasets to train models and make predictions. From healthcare to online shopping. A data breach can cause a lot negative consequences. Securing these datasets is important, and while encryption and secure storage methods exist, most of the time there is lack of transparency and tamper-proof (in blockchain, “tamper-proof” means that once data is recorded, it cannot be changed, making it highly secure) assurances needed in AI applications. Blockchain technology offers a solution that works well with AI’s need for data.

How Blockchain Secures AI Data

Blockchain technology, in easy words, is a system that spreads information across many computers and makes it very hard to change. Once data is recorded on the blockchain, it can’t be changed without changing all the data that came after it. And this is what makes it strong. This tamper-proof feature makes blockchain a good way for securing sensitive data transactions in AI systems.

What are the most important benefits:

Data Integrity: Blockchain creates records of data that cannot be changed and it also keeps the accuracy of the data that AI systems use for decision-making.

Transparency: Everyone in the blockchain network can see the same information, making it hard for anyone to change data without others noticing.

Decentralization: Removing a central authority reduces the risk of data breaches, there is not 1 server where are the data is stored, it is spread across many computers. Adding an additional layer of security.

Ted Talk:

I added a TED talk that talks about use the case of AI and blockchain in a different way. This shows the broad use and strong capabilities when you connect AI and blockchain

Real-World Applications and Use Cases

1.Healthcare: One area where the integration of AI and blockchain has shown good real-life use case, is in healthcare. For example, in their research, Tatineni (2022) discusses how combining AI and blockchain ensures data integrity, security, and accessibility in healthcare data management. By using blockchain, healthcare providers can guarantee that patient data remains tamper-proof, while AI systems can use this verified data to provide accurate diagnoses and treatment recommendations. In smart city healthcare applications, Rajawat et al. (2022) explores how blockchain and AI can improve the security of data in smart city environments, keeping the people privacy’s.

2.Industry Cyber-Physical Systems: In machinery in manufacturing like robotics or smart grids, blockchain ensures data integrity and trust in AI-driven processes. Hossain et al. (2024) explore how blockchain enhances security and traceability in cyber-physical systems, providing a tamper-proof record of all data transactions and giving trust in AI-powered systems.

Future of Blockchain and AI Integration

I see a lot of potential for AI and blockchain in the future. It can expand beyond healthcare or e-commerce, the applications are boundless. Chowdhury (2024) talk about how blockchain provides a tamper-proof mechanism for AI-driven data, giving the possibility for industries to improve their business intelligence processes securely.

There is also more and more use of internet of things, for example smart thermostats, wearable fitness trackers, and connected cars. Raparthi and Gayam (2021) talk about how blockchain and AI can enhance privacy and data security in IoT applications, creating a decentralised, tamper-proof platform for storing big amounts of data generated by connected devices.

By integrating AI with blockchain, future systems will be able to ensure that their data—whether it be financial, personal, or industrial—remains secure, trustworthy, and tamper-proof. The decentralised nature of blockchain will continue to complement AI’s growing role in data analysis, providing businesses and individuals with the security assurances they need in the digital age.

Challenges to Blockchain Securing AI Data

While blockchain shows promise in securing AI data, several obstacles need to be addressed. There are two issues I want to discuss a bit more in depth:

Scalability Issues

One of the biggest concerns with blockchain technology is scalability. So if the work amounts grow, how well can the system handle this?

Traditional blockchains, like Bitcoin and Ethereum, are designed to handle relatively small volumes of transactions compared to the huge amounts of data AI systems generate. It is like trying to fit a big lake through a small pipe. As more and more data is added to the blockchain, the system can become slow and expensive to use. Imagine if every time you wanted to use an AI application, you had to wait several minutes for it to process!

Possible solution: To overcome this challenge, new blockchain solutions such as Layer 2 scaling techniques, sharding, or hybrid blockchain models are being developed.

A lot of these new system are still being tested and we must see if there are able to process the big amount of data that AI uses.

High Energy Consumption

Probably a lot of you heard already that it cost a lot of energy to mine blocks, especially like Proof of Work (PoW) consensus algorithms. Some blockchain use even more electricity than entire countries. If AI and blockchain are combined, it could consume enormous amounts of energy.

Possible solution: There is some cryptocurrencies who are changing from PoW to more energy-efficient models such as Proof of Stake (PoS), but they do not have the same level of security.

Conclusion

Thinking about AI and blockchain creates many powerful possibilities. It can provide securing data, in a world where this becomes harder and harder. AI systems will only get bigger and more complicated, blockchain could be the decentralised, tamper proof system to keep the data integrity and security. Whether this is in healthcare or maybe in something we did not even think of.

References

TED Talk: https://www.ted.com/talks/gunjan_bhardwaj_how_blockchain_and_ai_can_help_us_decipher_medicine_s_big_data?subtitle=en

Tatineni, S. (2022). Integrating AI, blockchain, and cloud technologies for data management in healthcare. Journal of Computer Engineering and Technology. Retrieved from https://www.researchgate.net/publication/378669034

Rajawat, A. S., Bedi, P., Goyal, S. B., & Shaw, R. N. (2022). AI and blockchain for healthcare data security in smart cities. In AI and IoT for Smart City (pp. 123-138). Springer. Retrieved from https://link.springer.com/chapter/10.1007/978-981-16-7498-3_12

Chowdhury, R. H. (2024). Blockchain and AI: Driving the future of data security and business intelligence. World Journal of Advanced Research and Reviews. Retrieved from https://wjarr.com/content/blockchain-and-ai-driving-future-data-security-and-business-intelligence

Raparthi, M., & Gayam, S. R. (2021). Privacy-preserving IoT data management with blockchain and AI: A scholarly examination of decentralized data ownership and access control mechanisms. Internet of Things: Current Technologies and Applications. Retrieved from https://thesciencebrigade.com/iotecj/article/view/131

Hossain, M. I., Steigner, D. T., & Hussain, M. I. (2024). Enhancing data integrity and traceability in industry cyber-physical systems (ICPS) through blockchain technology. arXiv preprint. Retrieved from https://arxiv.org/abs/2405.04837