NutriNet – a personal assistant for your grocery shopping

18

October

2024

No ratings yet.

Have you ever taken hours browsing around supermarkets searching for the most nutritious food options? Did it take you too much time figuring out which recipes to cook best, with the groceries you bought? Struggles when dealing with food are numerous, starting from choosing products with appropriate nutrients simply not knowing enough recipes.

Therefore, we introduce NutriNet – a mobile application that is going to make shopping and meal planning easier!

• Grocery shopping & meal planning simplification:

NutriNet simplifies grocery shopping and meal planning, by analyzing which products and recipes fit the users’ desired grocery item wishes, nutrition values and store preferences best. NutriNet aims to address the challenges being implicit to personalized nutrition and helps consumers make healthier food choices by simplifying the grocery shopping and meal planning process. This shall be done by eliminating the need to perfectly understand all nutrition values or to search for numerous recipes. NutriNet completes all these tasks for you in real-time and provides clear and accessible recommendations for you.

• Real-time personal assitant: NutriNet acts as a multifunctional application providing value to consumers by solving various food related problems in real-time. Appearing as a chatbot, it is aimed at taking general grocery shopping lists or meal wishes as input query, combined with preferences for food characteristics (e.g., nutrients, allergies) and grocery stores. It then provides brand specific and personalized grocery shopping lists, as well as meal recommendations, if so desired. Moreover, grocery items can also be added into the initial grocery shopping list query, by scanning them with the integrated AR tool. The product will then be detected visually and thus will be integrated into the shopping list input query.

• Long-term customer engagement: NutriNet distinguishes from competition by providing personalized and customized advice. This is possible, as NutriNet consists of a database, having incorporated stock and product information of the major supermarkets in the Netherlands. In contrast, classic applications, which try to meet similar needs (e.g., meal recommendation) rather focus on counting nutrients for the purpose of short-term weight loss, instead of personalizing grocery shopping lists and meal recommendation to enable a healthier lifestyle for users. Those applications are usable on short term but are proven to have low adherence over the time (Chen et al., 2015).

• Personalized recommendations: NutriNet leverages generative AI to offer accurately personalized recommendations. Users can simply enter their preferences, while prompting a grocery list or a meal, such as gluten-free or high in protein, and the generative AI powered application will provide accurately personalized results.
However, personalization and raising awareness for healthy foods are not the only purposes of NutriNet. It also addresses sustainability issues that supermarkets are facing. By gathering consumer purchase and search data in the application, consulting services can be offered to supermarkets, enabling them to plan ordering and stockholding processes more efficient. Hence, supermarkets should be able to reduce food waste due to overstocking on long-term.

NutriNet offers a 2-sided network platform, yielding important value to both, consumers as well as supermarkets.

Contributors

574051 – Duong Dao
728070 – David Wurzer
738898 – David Do
562387 – Roxi Ni

References

Chen, J., Berkman, W., Bardouh, M., Ng, C. Y. K., & Allman-Farinelli, M. (2019). The use of a food logging app in the naturalistic setting fails to provide accurate measurements of nutrients and poses usability challenges. Nutrition, 57, 208-216.

Please rate this

Innovating Learning with Canv-AI: A GenAI Solution for Canvas LMS

17

October

2024

No ratings yet.

In today’s educational landscape, generative AI (GenAI) is reshaping how students and instructors interact with learning platforms. A promising example is Canv-AI, an AI-powered tool designed to integrate into the widely used Canvas Learning Management System (LMS). This tool aims to transform both student learning and faculty workload by leveraging advanced AI features to provide personalized, real-time support.

The integration of Canv-AI focuses on two primary groups: students and professors. For students, the key feature is a chatbot that can answer course-specific questions, provide personalized feedback, and generate practice quizzes or mock exams. These features are designed to enhance active learning, where students actively engage with course material, improving their understanding and retention. Instead of navigating dense course content alone, students have instant access to interactive support tailored to their learning needs.

Professors benefit from Canv-AI through a dashboard that tracks student performance and identifies areas where students struggle the most. This insight allows instructors to adjust their teaching strategies in real-time, offering targeted support without waiting for students to seek help. Additionally, the chatbot can help reduce the faculty workload by answering common questions about lecture notes or deadlines, allowing professors to focus more on core teaching tasks.

From a business perspective, Canv-AI aligns with Canvas’s existing subscription-based revenue model. It is offered as an add-on package, giving universities access to AI-driven tools for improving educational outcomes. The pricing strategy is competitive, with a projected $2,000 annual fee for universities already using Canvas. The integration also brings the potential for a significant return on investment, with an estimated 29.7% ROI after the first year. By attracting 15% of Canvas’s current university customers, Canv-AI is expected to generate over $700,000 in profit during its first year.

The technological backbone of Canv-AI relies on large language models (LLMs) and retrieval-augmented generation (RAG). These technologies allow the system to understand and respond to complex queries based on course materials, ensuring students receive relevant and accurate information. The system is designed to be scalable, using Amazon Web Services (AWS) to handle real-time AI interactions efficiently.

However, the integration of GenAI into educational systems does come with challenges. One concern is data security, especially the protection of student information. To address this, Canv-AI proposes the use of Role-Based Access Control (RBAC), ensuring that sensitive data is only accessible to authorized users. Another challenge is AI accuracy. To avoid misinformation, Canv-AI offers options for professors to review and customize the chatbot’s responses, ensuring alignment with course content.

In conclusion, Canv-AI offers a transformative solution for Canvas LMS by enhancing the learning experience for students and reducing the workload for professors. By integrating GenAI, Canvas can stay competitive in the educational technology market, delivering personalized, data-driven learning solutions. With the right safeguards in place, Canv-AI represents a promising step forward for digital education.

Authors: Team 50

John Albin Bergström (563470jb)

Oryna Malchenko (592143om)

Yasin Elkattan (593972yk)

Daniel Fejes (605931fd)

Please rate this

Bing Image Creator: Enabling Expression for All, but with its Limitations

11

October

2024

No ratings yet.

The rise of Generative AI (GenAI) for text-to-image creation, like Bing Image Creator, has created new opportunities for people who may lack traditional artistic skills, like myself. For those who struggle with drawing or other creative disciplines, these tools are a game-changer, providing an accessible way to visually express their own creative ideas. Simply by typing a description, anyone can bring to life images that reflect their imagination, whether it’s for personal projects, storytelling, or fun experiments.

Another advantage of Bing Image Creator is its ability to generate diverse and unique visuals quickly. This is especially useful for prototyping and brainstorming, as users can explore a wide variety of concepts in a short amount of time without needing to start from scratch for each new idea. We recently used it for a DBA assignment to quickly create a visualization of our application.

However, despite its benefits, there are significant limitations. One major drawback that I experienced was that achieving a specific, detailed image often requires dozens of attempts. This trial-and-error process can be time-consuming, especially if you have a particular vision in mind. Another common annoyance is the tool’s inability to handle text properly within images. Misspelled words and jumbled letters happen a lot, which means you would have to edit the image, which is time-consuming. These technical flaws can be a barrier for users who need precision in their visuals.

Beyond the technical issues, there is an ethical dilemma. GenAI tools, like Bing Image Creator, are trained on large amounts of data, including artwork by real creators. Using such tools for financial gain raises questions about intellectual property and whether it’s fair to profit from art generated by an AI system that learned from the work of others without compensation. For many, this remains a contentious issue, emphasizing the ongoing debate around AI’s role in creative industries.

While GenAI provides incredible opportunities for personal creativity, it’s important to be mindful of its limitations and the ethical concerns that come with it.

Please rate this

Generative AI – friend or foe?

9

October

2024

No ratings yet.

Tools like GitHub Copilot assisting you with coding, ChatGPT serving as a tutor or DALL-E boosting creative projects (actually the pictures for this blog post were created using AI) can often be invaluable. Even though I have some experience using Gen AI tools, I would not call myself an expert as there are still many useful ways of implementing this technology that I have not yet discovered. Gen AI often proved to be a great brainstorming partner or a companion helping explore uncharted territories of new information whether at work or university. However, there are significant challenges I came across, with intellectual property (IP) and accuracy.

Gen AI is an amazing tool if used wisely. Nonetheless, I came across a fair deal of issues when implementing Gen AI into my tasks. The biggest difficulty I had was dealing with intellectual property such as legacy code. Do not get me wrong, Gen AI proves to be a great aid with most coding assignments, however, there are points at which it is close to useless. Developing code that is working based on legacy code is one of these examples. This is one of the biggest concerns in adopting AI at businesses, the concerns about intellectual property. In a case like this one cannot just simply copy and paste the legacy code into an AI assistant due to the obvious breach of confidentiality. In addition, AI models can reuse said code for other users increasing the number of questions around sharing IP with these models. 

This image was created using ChatGPT.
This image was created using ChatGPT.

I also sourced Gen AI for help with university tasks. I often asked ChatGPT to help explain a difficult topic or to help work on new ideas, however, oftentimes I run into issues when asking Chat to provide sources for the information it’s giving me. I thought it would be a clever workaround to fact-check the information provided by the model, however, it turns out that these models have a tendency to fabricate citations. I was a bit shellshocked when I found out but again this was a lesson on the need to be incredibly cautious and aware while using the assistance of AI. 

In conclusion, GenAI is a force changing the world at a rapid pace and it would be unwise to not use it. However, the limitations and reliability issues must be kept in mind. I do believe that this situation will only improve, and I am looking forward to broadening the range of AI products I am familiar with but for now, I will stick to using AI as a supplementary tool to the work I do.

Please rate this

My Experience with GenAI: Improving Efficiency or Becoming Stupid?

9

October

2024

No ratings yet.

I work as a part-time data analyst at a software company, where I analyze sales data. My 9-5 mainly consists of writing code, specifically using SQL in Google Bigquery and creating dashboards in PowerBI. I love using GenAI to help me write queries faster which would have taken me a long time to compose by myself. Additionally, I am a student and use GenAI to help me better understand course content or inspire me on what to write about during assignments. Generally, I would say that GenAI benefits my life as I can get more done in less time, however, from time to time I start to question whether I am not just becoming lazy.

I use GenAI on a daily (almost hourly) basis and rely on it in many ways. I mainly use ChatGPT 3.5, when ChatGPT 4o’s free limit has been reached, and Gemini, when ChatGPT is down. Based on my own experience, I can say that being good at ‘AI prompting’ is a real skill in the field of data analytics as it can drastically improve the efficiency with which you write queries, and therefore, the speed with which you finish tasks. My manager recently even held a knowledge-sharing meeting in which he discussed the best practices to use for data analysts when interacting with ChatGPT. Using GenAI has become a real thing in the field of data analytics, and is not something to be ashamed of.

However, I cannot help but sometimes be slightly embarrassed when I read back the questions I’ve asked ChatGPT. It seems that with any task that requires a little bit of effort or critical thinking, I automatically open the ChatGPT tab in my browser to help me come up with the right approach to solve the task at hand. I don’t even try to solve things by myself anymore, which makes me question: is this something to be desired?

The image presents an interaction with ChatGPT regarding the risk of using GenAI on human intelligence.
The image presents an interaction with ChatGPT regarding the risk of using GenAI on human intelligence.

As explained by ChatGPT in the image, using GenAI indeed frees up more brain space for things that are important. If I can use less time to get more work done, this improves my work efficiency and also gives me more time for things that I find more valuable, such as spending time with family or friends. Right now, it is still too soon to be able to determine the impact that using GenAI will have on our own (human) intelligence. In the meantime, we should just continue using it for repetitive tasks that would normally take much of our valuable time and hope that it is not ChatGPT’s plan to stupidify humanity before it can take over the world.

Please rate this

Coding and GenAI – an ideal match

8

October

2024

No ratings yet.

I remember my first assignment with RStudio in the Business Information course in the first year of my bachelor’s (in 2021). I had no experience with coding so I had to learn this from scratch. After crying and asking my lovely dad for help, I managed to write some code. This was all fun and games until I encountered my very first error code – and soon after many more followed. So of course, I stressed out and I didn’t know what to do. I started searching on Google and clicked on a number of webpages. And before I knew it, I was in deep into Stack Overflow. When I finally managed to find a post describing the same problem I encountered, I figured out that that the specific solution did not entirely work for my code, so the cycle repeated itself again. This process of resolving errors was time-consuming and definitely frustrating going from webpage to webpage and then going back to my code to see that the solution did not apply to my situation.

My journey

In 2022, ChatGPT entered the scene. Back then, I was on exchange in Hong Kong. While calling my dad, he mentioned something about an “AI bot who can generate everything you want it to generate”. I brushed this off seeing it as something similar to the Metaverse: the hype will last for a couple of months and then it will just be over. But when I came back home months later, everyone was still talking about this ‘ChatGPT’. One day, I tried putting my error codes in ChatGPT and it came up with a clear solution and even provided example code! How convenient! Now, I still use ChatGPT to take a look at my error codes.

Reflection

As you, the reader, might have noticed, I am very satisfied with using GenAI for coding. As I now genuinely love coding in RStudio, I still write my code myself. However, when encountering error codes, ChatGPT is definitely my ‘bestie’. Solving an error code is not the scope of writing code or running analyses. By using GenAI, it saves me a ton of time. No need to dive into the deep rabbit holes of Stack Overflow. I just provide my code and the error code to the GenAI bot and it will help me instantly. How amazing! It still blows my mind.

You still need to use your brain!

Using GenAI for coding does not, however, mean that you just let ChatGPT write your code and you just shut off your brain. In my opinion, it is still crucial to use a critical attitude when using GenAI for coding purposes. You can let the bot write all the code you want and resolve all of your problems every time, but if you don’t understand what the output is, using GenAI might as well be pointless. In my opinion, learning from the output from GenAI is the key to success. By learning from the output, you can prevent the same error codes next time you write code.

Thus, in my opinion GenAI is definitely useful for coding. No more wasting time on Stack Overflow as error codes are resolved in just a couple of seconds thanks to GenAI. Combine this with maintaining a critical mind and you might be able to hack a government soon.

Questions for the readers

As readers, are you as enthusiastic as me about using GenAI for coding? What are your experiences? Or have you encountered any difficulties by using GenAI for coding?

Please rate this

Leveraging GenAI in Digital Markets

8

October

2024

No ratings yet.

Businesses in the digital age depend more and more on data to inform their strategy, especially in electronic markets. Key concerns, such as transaction costs and information asymmetry, play a significant role in how organizations evolve. However, the development of Generative AI (GenAI) opens up fresh opportunities for addressing these issues.

Information asymmetry is a common problem in digital marketplaces like eBay, and GenAI can change the way businesses handle it. Outdated issues like moral hazard and adverse selection are caused by sellers and purchasers having insufficient information. For instance, customers may have concerns about the credibility of a seller or the genuineness of a product. Large-scale datasets can be analyzed by GenAI-powered algorithms, which can identify possible dangers and estimate seller reliability more precisely than feedback-based methods. This lowers information risk and increases consumer trust.

Furthermore, GenAI greatly enhances the notion of digital experiments, such as targeted advertising. Businesses can more accurately adapt their marketing campaigns because of GenAI’s ability to combine massive datasets and generate fresh insights. For example, AI may create predictive models that predict user behavior based on interaction patterns, instead of just depending on measurements like click-through rates. By doing this, the emphasis is shifted from reactive analysis to proactive decision-making, which eventually improves ROI and optimizes ad spending.

GenAI proves to be another fresh ally for the “move to the middle” idea. Businesses may use AI-driven insights to create better, data-backed partnerships with suppliers as IT lowers coordination costs. As a result, businesses may handle their supply chains more agilely, reducing the chance of opportunism and guaranteeing better alignment with long-term objectives.

In summary, GenAI provides a strong toolkit to tackle ongoing problems in digital marketplaces. In an increasingly complicated digital ecosystem, businesses can create sustainable growth, foster trust, and unleash new efficiencies by fusing AI capabilities with a strong grasp of information strategy.

Please rate this

Chatbots in White Coats: A Personal Reflection

1

October

2024

5/5 (1)

Introduction: Balancing the Benefits and Risks of GenAI in Medicine

“Not all GenAI is safe, and in healthcare, the risks are particularly high,” said Aashima Gupta, Google Cloud’s Global Director of Healthcare Strategy and Solutions (Smith, 2023). This statement echoes my recent experience using a healthcare chatbot for a medical issue, highlighting the need to approach this technology with caution while recognizing its potential to transform patient care and alleviate the burden on medical professionals.

My Experience: Leveraging AI for Minor Symptoms

Recently, I found myself grappling with persistent bloating, nausea, and stomach discomfort. As the symptoms were not severe enough to warrant an immediate hospital visit, I reached out to my friend, a clinical medicine student, for advice. Intrigued by the potential of AI in healthcare, my friend decided to input my symptoms into several healthcare chatbots to see what insights they could provide. After analyzing the data, the chatbots recommended a specific medication to alleviate my symptoms. My friend, trusting the AI’s judgment, prescribed the medicine to me. However, in a surprising turn of events, my symptoms resolved on their own before I could even purchase the medication. While I was relieved to feel better, I couldn’t help but wonder about the accuracy of the chatbots’ diagnosis and whether I would have actually needed the prescribed treatment.

The Advantages of Conversational AI in Healthcare

Despite the potential risks, the emergence of Conversational AI in Healthcare offers several benefits. Firstly, it lowers the barrier for people seeking medical advice, making it more accessible and convenient. Secondly, it can help alleviate the immense pressure on doctors. My friend revealed that in China, doctors often work late into the night, which is a common occurrence. By handling minor cases and providing initial guidance, healthcare chatbots can reduce the workload of medical professionals, allowing them to focus on more critical cases.

Market Landscape: The Current State and Future of Healthcare Chatbots

The global healthcare chatbots market is expected to reach $703.2 million by 2025, growing at a CAGR of 21.5% from 2020 to 2025 (Grand View Research, 2020). This growth is driven by the increasing demand for accessible medical information, the need to reduce healthcare costs, and the growing adoption of smartphones and mobile applications. However, the market faces challenges such as concerns over data privacy, the need for regulatory compliance, and the potential for misdiagnosis or incorrect treatment recommendations (Deloitte, 2019).

Looking ahead, to maximize the benefits of healthcare chatbots while minimizing risks, it is essential to implement strict regulations and oversight to ensure the accuracy of diagnoses and recommendations. These AI systems should prioritize patient safety and provide clear guidance on when a hospital visit is necessary. As the technology continues to advance, collaboration among medical experts, researchers, and policymakers is crucial to develop ethical guidelines and best practices for the use of GenAI in healthcare (World Health Organization, 2022).

References:

Deloitte. (2019). The future of AI in healthcare: Challenges and opportunities. Deloitte Insights.

Grand View Research. (2020). Healthcare chatbots market size, share & trends analysis report by application, by end-use, by region, and segment forecasts, 2020-2025. Grand View Research.

Smith, J. (2023). Google Cloud’s Aashima Gupta on the future of AI in healthcare. HealthTech Magazine.

World Health Organization. (2022). Ethics and governance of artificial intelligence for health: WHO guidance. World Health Organization.

Please rate this

Using GenAI as a learning tool: A personal reflection

1

October

2024

No ratings yet.

Generative AI, to be more specific ChatGPT has become a key tool in my learning journey. I do not like to use ChatGPT as a way to do the work for me but rather as an instrument to make learning more fun and productive.

image from cegos.com

Some of the advantages I have found is the ability of this tool to turn complex concepts into more digestible and easy to understand explanations. Even recently when learning about UML diagrams I was a bit confused what exactly an object or class was and how they differed. instead of googling and spending time on finding a trustworthy source I could easily find the answer through ChatGPT. Of course there are pitfall to this, if you ask nuanced questions or vague ones the tool can give you a different answer than you actually need without knowing it. So it is important to ensure the questions you ask are clear and something you deem feasible to be asked to such a tool. As generative AI evolves even further, a time will come where it can smoothly ask questions back and ensure it understands the question completely which can reduce the risk of misinformation further.

Furthermore, when I am learning about a new software such as Notion or R, ChatGPT is the first platform I go to for simple functional questions such as “How do I create a progress bar”, or “How do you insert widgets”. This has always turned out correct and an easy way to find a solution.

Even existing platforms such as the famous Duolingo could gain a lot of value and productivity gains when making use of Generative AI improving the language learning experience for its users. Think of things like basic practice conversations which can be continued with the partial information that a learning student can provide. This is just one example, generative AI is not only limited to text-based information. With generated pictures and videos on the rise learning can be improved even further.

Please rate this

GenAI Video Clipping: Misinterpreting Context and (or) Going Viral

29

September

2024

No ratings yet.

Generative AI (GenAI) tools can be increasingly observed within the video-clipping industry for the creation of short videos. GenAI video clipping tools like OpusClip gain increased importance for creators and clippers. Creators and video-clippers use platforms for short videos like TikTok or Instagram which are heavily consumed and favored by many young people to gain virality and revenue. OpusClip as an example allows a creator or individual to turn long videos into shorts within seconds. Next to the fact that the services from OpusClip are costly, one is only required to insert a link and gets multiple shorts of the related video within seconds. 

What particularly sparked my interest here is that OpusClip provides a virality score with the possibility of the short going viral, therefore predicting the popularity of the short. This means that the GenAI from OpusClip can identify potentially viral sequences of a video and recognize what humans might prefer to see. By this, creators as a result can specifically focus on uploading shorts with a high virality score to increase their revenue, virality, and consumer engagement within the comment section. Additionally, the short videos can be directly uploaded to several social media channels within Instagram, TikTok, or YouTube (OpusClip, 2024). With the functionality of GenAI video clipping tools, let us look at the challenges these bring. With the example of a 2-hour-long video interview, our GenAI video clipping tool OpusClip can identify appealing sequences and turn them into shorts. While creating the short, there is one important aspect missing, namely the ability to identify the complete context. Identifying potentially viral sequences is of high importance not only to create virality scores for shorts but also to grab the attention of the viewer. This can lead to the creation of shorts where context will be potentially misinterpreted by the viewer, but at least the virality score is high a creator might think. OpusClip identifies a sequence that might grab the viewers’ attention but potentially misses the message’s complete meaning. Another aspect that one misses in such shorts is the personalized touch of the creator, but will this be compensated with the productivity of the creator in terms of the number of uploaded shorts? I guess this depends on the consumer of the short and his relation to the creator. For creators, GenAI video clipping enhances consumer engagement under viral shorts and therefore can increase their reach, while saving time and increasing revenue (Blanc, n.d.). However, there is an existing risk that their initial message might be misinterpreted or out of context. 

References

Opus. (n.d.). OpusClip: Repurpose your long videos into viral clips. Opus. https://www.opus.pro/

Submagic. (n.d.). Opus Clip review: AI video repurposing made easy. Submagic. https://www.submagic.co/blog/opus-clip-review#:~:text=Opus%20Cip%20harnesses%20AI%20to,%2C%20TikTok%2C%20and%20Instagram%20Reels

Please rate this