NutriNet – a personal assistant for your grocery shopping

18

October

2024

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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.

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Unlocking Culinary Potential: How tasteTAILOR elevates your Cooking Experience with Generative AI

18

October

2024

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In today’s fast-paced world, health-conscious individuals and families often struggle to find the time and inspiration to cook nutritious meals. Therefore, we are presenting tasteTAILOR: a revolutionary digital culinary assistant that harnesses the power of Generative AI (GenAI) to streamline your cooking experience. It is designed to create recipes tailored to users’ diet choices, available ingredients, and kitchen appliances – offering a unique and personalized way of cooking. To streamline the cooking experience even further, tasteTAILOR seamlessly integrates its ingredient list with partner supermarkets. TasteTAILOR even offers a social community where users can share their experiences in the kitchen, get inspiration from other users, and connect with individuals with similar culinary interests.

Objectives and Value Proposition

TasteTAILOR’s most important objective is to grant customers the highest level of user satisfaction possible by making meal planning easy, seamless shopping through APIs, and engaging with the community through food recommendations powered by GenAI. Fundamentally, tasteTAILOR is committed to reducing food waste and fostering sustainable and healthy cooking with recipes to fit every taste.
We at tasteTAILOR present a multi-dimensional value proposition. We propose new dishes tailored to specific tastes but it will also generate shopping lists in supermarket applications in a hassle-free manner. Our customers can benefit from the step-by-step visual and audio instructions so that cooking can become enjoyable for everybody. The community feature provides an exciting way to connect with other like-minded culinary enthusiasts. 

Target Customer Segments

TasteTAILOR’s services are a perfect fit for several customer segments, for instance, busy professionals who have limited time to cook  but desire healthy recipes, students who are on an extremely tight budget and advanced hobby cooks who want to experiment with different types of cuisine and connect with other home cooks. The application also applies to people with special diet needs, making it the most suitable solution for those looking for a personalized cooking experience.

Key Activities and Resources

Key activities of tasteTAILOR include the design of customized recipes, using the power of GenAI technology. The personalization power comes from the analysis of user behavior, tracking one’s activity in the community, and through capturing user’s (daily) preferences.  Logically, the key resource of tasteTAILOR is the GenAI system that is woven throughout the entire business model, from generating recipes to chatbots intended for user interaction, and analytics that are inducted continuously for better personalization.

Challenges and Solutions

While the integration of GenAI offers significant benefits to the tasteTAILOR platform, it also comes with potential pitfalls, such as data privacy issues or technical glitches. The platform has taken stringent actions to consider these risks, implementing robust security measures and algorithm enhancement. The education of users through tutorials and community building is another crucial component in the adoption of AI-powered cooking solutions.

The Future of Meal Planning

Among the new generation of meal-planning applications and technological substitutions in a broader perspective, tasteTAILOR stands at the very top of elevating one’s cooking experience. TasteTAILOR offers personalized solutions that are not just tailored to the individual but are also ecologically-conscious. Taking everything into account, tasteTAILOR is not a meal planner; instead, it is a community-driven solution to empower its users to cook healthy recipes based on their own preferences. TasteTAILOR powered by GenAI is making cooking joyful.

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

29

September

2024

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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

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The Rise of AI in Fashion: How Artificial Intelligence is Transforming Design and Retail

27

September

2024

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AI in Fashion Design

Artificial Intelligence is transforming the fashion industry leading to innovations in both design and retail activities. AI revolutionizes how designers create, how trends are predicted and how retailers manage their stock and interact with consumers with the analysis of large datasets and automated processes (Luce, 2018). In this blog, we’ll explore how AI is being applied across various stages of the fashion ecosystem, from creative design to personalized shopping experiences.

Using AI, designers blend technology and creativity. Machine learning alogrithms allow designers to analyze historical trends, consumer preferences and data to generate new or innovative designs. Fashion houses like Alexander McQueen have begun to incorporate AI into their design processes. This allows for experimenting with new styles and materials (Renee, 2023). In addition, AI contributes to sustainable fashion by helping brands reduce waste and optimize material choices, and production processes.

Trend Forecasting with AI

Predicting fashion trends has traditionally been a complex and instinct-driven process, but AI is providing brands with more accurate and data-driven insights. Machine learning algorithms scan social media platforms, online influencers, and sales patterns to identify emerging trends. Companies like Heuritech use AI to analyze millions of images on Instagram to predict future fashion trends months ahead of time (Poncelin, 2024). Brands react more quickly to consumer demands, reduce overproduction and meet better the market expectations due to the advanced forecasting.

AI-Driven Inventory Management

Inventory management is another area where AI is making a significant impact. AI systems can process vast amounts of sales data, seasonal trends, and even weather forecasts in order to help retailers optimize stock levels. AI helps brands maintain the right balance between supply and demand so that the chances of overstocking or stockouts are significantly reduced. Major fashion retailers like Zara and H&M have embraced AI to manage their inventories which leads to more efficient supply chains and less waste (Ünal et al., 2023). Therefore, retailers can ensure that popular items remain available while minimizing markdowns and unsold inventory.

Personalized Shopping Experiences

AI is enhancing the shopping experience by providing personalized recommendations and services tailored to individual customers. Retailers like ASOS and Stitch Fix use AI-powered recommendation engines and virtual stylists to analyze customer preferences and browsing behaviors. That way they deliver product suggestions uniquely suited to each shopper’s style (Fix, 2023). This personalization simultaneously improves customer satisfaction and helps retailers build stronger customer loyalty and increase sales.

AI is transforming the fashion industry by bringing innovation to design, trend forecasting, inventory management, and retail experiences. Brands that adopt AI technologies are staying ahead of consumer demands and improving efficiency and sustainability. As AI continues to advance, its role in fashion will only expand and lead to shaping the future of the industry in innovative ways.

References:

  1. Luce, L. (2018). Artificial Intelligence for Fashion: How AI is Revolutionizing the Fashion Industry. https://link.springer.com/content/pdf/10.1007/978-1-4842-3931-5.pdf
  2. Renee, K. (2023, December 15). How Artificial Intelligence is Revolutionizing the Fashion Industry. RYN. https://www.therynapp.com/post/how-artificial-intelligence-is-revolutionizing-the-fashion-industry
  3. Poncelin, C. (2024, June 28). How Heuritech forecasts fashion trends thanks to AI. Heuritech. https://heuritech.com/articles/how-heuritech-forecasts-fashion-trends-thanks-to-artificial-intelligence/
  4. Ünal, Ö. A., Erkayman, B., & Usanmaz, B. (2023). Applications of Artificial Intelligence in Inventory Management: A Systematic Review of the literature. Archives of Computational Methods in Engineering. https://doi.org/10.1007/s11831-022-09879-5
  5. Fix, S. (2023, June 29). How We’re Revolutionizing Personal Styling with Generative AI – Stitch Fix Newsroom. Stitch Fix Newsroom. https://newsroom.stitchfix.com/blog/how-were-revolutionizing-personal-styling-with-generative-ai/

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Digital Disruption: How Robotaxis Are Shaping the Future of Taxi Market

20

September

2024

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Current state of robotaxis

Since Google began developing autonomous driving technology in 2012, we have witnessed rapid advancements in the field. In 2020, China’s Baidu launched its autonomous driving trials, further accelerating the technology’s progress. By 2021, both Cruise and Waymo began limited commercial operations in the U.S. As of May 2024, Baidu’s RobotGo has launched Robotaxis across major cities in China. In the first half of 2024, RobotGo completed 680,000 rides in Wuhan, reflecting the growing adoption of Robotaxis in China.

How does Robotaxis work?

  • Customer experience

The customer experience of using a Robotaxi can be described as exceptionally smooth. Users simply request a ride through an app, much like Uber, by entering their pick-up and drop-off locations. The Robotaxi autonomously drives to the pick-up point, and customers unlock the door using the app. Once inside, customers can confirm the driving route, adjust the temperature, and even play music. Upon reaching their destination, the fare is displayed, and after confirming the bill and making payment, the ride experience is complete.

  • Algorithms and technologies behind Robotaxis

Robotaxis use technology like LIDAR, cameras, radar, and AI to navigate and operate without a driver. These cars have sensors to see what’s around them, software to plan routes and avoid obstacles, and controls for steering and braking. These technologies use high-definition mapping and, in some cases, vehicle-to-everything (V2X) communication to exchange data with infrastructure and other vehicles.

There are two main types of Robotaxis. The first involves retrofitting existing vehicles with sensors and software. This method is faster and cheaper but may limit design effectiveness. The second approach involves building vehicles from scratch, optimized for autonomy with advanced sensors and no manual controls, but at a higher cost and longer production time.

Robotaxis companies currently hire safety supervisors to make sure customers are safe. In-vehicle safety operators monitor the vehicle’s autonomous systems and are trained to take control in emergencies. Some companies are also transitioning to remote supervision, where operators monitor and intervene from a control centre.

The Impact of Robotaxis on Passengers, Drivers, and the Automotive Industry

  • For passengers

Robotaxis affect passengers in many ways. Fares are typically 60-80% of what Uber charges, making it a cost-effective alternative. Robotaxis are safer, especially for women travelling late at night. They follow traffic laws, so there is less speeding and reckless overtaking. Robotaxis are also quiet and private, which is good for business people.

However, passengers have concerns too. Some people think Robotaxis drive slowly or don’t handle traffic well because they follow the rules too strictly. There are also privacy concerns. Robotaxis have so many sensors that may capture personal information, which raises questions about passenger privacy. They could also be vulnerable to hacking, which could compromise passenger safety. Additionally, in situations of sudden network failure, passengers worry about how the Robotaxi would respond, highlighting the need for robust backup systems.

  • For Taxi Drivers

For taxi drivers, Robotaxis pose significant challenges. Robotaxis compete directly for passengers, reducing the customer base for traditional taxi drivers. Sharing the road with Robotaxis also introduces complications. As Robotaxis strictly follow traffic rules, they often hesitate to turn, occupy lanes for long periods or change lanes slowly, all of which can disrupt the flow of traffic and reduce the driving efficiency of taxi drivers, causing frustration and delays.

However, the situation creates new opportunities as well. Companies such as Robotgo are hiring numerous remote supervisors, which could represent a new career path for taxi drivers whose roles may be taken over by Robotaxis.

  • For Car industry

The rise of Robotaxis is transforming the automotive industry by shifting consumer preference from car ownership to mobility services, resulting in a decline in traditional vehicle sales. Manufacturers are adapting by redesigning vehicles specifically for autonomy, eliminating traditional controls to enhance passenger comfort and safety. Additionally, significant investments in advanced technologies, such as AI and sensor integration, are necessary to remain competitive and ensure the safe operation of these vehicles. As these changes take place, the industry will focus on innovation and the evolution of urban mobility.

Conclusion

In my view, Robotaxis represent the future of the taxi industry. I would definitely try and choose to ride in a Robotaxi, as I can no longer tolerate rude, reckless drivers. While there are currently many safety and technical concerns, I believe that with improved industry regulations and advancements in technology, Robotaxis will become increasingly sophisticated—much like many new technologies that faced initial skepticism but later gained widespread acceptance.

So, what are your opinions? Would you choose to ride in a Robotaxi instead of an Uber? Feel free to share your opinions in the comments!

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Seamlessness between Digital Ecosystems comes at a Heavy Price

19

September

2024

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We are living in a generation that yearns for convenience. The same can be said about the digital ecosystems provided by notable tech giants—Google, Amazon, Meta, Apple, and Microsoft (GAMAM)—which have become indispensable parts of our day-to-day lives. For instance, Apple’s ability to facilitate cross-connectivity among all its products and Microsoft’s 365 suite providing strong SaaS-based tools for productivity are prime examples of how simple it has become to interact with technology.

However, the ease between platforms and software costs digital giants a fortune, leading to speculation from regulatory bodies over potential violations of antitrust laws. Firms like GAMAM often produce incremental advances within their established systems but refrain from investing in new, digitally disruptive innovations to protect their revenue streams and existing user base. This strategy of focusing on refinement rather than disruption allows them to maintain control over vast ecosystems, raising concerns about reduced competition. Maintaining market position is their main focus, which has eventually led to domination and possible violations of antitrust laws.

The Monopoly Moment: Redefinition of Tech Regulation

Monopolistic practices first came under major scrutiny in the 1990s when Microsoft faced antitrust accusations for bundling its Internet Explorer browser with the Windows operating system, effectively pushing out competitors like Netscape Navigator. This practice not only harmed rivals but also limited consumer choice. Although the court initially ruled to break up the company, the decision was overturned, resulting in a settlement that required Microsoft to share its software interfaces with third-party developers.

The verdict of this case revolutionized the public’s perception of the tech industry, prompting governments to take a closer look at antitrust investigations involving companies like Google and Apple. Dozens of cases have been filed against these giants over the past few years.

Living Under the Microscope

Monopolistic practices leading to scrutiny include Google’s default search engine deals, Amazon’s use of non-public seller data for competitive advantage, and Microsoft bundling Teams with Office. While these practices create a frictionless and predictable user experience, they also make it difficult for smaller players to compete and lead to a digital lock-in for consumers. This reduces the incentive for these firms to innovate and encourages them to acquire or push out smaller firms that pose a threat.

There is a fundamental tension between innovation, consumer convenience, and market control. This seamless ecosystem can be both a blessing and a curse. The more entrenched these ecosystems become, the more competition shrinks, and technological advancements stagnate.

The question we must ask ourselves is whether today’s seamlessness comes at the expense of tomorrow’s innovation.

Sources

  • Altchek, A., & Shamsian, J. (2024). Google’s monopoly drama should have Apple, Meta, and Amazon nervous. Retrieved from https://www.businessinsider.nl/googles-monopoly-drama-should-have-apple-meta-and-amazon-nervous/
  • Hills, C. (2023). Big Tech’s dominance over US stocks poses no risk according to history. Retrieved from https://www.tradealgo.com/news/big-techs-dominance-over-us-stocks-poses-no-risk-according-to-history
  • Podesta, A., & Tsoni, M. (2022). Antitrust: Commission accepts commitments by Amazon barring it from using marketplace seller data, and ensuring equal access to Buy Box and Prime. Retrieved from https://ec.europa.eu/commission/presscorner/detail/en/ip_22_7777 
  • Zuber, L., & Simonini, S. (2024). Commission sends Statement of Objections to Microsoft over possibly abusive tying practices regarding Teams. Retrieved from https://ec.europa.eu/commission/presscorner/detail/en/ip_24_3446 

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Can even the oldest industries be platformized? VonWood tries to disrupt the timber marketplace

18

September

2024

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Two years ago I was asked by an acquaintance if I had time do a basic chore at his family member’s start-up. Some simple data entry work in Microsoft Excel. I agreed, and later that week found myself in a small office filled with three employees. Catalogs from Scandinavian sawmills were spread across the table. My task? Enter their product specifications into a spreadsheet. Their company was 2 months old, what exactly were they trying to do? Revolutionize one of the oldest existing industries; the timber industry.

The founders noticed a significant issue in the timber supply chain: numerous intermediaries, none of whom added real value. So, they created a platform to connect sawmills directly with customers—specifically, construction professionals. The model had clear potential: cutting out middlemen meant lower timber costs for buyers (provided they purchased in bulk), and sawmills could reach a broader customer base while offering lower prices, thereby boosting demand.

However, VonWood faces a familiar challenge for platform businesses—balancing supply and demand. While they’ve already raised €2.7 million in funding (VonWood, 2023), their key challenge is scaling both buyers and sawmills at the same pace. As more construction professionals use the platform, it becomes more attractive to sawmills, who can bypass middlemen. Yet, the platform must continue to offer enough variety and competitive pricing to entice new customers. Success hinges on achieving the network effects that fuel many platform businesses, but VonWood is still working toward that critical tipping point.

On the supply side, sawmills are intrigued by the platform’s potential to open new markets, but convincing them to join in significant numbers remains an ongoing effort. The founders emphasize that removing intermediaries not only makes timber more affordable but also makes pricing more transparent—a significant selling point. It is interesting to me – I would think to any BIM student – to keep track of their development. Even more so now that I learn more about information goods strategy. For instance, VonWood applied platform envelopment by partnering up with Finmid, a fintech company. This collaboration allows for the customers of the platforms to get access to financing for their timber (VonWood, n.d.)

The company adopts a transaction cut strategy, taking a small percentage from each sale. I figure the founders understand that pricing needs to remain competitive to gain traction. Charging too high a fee could deter early users, so the founders try to maintain a balance, working to encourage growth without sacrificing the platform’s financial sustainability. As the platform continues to grow, and demand-side economies of scale develop, the company can increase its fee accordingly.

VonWood remains in active pursuit of its ambitious vision: to revolutionize the timber trade by leveraging technology and platform dynamics. Although it has yet to reach the scale of a dominant marketplace, the foundation is being laid for a future where the platform can fully harness the power of network effects to transform an age-old industry

Sources

https://www.vonwood.com/nl/blog/collaboration-between-vonwood-and-finmid

https://www.vonwood.com/nl/blog/press-release-investment-announcement

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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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Deepfakes and digital business models

16

September

2024

5/5 (1)

Deepfakes are AI-generated media that can mimic real people’s appearances and voices. These have rapidly evolved from a technological curiosity to a significant force which reshape digital business models. Nowadays deepfakes offer a wide range of commercial applications from personalized advertising and virtual influencers to content creation and customer service automation (Herbert Smith Freehills, 2024; Ferraro et al., 2024). However, as the technology advances this brings complex ethical especially about misinformation. 

Digital business models are using deepfake technology to innovate and enhance customer engagement. Companies are exploring virtual influencers who engage with audiences and offer brands a new way to connect without the use of human influencers. Deepfakes also play a role in personalized marketing where tailored AI driven content creates more compelling advertisements. However, the rise of deepfakes goes hand in hand with ethical challenges. Which includes concerns about authenticity, consent, and misuse. As businesses adopt these technologies, they must carefully consider the potential risks alongside the opportunities.

Ethical challenges connected to the use of deepfakes are significant and particularly now when digital transformation spreads across industries. A major concern is the use in spreading misinformation such as deepfake videos of politicians on big social media platform like Meta and X. This as a result can have an undermining impact in public figures and institutions. Additionally, deepfakes involve consent and privacy issues. This because the media can be created and shared without the permission of an individual. Therefore, companies aiming or using this new technology must implement ethical guidelines that clearly label synthetic media and do their utmost best to prevent misuse and consent violations.  

As deepfakes influence digital business models companies must balance innovation with responsibility. While deepfakes offer immense potential in marketing, entertainment, and customer engagement, they also pose significant risks. Companies need to explore these opportunities but must also set ethical standards and develop safeguards to protect individuals and society. The future of deepfakes in business depends on leveraging their potential while carefully managing ethical implications.

Deepfakes in advertising – who’s behind the camera? | Herbert Smith Freehills (2024). https://www.herbertsmithfreehills.com/notes/tmt/2024-02/deepfakes-in-advertising-whos-behind-the-camera

Ferraro, C., Demsar, V., Sands, S., Restrepo, M., & Campbell, C. (2024). The paradoxes of generative AI-enabled customer service: A guide for managers. Business Horizons, 67(5), 549–559. https://doi.org/10.1016/J.BUSHOR.2024.04.013

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