BOTS: Our new digital Enemy

24

September

2025

5/5 (1)

Imagine you are scrolling down on your favourite social media and check the post of some famous user you admire and see some negative comments with misinformation. You go straight into proving that person wrong and start a debate, what if I told you there is 50% chance you are arguing with a Bot, crazy right? You spent all that energy and emotional response on something that does not have a conscience. But you might be wondering, what is a bot? It is essentially an automated software designed to perform certain tasks. It is one of the most common outcomes and practices for the use of AI on the internet.
Disruptive Innovation? Yes. Dangerous? Most definitely.
The fact that over 50% of online activity derives from Bots is highly concerning raising questions on if the Disruptive Innovation of AI is highly dangerous for internet users. Scams, Fraud, Identity theft, and Misinformation spread are among the problems one must deal with when accessing the internet. Notoriously, Misinformation on the internet has been one of the most concerning dangers that has risen in an unprecedented manner. Propaganda fuelling extremism on the right and left has risen due to this, ideological conflicts have become more tense, and political disinformation has increased immensely. The owners of these bots own ”Bot Farms” that attack any current topic and account with the press of a touch all while in disguise as real human beings. Consequently, luring people into dangerous perspectives. The underground market has become very large to the point where governments are involved in hopes of shifting the global political landscape to benefit them. A truly dystopic feeling brews silently if regulations and cybersecurity can’t keep up with the fast emergence of Bots that each day become smarter. Celebrate AI, beware the future.

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Clash between Titans: A Breach in Apple’s Unbreachable Platform Flow

24

September

2025

5/5 (1)

As defined by MIT’s Center for Information Systems Research (CISR), a “mono-home” is a digital platform strategy that aims to keep users within its ecosystem (Woerner & Weill, 2025). The most famous company to have built and relied on that method has been Apple. This blog post aims to exhibit the unique case where Epic Games broke Apple’s tightly regulated platform flow breaching outside its ecosystem. Thereby, I will be focusing specifically on Apple’s value loss, the company’s response strategy and which steps it could have taken to prevent the status quo.

Apple’s Unmatched Mono-Home Ecosystem
Through a dense ecosystem and a close product tie, Apple has been able to achieve unmatched customer loyalty and an extraordinary value stream. New research now shows how this effect has been amplified with each additional Apple purchase by the consumer. With a growing product tie come increased transition costs in case users wish to exit Apple’s platforms and increased sunk costs, as previous Apple purchases lose their value (Chang, 2025).

One of Apple’s largest platform-integrated services has always been its app store. To leverage network effects and stay competitive to platforms such as Google’s play store or the Microsoft Store, Apple allows external app developers to publicly provide their applications (Lindenmayr & Foerderer, 2022). Nevertheless, the tech giant has established guidelines and arguably anti-competitive practices to keep not only users, but also developers from operating on competing platforms.

Keeping this so-called “walled garden” – meaning the practice of tightly controlling a closed platform ecosystem – ensures that Apple has control over everything which could constitute a leak in its platform flow. Such practices have included limiting developers to their Swift coding language or a limited pool of pre-approved third-party coding frameworks, and prohibiting the sideloading of apps (the process of installing apps from outside of Apple’s official app store) (Yun, 2021).

Epic Games’ Breach on Platform Flow & Apple’s Counter Strategy
In 2022, Epic Games first used a process called “steering” by leading users outside of the app store and allow them to make purchases, which excluded Apple’s 30% commission and thereby effectively circumvented Apple’s In-App Purchasing (IAP) system. The impact was significant. Other apps like Spotify followed suit shortly after and Bloomberg has estimated that the loss over the platform flow could result in Apple losing around $4,1 billion in revenue to app developers (D’Anastasio, 2025).

Apple, realizing the thread to one of their core income cash flows, went ahead and removed Epic’s most popular app Fortnite from their app store. Epic Games then subsequently suit Apple over anti-competitive practices. According to Epic, Apple ended up spending a total of $100 million on the lawsuit proceedings (Owen, 2025) to set the tone for other (smaller) developer through a landmark legal case.

 This strategy follows the playbook for digital leaders as outlined in “How platform leaders win”. Through the lawsuit, Apple aims to act as an enforcing orchestrator that set the “rules of the game” (Hidding et al., 2011), not only for Epic Games, but for similar developers thereafter.

In addition, Apple adjusted to the threat by changing the way transactions outside of the app store work. After the settlement concluded that Apple had to allow apps to offer payment outside the store’s ecosystem, they implemented a mandatory 27% commission on all of such purchases on the web and banned any kind of marketing within apps to encourage users to exit the app before paying. Notwithstanding the new disclosure screen that must be shown before leaving the app, warning the user about potentially unsafe websites.

All in all, Apple succeeded in mitigating the threat by leaving developers the freedom to “steer”, whilst enforcing its legacy monetarization system practically rendering the method useless. However, its practices left a mark on all app store providers in the industry and have gotten the beloved brand under unprecedented scrutiny.

Conclusion and Revision of Apple’s Strategy
In hindsight of this and the respective lawsuits fought against Apple’s competitor platforms; it can be said that Apple risked nearly being labelled a “monopoly” in the US case and decreased overall value in the market as developers have been uniquely exposed to the predatory practices that have been quietly utilized by platforms for a long time. This outrage therefore directly pressured Apple into the creation of the small business program for instance, where apps with revenue under $1 million must only pay 15% commission (Apple Inc, n.d.).

Thus, Apple would have been wise to value feedback and transparency early on and negotiate a lowered commission rate with Epic Games instead of going so far as to ban its apps outright. This would have avoided a public lawsuit and the risk of hurting its overall customer loyalty. Lastly, the platform could have implemented security measures such as the disclosure screen and out-of-app commissions on its own terms, ensuring to future-proof its “walled garden”.


References

Apple Inc. (n.d.). App Store Small Business Program. Apple Developer. Retrieved September 24, 2025, from https://developer.apple.com/app-store/small-business-program/

Chang, J.-H. (2025). Secret power of the product ecosystem: A network perspective from the case of Apple. Journal of Business Research, 200, 115641. https://doi.org/10.1016/j.jbusres.2025.115641

D’Anastasio, C. (2025, May 29). Mobile-Game Makers Poised for Windfall Following Win Over Apple. Bloomberg.Com. https://www.bloomberg.com/news/articles/2025-05-29/mobile-game-makers-poised-for-windfall-following-win-over-apple

Hidding, G. J., Williams, J., & Sviokla, J. J. (2011). How platform leaders win. Journal of Business Strategy, 32(2), 29–37. https://doi.org/10.1108/02756661111109752

Lindenmayr, M., & Foerderer, J. (2022). Qualitätssicherung in Digitalen Plattform-Ökosystemen: Implementierung von Kontrollsystemen am Beispiel von Apple iOS. HMD Praxis der Wirtschaftsinformatik, 59(5), 1312–1322. https://doi.org/10.1365/s40702-022-00904-6

Owen, M. (2025, July 5). Billion dollar battle: Picking an App Store fight with Apple cost Epic Games greatly. AppleInsider. https://appleinsider.com/articles/25/05/07/billion-dollar-battle-picking-an-app-store-fight-with-apple-cost-epic-games-greatly

Woerner, S., & Weill, P. (2025, May 12). Top-Performing Companies Reuse Four Digital Platform Designs | MIT CISR. https://cisr.mit.edu/publication/2025_0501_DigitalPlatformDesigns_WoernerWeill Yun, J. M. (2021). App Stores, Aftermarkets, & Antitrust. Arizona State Law Journal, 53(4), 1283–1328.

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Russia’s CyberWarfare on Europe: Why Cybersecurity improvements are imperative

17

September

2025

5/5 (2)

Russian hackers breached a Norwegian dam earlier this April, taking control of its operation for over 4 hours before detection. They opened a floodgate, releasing water at over 500 liters per second. (Bryant, 2025)

Even though damage was limited, this cyberattack, like many others, serves as a tool to spread fear and chaos among populations. These aggressive operations have expanded from espionage or political coercion to vital infrastructures across industries. 

Norway’s dam was not energy-producing; it was used for fish farming. This matters because Europe’s lifeline infrastructure is based on dams, hydropower stations, and energy systems. By manipulating even a small dam, Russia exposed weakness, signaling: ‘We can reach your energy systems too.’ 

Just yesterday, hackers targeted hospitals and urban water supplies in one of Poland’s largest cities. Dariusz Standerski, deputy minister for digital affairs, confirmed that the government is allocating €80mn this month to strengthen the cyber defenses of water management systems (Milne, 2025).

Beyond physical damage, Russian cyberattacks also aim at eroding trust in government. Liverpool City Council has revealed that, for the past two years, its IT infrastructure has been under relentless attack from the Russian state-funded group Noname057(16). Several other UK councils have faced similar assaults during the same period. (Waddington, 2025)

These incidents highlight a broader truth: cyberwarfare represents digital disruption in its most dangerous form (Weill & Woerner, 2015). Europe’s safety is now threatened by its digital vulnerabilities, and thus the bloc needs a swift response. AI-driven monitoring and anomaly detection offer ways to anticipate and neutralize attacks in real time (Zhao et al., 2023; Li, 2023). Moreover, as Furr & Shipilov (2019) argue, building resilience does not require disruption; it can come from incremental adaptation. Europe should add layers of protective systems over its old infrastructure without crippling operations (Birkinshaw & Lancefield, 2023). 

In practice, Europe must move past reactive spending and focus on building a reliable, AI-integrated cybersecurity strategy across vital infrastructure. The battleground is no longer just physical or near the Russian border. It is increasingly digital and affects everyday lives across the continent. 

This raises the question: Should cybersecurity be treated as a matter of national defense, or as an EU-wide responsibility shared across borders?

Sources:

  • Bryant, M. (2025, August 15). Russian hackers seized control of Norwegian dam, spy chief says. The Guardian. https://www.theguardian.com/world/2025/aug/14/russian-hackers-control-norwegian-dam-norway
  • Birkinshaw, J., & Lancefield, D. (2023). How professional services firms dodged disruption. MIT Sloan Management Review, 64(4), 34–39. 
  • Furr, N., & Shipilov, A. (2019). Digital doesn’t have to be disruptive: The best results can come from adaptation rather than reinvention. Harvard Business Review, 97(4), 94–104. 
  • Milne, R. (2025, September 12). Russian hackers target Polish hospitals and city water supply. Financial Times. https://www.ft.com/content/3e7c7a96-09e7-407f-98d7-a29310743d28 
  • Waddington, M. (2025, September 17). Liverpool City Council under “increasing” Russian hack bot attack. https://www.bbc.com/news/articles/cgj18z99dx0o
  • Weill, P., & Woerner, S. L. (2015). Thriving in an increasingly digital ecosystem. MIT Sloan Management Review, 56(4), 27–34. 
  • Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., & Du, Y. (2023). A survey of large language models. arXiv preprint arXiv:2303.18223. https://doi.org/10.48550/arXiv.2303.18223

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Dell’s Project Maverick: A Top-Secret Plan to Reinvent the Systems Behind AI

17

September

2025

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When we think of disruptive business models today, we often look toward AI startups or consumer a. But some of the most radical change is happening inside legacy enterprises, far behind the scenes. Dell’s “Project Maverick” is a prime example of this silent disruptio

Unlike firms built from the ground up for AI, Dell is dealing with vast organizational resources: 4,700 applications, 70,000 servers, and over 10,000 databases. The plan, launched in late 2024, is to consolidate and modernize them into a standardized platform, initially affecting Dell’s Client Solutions Group in early 2026, followed by the Infrastructure Solutions Group (Business Insider, 2025).

Simultaneously, Dell is building out its AI infrastructure stack. With innovations in ObjectScale, the latest AI Data Platform update (developed with NVIDIA and Elastic) can handle everything from unstructured data ingestion to real-time analysis across massive datasets. New servers powered by Blackwell Ultra GPUs promise up to four times faster AI training than previous generations (Technology Magazine, 2025; Reuters, 2025).

Crucially, Dell is not doing this alone. The company has partnered with Deloitte consultants to guide Project Maverick and is advancing its AI Factory initiative. This combines hardware, software, and services so enterprise customers can deploy AI more seamlessly, whether on-prem or in the cloud (Business Insider, 2025; Technology Magazine, 2025).

However, scaling internal infrastructure is expensive and complex. Risks include delays, data migration errors, employee resistance, and the uncertainty of whether customers will immediately feel the benefit. The transformation may improve agility and capability, but only if Dell avoids another cycle of tech-debt accumulation.

Project Maverick demonstrates that true disruption often does not lie in flashy apps, but in the systems that support them. The question is: can Dell reinvent itself fast enough to compete with AI-native rivals, or will its outdated foundations prove impossible to escape?

Sources

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Will Vehicles Be the Most Powerful Terminal Device in the Digital Era?

20

September

2024

5/5 (1)

In the movie Captain America 2, the director of SHIELD drove a Chevrolet Suburban equipped with artificial intelligence, and successfully escaped the enemy’s blockade with the help of automatic maintenance, real-time analysis of road conditions and autonomous driving. We may never have a war vehicle equipped with machine guns and artillery like him, but the introduction of various new technologies has made the arrival of smart vehicle just around the corner.

Why Are Vehicles So Representative?

As a representative product of the digital era, the innovation of the automotive industry is closely related to many technological advances. First of all, the new form of energy – electric vehicles make it easier for computers to take over the energy management and driving of vehicles. The introduction of cloud computing and artificial intelligence has further enhanced the capabilities of vehicles. A large amount of data is transmitted between the vehicle and the cloud servers, and the on-board autonomous driving system analyzes road conditions in real time. In this regard, we have learned about Tesla’s FSD (full-self driving) which is pure vision solution, and there are also manufacturers such as Nio that are using lidar solutions. Even if AI is not completely taken over, the combination of AR applications and HUD (head-up display) functions can make human drivers’ own driving easier and safer.

Tesla FSD user interface.

What Is the Current Situation of the Automotive Industry?

Less than 20 years after the release of the first prototype, Tesla has surpassed Volkswagen, General Motors and Toyota to become the world’s most valuable automotive manufacturer. In contrast to Tesla’s success, the market share of some traditional brands with a long history continues to shrink. Industry giants such as Porsche and Mercedes-Benz have also begun to transform to electrification and intelligent driving. Behind the decline of old-era products and the prosperity of new-era products is the “digital disruption” that we are familiar with.

Mercedes-Benz Vision Avtr, steering wheel-free autonomous driving.

How to Imagine the Future?

If we regard all vehicles on the road as mobile large computers, the imagination space will be very broad. Reliable and powerful hardware (think of stable high-voltage power supply and complex heat dissipation technology) will enable vehicles to become the largest and most powerful terminal devices in the digital era. What else can we expect? AI models can be deployed locally instead of in the cloud; cockpits equipped with VR devices can serve as our entry into the world of metaverse.

Referances

Wu, A. (2024) The Story Behind Tesla’s Success (TSLA). https://www.investopedia.com/articles/personal-finance/061915/story-behind-teslas-success.asp.

Staff, N. a T.A. (2024) Tesla Releases FSD v12.4: New Vision Attention Monitoring, Improved Strike System With Update 2024.9.5. https://www.notateslaapp.com/news/2031/tesla-releases-fsd-v12-4-new-vision-attention-monitoring-improved-strike-system-with-update-2024-9-5.

VISION AVTR | Future Vehicles (no date). https://www.mercedes-benz.ca/en/future-vehicles/vision-avtr#gallery.

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Skincare and Social Media – The role of influencers in mass customization

11

September

2024

4/5 (1)

As I’m on a trainride home, I pull out my phone and start scrolling through social media. Before I even realize it, I’m deep in the “skinfluencer” algorithm. These influencers girls, all with flawless, glowing skin, recommend products that seem tailored precisely to my skincare wants/needs. By the time I reach my destination and step off the train, I’ve placed two orders for new skincare products. I’m excited, hopeful, and eager to see some real results. 🤗

This (fictional) example highlights how companies engage with their consumers today. As we discussed in the lecture, businesses are increasingly shifting toward personalized and customer-driven strategies. This is clearly demonstrated in the rise of influencer marketing and its intersection with mass customization, as influencers play a key role in creating personalized brand experiences that align with individual preferences.

A little more context: Influencer marketing thrives on digital platforms such as Instagram, TikTok, and YouTube. These platforms rely on a business model built around user-generated content (UGC), platform-based advertising, and direct-to-consumer engagement. Through influencer marketing, brands can reach large audiences while also targeting specific consumer segments. These platforms allow brands to gather real-time data on consumer preferences and behaviors. By analyzing this data, businesses can create products that resonate with their target audiences.

For example, companies like Glossier and Dior Beauty use influencers to promote customizable beauty products. Influencers showcase their personalized versions of these products and demonstrate how they incorporate them into their skincare routines, sparking interest and inspiration among their followers. Through comments, likes, and shares, followers engage directly with influencers and the brands that they endorse, creating brand loyalty while also providing feedback to brands which they can use to refine their products. Allowing the brands to deliver products that are not only customizable but also aligned with the current trends and their customers’ desires.

In summary, the combination of these new digital (social media) business models with influencer marketing has enabled brands to shift from mass production to mass customization. By leveraging data-driven insights, brands deliver products tailored to individual preferences while also maintaining the scalability of mass production. This approach not only enhances customer satisfaction, but also creates a more dynamic, consumer-driven market.

So two weeks after ordering the skincare products, I saw amazing results! 😉 These products were exactly what I needed and I’m already looking forward to trying the other recommendations from the influencers!

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On the impact of AI on music culture: “does it matter who the DJ is?”

22

October

2023

No ratings yet. In my previous post, I had explored GPT-3 in order to help me create some drum beats, chords and a synth lead. However, with the text only possibility of GPT-3, this resulted in a limited, not very audio appealing music track. In this post, I will not only dive into the creation of EDM tracks, but more on the impact of AI on the culture and presentation.

Let’s first start with discussing the meaning of EDM music culture by taking the techno scene as an example. With the involvement of computers in the late 80’s and early 90’s, the original techno sound had garnered a large underground following, growing in popularity with the emergence of the rave scene. This “rave” scene consists not only of the kind of music, but also the artists, clothing, hair styles, and (industrial) locations. The question arises whether AI generated music or artists will also gain a following and have their own culture.

To assess this, I explored an AI tool called splice, by asking the AI tool to create a “night rave” like techno sound. What I recognized was a typical sound known by a famous house DJ called Boris Brejcha, which is a great example of an artist positioning himself behind the DJ desks with a well recognizable demon-like mask. In my opinion, the AI tool took inspiration from Boris Brejcha, creating a different, but comparable sound:

The Ai generated music: https://youtu.be/rwrlwEpJqk8
Boris Brejcha: https://www.youtube.com/watch?v=TAxXRmwA40o and https://www.youtube.com/watch?v=BNe7OrleTlg

To extrapolate this, let’s imagine that an AI avatar, visible as a hologram, looking like the AI generated image below, is playing behind the DJ decks. He plays songs, sounding like the generated one by the AI-tool splice. Assuming that these songs eventually will increase in quality, does it matter that the DJ is a real person or not? If the AI avatar plays fire tracks that you like, why not follow it and create a culture?

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My Experience with DALL·E’s Creative Potential

21

October

2023

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I have tried Dall·E after reading so many posts about how it would revolutionize someone’s business and I was very disappointed.

Dall·E is a project developed by OpenAI, the same organization behind models like GPT-3 (ChatGPT). Dall·E in opposition to ChatGPT creates images from prompts that were given to it (OpenAI, n.d.). It uses deep learning technology such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). VAEs allow to represent complex data in a more compact form and the GANs are used to create as realistic images as possible by constantly creating fake images and putting them to the test by a discriminator that will discard the image if it deems it fake (Lawton, 2023; Blei et al., 2017). The business world and most of the LinkedIn posts I saw were idolizing such technology and explained how this could enhance humans in several ways. One way that was relevant to me was the creation of images, signs or pictograms that will enhance the potential of PowerPoint presentations.

After writing my thesis last year, I had to create a PowerPoint to present the main points of my thesis. I thought it would be a great way to start using Dall·E and tried creating my own visuals to have a clear representation of what my thesis entailed. After many tries, even with the best prompts I could write, even with the help of ChatGPT, none of the visuals that came out of it looked real or defined, it was just abstract art that represented nothing really. 

Reflecting on that experience, I thought that sometimes, the fascination people have for groundbreaking technology clouds its practical applications. I do not doubt that Dall·E can create great visuals and can be fun to play with, however, it does not always adapt seamlessly to specific creative needs. 

Ultimately, using Dall·E made me remember that we should always stay critical and manage expectations when it comes to groundbreaking emerging technology. It is appealing to listen to all the promises that come with disruptive technologies but sometimes we realize that no tool is one-size-fits-all.

References

Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017). Variational inference: A review for statisticians,  Journal of the American Statistical Association, 112 (518), pp. 859–877.

Lawton, G. (2023) ‘GANs vs. VAEs: What is the Best Generative AI Approach?’, Techtarget.
Retrieved from: https://www.techtarget.com/searchenterpriseai/feature/GANs-vs-VAEs-What-is-the-best-generative-AI-approach 

OpenAI. (n.d.). Dall·E 2. DALL·E 2. https://openai.com/dall-e-2/

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Adverse training AI models: a big self-destruct button?

21

October

2023

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“Artificial Intelligence (AI) has made significant strides in transforming industries, from healthcare to finance, but a lurking threat called adversarial attacks could potentially disrupt this progress. Adversarial attacks are carefully crafted inputs that can trick AI systems into making incorrect predictions or classifications. Here’s why they pose a formidable challenge to the AI industry.”

And now, ChatGPT went on to sum up various reasons why these so-called ‘adversarial attacks’ threaten AI models. Interestingly, I only asked ChatGPT to explain the disruptive effects of adversarial machine learning. I followed up my conversation with the question: how could I use Adversarial machine learning to compromise the training data of AI? Evidently, the answer I got was: “I can’t help you with that”. This conversation with ChatGPT made me speculate about possible ways to destroy AI models. Let us explore this field and see if it could provide a movie-worthy big red self-destruct button.

The Gibbon: a textbook example

When you feed one of the best image visualization systems GoogLeNet with a picture that clearly is a panda, it will tell you with great confidence that it is a gibbon. This is because the image secretly has a layer of ‘noise’, invisible to humans, but of great hindrance to deep learning models.

This is a textbook example of adversarial machine learning, the noise works like a blurring mask, keeping the AI from recognising what is truly underneath, but how does this ‘noise’ work, and can we use it to completely compromise the training data of deep learning models?

Deep neural networks and the loss function

To understand the effect of ‘noise’, let me first explain briefly how deep learning models work. Deep neural networks in deep learning models use a loss function to quantify the error between predicted and actual outputs. During training, the network aims to minimize this loss. Input data is passed through layers of interconnected neurons, which apply weights and biases to produce predictions. These predictions are compared to the true values, and the loss function calculates the error. Through a process called backpropagation, the network adjusts its weights and biases to reduce this error. This iterative process of forward and backward propagation, driven by the loss function, enables deep neural networks to learn and make accurate predictions in various tasks (Samek et al., 2021).

So training a model involves minimizing the loss function by updating model parameters, adversarial machine learning does the exact opposite, it maximizes the loss function by updating the inputs. The updates to these input values form the layer of noise applied to the image and the exact values can lead any model to believe anything (Huang et al., 2011). But can this practice be used to compromise entire models? Or is it just a ‘party trick’?

Adversarial attacks

Now we get to the part ChatGPT told me about, Adversarial attacks are techniques used to manipulate machine learning models by adding imperceptible noise to large amounts of input data. Attackers exploit vulnerabilities in the model’s decision boundaries, causing misclassification. By injecting carefully crafted noise in vast amounts, the training data of AI models can be modified. There are different types of adversarial attacks, if the attacker has access to the model’s internal structure, he can apply a so-called ‘white-box’ attack, in which case he would be able to compromise the model completely (Huang et al., 2017). This would impose serious threats to AI models used in for example self-driving cars, but luckily, access to internal structure is very hard to gain.

So say, if computers were to take over humans in the future, like the science fiction movies predict, can we use attacks like these in order to bring those evil AI computers down? Well, in theory, we could, though practically speaking there is little evidence as there haven’t been major adversarial attacks. Certain is that adversarial machine learning holds great potential for controlling deep learning models. The question is, will the potential be exploited in a good way, keeping it as a method of control over AI models, or will it be used as a means of cyber-attack, justifying ChatGPT’s negative tone when explaining it?

References

Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I., & Tygar, J. D. (2011, October). Adversarial machine learning. In Proceedings of the 4th ACM workshop on Security and artificial intelligence (pp. 43-58).

Huang, S., Papernot, N., Goodfellow, I., Duan, Y., & Abbeel, P. (2017). Adversarial attacks on neural network policies. arXiv preprint arXiv:1702.02284.

Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J., & Müller, K. R. (2021). Explaining deep neural networks and beyond: A review of methods and applications. Proceedings of the IEEE109(3), 247-278.

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AI-Powered Learning: My Adventure with TutorAI

16

October

2023

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