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.

Please rate this

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!

Please rate this

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?

Please rate this

My Experience with DALL·E’s Creative Potential

21

October

2023

No ratings yet.

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/

Please rate this

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

21

October

2023

No ratings yet.

“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.

Please rate this

AI-Powered Learning: My Adventure with TutorAI

16

October

2023

No ratings yet.

Subscribe to continue reading

Subscribe to get access to the rest of this post and other subscriber-only content.

Please rate this

Weapons of mass destruction – why Uncle Sam wants you.

14

October

2023

No ratings yet.

The Second World War was the cradle for national and geopolitical informational wars, with both sides firing rapid rounds of propaganda at each other. Because of the lack of connectivity (internet), simple pamphlets had the power to plant theories in entire civilizations. In today’s digital age, where everything and everyone is connected, the influence of artificial intelligence on political propaganda cannot be underestimated. This raises concern as, unlike in the Second World War, the informational wars being fought today extend themselves to national politics in almost every first-world country.

Let us take a look at the world’s most popular political battlefield; the US elections; in 2016, a bunch of tweets containing false claims led to a shooting in a pizza shop (NOS, 2016), these tweets had no research backing the information they were transmitting, but fired at the right audience they had significant power. Individuals have immediate access to (mis)information, this is a major opportunity for political powers wanting to gain support by polarising their battlefield.

Probably nothing that I have said to this point is new to you, so shouldn’t you just stop reading this blog and switch to social media to give your dopamine levels a boost? If you were to do that, misinformation would come your way six times faster than truthful information, and you contribute to this lovely statistic (Langin, 2018). This is exactly the essence of the matter, as it is estimated that by 2026, 90% of social media will be AI-generated (Facing reality?, 2022). Combine the presence of AI in social media with the power of fake news, bundle these in propaganda, and add to that a grim conflict like the ones taking place in East Europe or the Middle East right now, and you have got yourself the modern-day weapon of mass destruction, congratulations! But of course, you have got no business in all this so why bother to interfere, well, there is a big chance that you will share misinformation yourself when transmitting information online (Fake news shared on social media U.S. | Statista, 2023). Whether you want it or not, Uncle Sam already has you, and you will be part of the problem.

Artificial intelligence is about to play a significant role in geopolitics and in times of war the power of artificial intelligence is even greater, luckily full potential of these powers hasn’t been reached yet, but it is inevitable that this will happen soon. Therefore, it is essential that we open the discussion not about preventing the use of artificial intelligence in creating conflict and polarising civilisations, but about the use of artificial intelligence to repair the damages it does; to counterattack the false information it is able to generate, to solve conflicts it helps create, and to unite groups of people it divides initially. What is the best way for us to not be part of the problem but part of the solution?

References

Facing reality?: Law Enforcement and the Challenge of Deepfakes : an Observatory Report from the Europol Innovation Lab. (2022).

Fake news shared on social media U.S. | Statista. (2023, 21 maart). Statista. https://www.statista.com/statistics/657111/fake-news-sharing-online/

Langin, K. (2018). Fake news spreads faster than true news on Twitter—thanks to people, not bots. Science. https://doi.org/10.1126/science.aat5350

NOS. (2016, 5 december). Nepnieuws leidt tot schietpartij in restaurant VS. NOS. https://nos.nl/artikel/2146586-nepnieuws-leidt-tot-schietpartij-in-restaurant-vs

Please rate this

How AI might disrupt health care as we know it

29

September

2022

5/5 (3)

Artificial intelligence is everywhere. It can be used in almost any sector, with dozens of use cases. And one of the most exciting sectors AI could have a huge impact on is health care. According to IBM and Deepminds Healthcare, AI has the capability to help in early stages to specify certain diagnose of patients in a very accurate way. Further use cases of AI would be in decision making processes, care delivery, chronic care management and self-care.

Solutions which are already being used are: AI replacing repetitive task. Think about administration of patients, which now still takes a lot of time from doctors and nurses. Having these repetitive tasks reduced gives the doctors more time to focus on what they were meant to be doing in the first place: be doctors.

A second use case, one which needs more development, studying, and especially data and data analysis is AI predictive health care. In this case through the use of Big Data analytics AI can predict in what state a patient is at, and with this data prevent patients in an early stage to develop into more severe cases. With this insight, health care will make a switch from taking care of ill people, to prevent people get severe illnesses in the first place. Preventing people from getting sick, will not only save huge amounts of time and energy, but also save millions in current hospital costs.

A third and final use case I would like to bring up is precision medicine. Through AI and data analysis doctors can use this tool to take better decisions in their evaluation process. Think of someone who has a fracture, more often than not doctors might miss a genuine fracture (false negative) or on the other hand, classify something as a fracture (false positive). Through AI this decision-making process can be automized. Doctors can still give their insights on certain situations. However, if AI gives a completely different analysis than what the doctor expects the doctor here has the advantage to get triggered to pay extra attention to this case.

These are just three of the use cases which can immensely help our health care system. There are dozens of other applications for AI in healthcare. Where do you think AI can contribute the most in our healthcare system and why?

https://www.computerweekly.com/news/252524829/The-challenges-of-verifying-AI-for-healthcare

Please rate this

How data will save us from the next pandemic

10

October

2021

No ratings yet.

Even though epidemiologists have warned us about the threat of a pandemic, the world was far from prepared when the current COVID-19 pandemic hit. One and a half years later, we are finally getting our everyday lives back. However, looking back on the COVID-19 outbreak, what will hindsight tell us. More importantly, what have we learned to better prepare for the next pandemic.

Currently, the systems we have to combat the pandemic are too slow. First, a new case needs to be reported to the authorities and then recorded by the World Health Organization (WHO). They gather the data in their Global Outbreak Alert and Response Network to analyze and identify if an outbreak could be harmful. As we have seen with the current pandemic, COVID-19 had already spread around the globe by the time the WHO communicated their conclusion. Hence, to reduce the threat from new diseases, data worldwide has to be continuously gathered and analyzed for a quicker response. 

As seen from Israel’s experience analyzing real-world data and quick response helped decrease the number of serious infections. The country was a pioneer in rolling out the Pfizer vaccine to more than half of its population. Also, closely tracking their results, Israel’s hospitalizations and infections were quick to decline. Nevertheless, there are more ways to combat and even prevent disease outbreaks utilizing current technologies and data.

In Thailand, they believe that the community should be more involved in preventing outbreaks. A Thai national developed a digital surveillance app called Participatory One Health Disease Detection (PODD) for detecting diseases in animals that could eventually pass to humans. This system relies on volunteers to report data to identify disease outbreaks and will then notify the research organizations. Two hundred ninety-six volunteers reported 1029 abnormal events in their environment during a trial, including sick or diseased animals. Afterwards, a report stated that a total of 36 potential disease outbreaks were successfully detected and controlled.

The WHO is designing a hub as part of their Health Emergencies Programme; this will bundle the resources they are already utilizing. Diverse partnerships in multiple disciplines, data, the latest technology and intelligence will all be combined and shared for governments worldwide to use. This hub will support experts and health organizations to create better forecasts, detect and assess and pandemic risks faster. 

Everyone will have to contribute to preventing the next pandemic, starting with the global agencies down to the individuals. With the right experts to analyze the correct data, we can understand the ongoing impact of COVID-19 and gain the necessary insights to take appropriate actions to detect and hopefully prevent a future pandemic.

Sources:

https://www.ft.com/content/057ce6a6-c92d-4322-91fe-6b5de0e7c0bf
https://medcitynews.com/2021/09/we-can-prevent-the-next-pandemic-with-data/
https://news.un.org/en/story/2021/09/1098912
https://www.scientificamerican.com/article/how-real-world-data-can-help-us-better-prepare-for-the-next-pandemic/

Please rate this

Could DAOs be the future of business?

9

October

2021

No ratings yet.

At a basic level, such a stance makes sense. After all, decentralized finance (DeFi) is taking off starting from the idea that it is the inevitable successor of the traditional financial system. Moreover, NFTs are experiencing enormous success in a similar way. It is thus not surprising that a growing number of experts consider decentralized autonomous organizations, or DAOs, with their organization rules transparently programmed on blockchain, as the future of work. DAOs are seen as new structures that will replace the obsolete hierarchical ones of centralized companies. Yet, among non-experts, DAOs are either unknown or barely understood. Mainstream media, financial experts and regulators have occasionally shown minimal knowledge of DeFi and NFT, but DAOs remain largely a foreign concept – perhaps best known to blockchain novices for the infamous “The DAO” hack. , a first DAO investment experiment that collapsed in 2016.

DAOs are the ability of blockchain technology to provide a digital and secure ledger that tracks financial interactions on the internet and counteracts forgery through the concepts of timestamp, of trust and its presence in a distributed or non-centralized database. In recent years, DAOs have probably taken more steps forward in terms of development than any other blockchain industry. Many DeFi and NFT projects are governed by DAO; a large and growing percentage of the total market capitalization of approximately $2 trillion in cryptocurrencies is managed by these facilities. The tools and features have seen significant updates thanks to the work of organizations such as Colony, Aragon, and Coordinape.

“There is a group of Generation Z people who feel ripped off by late-stage capitalism,” said Kevin Owocki, CEO of the DAO-led grant organization Gitcoin. “We have inherited this economy where climate change is a big problem, disinformation is a big problem, where we don’t trust our institutions and have a new culture […] which is built around needs, values ​​and thoughts. of our generation “.

Ultimately, experts agree that the best way to generate value from this emerging trend is through active participation – what the investor known as Tracheopteryx has called “contribution mining”.

There are a number of guides on how to get close to joining a DAO, but according to Tracheopteryx the process isn’t as complex as, say, interacting with a DeFi contract – an investor just needs to find their favorite field and then get to work.

Please rate this