Hello, “Paul AI”

Have you ever tried to imagine a world where you could have your own digital twin — your own digital counterpart that could interact, learn, and even teach on your behalf? I knew that world wasn’t far off, but I have never thought it could become possible so quickly.

March 15, 2024 marks the day of “Paul AI” — it is my own digital twin that serves as a gateway for my friends, social media followers, my business partners and students to interact with my areas of expertise in a more discreet manner.

How does “Paul AI” work?

“Paul AI” is built on top my publications — dozens of law review articles, blog posts, and several books as well as my conference talks and webinars.

This knowledge base resides in my personal data cloud and is knowledgeable about topics I have been exploring in nearly 15 years of my professional career such as copyright, intellectual property and international law, and more recently, data privacy and generative AI.
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What “Paul AI” actually does, it provides a way for anyone a way to interact with my specific areas of expertise — and search my knowledge based in a new way.

Thanks to the advancement of AI and large language models, search has become a commodity. By having most of my publications and conference talks in one place, I can interact with such knowledge base by typing in any question, and get quite a solid response.

You can try interacting with my digital AI twin — “Paul AI” — here and experience it yourself.

What about security of data?

Data security is one of the most paramount issues. In the case of “Paul AI,” we utilized publicly available information — thousands of pages of published law review articles, recorded conference talks, podcast interviews, and blog posts.

Previously, one would have had to search online and read through this content to find an answer based on my area of expertise. However, in the age of AI, thanks to new data search capabilities, “Paul AI” offers the speed and previously unimaginable opportunities to interact with data in a new and dynamic way.

Since the components of “Paul AI’s” knowledge base come from public sources, there is very little concern about data security: this information is already available on various platforms and databases.

Moreover, as the creator of “Paul AI,” I have full control over what data sources constitute the knowledge base. I can add more sources if I find that necessary or remove some that are currently included.

In addition to having full control over the knowledge base, I can also benefit from the dashboard to see what conversations are taking place with my AI Twin: what questions are people asking? How is Paul AI responding? However, I cannot see the identities of the people interacting with Paul AI.

Paul AI: Today and Tomorrow

​As of today, the primary function of “Paul AI” is to provide a new way to interact with my specific knowledge base. This means you can search my knowledge base by typing in text and interacting with “Paul AI” via text.

But what about using your voice? Can “Paul AI” respond in the real Paul’s voice? You might wonder.

Indeed, numerous companies already offer solutions to clone your voice and likeness in a matter of seconds.
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At Prifina, we are tackling the more challenging task of allowing users to build a knowledge base from various data sources. These sources can include not only text, as in the current version of Paul AI, but also other types of data. Imagine incorporating heart rate data from your fitness tracker, or your medical records.
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Future versions of Paul AI will not only speak in my real voice but will also feature a virtual body avatar that reacts correspondingly.

Furthermore, I could create additional twins — like “Paul Health AI,” which would hold not just my medical history but also recent biometric data, such as information about my activity and sleep patterns. Who might be interested in interacting with “Paul Health AI”? Potentially my family doctor, my basketball coach, my nurse, or even my life insurance company representative.

The possibilities and opportunities for Personal AIs are immense:

First Use Case: Teaching at Vilnius University Law Faculty with “Paul AI”

I am particularly excited to deploy “Paul AI” as my AI assistant for the Data Privacy Law class I began teaching last week at Vilnius University Law Faculty.

This year, I’ve decided to go “all in,” requiring students to utilize all available AI tools for class preparation and assignment completion. This includes #ChatGPT, #Claude, Meta’s AI, Inflection AI’s #Pi, Perplexity , and, of course, Paul AI. Each of these tools has its own pre-programmed limitations, and I aim for students to learn how to effectively collaborate with them.

In my course, titled “Data Privacy Law: Human-Centric Approach,” “Paul AI” offers specialized knowledge on the subject. Over the past five years, I have extensively explored the human-centric approach to personal data — a topic that other popular AI assistants do not seem to cover as comprehensively.

​It has become clear, that “Paul AI” offers me an opportunity to curtail the duration of office hours, and reimagine the way how I organize the class, interact with the content and teach law-related subjects in such a rapidly evolving environment.

“AI is a gift to humanity that has been largely misunderstood.” — Prof. Mark Fenwick

There are many ways AI can be applied in the classroom beyond just having students write essays using AI chatbots. While the internet has already broadened access to education, some may be wary of introducing AI into the classroom. However, it’s undeniable that artificial intelligence is set to transform education.

The potential to further incorporate AI is substantial. It’s estimated that teachers spend 40% of their time on tasks such as grading and course planning. AI can assist with these essential but repetitive tasks, freeing up more time for professors to develop relationships with their students.

AI can also enable students to learn faster and access a higher quality of education. While many consider this the future of education, it’s actually already happening right now!

What do you think?  We’d love to hear your views. In the future, we’ll continue sharing my experiences and exciting opportunities with personal AIs — stay tuned!

Want to create your own AI twin?
Sign up here — powered by Prifina — Liberty. Equality. Data.

By Dr. Paul Jurcys (Co-Founder, Prifina)

Happy Valentine's Day! 

On this day when the world celebrates love and connection, I would like to share a few thoughts about a rather new paradigm - our relationships with Personal AI assistants and “AI buddies” that are gradually becoming our daily digital companions at work as well as our private lives. 
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I have no doubt that you have been increasingly relying on AI tools such as ChatGPT, or myriads of other AI assistants that helps solve various tasks, I wonder: 

• How will our interaction with the surrounding world evolve as personal AI assistants become more prevalent, and what implications will this have for our social behaviors and connections? 
• What does the future hold for data privacy in a world where AI assistants has access to our most intimate details, and how will we navigate the balance between personalization and privacy?
• In an age where trust in technology is both imperative and increasingly scrutinized, how should we approach building trust with AI assistants that know us better than we know ourselves?

These questions not only challenge us to reconsider our current relationship with technology but also to envision a future where AI assistants are not just tools, but partners in navigating the complexities of life. Let’s start by unpacking several possible categories of AI assistants.

Below is the English translation of a special article about Prifina which appeared on SITRA's webpage on December 11, 2023.

Prifina has built a solution where individuals manage their personal health and fitness data, for example, through their own data clouds. At the same time, it offers opportunities for all companies to build applications where they don't have to manage user data but instead access users' personal data clouds. 

Prifina's goal is nothing less than a revolution in personal data management. For instance, when a person goes for a run wearing a heart rate monitor, producing personal data, currently, that workout information typically remains in the ownership of the software company providing the health app.

In Prifina's vision, the data doesn't remain with the app provider but is stored in the user's personal data cloud. So, the running app only comes to work and, once the workout is done, it leaves the measurement results to the customer. Prifina offers a place for this data as a service.
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"The architectures, features, and pricing of major cloud services have evolved so much that it is possible to build infrastructure where people retain and manage their data. Applications simply use the data stored in people's personal data clouds," says Valto Loikkanen, Co-founder and Platform and Usability Architect at Prifina.

By James Donck, Prifina

On September 19-21, 2023, the Prifina team attended the largest frontier tech event of the year in the Bay Area - TechCrunch Disrupt 2023. We had an opportunity to meet new and current partners and showcase some of the use-cases where Prifina’s partners are leveraging Prifina’s personal data platform to offer services on top of users’ own data. 

In the post below, we summarize some of our main accomplishments during TechCrunch 2023, and also provide some insights about the current trends in the consumer data and AI market.

Prifina's Hub at TechCrunch Expo Hall

This year at TechCrunch Disrupt, the Prifina had a booth located in the very center of the expo hall thus becoming a hub of attraction for people who came to see what are the latest developments in frontier tech.

At our booth the Prifina team primarily aimed to focus on two areas of our work: 

• Showcasing how user-generated data from sensors could unlock the opportunities for brands;
• Showcasing how Prifina's human-centric approach to data unlocks new opportunities to build Personal AI assistants and agents. 

Showcase of sensorized products 
​At Prifina's stand, visitors could see and experience how sensors are added to the physical products we use daily. Specifically, the Prifina team brought real sensorized helmets and headbands produced by our partners Haierling (making sensorized ski boots) and Samphire Neuroscience (producing menstrual neuromodulation devices). 

Those devices helped us illustrate how Prifina empowers individuals to collect data from various sensors and platforms and build compelling solutions on top of consolidated users' data. In the case of a ski coach, for example, the user not only gets a report about the ski performance but can also talk to a personal AI ski coach about the recovery after an intense ski experience. Such recovery is possible because the user can collect data from various data sources (e.g., ski boot sensor and a sleep tracking device such as Apple Watch or Oura Ring).

Showcase of Personal AI Applications

As we enter a world where every service we use is transforming into AI-powered assistants, one question remains: how do we make AI assistants really personal to the user?

With Prifina's human-centric data infrastructure, it is rather simple to build AI-powered assistants: we need to build the assistant to the user, and tap into the combined of the user. 

Here is how it looks in practice: assume you want to build a "Personal AI travel agent" app. What data would such an AI assistant need to make recommendations that are truly personal to me? In this case, perhaps it is about: 

(i) my previous bookings on AirBnb platform, 
(ii) my starred locations on Google Maps (places I liked and places I marked as to be visited in the future); (iii) obviously, my calendar(s) so that I could plan accordingly, and 
(iv) perhaps my payments history.

In Prifina's architecture, the user downloads the "Travel AI Assistant App" and runs on top of his/her own data, which includes various data sources, also possible is the four mentioned above. Such Travel AI Assistant app runs privately (on top of device, or in my own personal data hub which only I can access). 

Prifina's human-centric data architecture received much attention; we are onboarding new partners to build applications that run privately on the combined data set that users have in their personal data environments.
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General Takeaways and Market Trends 

After spending three days and talking to hundreds of founders, partners and investors, we have the following three takeaways about the market trends: 

1. AI everywhere, even where it is not needed
There is so much buzz about Artificial Intelligence. There is no question that AI tools and AI solutions are coming to many areas of our work, social life and leisure. AI tools will help curtail the amount of redundant tasks and help people focus on things that matter more, increase efficiency. In fact, this process of AI tools has been already taking place for quite some time, but the emergence of OpenAI's tools such as ChatGPT has made it really obvious.

We also observed that the AI hype could get out of control: namely, we see how startup founders and builders are trying to add "AI" even to those areas where it does not make any sense. The result? Clumsy and complex UX, forced functionalities that are not needed. We understand that is a natural process of trial-and-error; we just hope that it will not take too much time and resources. 

2. Sensors and Empowerment with Raw Data 

Similarly to other technology conferences in 2023, we see witnessed the wave of sensors  rapidly entering every aspect of your physical lives. From sensorized running shoes, sensitized toilet seats, complex sensorized exoskeletons to sensorized workspaces, sensors and transforming how we  live, work and recover. 

3. Predictive Analytics for Consumers

At TechCrunch we evidenced many fertility, sexual, reproductive, and other solutions that rely on sensor data and provide insights and nudges for consumers. Some of those solutions are diagnostic and clinical focusing on sophisticated biometric data, others are simple consumer-facing apps.

Yes, one of the key common thread among sensors and AI-powered apps that offer predictive analytics is the need to adopt a more holistic approach to data. That's where Prifina's human-centric data model for user generated data has received most attention and validation.

We are looking forward to continue our work with Prifina's current and new partners to unlock the value from users' data created across platforms and devices. Showcase of sensorized products 


At Prifina's stand, visitors could see and experience how sensors are added to the physical products we use daily. Specifically, the Prifina team brought real sensorized helmets and headbands produced by our partners Haierling (making sensorized ski boots) and Samphire Neuroscience (producing menstrual neuromodulation devices). 


Those devices helped us illustrate how Prifina empowers individuals to collect data from various sensors and platforms and build compelling solutions on top of consolidated users' data. In the case of a ski coach, for example, the user not only gets a report about the ski performance but can also talk to a personal AI ski coach about the recovery after an intense ski experience. Such recovery is possible because the user can collect data from various data sources (e.g., ski boot sensor and a sleep tracking device such as Apple Watch or Oura Ring).


Showcase of Personal AI Applications
  
As we enter the world where every service we use is transforming into AI-powered assistants, one question remains: how do we make AI assistants really personal to the user?


With Prifina's human-centric data infrastructure, it is rather simple to build AI-powered assistants: we need to build the assistant to the user, and tap into the combined of the user. 


Here is how it looks in practice: assume you want to build a "Personal AI travel agent" app. What data would such an AI assistant need to make recommendations that are truly personal to me? In this case, perhaps it is about: 
(i) my previous bookings on AirBnb platform, 
(ii) my starred locations on Google Maps (places I liked and places I marked as to be visited in the future); (iii) obviously, my calendar(s) so that I could plan accordingly, and 
(iv) perhaps my payments history.


In Prifina's architecture, the user downloads the "Travel AI Assistant App" and runs on top of his/her own data, which includes various data sources, also possible is the four mentioned above. Such Travel AI Assistant app runs privately (on top of device, or in my own personal data hub which only I can access). 


Prifina's human-centric data architecture received much attention; we are onboarding new partners to build applications that run privately on the combined data set that users have in their personal data environments.

On September 5th, 2023, Prifina hosted a panel discusion "Revolutionizing Risk: How Sensors, Ambient Data and AI Are Reshaping the Insurance."

Among the panelists were:

• Prifina's CEO Markus Lampinen,
• Signe Amdi Holm, Co-Founder & CEO of Datasolvr, an insurance data technology company,
• Ashley Greenwald, Senior Associate from Huntsman Architectural Group and 
• The panel was moderated by Boudjemaa Nait Kaci, a financial services expert from New York Life Insurance Co. 

The panel discussion explored the current trends in the world where sensor-generated data and AI-powered applications permeate every aspect of our lives. Could such data be used to personalize insurance services and unlock new solutions to help us reconsider how we approach risk? 

The event on the implications of sensors and data on insurance gathered much attention from various representatives of different industries. In the following post, we review some of the discussion topics and provide a list of innovative solutions for insurance services.  

1. Paramount role of data for insurance 

The starting premise of this event was the realization of the huge amount of data generated by users themselves (e.g., data from sensor-equipped fitness trackers) and ambient data from sensors in office buildings and public spaces. Yet, appr. 80% of such data lies idle, unutilized.

Markus Lampinen from Prifina opened the panel discussion by explaining the paradigm shift that is happening in the personal data market - a new data framework the value from data is captured on the users' side (not locked away in centralized silos): 

“In the future, we want to see individuals getting the value from data captured across different data sources. We want to empower individuals with different types of applications that you own and that you run on top of your own combined data. Very simple things that help you sleep better or buy the right shoes, etc. But we already have those. The big picture? In 5 years, as an individual, I’d like to be able to predict what movie I’d like to see, and what better choices I should make.”

Ashley Greenwald from Huntsman AG explained the concept of "ambient data" - data from sensors embedded in spaces around us (temperature, noise, occupancy, vibrations, exposure light, humidity) - all of this data could be used to improve the efficiency of space management and human experiences. Such data from sensors in buildings is particularly interesting for building owners and renters.

2. Tight Regulations Limiting the Use of Personal Data

Signe from Datasolvr and Markus discussed the fact that data privacy regulations and well as risk-awareness of service providers limit the use of individual-level data for personalization. Companies don't want to face data discrimination lawsuits and opt not to use data in their services at all. While the individual-level data could be particularly interesting for creating different pricing models, data privacy regulations are tight both in Europe and in the US. 

3. Building Predictive Health Care System with Data

Participants of the panel discussed the ways how user-generated and ambient data could be used to enable predictive health and wellness services. This would be a major juxtaposition to the current "reactive" medicine which is extremely costly.

"In reality, when you go the hospital, you realize that many health-related data could have been avoided if various data sets were caught and utilized earlier." - Markus Lampinen

Another truth of the fact is that we are running short of doctors, physicians and experts: there are appr. 10 million doctors in the world for a population of 8 billion people.  And we will never have enough doctors. At the same time, people are increasingly eager to extend their well-being and the quality of life.  

That's where sensor-generated data and data-powered AI applications become increasingly appealing:

"Harnessing data from devices like the Apple Watch could help increase individual awareness of stress and potentially prevent harmful effects using predictive AI. That's one of the biggest opportunity for insurance service providers who ultimately want to mitigate risks." - Sille Amid Holm (Datasolvr)

​Individual-level use cases. The panelists explored several examples of utilizing user-generated data from wearables in insurance. For instance, renewing term life insurance is complex, with current pricing influenced largely by an individual's health condition, such as whether they smoke. For instance, smokers typically pay at least twice the premium compared to non-smokers of the same health. However, the potential use of comprehensive data collected over years could reshape this model. If data shows an individual quit smoking and improved their health over a decade, they might deserve a reduced premium, even if they're older. This concept suggests a more dynamic and responsive insurance model, though the decision rests with insurance companies.  

Group-level use-cases. Group-level data from wearables as well as ambient data could be a powerful tool to innovate in the insurance services market. Imagine, if an employer could prove certain habits of the employees (e.g., employees chose to walk up the stairs and sleep on average 6.5 hours a night) to the insurance company, this could offer additional insights and price adjustments for various insurance plans.

4. Creating New Consumer Experiences with Data

The panelists discussed various opportunities and challenges when it comes to building new consumer experiences with data. Many consumers need super simple interfaces and huge incentives; there is also a huge learning curve ("data literacy"). A huge part of this relates to the experts working in the User experience/user interface domain: How do you show the value from data to the individual? How to help people to better understand what to look for in their data?

There're many interesting insights about how people have adopted wearable devices: e.g., we see how much more aware about their biometric data people have become. 

However, it is quite difficult to build data-driven experiences in the insurance services market. The customer onboarding and customer journey has to be very different in insurance. Since more people interested in insurance services tend to be senior, the experience must be simple and understand. Here are some examples of gimmicks that could be used: 

• Savings notices: there can be an icon on a website which says that you can save xx%  with certain data
• Major rewards: Sille gave one of the most notable examples from Denmark - an advertising that looked like that "If you change your insurance provider, you will get a free car!" 
• Gamification & Incentives: insurance companies could use gamification strategies to nudge people adopt certain lifestyles; data could be used to offer better insights and rewards (premium discounts, points, gift cards, etc.) 

Accelerating innovation. The flip side argument of that is whether big players in the insurance market would like to innovate, or whether they'd rather prefer the status quo. Markus Lampinen from Prifina suggested that such a hold-out strategy by incumbents may not last too long especially when more innovative newcomers in the insurance market come up with more personalized and compelling products. 

5. Personal AI agents powering a new line of insurance services 

The panelists shared their common understanding that in the near future each of us will have our personal AI assistant experts (doctors, coaches, nutritionists, shopping agents). This world becomes possible as individuals get more ownership and control over their own data.

More generally, panelists agreed that user-generated and ambient opens new opportunities for virtually every stakeholder in the market, including innovation opportunities for insurance services that will become very personalized and specific.

Future vision. Imagine if your personal AI insurance agent negotiates on your behalf with AI-powered agents of insurance providers to find the best deal for your specific condition. The panelists even explored possible implications if an individual's own AI agent was aware of individual's DNA data and historical medical records  and how this degree of data accuracy and automatization could transform the whole understanding of risk. ​

We want to thank all the panelists and guests who attended the event and stayed after the panel to network and continue the conversation about the emerging personal data ecosystem.

“Whoever wins the personal agent, that’s [going to be] the big thing.  Because you will never go to a search site again, you’ll never go to Amazon again” - Bill Gates ​

By dr. Paul Jurcys

In the very near future, each of us will our personal AI assistants for every aspect of our lives. Each of us will have our personal AI doctor, personal AI coach, personal AI nutritionist, or digital assistant that will help us organize our calendars, schedule events, and purchase items on our behalf. 

Such AI-powered assistants will augment our abilities and help get rid of time-consuming tasks, and focus on things that matter most. 

• What are the current challenges to make such digital assistants really personal? 
• What technology infrastructure should be in place to make our personal AI assistants really personal? 
• How will these assistants interact with each other? 
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In this post, we explore (i) the current trends in the market, (ii) the types of personal AI assistants and (iii) emphasize the need to adopt a human-centric approach to data in order to build truly personal AI that runs privately, on top of your own data.

Buzz In the Market

In the past few months, there has been quite a bit of excitement about the possibility of building truly personal AI: 

• InflectionAI raised $1.3 Billion to build personal AI assistant “Pi”
• Jason Calacanis sees Personal AI as a $20 trillion opportunity
• Doug Clinton Suggests that truly Personal AI should not be built on “centralized” models
• Doug Clinton also conducted a poll and found that individuals are willing to pay for their Personal AI. 
  
  

Investors start to realize that Personal AI is a new market that both legacy players such as Microsoft and Apple, as well as startups are trying to explore. 

A massive investment in InflectionAI could be seen as an attempt to stave off any potential competition and secure dominance in this rapidly emerging domain. 

Shortage of Experts
Today, we are unfortunately facing a remarkable shortage of experts. With over 8 billion people on Earth, there are a mere 10 million doctors. Teachers struggle to spend enough time on direct interactions with every student in the class. 

On a personal level, we often lack someone equally knowledgeable or passionate about our favorite topics. In times of grief, suffering, and emotional pain, the desire for a close companion to provide advice and understanding becomes ever more acute.

This is where the transformative power of AI comes in. In the near future, countless individuals, potentially everyone, will be able to augment their personal capabilities with our own personal AI assistants. 

The trajectory toward universal access to intelligence is clearly mapped out: we can expect that any person with access to the internet and a hand-held device will have access to the same expert advice from a doctor, an educator, or any other specialist, all through their personal AI. 

2 Ways to Build Personal AI
As technology continues to leap forward at ever-increasing speed, personal AI assistants are evolving to become more sophisticated and versatile. 

There are two paths of bringing AI superpowers to humans: 

A top-down approach: after the release of ChatGPT and other generative AI tools, we are witnessing an influx of various personal AI assistants that are supposed to hello us find information, summarize text, or generate images. 

The top-down approach is rooted in creating software applications - personal AI assistants - that are marketed as tools to help us solve various daily tasks and assignments or offer us a companion, a buddy that is available 24/7. Think of InflectionAI’s Pi, Midjourney, Dalle, and countless others. 

However, most of our essential tasks are rooted in the physical world: we need to eat, sleep, and and go to the bathroom. As cheap sensors are being integrated in our wearable devices and environments around us, individuals have tools and ability to track personal biological clock - we start living quantified lives. If you are a runner, you probably have a fitness tracker to measure your running distance and heart rate, as well as recovery rate. Also, our cars track hundreds of data points (e.g., acceleration, braking, engine performance, and outside factors). 

Scientists around the world are building assistive robots to help us to recover from injuries or learn important skills.

These sensor-derived data sets also lead to the development of AI-powered applications that help optimize different aspects of our lives. This sensor-data-AI path resembles a bottom-up approach to building personal AI. 

Data Eats the World, But How to Access Data? 
AI assistants such as ChatGPT, or PI, are amazing in answering our general questions and performing general tasks. However, if you ask anything personal, something related to yourself, they stumble. Let’s see what responses we get to the question “How did I sleep last night?”:

How come!? Isn’t it odd that if you wear Apple watches that track our activity, sleep and process various biometric data, Siri - an AI assistant built by Apple for Apple’s products - is not able to answer such a simple question?!

Eventually, every company building personal AI will face the challenge of connecting the personal AI to the user’s own data.  
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To build a truly personal AI, we need not only general knowledge from publicly available sources and research repositories, we also need to bring this general, publicly available knowledge and correlate it with the specific data of each individual. In other words, to build a truly personal AI, Ai assistant must be able to access to the user’s personal data. Otherwise, the AI assistant will be a generalist, not a personal: ​

On July 13, 2023, an event called “Augmenting Consumer Experiences in the Age of Data & AI” took place at the offices of New York Life Insurance Company's offices in San Francisco (kindly hosted by Boudjemaa Nait Kaci).

​Augmented UX with AI

The event started with Jouko Ahvenainen from Prifina, who invited his fellow panelists to delve into their personal experiences, both triumphs and trials, relating to their interaction with the recently released AI and data-driven technologies.

Heather Whitney from Morrison Foerster shared her excitement at the launch of ChatGPT. She underscored the versatile potential of generative AI, highlighting its role in revolutionizing customer experiences across a variety of sectors, notably content creation, gaming, as well as gene editing and drug development. 

Following up, AJ Tomas from Touchdown Ventures shed light on the latest unveiling from Humane Inc. — a secretive startup founded by former Apple executives — who have recently announced the forthcoming release of a cutting-edge gadget that generated quite a buzz in the tech world.

Amit Sharma shared his vision: 

"We believe that generative AI has the potential to create innovative forms of content that go beyond the current video-based experiences on Instagram, YouTube, or Tiktok. These novel experiences will offer users the opportunity to engage with brands and services in a multimodal manner."

Aaron Mollin (CEO, Ichijiku) offered a fascinating observation, arguing that most AI-fuelled experiences are curated from a “top-down” perspective. He recognized the transformative nature of tools such as ChatGPT, but called to realize that oftentimes he felt that such tools are detached from how we interact with material objects around us. 

Aaron shared an alternate viewpoint related to integrating AI-powered insights based on the data collected from ubiquitous sensor technology found in everyday physical objects, like wearable tech, IoT devices, and environmental sensors. He called this the "bottom-up" approach. One such example is a collaboration between his company, Ichijiku, and Prifina: their work has resulted in the creation of sensor-imbued luxury jackets (one of which he was wearing that night).

This conversation immediately led to a conversation with audience members about the fact that we, individual consumers, all have cell phones, and we find it cumbersome to interact with hundreds of separate apps and services. From the consumer perspective, rather than multiplying points of interaction, it would be better to reduce our reliance of more and more devices and apps and make the UX more user-centric. 

Predicting the Future 

Jouko then asked panelists to share their thoughts on what changes consumers could expect in the next coming years.  

Amit did not hesitate to suggest that every aspect of consumer experience will change in the next few years. Some UX aspects will change faster than others, but gradually many industries will transform. E-commerce, online shopping, entertainment, and interactive experiences are the first ones to transform. 

AJ Tomas predicted that another area of fast-moving development is personal productivity. 
Aaron expressed his wonder about the impact of data on the way how individuals make decisions about their own lives. Lots of work appears to be needed to build personal recommendations and nudges for people. 

Jouko reiterated his favorite idea about predictions: although we do not have a crystal ball, we can actually make quite certain predictions about the future. Jouko noted that we can anticipate that certain things will happen (e.g., self-driving cars), but the challenge is to identify the timing: will happen in a year? Two years? Five years? 

Amit noted that the current developments in generative AI capture much attention of billions of people who are waiting to see what the actual breakthrough will actually look like:

“And if you go back to the 1980s, people knew there would be something called ‘a computer.’ But nobody knew what a computer would look like … it wasn't clear that it would be a box on your table with something called a keyboard and a mouse… This is true for smartphones and, most recently, Apple VisionPro. We can’t even imagine what Apple VisionPro 14 will look like. And that uncertainty is what makes people excited.” ​

Legal Issues To Be Resolved

Heather shared insights based on her hands-on experience from working with generative AI technology companies; she provided an overview of some of the key legal issues that revolve around copyright, the legality of the use of data, and data privacy. These issues are currently unclear and are currently discussed among legal experts and AI entrepreneurs:

• The legality of data scraping and using such publicly available data to train machine learning models: Do you have to license that data? Or can you scrape the entire internet and use that? Is that going to be deemed as copyright infringement and thus not permissible? Could it be excused under the copyright law doctrine of fair use? 
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• Data privacy: what if the data scraped from the internet contains some personal information of individuals? Could such data be used to train ML models? Which laws should be applied to such activities (the law of the state where the allegedly infringing  AI company is based or is operating? The law of the place where individuals whose personal information is use? Or some other country’s law?). 
  
  
• Then there's the question about the model itself. An understanding of what they receive from its training data. People don't fully understand that yet. And engineers don't understand exactly how to describe that yet. And what it really means from a legal perspective is still in flux. They just don't know. And it is not currently 100% knowable. 
  
  
• The legality of outputs generated with AI tools: what is the relationship between your training data and the outputs? Could output results be considered to be “derivative works” (in a copyright law perspective) of the training data? If so, then it is possible that there will be massive restrictions on how those outputs can be used. What about the commercialization of these models? The use of outputs are likely to be curtailed as long as there is no clear answer whether the outputs are not infringing (copyright law). 
  
  
• Rights to outputs generated with generative AI tools. Having (any) rights to the output generated with the help of AI tools is a huge problem that will determine whether such works can be commercialized. 

Heather and Jouko shared their views about the emotional narrative around these generative AI tools: many artists feel hurt and believe their works have been unlawfully used to train AI without their permission. 
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The ethics of this debate raise controversial questions. On one hand, information in the public domain is supposed to be free. On the other hand, some stakeholders invoke strong arguments based on the ideas of exclusive ownership rights. 

Ichijiku: Combining Fashion, Cultural Expressions and AIAaron shared his unique perspective on using traditional kimono silk materials to create bespoke fashion items. 

Differently from the prevailing ideals in the luxury fashion industry, where consumers are made to feel that the luxury fashion items purchased just a few months ago are out of style and invited to purchase the next generation of items, Ichijiku aims to create life-long items that people could hold on to for their entire lives.

Ichijiku embeds sensors in those fashion items and uses data to extend the lifetime quality of the garments. By implementing sensors and generating data, Ichijiku jacket owners can monitor explore to humidity, sunlight, and heat; the first, humidity, being the most kind of serious culprit for the degradation of silk. 

By using data and AI, Ichijiku aims to create a new type of experience that go even beyond the maintenance of one-of-its-kind silk. Additional information collected from other sensors, such as heart rate, could help people get insights into how they feel while wearing Ichijiku’s bespoke jackets. This is something that no one has ever been able to create. 

“For me, it's just all about, creating pieces that will stand the test of time. … Ichijiku aims to create these unbelievably beautiful, arguably one of the most intrinsically valuable items on the planet. And that I think, is what's going to really appeal to people. Technology will never be the selling point, although it is definitely appealing. We're using vintage silk fabrics, which ties closely to sustainability ideas. People understand just how beautiful the items they are buying. The use of data and technology helps us further the story.” -- Aaron Mollin

Post-Event Networking 

One of the remarkable features of the data and AI-related events currently happening in San Francisco is that people come to meet one another, learn about each other and what others are building. 

The same spirit could be felt at our event: attendees spent the remainder of the evening at our venue socializing, getting to know each other, and connecting on various social media platforms to stay in touch in the future. 

We at Prifina are excited to be at the heart of this historical moment; we feel inspired and motivated to continue building our Human-centric data platform for AI-powered apps and services.