Peep, the_magician from Team Illuminator is dispatching. Today we will dive into the future of personal intelligence. Imagine a future where computers can accurately predict your next question, effortlessly adapt to your preferences, and generate experiences tailored to your individual needs. This is the world of hyperpersonalization, and it's just around the corner, all thanks to intelligent computer systems (ICS) and large language models (LLM). Curious?

• The future of human-computer interaction lies in systems that adapt to your personality

• Corporations will try hard to log and analyze every piece of personal data

• Tinder will be the apex of human-computer interface. Why? Let's find out!

The development of ICS has been marked by several important milestones, each building on the foundations of the previous, and leading us to the current state of recommender systems and personalized AIs. Here's how we can trace that historical line:

1. The Know-It-All Era (1980s - 1990s):

Picture this - we're in the era of shoulder pads and neon leg warmers. But in the world of tech, we're seeing the birth of expert systems. These systems were the first attempt to mimic human experts using a bunch of "if-then" statements. But as we all know, humans are a bit more complicated than a simple set of rules, and these systems had their shortcomings.

2. The "Birds of a Feather" Phase (mid 1990s - early 2000s):

Fast forward to the 90s, when grunge was king, and AI took a leap forward with the introduction of Collaborative filtering. This was a game-changer. Sites like Amazon and Google started to user these systems to recommend products or websites based on your past behaviour and similarities with other users. Suddenly, AI was less about rules and more about learning from data.

3. The Deep Dive Era (2010s - present):

And here we are in the present, where deep learning has taken the AI world by storm. But hey, it's a storm we're happily caught in because the possibilities are endless. But look how far we've come! From bulky expert systems to sleek personalized AIs in our smartphones - it's been quite a ride, and I can't wait to see where we're headed next.

Okay, let's get real. Even the shiniest of tech has its dull spots, and intelligent systems were no exception:

1. Knowledge Acquisition: Picture trying to stuff an expert's brain into a computer. Tricky, isn't it? That's essentially what we were trying to do. Extracting that goldmine of knowledge and coding it into a computer-friendly format was a herculean task. Time-devouring and cost-heavy, it was one massive knot we had a tough time untying.

2. Lack of Common Sense Intelligent systems had a serious Achilles heel - common sense. Or rather, a lack thereof. Within their comfort zone, these systems were wizards. But step outside that narrow domain, and they were about as lost as a deer in headlights.

3. Lack of Learning Ability These systems were like old dogs struggling to learn new tricks. Adapting to fresh information or a changing environment? Not in their wheelhouse.

4. Maintenance Issues: Like a car needing regular oil changes, the knowledge in these systems aged fast, requiring constant upkeep. And let's face it, updates are a headache no one enjoys.

5. Lack of Explanation Capability: Yes, intelligent systems could spit out answers. But ask them to explain their thought process? That's where things got murky. Their reasoning often got lost in translation, leaving us scratching our heads.

The recent introduction of large language models has been a game-changer. These models, such as OpenAI's GPT-3, have demonstrated incredible capabilities in natural language understanding and generation. Their ability to perform few-shot learning enables them to adapt their behavior, reason, and understand language with minimal input. This has opened the door to a new era of personalization in computing.

Personalization and AI Democratization

With the development of AI and machine learning, corporations have gathered vast amounts of data to make inferences about their users. However, these attempts at personalization, like Apple's Siri or Microsoft's Cortana, have often fallen short of truly understanding individual user needs.

Historically, the journey of artificial intelligence (AI) and intelligent systems has been largely driven by the commercial world. Be it the recommendation algorithms of Netflix or the expert systems like IBM's Deep Blue, these sophisticated tools have primarily been employed as catalysts for corporate profit, their development dictated by marketability and financial gain.

However, the power and potential of intelligent systems extend far beyond their traditional, corporate applications. The true magic of AI lies not just in boosting bottom lines, but also in the capacity to astound us with its capabilities - to create those "wow" moments that truly exemplify the exciting possibilities of technology. Unfortunately, for most personal users, these magical experiences have been tantalizingly out of reach, locked behind the gates of corporate domains.

What could happen if these intelligent systems were made accessible to individual users, the tech enthusiasts, the hobbyists, the problem-solvers working late into the night in their home offices? Imagine the wave of innovation that could emerge from individuals motivated not by profit, but by curiosity, creativity, and the desire to make a difference.

When we democratize AI, we pave the way for a myriad of breakthroughs that might never occur within the strict profit-driven confines of the corporate world. It's high time we recognize that AI's full potential is yet to be explored and appreciated. By putting the power of intelligent systems into the hands of everyday users, we open up an entirely new landscape of opportunities for technological innovation and societal improvement.

As we move towards a future where users can run ICS on their own, this will lead to a shift in the space of technologies being developed, much like the transition from mainframe computers to PCs in the 1980s. This shift democratized computing, allowing individuals to have more control over their digital experiences, and the same could happen with personalized AI systems.

Enter hyperpersonal technology. It’s user-centric. It democratizes the benefits of intelligence providing value to users directly. The businesses are the second-class citizens as opposed to the classical ML applications where profit is put left right and center.

So, what could hyperpersonal systems look like in practice? Here are a few examples:

1. Ephemeral UI and Malleable Software: User interfaces generated on the fly, tailored to the task at hand and simplifying access to the features you need at that moment. For example, imagine a photo editing software that only shows tools relevant to the specific type of image you're working on, or a file management system that adapts its layout based on your current project.

2. Hyperpersonalized Education Systems: Intelligent tutors that generate a unique learning approach for each individual, understanding gaps in knowledge and providing useful analogies. Picture a virtual classroom that adapts its curriculum, pacing, and teaching style to each student's strengths and weaknesses, enabling everyone to reach their full potential.

3. Personalized Feedback and Guidance: AI Coaches that provide real-time feedback on performance, explanations of errors, and guidance on study plans. Envision a fitness app that not only tracks your workouts but also offers personalized advice on how to improve your form, prevent injuries, and achieve your goals.

4. Anticipatory Content Adaptation: AI systems that understand your mood and emotional state, adapting the content of your news feed accordingly, and even suggesting mood-boosting activities or providing mental health resources. Such a system could also customize advertisements to suit your current emotional state, making them more relevant and engaging.

5. Personalized Healthcare: AI-driven healthcare systems that tailor medical treatments and recommendations to each patient's unique needs, medical history, and genetic makeup. This could lead to more effective and personalized treatment plans, reducing the trial-and-error process often associated with medical care.

6. Entertainment Tailored to You: Streaming services that go beyond current recommendation algorithms, creating personalized playlists, movie selections, and even generating original content based on your tastes, interests, and viewing history.

The Tinder-like interface revolution is not about choosing products or technologies but selecting possible futures. This intuitive interaction simplifies the decision-making process, as users are no longer required to sift through countless options. Instead, they are presented with a series of highly personalized scenarios, each representing a different potential future.

Imagine this: in a shopping context, instead of browsing through hundreds of items, you are presented with complete, personalized outfits based on your past choices and preferences. In a business setting, executives could be presented with different strategic options, each predicting a different potential future for the company. A simple swipe sets a course towards that future.

The pivot towards this future relies heavily on hyperpersonal technology, designed to understand us better than we understand ourselves. By leveraging data and advanced machine learning algorithms, these systems can create an almost frictionless life adventure. They can extract potential hindrances, provide tailored suggestions, and enable us to make decisions quickly and efficiently.

Technology is evolving at an unprecedented rate, and at the heart of this revolution lies artificial intelligence. Here at Illuminator we are working hard to understand what lies ahead. What do YOU think?

1. Michael L. Umbricht and Carl R. Friend (Retro-Computing Society of RI)

2. Inside of a Google Datacenter (Google)