Generative AI’s Killer App: Hyper-Personalization

Moving from curation to creation. What is hyper-personalization? Is this the beginning of something great? or the end of something good?

Photo-realistic image results from Midjourney

TL;DR

Hyper-personalization will be one of the most powerful applications of generative AI with the ability to deliver highly-personalized and dynamic multimedia content to individuals for their personal enjoyment as well as improving the efficacy of targeted marketing of products and services. However it is not without its risks and can produce ideological echo chambers and feedback loops that make today’s social media pale in comparison. It’s imperative that steps are taken to prevent hyper-personalization from creating social harms, perpetrating biases and affecting the mental well-being of consumers while still being able to enjoy personalized multimedia experiences and content recommendations.

Table Of Contents

Introducing Hyper-Personalization
What is Hyper-Personalization?
How is Hyper-Personalization Used?
Generative AI Building Blocks
↳ Multi-modal Models
↳ Sparse and Retrieval Models
↳ Tool Calling Models
↳ Fine-Tuning and Reinforcement Learning
↳ Chatbots and Personal Assistants
↳ Investments
↳ The Missing Piece: Preference Models
↳ How does it all come together?
Hyper-Personalization Benefits
Hyper-Personalization Risks
How to Mitigate Risks
Conclusion

Introducing Hyper-Personalization

Imagine sitting at home on your living room couch, streaming the movie “Top Gun: Maverick” for the 10th time, each time you watch it, it feels like a new movie with different aesthetic elements, varying product placements, added twists and alternate endings. While watching the movie you see an advertisement for your favorite restaurant, and its almost as though the advertisement is speaking directly to you, the ad is showing your favorite meal from their restaurant, a delicious rib-eye steak with a side of veggies. Then a notification on your smartphone tells you there is a one-night only deal at that same restaurant for tonight, and guess what meal just happens to be half-off? You decide to go out for dinner at that restaurant and you load your favorite music playlist for the drive. You notice that the new song by Bad Bunny that you love seems to sound even better than it had the 100s of times that you had listened to it previously with a slightly different beat and instrumentation from the last time. It really feels like the world was tailor made for you… this is hyper-personalization.

Minority Report — Hyper-Personalized Advertising.

What is Hyper-Personalization?

Most will be familiar with personalization (not hyper-personalization) as a technique for targeted advertising or to procure content recommendations. What personalization really is at its core is an individualized experience aided by a system that procures content based on user preferences. These preferences are often gleaned from consumer’s stated and inferred preferences through past behaviors and revealed personal information, like demographic data.

Behind the scenes, algorithms generally select the most relevant content from a library of assets whether its an ad to be presented on a social media site or a generated list of recommendations such as movies to watch or products to purchase. User preferences are classified and associated with some predefined segment.

Personalization: The action of designing or producing something to meet someone’s individual requirements. — Oxford Dictionary

Personalization is the act of tailoring an experience or communication based on information a company has learned about an individual. — Salesforce

In more recent times the term “hyper-personalization” has been used liberally to refer to any personalization technique that involves the use of AI and the analysis of large amounts of customer data.

Hyper-personalization is the most advanced way brands can tailor their marketing to individual customers. It’s done by creating custom and targeted experiences through the use of data, analytics, AI, and automation — Deloitte

I would not consider what has been referred to as hyper-personalization up to this point as actually hyper-personalization, but rather personalization that has evolved to use machine learning models to improve accuracy. Fundamentally what is being referred to today as hyper-personalization is still using classification and prediction techniques to serve up or procure preexisting content. Largely hyper-personalization has been shopped around as a term to sell new marketing products and platforms, and although they may be improved, they do not go far enough to move into the domain of hyper-personalization.

Hyper-personalization goes beyond personalization by not only performing consumer classifications and predictions but is able to dynamically generate content that is highly tailored to a user’s preferences. This is not conventional procurement of existing content, but actually generating the media on-demand to achieve the highest level of engagement and appeal to the consumer.

To juxtapose hyper-personalization with personalization, personalization would display an ad image on a webpage that the ad placement system has predicted will resonate with a consumer from its library of assets, let’s say a pair of black Nike shoes. While the same use-case with hyper-personalization would modify the image advert to maximize its appeal, changing the type of shoe to make it more appealing, setting the color to blue, and adding a cute dog in the ad all based on the consumer’s preferences and then place the ad in the optimal location on the web page dynamically.

A non-marketing use case is music streaming service that not only provides recommendations for the music that the listener is most likely to appreciate but would partner with artists to generate alternate or dynamic versions of the same song that are most likely to maximize musical aesthetics potentially going so far as to mix in vocals from another favorite artist on demand without either artists having to head to the recording studio.

How is Hyper-Personalization Used?

Although personalization (not hyper-personalization) is often associated with targeted advertising and marketing use cases, personalization has many other non-marketing use cases with the most applied being content and decision recommendations for consumers. Hyper-personalization has all of the use cases of personalization but takes this further as it can generate and manipulate content in real-time, whether it be text, audio, images or video enabled through the use of generative AI. Here are some of the potential use cases of hyper-personalization:

• Personalized Advertising: Generate ads dynamically that will most resonate with consumers based on their preferences, behaviors, mood and state of mind. This includes videos, images, text and audio depending on the ad type and content.
• Product Recommendations: Create personalized product recommendations for individual customers based on their past purchase history, browsing history, and learned preferences. This is especially powerful when paired with a chatbot or virtual assistant interface driven by a large language model.
• Creative Content Generation: Generate customized content in real-time, such as marketing collateral, social media posts, or even product descriptions that that are tailored to the specific consumer.
• Personalized Visual Design: Create personalized visual designs for products, such as packaging or advertising. Can be extended beyond digital products and used with dynamic packaging and 3D printing.
• Voice and Speech Generation: Generate customized voice and speech experiences for customers, such as chatbots or voice assistants that utilize a voice, tone and speech mannerisms that are most appealing and trustworthy to the user.
• Personalized Music Generation: Create personalized music playlists and personalized songs for individual customers based on their listening history, environment, mood and preferences.
• Adaptive Multimedia: Make dynamic changes to movies, soundtracks and audio like audio books that adapt to the current users’ mood, state of mind and their preferences.
• Personalized Art Generation: Create personalized art, such as customized prints or paintings, for individual customers based on their preferences and tastes.
• Personalized Fashion Design: Use generative AI to create personalized fashion designs for individual customers based on their style preferences, body shape, and other factors.
• Personalized Game Design: Use generative AI to create personalized game experiences for individual customers based on their preferences and playing history. This goes beyond adapting game play from per-defined scripts reacting to in-game behaviors but understanding the user and tailoring the environment to their preferences. Autonomous agents powered by reinforcement learning as NPCs are also an important part of the experience.
• Personalized News and Content: Use generative AI to create personalized news and content experiences for individual customers based on their preferences and interests. Images used in a news story can be selected or generated based on user preferences and even the text can be worded for easy reading and appeal.
• Personalized Education: Use generative AI to create personalized education experiences for individual students based on their learning style and preferences. Educational and training courses can use dynamic and individualized pedagogy for specific individual learning types adapting content to maximize visual, auditory, read/write, and kinaesthetic learning styles.

Generative AI Building Blocks

Generative AI or more precisely generative deep learning models continue to evolve and impress the public with their uncanny ability to generate human-like conversational text, photo-realistic images and highly plausible synthetic voices. Platforms like OpenAI’s ChatGPT, Midjourney and ElevenLabs Prime Voice AI have put the technology into the hands of the public at low to no cost, making it highly accessible. Beyond generating realistic images, text and voices, research continues to improve in other generative media including the generation of music, video and 3D assets bringing us closer to seeing realistic video, audio and the generation of dynamic gaming and simulation environments.

There are a number of innovations, trends and factors that are driving generative models towards being much more capable, more accurate and more efficient. These will have the net effect of making generative models more accessible to a broader audience and create much more effective forms of media needed for hyper-personalization. The resultant platforms will be capable of delivering more accurate and temporally personalized results based on a more informed understanding of the consumer at any given moment in time. Here are some of the important innovations and activities that are advancing hyper-personalization.

Multi-modal Models

In the area of generative deep modeling architectures, advances in multi-modal zero-shot models (multi-modal: models that can work on different mediums like both text and images. Zero-shot: the ability of a model to recognize concepts or objects it has not previously seen) are evolving to allow users to interact with a single model that will understand and generate both text and images and understand the relationships between the two. This is a very powerful architecture as the interplay between text and images represents most of our interactions in the digital world. For instance you could ask a chat-bot like ChatGPT to process a document full of text, diagrams and images and provide a detailed summary of all the content it encounters, not only the text. You could prompt the chat-bot to generate a 10 page travel itinerary for your trip to Rome, complete with schedule, photos of the key sites and pertinent details about transportation and costs. Video understanding and generation is also now finding its way into multi-modal models as well however work is still rather nascent in this area.

Multi-modal architectures are continuing to evolve like Microsoft’s Kosmos-1, and it is rumored that GPT-4 will be multi-modal with both text and image comprehension. However these models have one large problem which is that they are “large” models. To build and train these complex models you need top industry talent and a lot of capital to pay for the compute needed to train large models. For instance GPT-3’s training run is estimated to have run for over a month and cost about $4.6 million dollars in Azure service charges. Scaling up with the transformer architecture has produced increasingly better model results however it has put the technology further away from anyone but large tech companies, a concern referred to as “accessible AI”.

Sparse and Retrieval Models

There is a counter-movement that has begun in AI research to reduce the size of large language models, and some techniques like sparse activation have reduced the size of models and versions of LLMs (Large Language Models) that have a small parameter count are now able to run on a single GPU like Meta’s LLaMA, however the model results are less stellar as the parameter count decreases. Retrieval models are an emerging method that moves away from a single large model that contains all the knowledge it is trained on and reduces its footprint by having specialized (sometimes called expert) models that are smaller in size and specialized for a particular task. You may have a base model that is a pruned (scaled down) large language model and it makes calls to a math model that specializes in math calculations, another that generates images, another that does object detection or semantic segmentation in images, and so forth.

Tool Calling Models

Further research with LLMs have shown that they can learn to make external calls to APIs in a self-supervised manner as demonstrated by Meta’s Toolformer. This is a powerful technique that allows LLMs to extend their reach beyond text generation and natural language understanding but can invoke external tools (not just models) and pass the correct parameters to the tool and return the results, which is very similar to the retrieval model architecture. One could imagine this being used as the basis of building an AI-based call center that can troubleshoot customer problems consistently and in a fraction of the time than that of a human customer service representative.

Fine-Tuning and Reinforcement Learning

The incorporation of fine-tuning on specific task-related (conversational) data was what took GPT-3 from being largely unrecognized by the mainstream and fine-tuned it into a conversational chat-bot that has become the most rapidly adopted application in history, ChatGPT. ChatGPT is based on GPT-3 (instructGPT actually), and through the process of pruning (removing model weights) and unfreezing parameters (making the parameters modifiable), the model was then was fine-tuned (the process of training on a specific task-related dataset) using a data set containing conversational dialog. The resultant model was then coupled with reinforcement learning from human feedback (RLHF) and a process called Proximal Policy Optimization (PPO) to create a model with which users interact with when they access ChatGPT. As a result ChatGPT has become a very convincing and a mostly accurate conversational AI platform (it is not without its problems.) The inclusion of reinforcement learning in the learning process is also a very important factor in continually improving the accuracy of the model and eliminating undesirable outputs, a feature that should not be overlooked.

OpenAI’s ChatGPT Training Workflow using Reinforcement Learning from Human Feedback (RLHF).

Chatbots and Personal Assistants

Using chatbots and personal assistants that are centered around a large language model creates a direct line of communication with a consumer. This is the ideal scenario for any platform in order to keep a consumer engaged and continually learn from them. There are some important elements to understand in this form of interaction. Firstly, let’s distinguish between chatbots and personal assistants. Chatbots are generally bound to a specific company’s operations and it’s interface is usually on a company’s website or in their app. They are less personalized platforms that are task specific such as customer service or a question and answer service. ChatGPT is a chatbot when interacted with on OpenAI’s website as are customer service support chat features for companies like Comcast and T-Mobile.

In contrast, a personal assistant‘s interface is generally run on a consumer’s device with its back-end in the cloud. A personal assistant isn’t bound to a single application or provider service but stays close to the consumer and can follow them around other applications understanding the context in which they are operating. They are generally always ready to interact with the consumer through voice or text commands and are highly personalized interacting with 3rd party applications on the user’s behalf. Siri and Alexa are examples of personal assistants.

Communication with chatbots and personal assistants leverage two user communication channels. The dialog sent from a chatbot or personal assistant is called the “forward or delivery channel” and the dialog sent to the chatbot or assistant from a user is called the the “return or feedback channel”. Today most chatbots and personal assistants are powered by systems that employ a number of interconnected services including natural language processing, text to speech and speech to text, and other deep learning and programmatic elements. However there is a movement towards backing these two conversational systems using large language models like GPT-3, LaMDA, BLOOM and others.

Personal assistants are strategically the better option for engaging with consumers and in fact, a personal assistant can interact with chatbots to achieve desired outcomes on behalf of the user. The industry trend is moving closer to conversational AI as a means of interacting with users, and personal assistants will become better at providing value, which will lead to more adoption and higher dependence. Personal assistants will also become a primary means of interaction for computational systems in general, it won’t be long before deeper integration is seen with productivity tools like Microsoft Office, Google Workspace and enterprise applications.

Personal assistants will no doubt be the next super apps. They are capable of so much, like assessing user responses to build deep understanding of a user and their preferences, they can also understand temporal context and infer sentiment. Personal assistants are a very powerful, perhaps the most powerful mechanism for collecting high quality information on a user and their behaviors and they can do so in real-time. When backed with a generative model personal assistants can be active learners who fine-tune on data provided by their users, creating a highly personalized experience. There is a lot more to be said about personal assistants that leverage hyper-personalization as this is the future of consumer interaction, so I will leave further details for another post. In the meantime here are some use cases where personal assistants can provide value with hyper-personalization.

• Personal Task Automation: Answer and craft emails, manage schedules, setup meetings, create presentations and documents, generate images and diagrams, create blog posts and tweets, summarize documents and news, and so forth.
• Question and Answer: Quickly answer questions with definitive and truthful answers as opposed to solely providing search results.
• Virtual Personal Shopping: Personalized product recommendations and help customers make purchases for mass-produced and customized products.
• Voice-Enabled Customer Service: Voice-enabled customer service that can handle inquiries, resolve issues, and answer questions in a personalized and efficient manner on behalf of the user, interacting with chatbots and provider customer service.
• Personalized Health and Wellness: Customized health and wellness recommendations, such as fitness routines or diet plans, and keep users honest about their progress and motivate them to take action.
• Intelligent Home Automation: Control and manage various devices and systems within a home, such as lighting, security, and temperature, based on a user’s preferences and behaviors.
• Personalized Travel and Hospitality: Customized travel and hospitality experiences, such as personalized itineraries, recommendations for local attractions, and personalized room amenities and then make all necessary bookings and arrangements.
• Personalized Financial Advice: Advice, such as investment recommendations or retirement planning, based on a user’s financial data and goals. This could go further to conducting trades and making investment decisions on a user behalf (but certainly carries a lot of risk.)
• Personalized Learning and Education: Personalized learning and educational experiences, such as customized lesson plans or educational content tailored to a user’s learning style and preferences.
• Intelligent Business Process Automation: Automate and optimize various business processes, such as sales, marketing campaigns or customer support, as well as technical and business operations.
• Personalized Advertising and Marketing: Personalized advertising and marketing experiences, such as customized promotions and offers.

Investments

The amount of investment being made by big tech companies is also a huge factor in the future of generative AI. Microsoft’s potential investment of up to $10 billion in OpenAI is a clear indicator of how important generative AI is to big tech’s overall strategy. With 100s of new startups entering the generative AI space and VCs shelling out 100s of millions of dollars in investments, the number and quality of platforms will only continue to grow for the foreseeable future.

The Missing Piece: Preference Models

Preference models are an evolved form of choice models however they don’t just model a consumer’s choice between product A or B, but go much further and model the consumer’s preferences both stated and inferred using deep learning. A preference model learns to model a users stated and inferred preferences from actions and information about the user.

The best way to think of it is the model imitates the conscious and sub-conscious of the user in order to best replicate how it would behave under any circumstance. The model is the aggregate of everything that has been learned about the user, and is used to predict what the user may do given a circumstance, such as the classic choice between product A and B. Or it could be a more advanced use case like how to generate a product image for an advertisement that will most appeal to and resonate with the consumer.

A pre-trained preference model may be fine-tuned on a data set from a class that most closely resembles the consumer based on conventional classification methods (profiles for similar demographics, interests, and preferences.) The preference model is an active learner, so its constantly updating its weights (its internal store of information) based on the interaction it has with the consumer or supplemental information provided to the model about the consumer. Some of the factors that update the model are:

• Demographics: Learned personal traits and characteristics, such as your age, gender, race, or personality type. Used initially to set a model or to correct the model when it drifts. Special attention needs to be paid to ensure no bias or stereotypes are introduced with this sensitive personal data.
• Personal preferences: These are both stated and inferred personal preferences and are learned by analyzing past interactions, such as your search history or purchase history, the content of conversations with a personal assistant or chatbot or any stated or inferred preference.
• Behavioral patterns: Learned behavioral patterns and habits, such as the times of day you are most active or the types of activities you engage in regularly. Less about what you say and more about what you do.
• Location and activity data: Information from location and activity data, such as your movements and the places you visit.
• State of mind, mood and sentiment: Learnt sentiment and emotion by analyzing your written or spoken language through a chatbot or personal assistant dialog, a phone call or public social media posts.
• Personal traits and characteristics: Learned personal traits and characteristics, such as your age, gender, or personality type, by analyzing various data sources and making predictions based on statistical patterns.
• Response to recommendations: The model can be corrected by assessing the success of past recommendations in order to perfect the model for future predictions.
• Conversational patterns: Through analyzing the way someone speaks or writes the model can adapt to carry out this conversation style in the generation of speech and text.
• Outside model behaviors: Any data not from direct user interactions that comes from 3rd parties and does not fit into one of the above categories.

How does it all come together?

Hyper-personalization is defined by two key elements, the ability to model consumer preferences (understanding the consumer) and the ability to generate content that will most resonate with the consumer (personalizing content.)

Understanding the consumer: Modeling the consumer preferences is achieved through the use of something like a preference model to best model the consumer. The model includes immutable attributes like demographic information, longer-term attributes like state of mind, personality, mannerisms and behaviors, short term attributes like mood and current location and historical attributes like travel patterns, purchase history and search history to name a few.

Personalizing Content: Conventional personalization techniques like content recommendation and ad serving are coupled with generative techniques like dynamic generation of highly-personalized marketing materials or generation or dynamic modification of multi-media like in-movie branding and video style transfer (where the video is altered to match the preferred style of the viewer.)

Deployment architectures for hyper-personalization platforms can vary, and are beyond the scope of this article but a very simplistic architecture may look like the following:

A Simplified High Level Architecture of a Hyper-Personalization System.

There are two channels in interacting with the consumer. The forward channel (delivery) and in the return channel (feedback.) The forward channel is the delivery of recommendations, predictions and dynamic content generation to the consumer as well as conversational responses when implemented with a chatbot, personal assistant or search engine. It is a communication channel back to the consumer. The reverse process is the way by which feedback is gathered from the consumer and processed by the hyper-personalization system (including storage of data.) If implemented in a conversational manner or using some form of search, the input into the system is used to respond to the query as well as to understand the consumer. Other inputs beyond text and speech may go through this channel, including location data and other behavioral data.

Actively Learning is an important feature of hyper-personalization. Behaviors, moods, interests can be temporal or can begin and end at any point in time. The input into the system is used to understand the consumer’s preferences and continually update the preference model to keep accurate account of their current interests, mood, and so forth. External data that updates the model can come from external sources and that data can be personal data like social media posts or class or general data like current world events or trends. This data also updates the preference model in real-time to ensure that the system is providing the best recommendations and content for that specific moment in time.

Architectural discussions are far beyond the scope of this article, however there are certainly both benefits and risks to implementing hyper-personalization. And in implementing hyper-personalization it is incumbent that the provider mitigate any of the potential risks. The next three sections cover the benefits, risks and the techniques for mitigation of risks in deploying and using hyper-personalization. This is not an exhaustive set of lists but is enough to convey the primary benefits, risks and mitigation techniques to inform the reader.

Hyper-Personalization Benefits

• Improved user experience: Can provide users with recommendations and content that are more relevant and tailored to their interests, improving their overall experience.
• Improved customer experience: Can lead to a more customized and personalized experience for customers, which can increase their satisfaction and loyalty.
• Enhanced user trust: Hyper-personalization can increase user trust in the platform and the company behind it, as users feel that their needs and interests are being understood and catered to.
• Increased revenue: Hyper-personalization can lead to increased revenue, as users are more likely to make purchases or engage with the platform when presented with personalized recommendations.
• Increased engagement: Can increase user engagement by providing them with content and recommendations that are more likely to capture their attention and keep them coming back.
• Enhanced customer satisfaction: By providing users with personalized recommendations and content, hyper-personalization can increase customer satisfaction and loyalty.
• Improved sales and marketing: Can improve sales and marketing efforts by providing targeted recommendations and advertising that are more likely to convert into sales.
• Increased efficiency: Can increase efficiency by automating the process of content and recommendation curation, allowing companies to provide a more personalized experience at scale.
• Improved retention: By providing users with content and recommendations that are more relevant to their interests, hyper-personalization can improve user retention and reduce churn.
• Better data analysis: Hyper-personalization requires the analysis of large amounts of user data, which can provide companies with valuable insights into user behavior and preferences.
• Greater personalization flexibility: Hyper-personalization allows companies to provide a high degree of personalization flexibility, allowing them to adjust recommendations and dynamic content based on changing user preferences and behavior.
• Competitive advantage: Companies that implement hyper-personalization with generative AI can gain a competitive advantage by providing a more personalized experience than their competitors.
• Improved accuracy: Generative AI models can provide more accurate recommendations and content generation as opposed to simply curation, improving overall effectiveness.
• Increased engagement: By delivering personalized dynamic content and recommendations, users are more likely to engage with the platform, which can lead to increased usage and revenue.
• Higher conversion rates: Can lead to higher conversion rates, as users are presented with recommendations and dynamic multimedia content that are more relevant to their interests and needs.
• Improved accuracy: Generative AI can analyze vast amounts of data to generate highly accurate and precise recommendations that are tailored to the individual user which are shared across all channels in a shared preference model.
• Better decision making: Can assist users in making better decisions by presenting them with recommendations that are more relevant and personalized.
• Improved efficiency: Can streamline the decision-making process and reduce the time and effort required to find relevant information or products.
• More effective marketing: Can lead to more effective marketing, as users are presented with content and recommendations that are more likely to resonate with them.

Hyper-Personalization Risks

• Reinforcing biases: If the generative AI models are trained on biased data, hyper-personalization could reinforce existing biases and stereotypes, leading to discriminatory or exclusionary recommendations.
• Limited exposure to diverse viewpoints: Could create a “filter bubble” effect, where users are only exposed to content and recommendations that reinforce their existing beliefs and values, limiting their exposure to diverse viewpoints.
• Over-reliance on technology: Could lead to an over-reliance on technology, where users become less capable of making decisions without the help of AI-generated recommendations.
• Privacy concerns: Hyper-personalization requires collecting and analyzing large amounts of personal data, which could raise concerns around privacy and data protection.
• Security risks: With more data being collected, there is an increased risk of security breaches, hacks, and data theft.
• Susceptibility to manipulation: Could make users more susceptible to manipulation, particularly if the generative AI models are misused for political or commercial purposes.
• Reduced serendipity: Could limit serendipitous discoveries or surprise recommendations that could broaden a user’s horizons.
• Loss of human touch: Could lead to a loss of human touch, where recommendations become purely algorithm-driven and lack the personal touch that comes with human interaction.
• Bias amplification: Could amplify biases that exist in the data, leading to recommendations that are even more biased than the original data set.
• Undermining trust: If hyper-personalization is not transparent, it could undermine trust in the technology and the companies that use it, leading to a backlash against AI-generated recommendations.

How to Mitigate Risks

• Transparency: Be transparent about the data being collected, how it will be used, and who it will be shared with. Explain to customers the reasoning behind personalized recommendations, content generation or decisions. Also solicit active feedback.
• Consent: Obtain explicit consent from customers before collecting and using their personal data for hyper-personalization. Allow customers to opt-out of personalized experiences if they choose to do so.
• Data security: Implement strong data security measures to prevent data breaches or theft. Ensure that data is encrypted, access is controlled, and regularly undergoes security audits.
• Explainability: Ensure that hyper-personalization algorithms are transparent and explainable. It should be possible to explain how the algorithm arrived at a particular decision or recommendation.
• Diversity and inclusion: Take steps to eliminate bias in data and algorithms, and ensure that personalized experiences are designed to be inclusive and diverse. Provide personalized experiences for a broad range of customers and take steps to avoid stereotypes.
• Human oversight: Provide human oversight of hyper-personalization algorithms. Ensure that humans are involved in the decision-making process and that they can step in when necessary to prevent errors or bias.
• Ethics and accountability: Develop ethical guidelines for hyper-personalization and ensure that they are followed. Hold individuals and organizations accountable for any breaches of trust or privacy.
• Model Monitoring: Ensure that models are monitored and that preferences are not favoring toxic or bias views.
• Ingress and Egress Filters: Filter content coming into the system and going out of the hyper-personalization system to ensure that bias is minimized.
• Reasoning models: The usage of models that can reason about truth and discern facts, opinion, belief and prejudice from one another can more easily eliminate bias and reduce hallucinations as they are able to discern facts from personal opinions, beliefs and prejudices. (I will link to another post on Reasoning models here as this is a substantial topic on its own.)

Conclusion

Hyper-personalization is fully dependent on advances in generative AI and deep learning and as such will continue to improve with new innovations in content generation, deep learning architectures and methods and as more commercial investment is made in developing the area. The applications of hyper-personalization are broad and far reaching, but the most compelling areas are coupled with personal assistants, dynamic content generation for multi-media like movies and music and in delivering highly-personalized ads based on learned consumer preferences.

Nearly any industry can benefit from hyper-personalization however it is not without its risks. Careful attention needs to be paid to the nature and type of data and content moving in and out of the system to minimize biases and harms, which can be mitigated using content filters and reasoning models. To prevent information bubbles, echo chambers and mental health issues arising out of exposure to the content produced by generative models they need to be actively monitored to ensure that the model doesn’t become toxic. Free speech advocates may claim this is some form of censorship, however hate, racism and other toxic behaviors are not protected by the First Amendment and have no place in society.

Stay tuned for a follow up article as this space evolves.

*ChatGPT helped write this article.