Key Takeaways
- Categorizing AI into public, private, and personal helps to address regulatory limitations, data privacy, and security issues.
- Public AI serves the global population, while private AI is tailored for specific organizations, and personal AI enhances user experience.
- Public AI is openly accessible, private AI has restricted access, and personal AI is limited to customers. Data handling and privacy vary among the three categories.
AIs can be categorized as public, private, or personal AIs. Training and designing an AI system based on these categories can help solve issues on regulatory limitations, data privacy, and security.
This categorization also helps people understand the purpose of the AI, who operates it, how it handles your data, and what restrictions may be imposed to safeguard public, personal, and organizational interests.
To understand the difference between public, private, and personal AI, let’s compare them based on their purpose, performance, data handling, and privacy.
What Is Public AI?
Public AI refers to AIs trained on user data and various open-source platforms like Wikimedia and ResNet. These types of AI are some of the most popular and widely accessible forms of AI that people use daily for work, school, and personal projects.
Purpose
A public AI is an AI service, program, or algorithm that’s openly accessible to anyone on the internet. Public AIs are typically general-purpose applications that serve the global population, providing effective AI solutions for problems and tasks that would normally require many man-hours to accomplish. Some of the most popular public AI you may be familiar with include search engines, social media algorithms, language translators, and modern text-to-speech engines.
Accessibility
As stated earlier, public AIs are openly accessible to anyone on the internet. Most public AIs are already integrated into search engines, social media platforms, and extensions, which do not require any special sign-up or payments to use. Many public AI models, such as Llama, ResNet, and BERT, are also freely available online for anyone to use and fine-tune to make their own models.
Performance
Public AI systems are made to handle a large number of users simultaneously. Due to the number of users (sometimes in the range of millions), public AIs are set to perform just well enough to support as many users as possible. Certain regulations are also imposed on public AI to safeguard the interests of the state and its people. Typical regulation would include denying users certain types of information and constraining AI in its actions and capabilities.
Data Handling and Privacy
One of the biggest concerns about public AIs is their way of handling data and privacy. These systems collect large amounts of user data to improve and operate their AI algorithms and services. This, however, can be quite concerning, as the data might be misused by the organizations that own and operate the service. Laws and regulations in protecting user data and privacy are limited due to the nature of how public AI operates.
What Is Private AI?
The reason why businesses require a private AI is that Public AIs like ChatGPT introduce privacy and security issues for organizations. A private AI refers to AI models trained and fine-tuned to cater to the needs of an organization without compromising the security of trade secrets and other intellectual properties. Many private AIs are fine-tuned from publicly available LLMs using private data to tailor fit the AI model to the organization’s specific needs.
Purpose
The purpose of a Private AI is to have an AI system specifically built for an organization. It is used to solve internal business problems and enhance efficiency and overall productivity within the company. Private AIs are often employed in a variety of internal systems such as customer relations management (CRM), supply chain optimization, and fraud detection.
Accessibility
Unlike public AI, private AI is not openly available to the public. In general, access to a private AI is restricted to only authorized personnel to ensure that sensitive data and processes remain protected. It is important to note that although businesses use private AI for internal productivity, they also utilize a separate personal AI for customers to access their services.
Performance
Private AI is trained and streamlined for the specific needs of an organization. This allows businesses to fine-tune pre-trained LLMs or their model to achieve optimal performance for a given task. This, in turn, lowers the computing power necessary to run the AI with good performance while saving costs. Since it’s not accessible to the public, private AI has fewer regulations and can use unconstrained AI models or algorithms to boost the capability of their AI.
Data Handling and Privacy
Data handling and privacy is the biggest reason companies require a private AI. With a private AI, organizations can control and secure their data, minimizing the risk of data breaches and unauthorized access. Data used to fine-tune a private AI is curated by a team of hired engineers, data scientists, and software developers to design and train the model, which ensures that publicly available data bias their AI.
What Is Personal AI?
Personal AI refers to an AI algorithm made to aid people in their daily lives. They can usually be accessed through personal devices such as smartphones, tablets, smart speakers, and wearables. Some examples of personal AI would include virtual assistants like Alexa, Bixby, Google Assistant, and Siri.
Purpose
Personal AIs are designed to enhance a person’s user experience when interacting with technology to use a specific service. Personal AI algorithms provide a tailored experience to the user by adapting to their preferences, making it easier for customers to use a certain service.
Performance
Though not as scalable as public AI, personalized AI excels in understanding and responding to individual user requests. Personalized AI may also be incrementally slower than public AI as it needs to consider the relevance of the data to the user before it provides any result. That said, personal AI is more capable than private AI as it provides better and more relevant outputs for the user. Of course, performance will also depend on what personal AI service you prefer and how the company that made the AI operates it.
Data Handling and Privacy
Privacy and data handling are some of the biggest concerns regarding personal AI. Due to how personal AI is utilized in a service, laws permit companies to collect personal user data after users agree to terms of service. This makes them liable for protecting user data privacy and security. However, due to the data’s sensitivity, any data breach could potentially harm the privacy and security of the users.
Comparing Public, Private, and Personal AI
Categorizing AI into Public AI, Private AI, and Personal AI enables us to apply AI in solving specific tasks while maintaining optimal performance, accessibility, data security, and user privacy. Here’s a table for easy comparison:
Aspect | Public AI | Private AI | Personal AI |
|---|---|---|---|
Purpose | Broad, general purpose | Broad, general purpose | Individual user needs |
Accessibility | Open to the public | Restricted access, employee only | Access is limited to customers |
Performance | Scalable, supports a large number of users simultaneously, optimal | Customized, optimized for specific operational tasks, fast | Personalized, optimal |
Data Handling and Privacy | Low data privacy, companies can use data in accordance with the law | High data security, company handles its own data | Medium-level privacy, users agree to company terms of service |
The Importance of Categorizing AI
Understanding the difference between public AI, private AI, and personal AI is important as these categories represent how we use AI in our daily lives. It also helps us to create certain laws and regulations to ensure the privacy and security of individuals and organizations. Without these distinctions, such regulations will be harder to impose, which may result in companies misusing user data, leaking business trade secrets, or even the death of AI democratization.