Assumption: AI systems share data vs Google Search/Wikipedia/Blog post
Why would a user choose AI over current data/learning systems?
- Speed. Can we communicate with AI more easily than google search to answer a question?
- Unbiased data. Can AI give me a clean answer to my question without tampering with the results?
- Comprehensive. The answer is more complete from a single source vs compiling results from various sources.
AI Answer
1. Personalization
- AI: Can provide personalized recommendations and data based on user behavior, preferences, and historical interactions.
- Traditional Systems: Offer static information that requires user navigation and interpretation.
2. Proactive Assistance
- AI: Predicts user needs and proactively provides relevant information and assistance without explicit queries.
- Traditional Systems: Generally, react to user inputs and do not anticipate user needs.
3. Dynamic Learning and Adaptation
- AI: Learns and adapts to the user’s evolving needs and preferences, continuously improving its interactions and recommendations.
- Traditional Systems: Do not adapt or learn from user interactions and provide the same experience to all users.
4. Accessibility and Inclusivity
- AI: Can provide more accessible and inclusive experiences, adapting to different user needs (e.g., voice commands for visually impaired users).
- Traditional Systems: May not cater to the specific accessibility needs of diverse users.
Trade-offs
Are there trade-offs to be considered before or while using AI as your primary source of knowledge? Always, and when embarking on a journey its in our best interest to consider them before proceeding.
1. Privacy and Data Security Concerns
- Detail: AI systems often require access to personal data to deliver personalized experiences, which might raise concerns about data privacy and security.
- Implication: Users might be reluctant to share personal information, and there is a risk of data breaches or misuse, potentially harming individuals and diminishing trust in AI systems.
2. Potential for Bias and Inaccuracy
- Detail: AI systems can inherit biases present in their training data or algorithms, which might result in unfair or inaccurate recommendations and decisions.
- Implication: This could lead to perpetuating stereotypes, providing skewed information, or making unjust decisions, which might disadvantage certain user groups.
3. Over-reliance on Technology
- Detail: As AI systems take over more functions, there’s a risk of users becoming overly reliant on technology for information, decisions, and tasks.
- Implication: This could potentially diminish critical thinking skills, reduce human oversight in decision-making, and make users vulnerable to errors or limitations of the AI.
4. Accessibility and Digital Divide
- Detail: Advanced AI functionalities might require sophisticated devices and reliable internet access, which may not be available to all user demographics.
- Implication: This can exacerbate the digital divide, where individuals without access to advanced technology are disadvantaged and unable to benefit from AI-enhanced systems.
Where do we go from here?
This series is designed to open experiments and test AI. In this case, we will experiment with AI as a primary education tool and survey the landscape. As a creator, it is exciting to think of the potential in building a tool that will allow for potentially non-biased, fast, accurate, tailored data. My presumption is this is a crowded field with many candidates vying for position, including household tech names.
Along with the high potential for competition, revenue creation is unclear as a data retrieval and creation tool needs to target a wide audience which can/should lower price to entry. I believe, we grow as we broaden educational access, seeing this space go to zero would be an amazing outcome for society.
Stay tuned for the experiment and outcome
Cheers
Nigel A.