AI and machine learning often make relatively trivial errors, such as speech-to-text errors, misidentification of photos, and failure to understand requests for specific information, all of which are generally seen as technical errors and not fundamental flaws in AI.
- [Instructor] The challenge of AI,…it's really the challenge of classification errors.…And the reason for this is AI…is fundamentally automated categorization.…There are two major approaches used…when dealing with data in AI.…The first one is called supervised learning.…And that's where the AI puts new observations…into existing criterion categories…based on matches with existing data.…It's like sorting the mail and who does it go to?…And there's also unsupervised learning…where AI groups cases based on similarity…of measured attributes without a specific criterion.…
It's simply a way of trying to put like with like.…And this is how AI categorizes tumors,…it's how it drives cars,…and it's how it transcribes speech to text.…And then the miracle of AI gives you something like this.…We've all had experiences with auto correct disasters.…Fundamentally, they're just errors…in classification or categorization,…but somehow Siri always seems to be reaching…into the deepest recesses of our unconscious.…And things have gone wrong in spectacular fashion…
- Bias in AI
- Navigating the social challenges of AI
- Moral reasoning and relational ethics
- General Data Protection Regulation (GDPR) and AI
- Discrimination in data
- Liability and AI
- AI in life-and-death situations
- Confronting the challenges of AI as a developer, executive, and consumer
Skill Level Intermediate
1. The Context for AI
2. Technical Challenges of AI
3. Social Challenges of AI
4. Legal Challenges of AI
5. Safety Challenges of AI
AI in the military4m 36s
6. Confronting the Challenges of AI
Next steps2m 52s
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