AI is no longer for data scientists only. Most businesses have made AI tools part of their workflow. For example, we’ve seen chatbots, automated emails, and the like. As we embrace artificial intelligence, we have to discuss it. What is AI? What is it not? Should we use it for everything?
In this episode, Sarah Hoffman, VP of AI and Machine Learning Research in Fidelity Investments, defines AI and its impact on our lives. She also describes the ethical challenges of data bias and what people are doing to overcome it. Finally, Sarah explains why diversity and prejudice are significant concerns in AI development.
Tune in to this episode to learn how AI pushes for innovation.
Here are three reasons why you should listen to this episode:
- Discover artificial intelligence as a new approach to learning and training.
- Learn how AI is a reflection of our own beliefs and understanding.
- Understand the relevance of diversity in the field of artificial intelligence.
Resources
Episode Highlights
[00:47] AI and ML Research at FCAT
- The Fidelity Center for Applied Technology (FCAT) has a long history of innovation and commitment to technology.
Sarah: "We invest deeply in technology. But we've also always recognized that technology is just a tool. It's really how we apply it that matters."
- FCAT develops platforms and products to empower the next generation.
- Sarah is a part of FCAT's research team. They explore the future of artificial intelligence (AI).
[02:29] Defining AI
- Sarah uses AI and machine learning (ML) interchangeably.
- ML refers to code learning from data. It produces answers based on stored information to make predictions.
- AI is math, not magic.
[03:43] AI in the Finance World
- FCAT provides services that harness AI’s true potential.
- Financial services use AI tools often. Many people use chatbots, robo-advisers, and automated email responders.
- Several companies have adopted personalization and sentiment analysis.
- Models need to adjust when something changes in the world.
[06:46] Data Ethics Concerns
- AI learns biases through data.
- Ethics boards address ethical issues regarding AI projects and decide whether a problem needs AI.
- Fairness and explainability tools are available to protect against inadvertent biases.
- Using AI can enhance how we train people and use fairness and explainability tools.
