In this conversation, Sarah Hoffman, Vice President for AI and Machine Learning Research at Fidelity, sheds light on the practical applications of AI in modern business. She breaks down common myths about AI, explaining its mathematical foundation and ethical implications related to data bias. Sarah emphasizes the need for diversity in AI development and how it reflects our societal values. Additionally, she discusses AI's transformative role in enhancing workplace innovation and accessibility through democratized technology.
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insights INSIGHT
AI: Math, Not Magic
AI, often used interchangeably with machine learning, learns from data and produces outputs.
It adapts to changes by retraining on new data, eliminating manual code adjustments.
question_answer ANECDOTE
COVID-19 and Sentiment Analysis
When COVID-19 hit, negative social media sentiment impacted investment decisions based on sentiment analysis.
This highlighted the need to adapt AI models to significant world events.
question_answer ANECDOTE
AI Bias in Recruiting
A tech company's AI recruiting tool unintentionally discriminated against women despite lacking gender input.
It learned biases from historical male-dominated resumes, highlighting how AI can perpetuate existing biases.
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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.