IBM's Rometty on Real-World Accomplishments of Watson

The transcript discusses the evolution of IBM's Watson from its Jeopardy debut to its current applications in business. It highlights the shift from programmable machines to AI that learns and reasons. The focus is on business AI, which is trained for specific industries, unlike consumer AI. Key differences include vertical knowledge and data ownership. Real-world examples illustrate Watson's impact in sectors like finance, oil, and weather forecasting.

What was the significant shift in AI development highlighted in the introduction?

• From data analysis to data storage

• From programmable machines to learning machines

• From cloud computing to local computing

• From manual input to automated input

What is a key difference between consumer AI and business AI?

• Consumer AI uses more data

• Business AI is less secure

• Business AI requires vertical training

• Consumer AI is more expensive

Why is vertical training important for business AI?

• It helps AI understand specific industries

• It increases the speed of AI processing

• It allows AI to be more general-purpose

• It reduces the cost of AI development

What percentage of the world's data is searchable, according to the transcript?

• 60%

• 10%

• 20%

• 40%

Why is data ownership crucial in business AI?

• To protect proprietary insights and competitive advantage

• To ensure data is shared with competitors

• To increase the amount of data available

• To reduce the cost of data storage

How has Watson impacted the financial services industry?

• By reducing the number of employees

• By improving customer sentiment analysis

• By eliminating the need for call centers

• By increasing the number of transactions

What role does Watson play in weather forecasting?

• It predicts earthquakes

• It controls climate change

• It provides real-time weather updates

• It measures air quality