Summary of How to Succeed With AI Augmentation

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00:00:00 - 00:55:00

This video discusses how AI is already impacting the workforce, and how to use it responsibly to improve efficiency and effectiveness in work. It highlights the importance of understanding the limitations of AI systems, and concludes that humans need to get comfortable coexisting with the fact that AI simultaneously exceeds some aspects of human capability.

  • 00:00:00 This webinar discusses how to succeed with AI augmentation, which includes identifying what tasks AI can't do yet, designing for what people and machines each do best, and engaging the right people in the work. Tom Davenport and Steven Miller discuss how AI is good at taking the first cut at different tasks, prioritizing human activity, and predicting the future.
  • 00:05:00 This video discusses how AI and robots are already taking the jobs of human beings in various industries, and how we need to be careful not to replace human beings with machines too quickly. It also discusses how ShotSpotter, Gravyty, and other police systems are using data to prioritize human activities.
  • 00:10:00 This video explains how to use artificial intelligence (AI) to improve efficiency and accuracy in various areas of work. AI can be used to help attorneys avoid having to read through contracts, healthcare professionals predict stock inventory and next best action, and machine learning can sometimes get wrong what data really matters. The video also provides a case study of how AI was used to help a company through a pandemic.
  • 00:15:00 The video discusses the effects of AI on the workforce, and concludes that, so far, it has not led to job loss. It describes a number of cases where AI has been used to complement or replace human labor, and discusses the potential implications of this trend for the future of work.
  • 00:20:00 The video discusses how automation and augmentation can be used to improve efficiency and effectiveness in work. Steve Miller goes on to list several examples of where automation and augmentation are used in tandem, including Mandiant's attribution stage of cyber threat analysis, DBS Bank's financial transaction surveillance, and the high precision required in certain industries.
  • 00:25:00 The key point that Tom emphasized in this video is that full automation works best when you're in familiar territory, and even the nature of variance and unexpected is somewhat familiar. This is key when it comes to driving productivity. However, people will still need to use tools like AI-enabled automation in order to be effective.
  • 00:30:00 The speaker discusses the concept of intelligence and its many dimensions. He notes that there is no agreed-upon framework for intelligence, but that there are four main types of intelligence: analytical, creative, practical, and emotional. He says that humans are always cycling through these types of intelligence, and that even though machines can do some things better than humans, humans are still very capable. The speaker concludes by saying that humans need to get comfortable coexisting with the fact that AI simultaneously exceeds some aspects of human capability.
  • 00:35:00 The author discusses the different aspects of AI and how it can be used responsibly. He also highlights the importance of understanding the limitations of AI systems.
  • 00:40:00 In this video, Tom and Abbie discuss the different differences and similarities between the US and Europe when it comes to using AI. They mention that in Europe, there is a greater focus on helping employees learn new skills, while in the US, people tend to figure things out on their own. They also mention that there are a few cases in Europe where the company helped train employees for new AI-related jobs, while in the US, this assistance is not as common. Finally, they mention that Singapore has a strong private sector and government partnership in training employees for new AI jobs.
  • 00:45:00 Tom discusses how successful AI implementations involve embedding AI into other aspects of work processes, such as platforms and systems. He also notes that change management efforts around AI are intensifying and becoming more complex.
  • 00:50:00 Tom talks about how it is important for companies to engage with AI augmentation to have a successful implementation. He discusses how early engagement can speed up the iterative feedback loop between machine and human. He also talks about how other stakeholders, such as the people who will be affected by the AI, can affect managers' decisions about the design of the AI.
  • 00:55:00 The speaker discusses the benefits of using AI to augment human abilities, emphasizing the importance of clear use cases and human input. They suggest that AI projects require upper management support in order to be successful. Finally, they mention that AI projects are not projects, but products that must be carefully managed.

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