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One year later, how has ChatGPT changed the way we work?


News Archive — Stanford Digital Economy Lab

One year later, how has ChatGPT changed the way we work?

Shawneric Hachey

2023-11-29 19:22:01

Full article at: Source link

Summary

Summary: The article explores the impact of ChatGPT on the way we work one year after its release, highlighting its influence on communication, productivity, and efficiency in various industries.

Key Themes

Communication: ChatGPT has revolutionized workplace communication by enabling more efficient and effective interaction between team members.
Productivity: The use of ChatGPT has boosted productivity by automating repetitive tasks and streamlining workflows.
Efficiency: ChatGPT has enhanced workplace efficiency by providing instant answers to queries and facilitating quicker decision-making processes.

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What to expect in AI in 2024


News Archive — Stanford Digital Economy Lab

What to expect in AI in 2024

Shawneric Hachey

2023-12-11 15:44:04

Full article at: Source link

Summary

In the article “What to expect in AI in 2024” by Stanford Digital Economy Lab, the future of artificial intelligence is explored, highlighting key trends and developments expected by the year 2024.

Key Themes

1. Robotics Technology: The increasing integration of robotics technology with AI systems is expected to revolutionize industries.
2. Ethical AI: The importance of ethical considerations in the development and implementation of AI technologies is emphasized.
3. Market Growth: The article discusses the projected market growth in the AI sector by 2024, highlighting opportunities for businesses and investors.

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Brainstorm AI 2023: Economic Impacts of AI and ML on the Workforce


News Archive — Stanford Digital Economy Lab

Brainstorm AI 2023: Economic Impacts of AI and ML on the Workforce

Shawneric Hachey

2023-12-14 17:35:02

Full article at: Source link

Summary

The article “Brainstorm AI 2023: Economic Impacts of AI and ML on the Workforce” discusses the potential effects of artificial intelligence and machine learning on the workforce in the upcoming years, highlighting the importance of understanding and preparing for these changes.

Key Themes

AI and ML: The article delves into the economic impacts of artificial intelligence (AI) and machine learning (ML) on the workforce, emphasizing the need for proactive measures to address these advancements.
Workforce: Focus is placed on how AI and ML technologies will affect the workforce, highlighting the importance of adapting and upskilling to meet the changing demands of the digital economy.
Economic Impacts: The article explores the broader economic implications of AI and ML on the workforce, urging leaders to consider the potential disruptions and opportunities presented by these technologies.

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Seminar Series 2023 — Stanford Digital Economy Lab


News Archive — Stanford Digital Economy Lab

Seminar Series 2023 — Stanford Digital Economy Lab

Shawneric Hachey

2023-12-15 16:51:02

Full article at: Source link

Summary

The article discusses the topics covered in the 2023 Seminar Series by the Stanford Digital Economy Lab, including productivity during the pandemic, supporting black-owned businesses, and data deserts and inequality.

Key Themes

1. Productivity during the pandemic: Focuses on how companies and individuals have adapted to maintain productivity amidst the challenges of the pandemic.
2. Consumer demand to support black-owned businesses: Addresses the increasing interest and support for black-owned businesses in the marketplace.
3. Data deserts and inequality: Explores the issues of unequal access to data and its implications for societal inequality.

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How Much Is Your Favorite Free App Worth to You?


News Archive — Stanford Digital Economy Lab

How Much Is Your Favorite Free App Worth to You?

Shawneric Hachey

2024-01-17 19:45:31

Full article at: Source link

Summary

Summary: The article explores the concept of the value of free apps to users, highlighting the various ways in which these apps generate revenue and engage with consumers through data collection and advertising.

Key Themes

1. Value of Free Apps: The article delves into the concept of free apps and how they create value for users despite not charging for the initial download or use.
2. Revenue Generation: It discusses the different methods through which free apps generate revenue, such as in-app purchases and targeted advertising.
3. User Engagement: The article touches on how free apps engage with users through data collection and personalization to enhance the user experience.

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How Data Collaboration Platforms Can Help Companies Build Better AI


News Archive — Stanford Digital Economy Lab

How Data Collaboration Platforms Can Help Companies Build Better AI

Shawneric Hachey

2024-01-26 20:16:04

Full article at: Source link

Summary

Summary:
The article discusses how data collaboration platforms can enhance the development of AI within companies by enabling data sharing, encouraging interdisciplinary collaboration, and facilitating knowledge transfer.

Key Themes

Data Collaboration Platforms:
Platforms that facilitate data sharing and collaboration among different teams within a company to improve AI development.

Interdisciplinary Collaboration:
Encouraging collaboration between individuals from different fields to bring diverse perspectives and expertise to AI projects.

Knowledge Transfer:
The process of transferring knowledge gained from one project or team to another to improve overall AI development within the company.

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Erik Brynjolfsson: ‘This could be the best decade in history — or the worst’


News Archive — Stanford Digital Economy Lab

Erik Brynjolfsson: ‘This could be the best decade in history — or the worst’

Shawneric Hachey

2024-01-31 16:25:21

Full article at: Source link

Summary

The article discusses the potential outcomes of the upcoming decade, with Erik Brynjolfsson highlighting the significant impact that technological advancements could have on society. He argues that this decade could bring about unprecedented prosperity, but also warns of the risks and challenges that come with it.

Key Themes

1. Technological Advancements:
– The article delves into the potential positive impact of technological advancements on society, emphasizing the opportunities they bring for prosperity and growth.

2. Risks and Challenges:
– Brynjolfsson also highlights the potential risks and challenges that come with technological progress, cautioning against overlooking the negative implications.

3. Future Outlook:
– The article explores the contrasting possibilities for the future, presenting a nuanced perspective on the potential outcomes of the upcoming decade.

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The Jobs Equation: A Conversation with Erik Brynjolfsson


News Archive — Stanford Digital Economy Lab

The Jobs Equation: A Conversation with Erik Brynjolfsson

Shawneric Hachey

2024-03-27 11:18:41

Full article at: Source link

Summary

Summary: In the article “The Jobs Equation: A Conversation with Erik Brynjolfsson,” the discussion around the impact of automation and technological advancement on the labor market is analyzed, with a focus on potential solutions to mitigate job displacement.

Key Themes

Automation: The article delves into the implications of automation on the labor market and the workforce, discussing how technological advancements are reshaping traditional job roles.
Technological Advancement: The progression of technology and its influence on various industries and job sectors is explored, highlighting the need for upskilling and adaptability in the workforce.
Job Displacement: The potential consequences of job displacement due to automation are discussed, along with proposed solutions to address the issue and support affected workers.

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Unmasking the Bias in Facial Recognition Algorithms | by MIT IDE | MIT Initiative on the Digital Economy | Jan, 2024


MIT Initiative on the Digital Economy – Medium

Unmasking the Bias in Facial Recognition Algorithms | by MIT IDE | MIT Initiative on the Digital Economy | Jan, 2024

MIT IDE

2024-01-02 23:43:13

Full article at: Source link

Summary

Machine learning models are influenced by biased data, as seen in the case of Amazon’s hiring model that discriminated against women. Data reflects societal biases, leading to power shadows in datasets, favoring lighter-skinned individuals and men. Power shadows must be acknowledged and addressed in the development of technology to avoid perpetuating existing social hierarchies.

Key Themes

1. Amazon Hiring Example: Biased data in machine learning models.
2. Skewed Data: Representation issues in datasets.
3. Colonialism and Colorism: Impact of historical injustices on data biases.

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Unilever Takes AI Ethics From Policy to Process | by MIT IDE | MIT Initiative on the Digital Economy


MIT Initiative on the Digital Economy – Medium

Unilever Takes AI Ethics From Policy to Process | by MIT IDE | MIT Initiative on the Digital Economy

MIT IDE

2024-01-09 17:18:37

Full article at: Source link

Summary

Many companies recognize the importance of ethical AI, but few have developed guidelines, with only 6% of U.S. senior leaders having done so. Unilever exemplifies best practices for embedding ethical AI into corporate strategies, with a focus on transparency, bias mitigation, and fairness in AI applications.

Key Themes

Transparency: Companies must ensure transparency in their AI systems, disclosing how data is used and decisions are made to build trust and accountability.
Bias Mitigation: Addressing biases in AI applications is crucial to prevent discriminatory outcomes and ensure fairness.
Fairness: Upholding fairness standards in AI systems involves systematic analysis and decision-making processes to align with ethical norms.

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