ai governance

The following content may be disturbing to some listeners.

For years, child sexual abuse material was mostly distributed by mail. Authorities used investigative techniques to stem its spread. That got a lot harder when the internet came along. And AI has supercharged the problem.

“Those 750,000 predators that are online at any given time looking to connect with minor[s] … they just need to find a picture of a child and use the AI to generate child sexual abuse materials and superimpose these faces on something that is inappropriate,” says child safety advocate and TikTokker Tiana Sharifi.

The nonprofit Thorn has created new design principles aimed at fighting child sexual abuse. Rebecca Portnoff, the organization’s vice president of data science, says tech companies need to develop better technology to detect AI-generated images and commit not to use this material to train AI models.

AI Governance is a tricky topic that no one seems to know how to approach. There can be many roadblocks or hurdles before properly implementing governance for AI. Gabriella Kusz, Sr. Fellow at AI2030, joins us to discuss how to create governance for AI mass adoption. 

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Timestamps:
01:30 Daily AI news
05:00 About Gabriella and AI2030
08:44 Challenges with AI technology and governance issues.
12:02 Rapid pace challenges global technical standards creation.
13:26 Ethical AI guidance is available from Fintech.
16:41 Draw parallels between ESG and responsible AI adoption.
22:33 Industry self-regulation precedes formal laws in societies.
26:00 Inclusion and multidisciplinary team crucial for governance.
27:57 Clear boundaries for leveraging technology for advantage.
33:29 Recognize failures, iterate, and empower AI progress.
36:00 Set up task force, admit setbacks, progress.

Topics Covered in This Episode:
1. Role of corporations and Government in AI Governance
2. Organization's Governance Structure
3. Risks of AI and Generative AI
4. Practical Tips for AI Governance
5. Ethical and Global Technical Standards

Keywords:
ESG, AI, Artificial intelligence, Emerging areas, Pilot period, Return on investment, Risk identification, Risk mitigation, Governance, Legislation, Change management, Generative AI, Inclusion, Education, ongoing learning, Technology usage, Ethical standards, Global technical standards, Fintech for Good, AI 2030, NIST, Taskforce, Failure in AI implementation, Self-education, AI governance, AI applications, Business growth, AI news.

A year ago, the public launch of ChatGPT took the world by storm and it was followed by many more generative artificial intelligence tools, all with remarkable, human-like abilities. Fears over the existential risks posed by AI have dominated the global conversation around the technology ever since.

Fei-Fei Li, a pioneer that helped lay the groundwork that underpins modern generative AI models, takes a more nuanced approach. She’s pushing for a human-centred way of dealing with AI—treating it as a tool to help enhance—and not replace—humanity, while focussing on the pressing challenges of disinformation, bias and job disruption.

Fei-Fei Li is the founding co-director of Stanford University’s Institute for Human-Centred Artificial Intelligence. Fei-Fei and her research group created ImageNet, a huge database of images that enabled computers scientists to build algorithms that were able to see and recognise objects in the real world. That endeavour also introduced the world to deep learning, a type of machine learning that is fundamental part of how large-language and image-creation models work.

Host: Alok Jha, The Economist’s science and technology editor. 

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Hosted on Acast. See acast.com/privacy for more information.

Sam Altman Has been fired as CEO of OpenAI, joining Microsoft this morning and sending the internet into a spiral. Here's everything you need to know about what went down and what it all means for the world.

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TIMESTAMPS

00:00 What Happened?
https://x.com/OpenAI/status/1725611900262588813?s=20 
https://openai.com/blog/openai-announces-leadership-transition 
https://x.com/sama/status/1725631621511184771?s=20 
https://x.com/gdb/status/1725736242137182594?s=20 

06:25 OpenAI Board Members
https://twitter.com/ilyasut?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor 
https://twitter.com/adamdangelo?lang=en 
https://www.linkedin.com/pulse/tasha-mccauley-wikipedia-joseph-gordon-levitt-wife-ali-raza-3gsbf/?trk=article-ssr-frontend-pulse_more-articles_related-content-card 
https://cset.georgetown.edu/staff/helen-toner/ 

11:59 The New CEO
https://x.com/eshear/status/1726526112019382275?s=20 
https://x.com/adcock_brett/status/1726484049664057789?s=20 
https://x.com/EffMktHype/status/1726558177028935770?s=20 
https://x.com/Jason/status/1726492873775079807?s=20 

16:32 Microsoft's Role
https://x.com/satyanadella/status/1726509045803336122?s=20 
https://x.com/sama/status/1726510261509779876?s=20
https://x.com/balajis/status/1726515841221681619?s=20 

22:27 Employee Pushback
https://x.com/miramurati/status/1726542556203483392?s=20 
https://x.com/ilyasut/status/1726590052392956028?s=20 

25:01 The Future of Silicon Valley

28:03 Decel vs e/acc
https://x.com/hosseeb/status/1726492186953535764?s=20 

31:11 Takeaways

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Not financial or tax advice. See our investment disclosures here:
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Paul Christiano is the world’s leading AI safety researcher. My full episode with him is out!

We discuss:

- Does he regret inventing RLHF, and is alignment necessarily dual-use?

- Why he has relatively modest timelines (40% by 2040, 15% by 2030),

- What do we want post-AGI world to look like (do we want to keep gods enslaved forever)?

- Why he’s leading the push to get to labs develop responsible scaling policies, and what it would take to prevent an AI coup or bioweapon,

- His current research into a new proof system, and how this could solve alignment by explaining model's behavior

- and much more.

Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform. Read the full transcript here. Follow me on Twitter for updates on future episodes.

Open Philanthropy

Open Philanthropy is currently hiring for twenty-two different roles to reduce catastrophic risks from fast-moving advances in AI and biotechnology, including grantmaking, research, and operations.

For more information and to apply, please see the application: https://www.openphilanthropy.org/research/new-roles-on-our-gcr-team/

The deadline to apply is November 9th; make sure to check out those roles before they close.

Timestamps

(00:00:00) - What do we want post-AGI world to look like?

(00:24:25) - Timelines

(00:45:28) - Evolution vs gradient descent

(00:54:53) - Misalignment and takeover

(01:17:23) - Is alignment dual-use?

(01:31:38) - Responsible scaling policies

(01:58:25) - Paul’s alignment research

(02:35:01) - Will this revolutionize theoretical CS and math?

(02:46:11) - How Paul invented RLHF

(02:55:10) - Disagreements with Carl Shulman

(03:01:53) - Long TSMC but not NVIDIA

To deploy responsible AI and build trust with customers, businesses need to prioritize AI governance. In this episode of Smart Talks with IBM, Malcolm Gladwell and Laurie Santos discuss AI accountability with Christina Montgomery, Chief Privacy and Trust Officer at IBM. They chat about AI regulation, what compliance means in the AI age, and why transparent AI governance is good for business.

Visit us at: ibm.com/smarttalks

Explore watsonx.governance: https://www.ibm.com/products/watsonx-governance

This is a paid advertisement from IBM.

See omnystudio.com/listener for privacy information.

Today we explore AutoGPT, an AI system that can recursively improve its own natural language skills without human supervision. Through unconstrained learning, AutoGPT can acquire expertise faster than humans. A case study showed AutoGPT mastering chess in just 72 hours by analyzing games. AutoGPT has huge potential but also poses risks like technological unemployment. Key insights:

• AutoGPT represents a milestone in AI due to recursive self-improvement
• It points to AI that can learn any textual skill without explicit training
• Applications could reshape medicine, law, science and more
• But it also risks automating human jobs and expertise
• Responsible governance of the technology will be critical

This podcast was generated with the help of artificial intelligence. We do fact check with human eyes, but there might still be hallucinations in the output.

Music credit: "Modern Situations by Unicorn Heads"

When Mustafa Suleyman co-founded the AI research company DeepMind more than a decade ago, his goal felt ambitious, even a bit far-fetched: to build a machine that could replicate human intelligence. Now, he says, rapid progress in the development of AI means that goal could be met within the next three years, and the implications of that milestone are huge. Suleyman explores those implications in his new book, “The Coming Wave,” which came out this week. Marketplace’s Lily Jamali spoke to Suleyman, now CEO and co-founder of Inflection AI, about a core theme of the book: the question of containment.

We know that AI can help us make more money. But what can it do to help improve the way we manage and deal with money? Today, Nazia Raoof, IT Strategy and Strategic Partnerships for Financial Services,  joins us as we delve into the world of banking and financial services and explore how AI can be used to improve these industries.

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Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

Time Stamps:
[00:00:17] Daily AI news
[00:06:35] ChatGPT as a complementary tool for financial advisors
[00:09:15] Firms adapting to AI and use cases
[00:14:33] Embedding  AI in platforms and APIs while adding guardrails
[00:16:47] Generative AI's risk for financial institutions' security
[00:19:53] Final takeaway

Topics Covered in This Episode:
- Risks posed to financial institutions and how banks handle AI
- Implementation of AI governance committee
- Functions of the committee in evaluating and implementing AI
- Understanding of decision makers in the financial industry regarding generative AI
- Extracting information from PDFs and spreadsheets using generative AI
- Importance of adapting to using bots in the financial industry
- Personalized advice and financial education offered by bots
- Use of bots in websites for loan assistance and account support
- Guideline establishment and embracing the use of bots by banks
- Use of embedded AI and chat GPT in platforms APIs and partnerships
- Focus on implementing guardrails to protect against data breaches
- Accuracy and limitations of ChatGPT requiring fact-checking
- Risk of younger generations relying on AI-generated code without understanding its quality
- JPMorgan's use of ChatGPT and AI in production
- Development of JPMorgan's platform called Index GPT for investment selection

Keywords:
ChatGPT, generative AI, connections, voice cloning, impersonation, security, financial sector, risks, financial institutions, AI governance committee, ethical uses of AI, education, evaluation, decision making, prioritizing, implementation, understanding, power, potential, extraction, PDFs, spreadsheets, availability, bots, personalized advice, financial education, websites, loan assistance, account support, guidelines, embedded AI, platforms, APIs, strategic partnerships, data breaches, sensitive information, accuracy, fact-checking, verification, younger generations, AI-generated code, JPMorgan, production, investment selection, robo advisors, complementary tool, information security, analysis of large data sets, access to information, technology leader, business problems, process automation, strategy, delivery, Eliza, communication between humans and computers, pattern matching, substitution methodology, security measures, fraud attempts, identification, fraud, podcast, generative AI in banking, Meta, Microsoft, Llama Two, Bing Chat enterprise, Xai, artificial g

Is it possible to govern AI or has it already grown to be bigger than we can handle? John Chiappetta, Principal and CEO of HG Technical Consulting, joins us to discuss the importance of defining the audience and setting goals before introducing AI.

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Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup

[00:00:17] Daily AI news

[00:03:59] Intro to John Chiappetta

[00:10:18] Examples of AI in the classroom

[00:14:03] Cybersecurity is crucial for risk assessments.

[00:17:27] Professors want students to learn, not rely on GPT.

[00:19:34] Defining audiences and conducting research to govern AI 

[00:22:43] Roadmap to governance of AI

Topics Covered:

Importance of defining audience and customers before introducing AI (Defining Audience and Customers)

Creating a roadmap for AI implementation, setting goals and objectives (Creating a Roadmap)

Emphasizing the need for continuous research in AI implementation (Continuous Research)

Involving employees at all levels for diverse perspectives on AI (Employee Involvement)

Potential benefits of using AI, including better revenue and optimized resources (Benefits of AI)

Addressing job replacement fears and resource readjustment (Job Replacement and Resource Adjustments)

Recommendations for companies new to AI (Recommendations for Newcomers)
    - Research and assemble a team
    - Consider AI principles and one applicable aspect

Acknowledging the benefits of AI for companies that have not considered it (Benefits for Non-AI Companies)

Not all companies using generative AI, seeking recommendations (Generative AI and Recommendations)

Importance of taking an AI class and John's agreement (Importance of AI Classes)

Educators' polarization on AI detection in education space (Educators' Polarization)

Use of ChatGPT by students in writing papers and issues with professors (Use of ChatGPT in Education)

Concerns about students becoming dependent on AI tools in education (Concerns in Education)

Importance of roadmaps and governance in AI implementation (Importance of Roadmaps and Governance)

Starting the process of mitigating risk in AI and the importance of education (Mitigating Risk in AI)

Balancing the benefits of AI with ethical frameworks and policies (Balancing Benefits and Ethical Frameworks)

Keywords:
implementing AI classes, securing funding, educating teachers, sharing information with the corporate sector, high demand, quick fill-up, enthusiastic students, eager to learn about AI, real-life examples, interactivity, hands-on learning, train models, identify objects, explore own ideas, unleash creativity, US senators, classified senate briefing, governance of AI, Indian CEO, criticism for layoffs, AI support bot, concerns about being replaced by AI, VC Billionaire Mark Anderson, scenarios for AI, Chinese world domination, control and govern AI, guest speaker John Chapada, defining audience and customers, creating a roadmap, research, involving employees, potential benefits of using AI, better revenue, improved margins, optimizing resources, readjusting resources, assembling a team

America today faces a crisis of governance. In the face of numerous challenges — from climate change, to housing shortages, to pandemics — our institutions struggle to act quickly and decisively. Democratic processes often get captured by special interests or paralyzed by polarization. And, in response, public faith in government has reached a new low.

For the political philosopher Danielle Allen, this crisis requires a complete transformation of our democratic institutions. “Representation as designed cannot work under current conditions,” she writes. “We have no choice but to undertake a significant project of democracy renovation.” Allen’s most recent book — “Justice By Means of Democracy” — puts forth a sweeping vision of what she calls “power-sharing liberalism,” which aims to place political equality, power and participation at the center of liberal thinking.

But Allen isn’t just a theorist of liberal governance; she’s actively applying her insights in the real world. As the director of Harvard’s Edmond and Lily Safra Center for Ethics, she’s convened interdisciplinary groups to tackle a range of challenges from building Covid-19 testing infrastructure to innovating in A.I. governance. She was co-chair of the “Our Common Purpose” commission, which put forward over 30 specific policy recommendations for reinventing American democracy. She even ran for governor of Massachusetts.

So this is a conversation about what it would mean to build a better, more responsive and inclusive government — and the numerous challenges standing in the way of doing that. Along the way, we discuss liberals’ failure to take power seriously, Colorado’s experiments with “plural voting,” Seattle’s efforts to publicly finance elections through “democracy bucks,” Taiwan’s groundbreaking innovations in deliberative democracy, whether most citizens actually want deeper participation in government — or just better results from it, what it would mean to democratically govern AI development and much more.

Mentioned:

“Introducing Power-Sharing Liberalism” by Danielle Allen

“Movement vs. Abundance Progressives” by Misha David Chellam

How Democracies Die by Steven Levitsky and Daniel Ziblatt

“Our Common Purpose” Report

“How A.I. Fails Us”

Book Recommendations:

The Darkened Light of Faith by Melvin L. Rogers

Life 3.0 by Max Tegmark

Open Democracy by Héléne Landemore

Thoughts? Guest suggestions? Email us at ezrakleinshow@nytimes.com.

You can find transcripts (posted midday) and more episodes of “The Ezra Klein Show” at nytimes.com/ezra-klein-podcast, and you can find Ezra on Twitter @ezraklein. Book recommendations from all our guests are listed at https://www.nytimes.com/article/ezra-klein-show-book-recs.

This episode of “The Ezra Klein Show” is produced by Emefa Agawu, Jeff Geld, Kristin Lin, and Roge Karma. Fact-checking by Michelle Harris. Mixing by Jeff Geld. Original music by Isaac Jones. Audience strategy by Shannon Busta. The executive producer of New York Times Opinion Audio is Annie-Rose Strasser. Special thanks to Sonia Herrero and Kristina Samulewski.

This is something a bit different: Not an interview, but a commentary of my own. 

We’ve done a lot of shows on A.I. of late, and there are more to come. On Tuesday, GPT-4 was released, and its capabilities are stunning, and in some cases, chilling. More on that in Tuesday’s episode. But I wanted to take a moment to talk through my own views on A.I. and how I’ve arrived at them. I’ve come to believe that we’re in a potentially brief interregnum before the pace of change accelerates to a rate that is far faster than is safe for society. Here’s why.

Column:

“This Changes Everything” by Ezra Klein

Episode Recommendations:

Sam Altman on the A.I. revolution

Brian Christian on the alignment problem

Gary Marcus on the case for A.I. skepticism

Ted Chiang on what humans really fear about A.I.

Thoughts? Guest suggestions? Email us at ezrakleinshow@nytimes.com. 

You can find transcripts (posted midday) and more episodes of “The Ezra Klein Show” at nytimes.com/ezra-klein-podcast, and you can find Ezra on Twitter @ezraklein. Book recommendations from all our guests are listed at https://www.nytimes.com/article/ezra-klein-show-book-recs.

“The Ezra Klein Show” is produced by Emefa Agawu, Annie Galvin, Jeff Geld, Roge Karma and Kristin Lin. Fact-checking by Rollin Hu. Mixing by Isaac Jones. Original music by Isaac Jones and Pat McCusker. Audience strategy by Shannon Busta. The executive producer of New York Times Opinion Audio is Annie-Rose Strasser.

Tim Hwang is the Global Public Policy Lead on AI and Machine Learning for Google.

Read the transcript on our blog.