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Taylor Swift's 'Down Bad' Prophecy to AI Dread and Safety Challenge We Can't Ignore

May 21, 202411 min read

Okay, so I’m listening to “Down Bad” by Taylor Swift and this lyric hits me hard:

“Did you really beam me up / In a cloud of sparkling dust / Just to do experiments on? / Tell me I was the chosen one / Showed me that this world is bigger than us / Then sent me back where I came from”

Taylor herself confirmed in her iHeartRadio commentary that “The metaphor in ‘Down Bad’ is that I was comparing sort of the idea of being love bombed. Where someone…rocks your world and dazzles you and then just kind of abandons you….This girl is abducted by aliens but she wanted to stay with them…And then when they drop her back off in her hometown, she’s like, ‘Wait, no, where are you going? I liked it there! It was weird but it was cool. Come back!’ And so…the girl in the character in the song felt like, ‘I’ve just been exposed to a whole different galaxy and universe and didn’t know it was possible, how could you just put me back where I was before?”

She’s using Star Trek and alien references, and it got me thinking: what if the aliens are already here? Hear me out: AI is kinda like an alien intelligence, right? It’s beamed me up into this mind-blowing world of endless possibilities, and honestly, I’m here for it. It’s like mainlining pure potential. But then I remember all the sci-fi stories where AI goes rogue, and I’m like, “Wait a sec, is this safe? Are we even trying to make this thing behave?”

I’ve been diving deep into AI safety, ethics, and all that jazz these past few months. It’s been a total rollercoaster. Part of me is hyped about the potential – imagine curing cancer, Alzheimer’s, cracking the code of consciousness! We could save so many lives, give kids back their childhoods instead of endless chemo sessions. But another part of me is like, “Whoa there, hold your horses. Is this even safe? At what cost?”

The Suffering Conundrum

A while back, I got into a deep convo about this with a philosophy group. Let’s just say I left more confused than ever. Some people were all, “Suffering is part of life, embrace the pain, find meaning in it.” And I’m over here like, “Hard disagree.”

Don’t get me wrong, I used to be obsessed with Emil Cioran’s whole suffering-as-human-condition thing.1 But now? Not really. I think the whole reason religions and philosophies teach us how to deal with suffering is because they didn’t have the tech to solve it back then. What if we do now? Should we go for it?

My AI Dumpster Fire

This little corner of the internet is where I’m dumping all my AI thoughts, from the mind-blowing potential to the “oh hell no” scenarios. Let’s see if we can figure out how to reap the benefits without ending up in an Ex Machina situation.

AI Safety 101

AI safety isn’t just about cool trends. It’s about ensuring that AI technology is developed and used responsibly, in a way that benefits humanity rather than harms it. Here’s why we need to take AI safety seriously:

Existential Risks: The “Oh Crap” Scenario

  • This is the big one, could AI become so powerful that it poses an existential threat to humanity? Some experts believe this is a real possibility if AI systems become significantly more intelligent than humans and develop goals that conflict with our own.2 Imagine an AI system that decides humans are a hindrance to its objectives—not exactly a comforting thought.

Societal Impacts: The Disruption We Can’t Ignore

  • Even if AI doesn’t try to wipe us out, it could still have a major impact on society. We’re already seeing AI automate jobs and change the way we work. But the consequences could go much further, leading to increased inequality, social unrest, and even the weaponization of AI by malicious actors.3

Ethical Nightmares: AI’s Shady Side

  • AI is only as good as the data it’s trained on, and that data often reflects our own biases and prejudices. This means AI systems can perpetuate and even amplify discrimination, leading to unfair outcomes in areas like hiring, lending, and criminal justice.4
  • Another ethical concern is the lack of transparency in many AI systems. How can we trust decisions made by a “black box” algorithm that we don’t fully understand? This is especially worrisome in high-stakes situations like medical diagnoses or self-driving cars. 5
  • As AI becomes more powerful, we also need to grapple with questions of accountability. Who is responsible when an AI system makes a mistake or causes harm? And how do we ensure that AI systems are held to the same ethical standards as humans?6

In short, AI safety is about more than just preventing a sci-fi apocalypse. It’s about ensuring that AI is developed and used in a way that aligns with our values, promotes fairness and justice, and benefits all of society, not just the privileged few. rich and powerful

Key Areas of AI Safety Research

  • Alignment: AI’s goals match up with ours. Basically, teaching it to be a team player, not a dictator.7
  • Robustness: Building AI that can handle curveballs and unexpected situations without losing its mind.8
  • Monitoring and Control: - Keeping an eye on AI and having a plan B if it starts acting sketchy.9

More AI Safety Tea

Okay, so I mentioned the big three: alignment, robustness, and monitoring/control. But the AI safety rabbit hole goes way deeper. Here’s the lowdown on some other major areas that researchers are sweating over:

  • Transparency and Explainability: AI shouldn’t be a mysterious black box. We need to be able to peek inside and understand how it’s making decisions. Otherwise, how can we trust it? This is like being able to see the receipts for why your algorithm thinks you’d be into a certain song or product.10
  • Fairness and Bias Mitigation: We all know the world’s full of biases, and AI can pick up on those bad vibes from the data it learns from. Researchers are working on ways to spot and squash those biases before they lead to discriminatory outcomes. Like, we don’t want AI deciding who gets a loan based on their skin color, right?11
  • Value Alignment and Moral Decision-Making: This is the deep stuff. How do we teach AI right from wrong? It’s not as simple as programming “be good.” It’s about figuring out how to get AI to understand and respect human values. We’re talking Westworld levels of complexity here.12
  • Scalable Oversight and Governance: As AI gets more powerful, we need better rules and systems to keep it in check. Think of it like setting up guardrails for a self-driving car. We need ethical guidelines, safety standards, and ways to make sure AI is actually being used for good.13
  • Long-Term Safety and Control: This is where things get really sci-fi. What happens if AI gets smarter than us? Can we control it? Researchers are looking into ways to mitigate those risks and make sure AI stays on our team, not plotting world domination.14

This is just the tip of the iceberg. There are tons of other areas where researchers are trying to make sure AI doesn’t turn into a dystopian nightmare. As AI keeps evolving, new challenges and issues will pop up faster than you can say “Alexa, play Speak Now by Taylor Swift.” It’s gonna take a whole squad of experts from different fields to keep this thing in check. No pressure or anything.

Resources to Get You Thinking

So, you’re intrigued by this whole AI safety thing? Want to avoid accidentally summoning the digital devil? Good news: there’s a whole universe of resources out there to get you up to speed. Here’s some of my fave spots to geek out on AI safety:

Organizations:

These folks are doing the heavy lifting on AI safety research and advocacy

Websites and Articles:

  • AI Safety Fundamentals: This course by BlueDot Impact is like AI Safety 101. It’ll give you a good foundation in the basics.
  • 80,000 Hours AI Safety Career Guide: Thinking about a career in AI safety? This guide has everything you need to know.
  • Wait But Why on AI: These articles are a super fun and informative way to dive into the world of AI. Think of it like a BuzzFeed listicle, but for brainy folks.
  • LessWrong: This community is where all the AI safety nerds hang out. It’s full of thought-provoking articles and discussions.

Books:

  • Human Compatible by Stuart Russell: This book explores how we can build AI that’s actually aligned with human values. It’s like a relationship guide for humans and AI.
  • Superintelligence by Nick Bostrom: This one dives deep into the potential risks of superintelligent AI. It’s not exactly a beach read, but it’s essential if you want to understand the stakes.
  • The Alignment Problem by Brian Christian: This book breaks down the complex issue of aligning AI with human values in a way that’s easy to understand. It’s like a TED Talk in book form.
  • Rationality: From AI to Zombies by Eliezer Yudkowsky: This collection of essays explores the concept of rationality and its implications for AI safety. It’s like philosophy class, but way more interesting.

This is just a taste of what’s out there. If you’re ready to go down the rabbit hole, there’s a whole world of AI safety knowledge to explore and geek out! Just remember, knowledge is power. The more we know about AI, the better equipped we’ll be to make sure it’s a force for good in the world.

My Two Cents

Okay, I’m not gonna pretend I’m some AI guru, but I’m definitely low-key freaking out about the potential risks. Like, we need to have some serious heart-to-hearts about this stuff so we can make sure AI actually helps humanity instead of, you know, ending it. We gotta get ahead of the game on AI safety before it’s too late.

I’m still kinda optimistic, though. AI could be a total game-changer for solving pressing problems like climate change and disease. But we can’t get ahead of ourselves. We need to proceed with caution and make sure this tech is developed and used responsibly. No one wants a real-life sci-fi horror story, right?

Look, I don’t have all the answers, but one thing’s for sure: we need to be talking about AI safety nonstop. Like, it should be trending more than the latest TikTok challenge. We gotta be proactive, not just sitting around waiting for disaster to strike. I’m hoping we can steer AI in a positive direction, but we can’t be naive about it.

The future’s a mystery box, but if we stay informed, ask the hard questions, and work together, maybe we can dodge the whole AI apocalypse bullet. Just sayin’.

- your cyborg girl, alta :)

Footnotes

  1. Emil Cioran, The Trouble with Being Born. Translated from the French by Richard Howard (New York: Seaver Books, 1976) ↩

  2. Eliezer Yudkowsky, “Artificial Intelligence as a Positive and Negative Factor in Global Risk.” In Global Catastrophic Risks, pp. 308-345. Oxford University Press, 2008. ↩

  3. Brundage, Miles, et al. “The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation” (arXiv preprint arXiv:1802.07228, 2018). ↩

  4. Buolamwini, Joy, and Timnit Gebru. “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification,” In Conference on Fairness, Accountability and Transparency, PMLR 81 (2018): 77-91. ↩

  5. Cynthia Rudin, “Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,” Nature Machine Intelligence 1, no. 5 (2019): 206-215. ↩

  6. Virginia Dignum, Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way (Springer Nature, 2019). ↩

  7. Stuart Russell, Human Compatible: Artificial Intelligence and the Problem of Control (Viking, 2019). ↩

  8. Dario Amodei et al., “Concrete Problems in AI Safety” (arXiv preprint arXiv:1606.06565, 2016). ↩

  9. Yampolskiy, Roman V. “Artificial intelligence safety and cybersecurity: a timeline of AI failures.” arXiv preprint arXiv:1904.01183 (2019). ↩

  10. Zachary C. Lipton, “The mythos of model interpretability,” Queue 16, no. 3 (2018): 31-57. ↩

  11. Solon Barocas and Andrew D. Selbst, “Big data’s disparate impact,” California Law Review 104 (2016): 671. ↩

  12. Arnold, Thomas, Daniel Kasenberg, and Matthias Scheutz. “Value alignment or Misalignment–What Will Keep Systems Accountable?.” In Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence. 2018. ↩

  13. Jess Whittlestone, Rune Nyrup, and Stephen Cave. “The role and limits of principles in AI ethics: towards a focus on tensions.” Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society. (2019). ↩

  14. Nick Bostrom, Superintelligence: Paths, Dangers, Strategies (Oxford University Press, 2014). ↩

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