Will AI take over the world?

HuggingFace · Advanced ·📄 Research Papers Explained ·4y ago

Key Takeaways

The video discusses the potential risks and benefits of AI, with a focus on the importance of considering the worst-case scenarios and mitigating them, featuring insights from Meg, an Ethical AI Researcher at Hugging Face, who highlights the need for responsible AI development and the potential for AI to exacerbate existing social inequalities, emphasizing the importance of designing systems that benefit all, using techniques from AI alignment and safety engineering, such as value alignment and

Full Transcript

should people be afraid of ai taking over the world there's a lot of things to be afraid of with ai i like to see it as we have a distribution over different kinds of outcomes some more positive than others so there isn't some set at least not one that we can know there are a lot of different things where ai can be just super helpful super assistive possibly task based as opposed to more general intelligence and you could see it going the other way similar to this example i had given about um thinking something destructive was beautiful like that's one hop away from the system is able to like press a button or the equivalent to press a button to like set off a missile or whatever like yeah so i don't think people should be scared per se but i do think that they should think about the worst ways that things could go and the best ways that things can go and aim for the best ways and try and mitigate or stop the worst ways i think the biggest issue right now is just on how these systems can can widen the divide between you know haves and have not really like further further give power to people who have power and you know further worse and things for people who don't as the people designing these systems are often people with more power and wealth and they design for their kinds of interests yeah so i think that's the the thing that's happening now and one of the riskiest things to be thinking about in the future but ideally if if we can focus on what's actually most beneficial then we can lead it in that direction

Original Description

Thanks for watching! Full Interview: https://bit.ly/3tAGyiq Meg is an Ethical AI Researcher at Hugging Face who previously founded & co-led Google's Ethical AI Group. 🚀 If you're interested in learning how ML Experts, like Meg, can help accelerate your ML roadmap visit https://bit.ly/3isEbYH
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← Previous Next →
1 The Future of Natural Language Processing
The Future of Natural Language Processing
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2 Trends in Model Size & Computational Efficiency in NLP
Trends in Model Size & Computational Efficiency in NLP
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3 Increasing Data Usage in Natural Language Processing
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4 In Domain & Out of Domain Generalization in the Future of NLP
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5 The Limits of NLU & the Rise of NLG in the Future of NLP
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6 The Lack of Robustness in the Future of NLP
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7 Inductive Bias, Common Sense, Continual Learning in The Future of NLP
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8 Train a Hugging Face Transformers Model with Amazon SageMaker
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9 What is Transfer Learning?
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10 The pipeline function
The pipeline function
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11 Navigating the Model Hub
Navigating the Model Hub
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12 Transformer models: Decoders
Transformer models: Decoders
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13 The Transformer architecture
The Transformer architecture
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14 Transformer models: Encoder-Decoders
Transformer models: Encoder-Decoders
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15 Transformer models: Encoders
Transformer models: Encoders
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16 Keras introduction
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17 The push to hub API
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18 Fine-tuning with TensorFlow
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19 Learning rate scheduling with TensorFlow
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20 TensorFlow Predictions and metrics
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21 Welcome to the Hugging Face course
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22 The tokenization pipeline
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23 Supercharge your PyTorch training loop with Accelerate
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24 The Trainer API
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25 Batching inputs together (PyTorch)
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26 Batching inputs together (TensorFlow)
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27 Hugging Face Datasets overview (Pytorch)
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28 Hugging Face Datasets overview (Tensorflow)
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29 What is dynamic padding?
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30 What happens inside the pipeline function? (PyTorch)
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31 What happens inside the pipeline function? (TensorFlow)
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32 Instantiate a Transformers model (PyTorch)
Instantiate a Transformers model (PyTorch)
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33 Instantiate a Transformers model (TensorFlow)
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34 Preprocessing sentence pairs (PyTorch)
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35 Preprocessing sentence pairs (TensorFlow)
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36 Write your training loop in PyTorch
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37 Managing a repo on the Model Hub
Managing a repo on the Model Hub
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38 Chapter 1 Live Session with Sylvain
Chapter 1 Live Session with Sylvain
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39 Chapter 2 Live Session with Lewis
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40 The push to hub API
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41 Chapter 2 Live Session with Sylvain
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42 Chapter 3 live sessions with Lewis (PyTorch)
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43 Day 1 Talks: JAX, Flax & Transformers 🤗
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44 Day 2 Talks: JAX, Flax & Transformers 🤗
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45 Day 3 Talks JAX, Flax, Transformers 🤗
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46 Chapter 4 live sessions with Omar
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47 Deploy a Hugging Face Transformers Model from S3 to Amazon SageMaker
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48 Deploy a Hugging Face Transformers Model from the Model Hub to Amazon SageMaker
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49 Run a Batch Transform Job using Hugging Face Transformers and Amazon SageMaker
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50 [Webinar] How to add machine learning capabilities with just a few lines of code
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51 Hugging Face + Zapier Demo Video
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52 Hugging Face + Google Sheets Demo
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53 Hugging Face Infinity Launch - 09/28
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54 Build and Deploy a Machine Learning App in 2 Minutes
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55 Hugging Face Infinity - GPU Walkthrough
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56 Otto - 🤗 Infinity Case Study
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57 Workshop: Getting started with Amazon Sagemaker Train a Hugging Face Transformers and deploy it
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58 Workshop: Going Production: Deploying, Scaling & Monitoring Hugging Face Transformer models
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59 🤗 Tasks: Causal Language Modeling
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60 🤗 Tasks: Masked Language Modeling
🤗 Tasks: Masked Language Modeling
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The video discusses the importance of considering the potential risks and benefits of AI and the need for responsible AI development, highlighting the potential for AI to exacerbate existing social inequalities and emphasizing the importance of designing systems that benefit all, using techniques from AI alignment and safety engineering.

Key Takeaways
  1. Consider the potential risks and benefits of AI
  2. Design AI systems with value alignment
  3. Implement robustness testing
  4. Develop responsible AI
  5. Analyze AI risks
  6. Mitigate AI risks
  7. Test AI systems for safety
  8. Improve AI reliability
💡 The development of AI systems must prioritize responsible AI development and consider the potential risks and benefits of AI, including the potential for AI to exacerbate existing social inequalities.

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