Phi-2, Imagen-2, Optimus-Gen-2: Small New Models to Change the World?
Key Takeaways at a Glance
00:00Phi-2 and Imagen-2 are small models that could change the AI landscape.08:09Optimus Gen-2 is a new humanoid robot from Tesla.11:01The MML U benchmark has significant flaws.14:31Models are sensitive to inputs and can be easily confused by errors or ambiguities in the source.15:32Multiple question dependence and lack of clear answers are common challenges for models.16:47Models struggle with complex and controversial topics that require nuanced understanding.
1. Phi-2 and Imagen-2 are small models that could change the AI landscape.
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Phi-2 and Imagen-2 are small models with 2.7 billion parameters that outperform models of comparable size and even models 25 times their size.
- Phi-2 can fit locally on a smartphone.
- These models were trained using synthetic data, resulting in less toxic data and improved performance.
2. Optimus Gen-2 is a new humanoid robot from Tesla.
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Optimus Gen-2 is a 10 kg lighter humanoid robot that shows potential for touch, temperature, and pressure sensitivity.
- This robot represents a new exploration of modalities in robotics.
- It could have various applications in the future.
3. The MML U benchmark has significant flaws.
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The MML U benchmark is flawed and has numerous factual errors and missing context.
- The benchmark includes incorrect answers and wrong answer options.
- It lacks accuracy and reliability in assessing AI models.
4. Models are sensitive to inputs and can be easily confused by errors or ambiguities in the source.
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Errors, misspellings, grammatical ambiguity, and formatting ambiguity in the source can potentially confuse a model.
- Models are particularly sensitive to the inputs they receive.
- Ambiguities in the source can lead to incorrect answers.
5. Multiple question dependence and lack of clear answers are common challenges for models.
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Models struggle with questions that depend on multiple factors or have no clear answer.
- Questions that require context or depend on multiple principles can be challenging for models.
- Questions with conflicting answers or ambiguous options can also confuse models.
6. Models struggle with complex and controversial topics that require nuanced understanding.
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Models find it difficult to capture the nuance and complexity of topics like biology, gender roles, society, and state responsibilities.
- Complex and controversial topics require a deep understanding of various factors.
- Models may provide answers that lack the nuanced relationship between different elements.
