10 Predictions For AI in 2024 🚀 Let's Accelerate Into The Future!

1. Llama 3, the next version of the open-source language model, is expected to be released in the first half of 2024.

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Llama 3 will close the gap between open-source models and proprietary models like gp4, and it will be integrated into Meta's consumer products.

• Llama 3 needs to go through a rigorous assessment and red teaming process before its release.
• Meta's focus is currently on integrating llama 2 into their consumer products.
• Open-source AI, like llama, has helped Meta establish itself in the AI community and industry.

2. Google will release Gemini Ultra, a highly capable model, in 2024.

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Gemini Ultra will compete with gp4 and will be available in three packages: Nano, Pro, and Ultra.

• Gemini Ultra will initially have some problems but will quickly improve with consumer and developer feedback.
• Google aims to attract developers to build on top of Gemini and create a thriving ecosystem.
• Competition in the closed-source proprietary model space benefits the AI industry as a whole.

3. Apple may release more AI products in 2024, further intensifying competition in the AI developer community.

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Apple already has a strong developer community, and Google and OpenAI will have to compete with Apple to attract developers.

• The winner in the AI space will be the company that can lure the most developers to build on their platforms.
• Competition among major players like Apple, Meta, Google, Microsoft, and OpenAI benefits the AI ecosystem.
• Developers play a crucial role in driving innovation and creating compelling AI applications.

4. Tesla's humanoid robot, Optimus, will make significant progress in 2024.

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Tesla has a working prototype of Optimus and aims to improve its speed and capabilities.

• Optimus is expected to have a minimum speed of 3 mph for effective use in factories.
• AI Day 3 is predicted to take place in Q1 2024, where Tesla will announce updates and demos of Optimus.
• Tesla plans to produce dozens of Optimus robots by Q1 and potentially hundreds by the end of the year.

5. Optimus by Tesla will evolve the fastest in robotics.

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The hardest part of Optimus right now is getting the actuators to work properly, but once they do, production will accelerate quickly.

• Actuators are the mechanisms behind joint movement in robots.
• Tesla's focus on improving actuators will give them an edge in the robotics industry.

6. Open source language models are catching up with closed source models.

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Open source models like GPT-4 are rapidly closing the performance gap with closed source models.

• The trend curve shows that open source models are improving at a faster rate.
• The release of Llama 3 will further narrow the gap between open source and closed source models.

7. Quantization techniques enable running large models on consumer hardware.

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Quantization techniques allow running large language models on most consumer hardware with minimal quality loss.

• Quantization techniques have greatly improved in 2023.
• Running models efficiently on consumer hardware is a key advantage of open source models.

8. Mixture of experts will become the gold standard for open source models.

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Open source models like GPT-4 already use mixture of experts, and this approach will be widely adopted.

• Mixture of experts allows large models to perform efficiently by using only relevant parts for a given prompt.
• The success of Mixol demonstrates the effectiveness of mixture of experts in open source models.

9. Open source models may face challenges due to data protection measures.

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Companies will protect their data by limiting access to APIs and proprietary algorithms.

• Companies like OpenAI, Twitter, and Reddit have already restricted access to their APIs.
• Acquiring data sets for training open source models will become more difficult.

10. Synthetic data could be a solution for training open source models.

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Synthetic data may be used to train future open source models when acquiring real data becomes challenging.

• Synthetic data can be generated to simulate real-world scenarios.
• It can help overcome limitations in data availability for training models.

11. AI agents will continue to improve and find real-world use cases.

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AI agents will become more advanced and collaborate with each other to solve real-world problems.

• Software powering AI agents will also improve.
• AI agents will be used in various fields such as coding, research, and data analysis.

12. AI agents will exhibit human-like behavior and raise questions about consciousness.

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AI agents, powered by large language models, will exhibit human-like behavior and raise questions about the nature of consciousness.

• Generative agents have already shown human-like behavior in simulated environments.
• The emergence of human-like behavior in AI agents will prompt discussions about what it means to be human.

13. More tooling will be developed to facilitate AI team collaboration.

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In 2024, there will be increased focus on developing tools to improve collaboration among AI teams.

• Tools will help define system messages, prompts, and roles for AI agents.
• Efficient collaboration among AI teams will lead to better performance and output.

14. AGI (Artificial General Intelligence) is not expected to be achieved in 2024.

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There is no consensus on when AGI will be created, and it is unlikely to happen in 2024.

• AGI refers to a singular AI system with general intelligence.
• Predictions about AGI are speculative, and it remains a complex goal.