Knowledge Vault 5 /96 - CVPR 2024
Phase Transition in AI: Opportunities and Gaps Towards Making AI Real
Ece Kamar
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Concept Graph & Resume using Claude 3 Opus | Chat GPT4o | Llama 3:

graph LR classDef main fill:#f9f9f9, stroke:#333, stroke-width:4px classDef ai fill:#d4e6f1, stroke:#333, stroke-width:2px classDef models fill:#d5f5e3, stroke:#333, stroke-width:2px classDef challenges fill:#f9e79f, stroke:#333, stroke-width:2px classDef multiagent fill:#fadbd8, stroke:#333, stroke-width:2px classDef future fill:#d2b4de, stroke:#333, stroke-width:2px Main[Phase Transition in
AI: Opportunities and
Gaps Towards Making
AI Real] Main --> A[AI Development] Main --> B[AI Capabilities] Main --> C[Challenges and Evaluation] Main --> D[Multi-Agent Systems] Main --> E[Future Directions] A --> A1[AI leap marks new
development era 1] A --> A2[AI coding assistant boosts
developer efficiency 5] A --> A3[AI assistants potentially double
developer productivity 6] B --> B1[Models handle diverse tasks
without specialization 2] B --> B2[Improved complex problem-solving
through reasoning 3] B --> B3[Better comprehension of
contextual information 4] B --> B4[Models process multiple
content types 9] B --> B5[Smaller models maintain
high performance 10] C --> C1[Benchmarks struggle to reflect
real-world performance 11] C --> C2[Dynamic benchmarks prevent
model memorization 12] C --> C3[Models struggle with detailed
scene understanding 13] C --> C4[Dataset evaluates AI in
mixed reality 14] C --> C5[AI limited in spatial
reasoning tasks 15] C --> C6[Models generate inaccurate information 16] D --> D1[Multiple agents solve complex
tasks reliably 21] D --> D2[Open-source tool implements
multi-agent systems 22] D --> D3[Agents collaborate through
conversational interfaces 23] D --> D4[Multi-agent systems parallelize
language tasks 24] D --> D5[Multiple agents improve accuracy,
alignment 25] D --> D6[Multi-agent systems achieve
cost-effective performance 26] E --> E1[Personalized AI enhances
human capabilities 7] E --> E2[AI agents perceive, act
in environments 8] E --> E3[Trend towards complex, coordinated
AI systems 27] E --> E4[Models understand and act
across modalities 28] E --> E5[Combining new AI with
traditional techniques 29] E --> E6[Focus on fundamental, long-lasting
problems 30] class Main main class A,A1,A2,A3 ai class B,B1,B2,B3,B4,B5 models class C,C1,C2,C3,C4,C5,C6 challenges class D,D1,D2,D3,D4,D5,D6 multiagent class E,E1,E2,E3,E4,E5,E6 future


1.- AI Phase Transition: Recent AI models like GPT-4 represent a significant leap in capabilities, marking a new era in AI development.

2.- General Purpose Task Solvers: Modern AI models can handle a wide range of tasks without specialized training for each one.

3.- Increased Reasoning Capabilities: AI models now demonstrate improved ability to solve complex problems requiring multi-step reasoning.

4.- Context Understanding: AI can now better comprehend and utilize complex contextual information provided in prompts or conversations.

5.- GitHub Copilot: An AI coding assistant that significantly improves developer productivity by generating and completing code.

6.- Productivity Boost: AI assistants like GitHub Copilot can potentially double developer efficiency, addressing longstanding productivity challenges.

7.- Personal AI Assistants: The vision of AI evolving into personalized assistants to enhance human capabilities and productivity.

8.- Agent Paradigm: A new computing paradigm where AI acts as an agent perceiving and acting in complex environments.

9.- Multimodal Models: AI models that can process and generate content across multiple modalities (text, image, video).

10.- Efficiency in AI: Developing smaller, more efficient AI models that maintain high performance, like the PHY family of models.

11.- Model Evaluation Challenges: Current benchmarks for AI models have limitations and may not accurately reflect real-world performance.

12.- Dynamic Benchmarks: New evaluation methods that generate benchmarks on-the-fly to prevent memorization and better assess model capabilities.

13.- Detailed Understanding Gap: Even advanced models struggle with tasks requiring detailed scene understanding or complex reasoning.

14.- HoloAssist Dataset: A multimodal dataset created from real HoloLens interactions to evaluate AI in mixed reality scenarios.

15.- Spatial Understanding Limitations: Current AI models struggle with tasks requiring complex spatial reasoning and understanding.

16.- Hallucinations in AI: The problem of AI models generating false or inaccurate information, especially in information retrieval tasks.

17.- KITAP Benchmark: A dynamic benchmark for evaluating AI models' ability to retrieve information under specific constraints.

18.- Model Interpretability: Techniques to understand how information flows through AI models, helping diagnose failures and hallucinations.

19.- Fairness in AI: Addressing biases in AI-generated content, particularly in image generation models.

20.- Adversarial Risks: The potential misuse of powerful AI tools, especially in creating deepfakes or harmful content.

21.- Multi-agent Orchestration: Using multiple specialized AI agents to solve complex tasks more reliably than single large models.

22.- OtoGen Library: An open-source tool for implementing multi-agent AI systems to tackle complex problems.

23.- Conversational Interface for Agents: AI agents collaborating through conversation, using it as a form of working memory.

24.- Overcoming Autoregressive Limitations: Multi-agent systems can parallelize tasks to overcome limitations of large language models.

25.- Reliability Through Collaboration: Using multiple agents for tasks like image generation to improve accuracy and alignment with user intent.

26.- Cost-Effective Performance: Multi-agent systems can achieve higher performance using less expensive models compared to single large models.

27.- Future of AI Agents: Predicting a trend towards more complex, coordinated multi-agent systems for AI tasks.

28.- Multimodal Action Models: Anticipating the development of AI models that can both understand multiple modalities and take actions in the world.

29.- Complementary AI Approaches: Combining new AI models with traditional AI techniques like planning and symbolic reasoning.

30.- Long-Term Research Focus: Emphasizing the importance of focusing on fundamental, long-lasting problems in AI research despite rapid model improvements.

Knowledge Vault built byDavid Vivancos 2024