from dataclasses import dataclass
from typing import List, Dict, Optional
import matplotlib.pyplot as plt
import numpy as np

@dataclass
class ModelEvolution:
    """Track LLM evolution over time"""
    year: int
    name: str
    parameters: float  # in billions
    context_window: int
    training_cost: Optional[float] = None  # in millions USD
    performance_score: Optional[float] = None  # Standardized benchmark

class LLMTrends:
    """Analyze trends in LLM development"""

    def __init__(self):
        self.milestones = [
            ModelEvolution(2018, "BERT", 0.34, 512),
            ModelEvolution(2019, "GPT-2", 1.5, 1024),
            ModelEvolution(2020, "GPT-3", 175, 2048),
            ModelEvolution(2021, "GPT-3.5", 175, 4096),
            ModelEvolution(2022, "GPT-4", 1700, 8192),  # Estimated
            ModelEvolution(2023, "GPT-4 Turbo", 1700, 128000),
            ModelEvolution(2023, "Claude 2", 100, 200000),  # Estimated
            ModelEvolution(2024, "Gemini 1.5 Pro", 1000, 1000000),  # Estimated
        ]

    def analyze_scaling_trends(self) -> Dict:
        """Analyze how models are scaling"""

        years = [m.year for m in self.milestones]
        params = [m.parameters for m in self.milestones]
        context = [m.context_window for m in self.milestones]

        # Calculate growth rates
        param_growth = (params[-1] / params[0]) ** (1 / (years[-1] - years[0]))
        context_growth = (context[-1] / context[0]) ** (1 / (years[-1] - years[0]))

        return {
            "parameter_growth_rate": param_growth,
            "context_growth_rate": context_growth,
            "years_to_double_params": np.log(2) / np.log(param_growth),
            "years_to_double_context": np.log(2) / np.log(context_growth),
            "observations": self._generate_observations(param_growth, context_growth)
        }

    def _generate_observations(
        self,
        param_growth: float,
        context_growth: float
    ) -> List[str]:
        """Generate insights from trends"""

        observations = []

        if param_growth > 2:
            observations.append(
                f"Model size is growing {param_growth:.1f}x per year - "
                "approaching physical and economic limits"
            )

        if context_growth > 5:
            observations.append(
                f"Context windows growing {context_growth:.1f}x per year - "
                "enabling new long-document applications"
            )

        observations.append(
            "Trend: Shift from pure scaling to efficiency and capabilities"
        )

        return observations

    def project_future_capabilities(self, years_ahead: int = 5) -> Dict:
        """Project potential capabilities in the future"""

        current_year = 2024

        projections = {
            "year": current_year + years_ahead,
            "likely_developments": [],
            "possible_breakthroughs": [],
            "challenges": []
        }

        # Likely developments (extrapolating current trends)
        projections["likely_developments"] = [
            f"Context windows: 10M+ tokens (entire codebases, books)",
            "Multimodal by default: text, image, video, audio",
            "Real-time interaction with <100ms latency",
            "Personalization: models that adapt to individual users",
            "Specialized models: domain-specific LLMs (medical, legal, scientific)",
            "Improved efficiency: 10x cheaper inference costs"
        ]

        # Possible breakthroughs
        projections["possible_breakthroughs"] = [
            "Continuous learning: models that learn from interactions",
            "True reasoning: solving complex math/logic problems",
            "Multi-step planning: autonomous agents solving complex tasks",
            "Verifiable outputs: provably correct reasoning chains",
            "Energy efficiency: brain-like efficiency (20 watts)",
            "Generalization: AGI-level capabilities"
        ]

        # Challenges
        projections["challenges"] = [
            "Alignment: ensuring AI goals match human values",
            "Safety: preventing misuse and harmful outputs",
            "Interpretability: understanding how models make decisions",
            "Bias: eliminating unfair biases in model behavior",
            "Privacy: protecting training and user data",
            "Sustainability: reducing environmental impact of training",
            "Regulation: developing appropriate governance frameworks"
        ]

        return projections

# Example usage
trends = LLMTrends()

# Analyze current trends
print("LLM Scaling Trends Analysis:")
analysis = trends.analyze_scaling_trends()

print(f"\nParameter growth: {analysis['parameter_growth_rate']:.2f}x per year")
print(f"Context growth: {analysis['context_growth_rate']:.2f}x per year")
print(f"Parameters double every {analysis['years_to_double_params']:.1f} years")
print(f"Context doubles every {analysis['years_to_double_context']:.1f} years")

print("\nKey Observations:")
for obs in analysis['observations']:
    print(f"  - {obs}")

# Project future
print("\n" + "="*60)
print("Future Projections (5 years):")
future = trends.project_future_capabilities(5)

print("\nLikely Developments:")
for dev in future['likely_developments']:
    print(f"  - {dev}")

print("\nPossible Breakthroughs:")
for breakthrough in future['possible_breakthroughs']:
    print(f"  - {breakthrough}")

print("\nKey Challenges:")
for challenge in future['challenges']:
    print(f"  - {challenge}")

class EmergingArchitectures:
    """Overview of emerging LLM architectures"""

    @staticmethod
    def describe_retentive_networks():
        """
        RetNet: Retention Networks

        Key innovation: Replace attention with retention mechanism
        - Training parallelizable like Transformers
        - Inference efficient like RNNs
        - Better scaling for long sequences
        """

        return """
RetNet Architecture:

Traditional Transformer:
  - O(N²) attention complexity
  - Parallel training
  - Parallel inference (with KV cache)

RetNet:
  - O(1) per-step inference
  - Parallel training
  - Recurrent inference
  - Linear scaling with sequence length

Benefits:
  - 8x faster inference for long sequences
  - Lower memory usage
  - Better long-context scaling
  - Maintains model quality

Status: Emerging research (2023+)
"""

    @staticmethod
    def describe_state_space_models():
        """
        State Space Models (SSMs) like Mamba

        Key innovation: Structured state space layers
        - Linear complexity in sequence length
        - Selective memory mechanism
        - Competitive with Transformers
        """

        return """
State Space Models (Mamba):

Problem with Transformers:
  - Quadratic attention cost
  - Fixed context window
  - Expensive for long sequences

SSM Solution:
  - Linear time complexity: O(N)
  - Selective state updates
  - Infinite context (theoretically)
  - Efficient long-range dependencies

Architecture:
  Input → Selective SSM Layers → Output

  Each layer:
    - Selective copying (remember important info)
    - Compression (forget irrelevant info)
    - Linear computation

Performance:
  - Matches Transformers on many tasks
  - 5x faster inference on long sequences
  - Better scaling to millions of tokens

Status: Active research (2023-2024)
"""

    @staticmethod
    def describe_mixture_of_depths():
        """
        Mixture of Depths

        Key innovation: Dynamic computation depth
        - Different tokens get different computation
        - More efficient than uniform depth
        """

        return """
Mixture of Depths:

Standard Transformer:
  - Every token goes through all N layers
  - Uniform computation
  - Wasteful for simple tokens

Mixture of Depths:
  - Tokens can "skip" layers
  - Router decides which tokens need deep processing
  - Simple tokens exit early
  - Complex tokens get full depth

Example:
  Token "the" → Skip 50% of layers (simple)
  Token "quantum" → Use all layers (complex)

Benefits:
  - 2-3x faster inference
  - Same quality with less computation
  - Adaptive complexity

Status: Research (2024)
"""

    @staticmethod
    def compare_architectures():
        """Compare architectural approaches"""

        architectures = {
            "Standard Transformer": {
                "complexity": "O(N²)",
                "strengths": ["Proven at scale", "Well understood"],
                "weaknesses": ["Expensive attention", "Fixed context"],
                "status": "Dominant (current)"
            },
            "RetNet": {
                "complexity": "O(N) inference",
                "strengths": ["Fast inference", "Linear scaling"],
                "weaknesses": ["New/unproven", "Training complexity"],
                "status": "Emerging"
            },
            "Mamba (SSM)": {
                "complexity": "O(N)",
                "strengths": ["Linear complexity", "Selective memory"],
                "weaknesses": ["Limited adoption", "Ongoing research"],
                "status": "Promising research"
            },
            "MoE": {
                "complexity": "O(N²) but sparse",
                "strengths": ["Massive capacity", "Efficient per-token"],
                "weaknesses": ["Training complexity", "Load balancing"],
                "status": "Production (Mixtral, GPT-4)"
            }
        }

        return architectures

# Display information
emerging = EmergingArchitectures()

print("EMERGING ARCHITECTURES")
print("="*60)

print("\n" + emerging.describe_retentive_networks())
print("\n" + "="*60)
print("\n" + emerging.describe_state_space_models())
print("\n" + "="*60)
print("\n" + emerging.describe_mixture_of_depths())

print("\n" + "="*60)
print("\nARCHITECTURE COMPARISON:")
comparison = emerging.compare_architectures()

for arch, details in comparison.items():
    print(f"\n{arch}:")
    print(f"  Complexity: {details['complexity']}")
    print(f"  Strengths: {', '.join(details['strengths'])}")
    print(f"  Status: {details['status']}")

from typing import List, Dict, Callable, Any

class FutureAgentCapabilities:
    """Explore future AI agent capabilities"""

    @staticmethod
    def describe_current_state():
        """Current state of AI agents (2024)"""

        return {
            "capabilities": [
                "Tool use: Call functions and APIs",
                "Multi-step planning: Break down tasks",
                "Code execution: Run and debug code",
                "Web browsing: Search and extract information",
                "Memory: Short-term context management"
            ],
            "limitations": [
                "Limited planning horizon (few steps)",
                "No persistent memory across sessions",
                "Can't truly learn from experience",
                "Struggles with complex reasoning chains",
                "No self-improvement capability"
            ]
        }

    @staticmethod
    def describe_near_future():
        """Near-future capabilities (1-2 years)"""

        return {
            "developments": [
                "Persistent memory: Remember user preferences and history",
                "Better planning: 10+ step task decomposition",
                "Multi-agent collaboration: Agents working together",
                "Self-debugging: Recognize and fix own mistakes",
                "Proactive assistance: Anticipate needs"
            ],
            "examples": [
                "Personal AI assistant that knows your preferences",
                "Development agent that can build entire features",
                "Research agent that conducts literature reviews",
                "Teaching agent that adapts to learning style"
            ]
        }

    @staticmethod
    def describe_long_term_vision():
        """Long-term vision (5+ years)"""

        return {
            "capabilities": [
                "Continuous learning: Learn from every interaction",
                "Long-term planning: Days/weeks of autonomous work",
                "True reasoning: Novel problem solving",
                "Self-improvement: Improve own capabilities",
                "Human collaboration: Equal partners in complex tasks"
            ],
            "potential_applications": [
                "Scientific research: Autonomous hypothesis generation and testing",
                "Software development: End-to-end system design and implementation",
                "Education: Fully personalized curriculum and tutoring",
                "Healthcare: Comprehensive diagnosis and treatment planning",
                "Business: Strategic planning and execution"
            ],
            "requirements": [
                "Verifiable reasoning: Prove correctness of plans",
                "Safety guarantees: Provably aligned behavior",
                "Interpretability: Explain all decisions",
                "Resource efficiency: Operate within bounds",
                "Value alignment: Follow human values"
            ]
        }

class NextGenAgentArchitecture:
    """Conceptual next-generation agent architecture"""

    def __init__(self):
        self.components = {
            "perception": "Multi-modal understanding (text, vision, audio)",
            "memory": "Hierarchical long-term and working memory",
            "reasoning": "Chain-of-thought and tree-search planning",
            "learning": "Continuous learning from interactions",
            "tools": "Extensible tool use and API integration",
            "communication": "Natural multi-turn dialogue",
            "safety": "Built-in alignment and safety checks"
        }

    def describe_architecture(self):
        """Describe the architecture"""

        arch_description = """
Next-Generation AI Agent Architecture:

┌─────────────────────────────────────────────────────┐
│                    User Interface                    │
└──────────────────────┬──────────────────────────────┘
                       │
┌──────────────────────▼──────────────────────────────┐
│              Perception & Understanding              │
│  (Multimodal: Text, Images, Video, Audio, Sensors)  │
└──────────────────────┬──────────────────────────────┘
                       │
┌──────────────────────▼──────────────────────────────┐
│                  Working Memory                      │
│     (Current context, recent interactions)          │
└──────────────────────┬──────────────────────────────┘
                       │
           ┌───────────┴───────────┐
           │                       │
┌──────────▼──────────┐  ┌────────▼────────┐
│  Planning Engine    │  │ Long-term Memory│
│  (Task decomp,      │  │ (User prefs,    │
│   reasoning)        │  │  knowledge)     │
└──────────┬──────────┘  └────────┬────────┘
           │                       │
           └───────────┬───────────┘
                       │
┌──────────────────────▼──────────────────────────────┐
│                 Action Selection                     │
│        (Tool use, API calls, responses)              │
└──────────────────────┬──────────────────────────────┘
                       │
┌──────────────────────▼──────────────────────────────┐
│                Safety & Alignment                    │
│    (Verify actions align with values/constraints)   │
└──────────────────────┬──────────────────────────────┘
                       │
┌──────────────────────▼──────────────────────────────┐
│                     Execution                        │
│         (Perform actions in environment)             │
└──────────────────────┬──────────────────────────────┘
                       │
┌──────────────────────▼──────────────────────────────┐
│              Learning & Adaptation                   │
│      (Update memory and improve from feedback)       │
└─────────────────────────────────────────────────────┘

Key Innovations:
1. Persistent memory across sessions
2. Hierarchical planning (strategic → tactical)
3. Continuous learning loop
4. Multi-modal perception
5. Built-in safety constraints
6. Verifiable reasoning chains
"""

        return arch_description

# Display information
print("FUTURE OF AI AGENTS")
print("="*60)

capabilities = FutureAgentCapabilities()

current = capabilities.describe_current_state()
print("\nCurrent State (2024):")
print("Capabilities:")
for cap in current['capabilities']:
    print(f"  ✓ {cap}")
print("\nLimitations:")
for lim in current['limitations']:
    print(f"  ✗ {lim}")

near_future = capabilities.describe_near_future()
print("\n" + "="*60)
print("\nNear Future (1-2 years):")
print("Expected Developments:")
for dev in near_future['developments']:
    print(f"  • {dev}")

long_term = capabilities.describe_long_term_vision()
print("\n" + "="*60)
print("\nLong-term Vision (5+ years):")
print("Potential Capabilities:")
for cap in long_term['capabilities']:
    print(f"  • {cap}")

print("\nApplications:")
for app in long_term['potential_applications']:
    print(f"  • {app}")

print("\n" + "="*60)
architecture = NextGenAgentArchitecture()
print(architecture.describe_architecture())

class ResearchFrontiers:
    """Key research challenges and frontiers"""

    @staticmethod
    def get_open_problems():
        """Major open problems in LLM research"""

        return {
            "Reasoning and Planning": {
                "problem": "LLMs struggle with complex multi-step reasoning and planning",
                "current_approaches": [
                    "Chain-of-thought prompting",
                    "Tree-of-thought search",
                    "Process supervision"
                ],
                "future_directions": [
                    "Verifiable reasoning chains",
                    "Formal logic integration",
                    "Neurosymbolic methods"
                ]
            },
            "Factual Accuracy": {
                "problem": "Hallucination - generating plausible but false information",
                "current_approaches": [
                    "Retrieval augmentation (RAG)",
                    "Fact-checking modules",
                    "Uncertainty quantification"
                ],
                "future_directions": [
                    "Grounded generation",
                    "Calibrated uncertainty",
                    "Citation and provenance tracking"
                ]
            },
            "Efficiency": {
                "problem": "Training and inference are computationally expensive",
                "current_approaches": [
                    "Quantization",
                    "Distillation",
                    "Sparse models (MoE)"
                ],
                "future_directions": [
                    "Novel architectures (SSMs, RetNet)",
                    "Algorithmic improvements",
                    "Specialized hardware"
                ]
            },
            "Alignment": {
                "problem": "Ensuring AI systems follow human values and intentions",
                "current_approaches": [
                    "RLHF",
                    "Constitutional AI",
                    "Red teaming"
                ],
                "future_directions": [
                    "Scalable oversight",
                    "Value learning",
                    "Mechanistic interpretability"
                ]
            },
            "Continual Learning": {
                "problem": "Models can't learn from interactions or update knowledge",
                "current_approaches": [
                    "Fine-tuning",
                    "In-context learning",
                    "External memory"
                ],
                "future_directions": [
                    "Online learning",
                    "Lifelong learning",
                    "Selective memory updates"
                ]
            }
        }

    @staticmethod
    def get_breakthrough_scenarios():
        """Potential breakthrough scenarios"""

        return [
            {
                "breakthrough": "Verifiable Reasoning",
                "description": "LLMs that can prove their reasoning is correct",
                "impact": "Revolutionary for high-stakes applications (medical, legal, safety-critical)",
                "timeline": "3-5 years",
                "prerequisites": ["Formal methods integration", "Better interpretability"]
            },
            {
                "breakthrough": "True Continual Learning",
                "description": "Models that learn continuously from interactions",
                "impact": "Personalized AI that improves with use",
                "timeline": "2-4 years",
                "prerequisites": ["Efficient online learning", "Catastrophic forgetting solutions"]
            },
            {
                "breakthrough": "AGI-level Reasoning",
                "description": "Human-level reasoning and problem-solving",
                "impact": "Transformative for science, engineering, all knowledge work",
                "timeline": "10+ years (uncertain)",
                "prerequisites": ["Multiple breakthroughs", "Significant compute scaling"]
            },
            {
                "breakthrough": "Energy-Efficient AI",
                "description": "Brain-like efficiency (20 watts)",
                "impact": "Ubiquitous AI on edge devices, sustainable scaling",
                "timeline": "5-10 years",
                "prerequisites": ["Novel architectures", "Specialized hardware"]
            }
        ]

# Display information
frontiers = ResearchFrontiers()

print("RESEARCH FRONTIERS")
print("="*60)

problems = frontiers.get_open_problems()

for problem_name, details in problems.items():
    print(f"\n{problem_name}:")
    print(f"  Problem: {details['problem']}")
    print(f"  Current approaches:")
    for approach in details['current_approaches']:
        print(f"    • {approach}")
    print(f"  Future directions:")
    for direction in details['future_directions']:
        print(f"    → {direction}")

print("\n" + "="*60)
print("\nPOTENTIAL BREAKTHROUGHS:")

breakthroughs = frontiers.get_breakthrough_scenarios()

for bt in breakthroughs:
    print(f"\n{bt['breakthrough']}:")
    print(f"  Description: {bt['description']}")
    print(f"  Impact: {bt['impact']}")
    print(f"  Timeline: {bt['timeline']}")
    print(f"  Prerequisites: {', '.join(bt['prerequisites'])}")

class FuturePreparation:
    """How to prepare for the future of LLMs"""

    @staticmethod
    def get_recommendations():
        """Recommendations for staying current"""

        return {
            "For Developers": [
                "Master fundamentals: Architecture, training, fine-tuning",
                "Build with latest tools: Keep up with LangChain, LlamaIndex, etc.",
                "Experiment with new models: Try each major release",
                "Focus on applications: Build real solutions, not just demos",
                "Learn adjacent fields: Understand agents, reasoning, safety"
            ],
            "For Researchers": [
                "Focus on open problems: Reasoning, alignment, efficiency",
                "Interdisciplinary work: Combine AI with domain expertise",
                "Reproducibility: Open source and share findings",
                "Ethics: Consider societal impact of research",
                "Collaboration: Work across institutions and companies"
            ],
            "For Organizations": [
                "Invest in AI literacy: Train entire organization",
                "Build infrastructure: GPU clusters, data pipelines",
                "Hire diverse talent: ML engineers, domain experts, ethicists",
                "Responsible deployment: Safety, privacy, fairness first",
                "Stay adaptable: Technology changes rapidly"
            ],
            "For Everyone": [
                "Continuous learning: Field evolves monthly",
                "Hands-on practice: Build, experiment, iterate",
                "Community engagement: Share learnings, get feedback",
                "Think critically: Understand limitations, not just capabilities",
                "Consider impact: How will this affect society?"
            ]
        }

    @staticmethod
    def get_learning_resources():
        """Key resources to stay current"""

        return {
            "Papers": [
                "ArXiv cs.CL and cs.LG (daily papers)",
                "Key conferences: NeurIPS, ICML, ACL, EMNLP",
                "Company research blogs: OpenAI, Anthropic, Google DeepMind"
            ],
            "Courses": [
                "Stanford CS224N (NLP with Deep Learning)",
                "DeepLearning.AI specializations",
                "Fast.ai courses"
            ],
            "Communities": [
                "Hugging Face forums",
                "EleutherAI Discord",
                "Reddit r/MachineLearning",
                "Twitter/X AI researchers"
            ],
            "Tools to Master": [
                "Hugging Face Transformers",
                "LangChain / LlamaIndex",
                "PyTorch / JAX",
                "Weights & Biases / MLflow",
                "Vector databases (Pinecone, Weaviate)"
            ]
        }

# Display recommendations
prep = FuturePreparation()

print("PREPARING FOR THE FUTURE")
print("="*60)

recommendations = prep.get_recommendations()

for category, items in recommendations.items():
    print(f"\n{category}:")
    for item in items:
        print(f"  • {item}")

print("\n" + "="*60)
print("\nKEY LEARNING RESOURCES:")

resources = prep.get_learning_resources()

for category, items in resources.items():
    print(f"\n{category}:")
    for item in items:
        print(f"  • {item}")