diff --git a/quickstarts/Get_started_LyriaRealTime.ipynb b/quickstarts/Get_started_LyriaRealTime.ipynb index 3f6d2b164..3fb9ac9a2 100644 --- a/quickstarts/Get_started_LyriaRealTime.ipynb +++ b/quickstarts/Get_started_LyriaRealTime.ipynb @@ -40,6 +40,15 @@ "# Get started with Music generation using Lyria RealTime" ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "d4f919f05306" + }, + "source": [ + "" + ] + }, { "cell_type": "markdown", "metadata": { diff --git a/quickstarts/Get_started_Veo.ipynb b/quickstarts/Get_started_Veo.ipynb index a37f47023..da40403da 100644 --- a/quickstarts/Get_started_Veo.ipynb +++ b/quickstarts/Get_started_Veo.ipynb @@ -864,4 +864,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/quickstarts/Grounding.ipynb b/quickstarts/Grounding.ipynb index f67396e9d..924dbaa55 100644 --- a/quickstarts/Grounding.ipynb +++ b/quickstarts/Grounding.ipynb @@ -46,7 +46,7 @@ "id": "VkR4fWudrHCs" }, "source": [ - "" + "" ] }, { @@ -180,7 +180,14 @@ "outputs": [ { "data": { - "text/markdown": "Response:\n The latest completed Indian Premier League (IPL) match was played on May 19, 2025, between Sunrisers Hyderabad and Lucknow Super Giants. Sunrisers Hyderabad won the match by 6 wickets.\n\nPrior to that, on May 18, 2025, Gujarat Titans defeated Delhi Capitals by 10 wickets, and Punjab Kings won against Rajasthan Royals by 10 runs. The match between Royal Challengers Bengaluru and Kolkata Knight Riders on May 17, 2025, was abandoned.\n\nThe 2024 IPL season concluded on May 26, 2024, with the Kolkata Knight Riders winning their third IPL title by defeating Sunrisers Hyderabad in the final.", + "text/markdown": [ + "Response:\n", + " The latest completed Indian Premier League (IPL) match was played on May 19, 2025, between Sunrisers Hyderabad and Lucknow Super Giants. Sunrisers Hyderabad won the match by 6 wickets.\n", + "\n", + "Prior to that, on May 18, 2025, Gujarat Titans defeated Delhi Capitals by 10 wickets, and Punjab Kings won against Rajasthan Royals by 10 runs. The match between Royal Challengers Bengaluru and Kolkata Knight Riders on May 17, 2025, was abandoned.\n", + "\n", + "The 2024 IPL season concluded on May 26, 2024, with the Kolkata Knight Riders winning their third IPL title by defeating Sunrisers Hyderabad in the final." + ], "text/plain": [ "" ] @@ -370,7 +377,13 @@ "outputs": [ { "data": { - "text/markdown": "As of my last update, the **IPL 2024 season is currently underway**.\n\nThe latest match played was between **Rajasthan Royals (RR)** and **Royal Challengers Bangalore (RCB)** on **April 6, 2024**.\n\n**Rajasthan Royals (RR) won** the match by 6 wickets, successfully chasing down RCB's total of 183 with Jos Buttler scoring a magnificent century.", + "text/markdown": [ + "As of my last update, the **IPL 2024 season is currently underway**.\n", + "\n", + "The latest match played was between **Rajasthan Royals (RR)** and **Royal Challengers Bangalore (RCB)** on **April 6, 2024**.\n", + "\n", + "**Rajasthan Royals (RR) won** the match by 6 wickets, successfully chasing down RCB's total of 183 with Jos Buttler scoring a magnificent century." + ], "text/plain": [ "" ] @@ -415,7 +428,16 @@ "outputs": [ { "data": { - "text/markdown": "The video introduces \"Gemma Chess,\" an application of Google's Gemma AI model designed to enhance the chess experience. Instead of replacing traditional powerful chess engines for calculating optimal moves, Gemma leverages its natural language processing capabilities to add a new dimension to learning and analysis.\n\nKey functionalities demonstrated include:\n* **Move Explanations:** Gemma can analyze chess games from PGN data, identify interesting moves, and explain their strategic and psychological significance in human-readable text, contrasting this with the raw data provided by traditional engines.\n* **Storytelling:** It can generate narratives about chess matches, bringing the game's atmosphere, player emotions, and unfolding drama to life.\n* **Learning Support:** Gemma acts as an intelligent study assistant, explaining complex chess concepts (like openings or specific pawn structures) in an easy-to-understand manner, adaptable to different skill levels and multiple languages.\n\nBy combining the computational strength of chess AI with Gemma's linguistic abilities, this approach aims to make chess learning and analysis more intuitive, accessible, and engaging for players.", + "text/markdown": [ + "The video introduces \"Gemma Chess,\" an application of Google's Gemma AI model designed to enhance the chess experience. Instead of replacing traditional powerful chess engines for calculating optimal moves, Gemma leverages its natural language processing capabilities to add a new dimension to learning and analysis.\n", + "\n", + "Key functionalities demonstrated include:\n", + "* **Move Explanations:** Gemma can analyze chess games from PGN data, identify interesting moves, and explain their strategic and psychological significance in human-readable text, contrasting this with the raw data provided by traditional engines.\n", + "* **Storytelling:** It can generate narratives about chess matches, bringing the game's atmosphere, player emotions, and unfolding drama to life.\n", + "* **Learning Support:** Gemma acts as an intelligent study assistant, explaining complex chess concepts (like openings or specific pawn structures) in an easy-to-understand manner, adaptable to different skill levels and multiple languages.\n", + "\n", + "By combining the computational strength of chess AI with Gemma's linguistic abilities, this approach aims to make chess learning and analysis more intuitive, accessible, and engaging for players." + ], "text/plain": [ "" ] @@ -461,7 +483,47 @@ "outputs": [ { "data": { - "text/markdown": "Gemma models, as large language models (LLMs), are primarily designed for text generation, understanding, and reasoning. While they are **not** specialized chess engines like Stockfish, AlphaZero, or Leela Chess Zero (which use sophisticated search algorithms and neural networks trained specifically on game play), they can still be incredibly helpful in various *language-based* aspects of chess:\n\nHere's how Gemma models can assist with chess:\n\n1. **Learning and Understanding Chess Concepts:**\n * **Explaining Rules and Mechanics:** Ask Gemma to explain castling, en passant, stalemate, or any other fundamental rule.\n * **Defining Terminology:** \"What is zugzwang?\" \"Explain a discovered attack.\" \"What is a pawn structure?\"\n * **Elaborating on Openings and Endgames:** \"Describe the main ideas behind the Sicilian Defense.\" \"Explain the King and Pawn endgame theory.\"\n * **Tactics and Strategy:** \"What are the common tactical motifs?\" \"Explain the concept of prophylaxis.\"\n\n2. **Game Analysis and Review (Textual):**\n * **Translating PGN to Natural Language:** You can provide a PGN (Portable Game Notation) string, and Gemma can describe the moves in plain English, making the game more accessible to beginners.\n * **Commentary Generation:** Based on a game's PGN, Gemma can generate descriptive commentary, highlighting key moments, blunders (if explicitly pointed out or easily inferred from standard PGN annotations like '??' or '?'), and strategic themes, acting like a virtual commentator.\n * **Answering \"Why\" Questions:** \"Why was 12. Ne5 a good move in this position?\" (Requires the model to have absorbed chess knowledge to reason about common positional ideas, though it won't perform deep calculation like an engine).\n * **Summarizing Games:** Provide a PGN, and ask Gemma to summarize the critical phases of the game.\n\n3. **Study Material Creation and Organization:**\n * **Generating Study Questions:** \"Give me 5 questions about common mistakes in the endgame.\"\n * **Creating Explanations for Puzzles:** If you have a chess puzzle, Gemma can help you formulate the textual explanation of the solution or the tactical theme involved.\n * **Brainstorming Lesson Plans:** For a chess coach, Gemma can help outline topics, exercises, and examples for a lesson on a particular chess concept.\n * **Writing Articles or Blog Posts:** Generate content about specific openings, historical games, famous players, or general chess advice.\n\n4. **Personalized Learning and Coaching:**\n * **Suggesting Training Regimens:** Based on your described weaknesses (e.g., \"I struggle with tactics\"), Gemma can suggest types of exercises or areas to focus on.\n * **Providing Feedback on Ideas:** You can propose a strategic idea or a move, and Gemma can discuss its merits or drawbacks based on general chess principles.\n\n5. **Historical and Cultural Context:**\n * **Information on Players:** \"Tell me about Bobby Fischer's career.\"\n * **Chess History:** \"When did chess originate?\" \"Describe the evolution of chess rules.\"\n * **Chess in Culture:** \"How has chess been depicted in literature or film?\"\n\n**What Gemma Models CANNOT Do (and why specialized chess engines are needed):**\n\n* **Play Chess Competitively:** Gemma cannot *play* a chess game in real-time or make optimal moves. It doesn't have a built-in search tree, evaluation function, or understanding of board state in the way a chess engine does. It operates on text sequences, not game logic.\n* **Perform Deep Tactical Calculation:** It cannot calculate variations 20 moves deep or find hidden tactical blows that an engine would. Its \"understanding\" is based on learned patterns from text, not on a simulation of the game.\n* **Evaluate Positions with Engine-Level Accuracy:** It won't give you an objective evaluation score (+0.5, -2.1) or identify the absolute best move in a complex position.\n* **Generate Sound Chess Puzzles Reliably:** While it can describe what a puzzle *is*, generating a *valid* and *solvable* chess puzzle from scratch that works perfectly and isn't trivially broken is extremely difficult for an LLM without specialized fine-tuning and validation against a chess engine.\n\n**In summary, Gemma models are powerful textual assistants for chess. They excel at explaining, summarizing, generating content, and answering questions about chess concepts, history, and strategy. They are a fantastic complementary tool for learning, teaching, and discussing chess, but they do not replace the analytical power of dedicated chess engines for game play and deep strategic/tactical analysis.**", + "text/markdown": [ + "Gemma models, as large language models (LLMs), are primarily designed for text generation, understanding, and reasoning. While they are **not** specialized chess engines like Stockfish, AlphaZero, or Leela Chess Zero (which use sophisticated search algorithms and neural networks trained specifically on game play), they can still be incredibly helpful in various *language-based* aspects of chess:\n", + "\n", + "Here's how Gemma models can assist with chess:\n", + "\n", + "1. **Learning and Understanding Chess Concepts:**\n", + " * **Explaining Rules and Mechanics:** Ask Gemma to explain castling, en passant, stalemate, or any other fundamental rule.\n", + " * **Defining Terminology:** \"What is zugzwang?\" \"Explain a discovered attack.\" \"What is a pawn structure?\"\n", + " * **Elaborating on Openings and Endgames:** \"Describe the main ideas behind the Sicilian Defense.\" \"Explain the King and Pawn endgame theory.\"\n", + " * **Tactics and Strategy:** \"What are the common tactical motifs?\" \"Explain the concept of prophylaxis.\"\n", + "\n", + "2. **Game Analysis and Review (Textual):**\n", + " * **Translating PGN to Natural Language:** You can provide a PGN (Portable Game Notation) string, and Gemma can describe the moves in plain English, making the game more accessible to beginners.\n", + " * **Commentary Generation:** Based on a game's PGN, Gemma can generate descriptive commentary, highlighting key moments, blunders (if explicitly pointed out or easily inferred from standard PGN annotations like '??' or '?'), and strategic themes, acting like a virtual commentator.\n", + " * **Answering \"Why\" Questions:** \"Why was 12. Ne5 a good move in this position?\" (Requires the model to have absorbed chess knowledge to reason about common positional ideas, though it won't perform deep calculation like an engine).\n", + " * **Summarizing Games:** Provide a PGN, and ask Gemma to summarize the critical phases of the game.\n", + "\n", + "3. **Study Material Creation and Organization:**\n", + " * **Generating Study Questions:** \"Give me 5 questions about common mistakes in the endgame.\"\n", + " * **Creating Explanations for Puzzles:** If you have a chess puzzle, Gemma can help you formulate the textual explanation of the solution or the tactical theme involved.\n", + " * **Brainstorming Lesson Plans:** For a chess coach, Gemma can help outline topics, exercises, and examples for a lesson on a particular chess concept.\n", + " * **Writing Articles or Blog Posts:** Generate content about specific openings, historical games, famous players, or general chess advice.\n", + "\n", + "4. **Personalized Learning and Coaching:**\n", + " * **Suggesting Training Regimens:** Based on your described weaknesses (e.g., \"I struggle with tactics\"), Gemma can suggest types of exercises or areas to focus on.\n", + " * **Providing Feedback on Ideas:** You can propose a strategic idea or a move, and Gemma can discuss its merits or drawbacks based on general chess principles.\n", + "\n", + "5. **Historical and Cultural Context:**\n", + " * **Information on Players:** \"Tell me about Bobby Fischer's career.\"\n", + " * **Chess History:** \"When did chess originate?\" \"Describe the evolution of chess rules.\"\n", + " * **Chess in Culture:** \"How has chess been depicted in literature or film?\"\n", + "\n", + "**What Gemma Models CANNOT Do (and why specialized chess engines are needed):**\n", + "\n", + "* **Play Chess Competitively:** Gemma cannot *play* a chess game in real-time or make optimal moves. It doesn't have a built-in search tree, evaluation function, or understanding of board state in the way a chess engine does. It operates on text sequences, not game logic.\n", + "* **Perform Deep Tactical Calculation:** It cannot calculate variations 20 moves deep or find hidden tactical blows that an engine would. Its \"understanding\" is based on learned patterns from text, not on a simulation of the game.\n", + "* **Evaluate Positions with Engine-Level Accuracy:** It won't give you an objective evaluation score (+0.5, -2.1) or identify the absolute best move in a complex position.\n", + "* **Generate Sound Chess Puzzles Reliably:** While it can describe what a puzzle *is*, generating a *valid* and *solvable* chess puzzle from scratch that works perfectly and isn't trivially broken is extremely difficult for an LLM without specialized fine-tuning and validation against a chess engine.\n", + "\n", + "**In summary, Gemma models are powerful textual assistants for chess. They excel at explaining, summarizing, generating content, and answering questions about chess concepts, history, and strategy. They are a fantastic complementary tool for learning, teaching, and discussing chess, but they do not replace the analytical power of dedicated chess engines for game play and deep strategic/tactical analysis.**" + ], "text/plain": [ "" ] @@ -502,7 +564,24 @@ "outputs": [ { "data": { - "text/markdown": "Gemma models, as demonstrated in the video, can enhance chess games and learning in several innovative ways by leveraging their language understanding and generation capabilities:\n\n1. **Enhanced Explanations and Analysis**:\n * **Translating Technical Jargon**: Traditional chess engines often provide technical numerical evaluations and complex move sequences that can be hard for humans to understand. Gemma can translate these into plain, natural language.\n * **Explaining Strategic Rationale**: Instead of just showing the best move, Gemma can explain *why* a move is good, detailing the underlying strategic ideas, tactical opportunities, or potential dangers (e.g., explaining why a pawn sacrifice is interesting).\n * **Summarizing Complexity**: Gemma can condense complicated game phases, highlighting key tactical moments and strategic concepts, making it easier for players to grasp important takeaways from a game.\n\n2. **Storytelling and Narrative**:\n * **Bringing Games to Life**: Gemma can analyze game data (moves, players, tournament context) and generate engaging narratives or short stories about how a chess match unfolded. This adds a \"backstory\" and emotional depth, making the analysis more interesting and memorable than just reviewing moves on a board.\n\n3. **Personalized Chess Learning**:\n * **Intelligent Study Buddy**: Gemma can act as a personal chess coach or study companion, explaining concepts like openings (e.g., the Sicilian Defense) or specific chess terms (e.g., \"passed pawn\") in a way that is tailored to the user's skill level (beginner, intermediate, advanced).\n * **Multilingual Support**: It can provide explanations in various languages, removing language barriers for learners.\n * **Interactive Feedback**: Gemma can offer feedback on a user's understanding of chess ideas and even suggest areas for improvement.\n\nEssentially, Gemma models bring a more human-like, intuitive, and narrative dimension to chess, making analysis and learning more accessible and engaging for players of all levels by combining the raw computational power of chess engines with advanced language intelligence.", + "text/markdown": [ + "Gemma models, as demonstrated in the video, can enhance chess games and learning in several innovative ways by leveraging their language understanding and generation capabilities:\n", + "\n", + "1. **Enhanced Explanations and Analysis**:\n", + " * **Translating Technical Jargon**: Traditional chess engines often provide technical numerical evaluations and complex move sequences that can be hard for humans to understand. Gemma can translate these into plain, natural language.\n", + " * **Explaining Strategic Rationale**: Instead of just showing the best move, Gemma can explain *why* a move is good, detailing the underlying strategic ideas, tactical opportunities, or potential dangers (e.g., explaining why a pawn sacrifice is interesting).\n", + " * **Summarizing Complexity**: Gemma can condense complicated game phases, highlighting key tactical moments and strategic concepts, making it easier for players to grasp important takeaways from a game.\n", + "\n", + "2. **Storytelling and Narrative**:\n", + " * **Bringing Games to Life**: Gemma can analyze game data (moves, players, tournament context) and generate engaging narratives or short stories about how a chess match unfolded. This adds a \"backstory\" and emotional depth, making the analysis more interesting and memorable than just reviewing moves on a board.\n", + "\n", + "3. **Personalized Chess Learning**:\n", + " * **Intelligent Study Buddy**: Gemma can act as a personal chess coach or study companion, explaining concepts like openings (e.g., the Sicilian Defense) or specific chess terms (e.g., \"passed pawn\") in a way that is tailored to the user's skill level (beginner, intermediate, advanced).\n", + " * **Multilingual Support**: It can provide explanations in various languages, removing language barriers for learners.\n", + " * **Interactive Feedback**: Gemma can offer feedback on a user's understanding of chess ideas and even suggest areas for improvement.\n", + "\n", + "Essentially, Gemma models bring a more human-like, intuitive, and narrative dimension to chess, making analysis and learning more accessible and engaging for players of all levels by combining the raw computational power of chess engines with advanced language intelligence." + ], "text/plain": [ "" ] @@ -561,7 +640,11 @@ "outputs": [ { "data": { - "text/markdown": "The Gemini API offers various models optimized for specific use cases. Here's a comparison of Gemini 1.5, Gemini 2.0, and Gemini 2.5 models based on the provided documentation:\n\n| Feature | Gemini 1.5 Pro | Gemini 1.5 Flash | Gemini 2.0 Flash | Gemini 2.0 Flash Preview Image Generation | Gemini 2.0 Flash-Lite | Gemini 2.0 Flash Live | Gemini 2.5 Pro (Preview) ", + "text/markdown": [ + "The Gemini API offers various models optimized for specific use cases. Here's a comparison of Gemini 1.5, Gemini 2.0, and Gemini 2.5 models based on the provided documentation:\n", + "\n", + "| Feature | Gemini 1.5 Pro | Gemini 1.5 Flash | Gemini 2.0 Flash | Gemini 2.0 Flash Preview Image Generation | Gemini 2.0 Flash-Lite | Gemini 2.0 Flash Live | Gemini 2.5 Pro (Preview) " + ], "text/plain": [ "" ] @@ -611,7 +694,38 @@ "outputs": [ { "data": { - "text/markdown": "It's important to clarify that as of my last update, **Gemini 2.0 and Gemini 2.5 are not publicly announced or released models by Google in the same way Gemini 1.0 and Gemini 1.5 Pro/Flash are.**\n\nGoogle often uses internal versioning or refers to \"next-generation\" capabilities. The major public release and current flagship model is **Gemini 1.5 Pro** (and its smaller, faster variant, Gemini 1.5 Flash). Any mention of \"Gemini 2.0\" or \"Gemini 2.5\" would refer to future, unreleased iterations, or internal development stages.\n\nTherefore, the comparison for 2.0 and 2.5 will be speculative, based on the general trajectory of large language model development and what one would expect from future generations.\n\nHere's a comparison table, with the understanding that Gemini 2.0 and 2.5 are hypothetical future versions:\n\n---\n\n### Gemini Model Comparison\n\n| Feature/Aspect | Gemini 1.5 Pro | Gemini 2.0 (Hypothetical/Future Iteration) | Gemini 2.5 (Further Hypothetical/Future Iteration) |\n| :--------------------- | :---------------------------------------------- | :-------------------------------------------------- | :-------------------------------------------------- |\n| **Status/Release** | Publicly available (API, Google AI Studio, Vertex AI) since early 2024. | Not publicly announced or released as a distinct model. | Not publicly announced or released as a distinct model. |\n| **Role/Iteration** | Major step forward from Gemini 1.0; current flagship for complex, multimodal tasks. | Would represent the next significant leap in core capabilities and efficiency after 1.5. | Would signify even more profound advancements, possibly towards human-level intelligence in many domains. |\n| **Key Innovation** | Groundbreaking 1 million (or 2 million) token context window, Mixture-of-Experts (MoE) architecture. | **Speculative:** Further significant architectural advancements, more robust reasoning, potentially new modalities or real-time processing. | **Speculative:** Even more advanced capabilities, potentially true multi-agent systems, deep understanding of physical world. |\n| **Context Window** | Up to 1 million tokens (preview), 2 million tokens (private access/select use cases). Game-changing for long-form content. | **Speculative:** Potentially even larger context windows, or significantly more efficient and accurate use of existing large contexts for recall and reasoning. | **Speculative:** Breakthroughs in context management, possibly dynamic and adaptive context handling, going beyond linear token limits. |\n| **Multimodality** | Highly advanced (native understanding of text, image, audio, video inputs, and cross-modal reasoning). | **Speculative:** Deeper, more nuanced understanding across modalities; potentially real-time, bidirectional interaction with multimodal inputs. | **Speculative:** Near-human level multimodal reasoning and generation, seamless integration of physical world understanding, potentially for robotics or augmented reality. |\n| **Architecture** | Mixture-of-Experts (MoE) provides efficiency and performance scalability. | **Speculative:** Continued refinement of MoE, possibly hybrid architectures combining different strengths, or entirely novel designs. | **Speculative:** Revolutionary architectural changes, potentially leveraging biological inspirations, leading towards Artificial General Intelligence (AGI). |\n| **Performance (General)** | Significant leap over 1.0, especially in long-context reasoning, complex problem-solving, and multimodal understanding. | **Speculative:** Expected to significantly surpass 1.5 Pro in benchmarks, exhibit more robust and less \"hallucinatory\" reasoning, and handle more complex, open-ended tasks. | **Speculative:** Major leaps in intelligence, potentially approaching or surpassing human-level cognitive abilities in a wide range of tasks and domains. |\n| **Efficiency** | Highly efficient for its capabilities due to MoE. Enables high-throughput and cost-effective large-scale use. | **Speculative:** Further optimized for cost and speed of training and inference, allowing for broader deployment and more complex real-time applications. | **Speculative:** Breakthroughs in energy efficiency and computational demands, making highly advanced AI more accessible and sustainable. |\n| **Typical Use Cases** | Code analysis across large repositories, summarizing hours of video/audio, complex data analysis, building sophisticated multimodal agents. | **Speculative:** More sophisticated scientific research, highly complex and nuanced problem-solving, advanced creative content generation, autonomous decision-making in constrained environments. | **Speculative:** AGI-like applications, fundamental scientific discovery, complex systems design and management, advanced autonomous systems capable of learning and adapting in novel situations. |\n\n---\n\n**In summary:**\n\n* **Gemini 1.5 Pro** is the current leading edge from Google, known for its massive context window and advanced multimodal capabilities.\n* **Gemini 2.0 and 2.5** are not public models. If they were to exist, they would represent future generations building upon the foundations of 1.5 Pro, focusing on even greater intelligence, efficiency, and perhaps novel ways of interacting with information and the world.", + "text/markdown": [ + "It's important to clarify that as of my last update, **Gemini 2.0 and Gemini 2.5 are not publicly announced or released models by Google in the same way Gemini 1.0 and Gemini 1.5 Pro/Flash are.**\n", + "\n", + "Google often uses internal versioning or refers to \"next-generation\" capabilities. The major public release and current flagship model is **Gemini 1.5 Pro** (and its smaller, faster variant, Gemini 1.5 Flash). Any mention of \"Gemini 2.0\" or \"Gemini 2.5\" would refer to future, unreleased iterations, or internal development stages.\n", + "\n", + "Therefore, the comparison for 2.0 and 2.5 will be speculative, based on the general trajectory of large language model development and what one would expect from future generations.\n", + "\n", + "Here's a comparison table, with the understanding that Gemini 2.0 and 2.5 are hypothetical future versions:\n", + "\n", + "---\n", + "\n", + "### Gemini Model Comparison\n", + "\n", + "| Feature/Aspect | Gemini 1.5 Pro | Gemini 2.0 (Hypothetical/Future Iteration) | Gemini 2.5 (Further Hypothetical/Future Iteration) |\n", + "| :--------------------- | :---------------------------------------------- | :-------------------------------------------------- | :-------------------------------------------------- |\n", + "| **Status/Release** | Publicly available (API, Google AI Studio, Vertex AI) since early 2024. | Not publicly announced or released as a distinct model. | Not publicly announced or released as a distinct model. |\n", + "| **Role/Iteration** | Major step forward from Gemini 1.0; current flagship for complex, multimodal tasks. | Would represent the next significant leap in core capabilities and efficiency after 1.5. | Would signify even more profound advancements, possibly towards human-level intelligence in many domains. |\n", + "| **Key Innovation** | Groundbreaking 1 million (or 2 million) token context window, Mixture-of-Experts (MoE) architecture. | **Speculative:** Further significant architectural advancements, more robust reasoning, potentially new modalities or real-time processing. | **Speculative:** Even more advanced capabilities, potentially true multi-agent systems, deep understanding of physical world. |\n", + "| **Context Window** | Up to 1 million tokens (preview), 2 million tokens (private access/select use cases). Game-changing for long-form content. | **Speculative:** Potentially even larger context windows, or significantly more efficient and accurate use of existing large contexts for recall and reasoning. | **Speculative:** Breakthroughs in context management, possibly dynamic and adaptive context handling, going beyond linear token limits. |\n", + "| **Multimodality** | Highly advanced (native understanding of text, image, audio, video inputs, and cross-modal reasoning). | **Speculative:** Deeper, more nuanced understanding across modalities; potentially real-time, bidirectional interaction with multimodal inputs. | **Speculative:** Near-human level multimodal reasoning and generation, seamless integration of physical world understanding, potentially for robotics or augmented reality. |\n", + "| **Architecture** | Mixture-of-Experts (MoE) provides efficiency and performance scalability. | **Speculative:** Continued refinement of MoE, possibly hybrid architectures combining different strengths, or entirely novel designs. | **Speculative:** Revolutionary architectural changes, potentially leveraging biological inspirations, leading towards Artificial General Intelligence (AGI). |\n", + "| **Performance (General)** | Significant leap over 1.0, especially in long-context reasoning, complex problem-solving, and multimodal understanding. | **Speculative:** Expected to significantly surpass 1.5 Pro in benchmarks, exhibit more robust and less \"hallucinatory\" reasoning, and handle more complex, open-ended tasks. | **Speculative:** Major leaps in intelligence, potentially approaching or surpassing human-level cognitive abilities in a wide range of tasks and domains. |\n", + "| **Efficiency** | Highly efficient for its capabilities due to MoE. Enables high-throughput and cost-effective large-scale use. | **Speculative:** Further optimized for cost and speed of training and inference, allowing for broader deployment and more complex real-time applications. | **Speculative:** Breakthroughs in energy efficiency and computational demands, making highly advanced AI more accessible and sustainable. |\n", + "| **Typical Use Cases** | Code analysis across large repositories, summarizing hours of video/audio, complex data analysis, building sophisticated multimodal agents. | **Speculative:** More sophisticated scientific research, highly complex and nuanced problem-solving, advanced creative content generation, autonomous decision-making in constrained environments. | **Speculative:** AGI-like applications, fundamental scientific discovery, complex systems design and management, advanced autonomous systems capable of learning and adapting in novel situations. |\n", + "\n", + "---\n", + "\n", + "**In summary:**\n", + "\n", + "* **Gemini 1.5 Pro** is the current leading edge from Google, known for its massive context window and advanced multimodal capabilities.\n", + "* **Gemini 2.0 and 2.5** are not public models. If they were to exist, they would represent future generations building upon the foundations of 1.5 Pro, focusing on even greater intelligence, efficiency, and perhaps novel ways of interacting with information and the world." + ], "text/plain": [ "" ] diff --git a/quickstarts/websockets/Get_started_LyriaRealTime_websockets.ipynb b/quickstarts/websockets/Get_started_LyriaRealTime_websockets.ipynb index 0b5488bf5..a783ab821 100644 --- a/quickstarts/websockets/Get_started_LyriaRealTime_websockets.ipynb +++ b/quickstarts/websockets/Get_started_LyriaRealTime_websockets.ipynb @@ -40,6 +40,15 @@ "# Get started with Music generation using Lyria RealTime and websockets" ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "c249c5f4ec7e" + }, + "source": [ + "" + ] + }, { "cell_type": "markdown", "metadata": {