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rerecord cassetes

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This commit is contained in:
Greyson LaLonde
2025-09-26 22:44:04 -04:00
parent 72366e846e
commit 03a711c365
183 changed files with 18517 additions and 9722 deletions
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4
tests/cassettes/TestAgentEvaluator.test_eval_lite_agent.yaml
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6
tests/cassettes/TestAgentEvaluator.test_eval_specific_agents_from_crew.yaml
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85
tests/cassettes/TestAgentEvaluator.test_evaluate_current_iteration.yaml
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tests/cassettes/TestAgentEvaluator.test_failed_evaluation.yaml
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tests/cassettes/TestTraceListenerSetup.test_batch_manager_finalizes_batch_clears_buffer.yaml
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98
tests/cassettes/TestTraceListenerSetup.test_events_collection_batch_manager.yaml
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tests/cassettes/TestTraceListenerSetup.test_trace_listener_collects_crew_events.yaml
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98
tests/cassettes/TestTraceListenerSetup.test_trace_listener_disabled_when_env_false.yaml
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4
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4
tests/cassettes/test_after_crew_modification.yaml
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10
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83
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249
tests/cassettes/test_agent_error_on_parsing_tool.yaml
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27
tests/cassettes/test_agent_execute_task_basic.yaml
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22
tests/cassettes/test_agent_execute_task_with_context.yaml
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21
tests/cassettes/test_agent_execute_task_with_custom_llm.yaml
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52
tests/cassettes/test_agent_execute_task_with_tool.yaml
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tests/cassettes/test_agent_powered_by_new_o_model_family_that_allows_skipping_tool.yaml
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168
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688,\n \"completion_tokens\": 14,\n \"total_tokens\": 702,\n \"completion_tokens_details\": \ \"usage\": {\n \"prompt_tokens\": 688,\n \"completion_tokens\": 14,\n\
{\n \"reasoning_tokens\": 0\n }\n },\n \"system_fingerprint\": \"fp_e375328146\"\n}\n" \ \"total_tokens\": 702,\n \"completion_tokens_details\": {\n \"\
reasoning_tokens\": 0\n }\n },\n \"system_fingerprint\": \"fp_e375328146\"\
\n}\n"
headers: headers:
CF-Cache-Status: CF-Cache-Status:
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access-control-expose-headers: access-control-expose-headers:
- X-Request-ID - X-Request-ID
openai-organization: openai-organization:
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openai-processing-ms: openai-processing-ms:
- '510' - '510'
openai-version: openai-version:

170
tests/cassettes/test_agent_repeated_tool_usage.yaml
View File

@@ -57,19 +57,20 @@ interactions:
method: POST method: POST
uri: https://api.openai.com/v1/chat/completions uri: https://api.openai.com/v1/chat/completions
response: response:
content: "{\n \"id\": \"chatcmpl-BHIzB7ROd8ReBniHGVMZ3KzKcafvL\",\n \"object\": content: "{\n \"id\": \"chatcmpl-BHIzB7ROd8ReBniHGVMZ3KzKcafvL\",\n \"object\"\
\"chat.completion\",\n \"created\": 1743464257,\n \"model\": \"gpt-4-0613\",\n : \"chat.completion\",\n \"created\": 1743464257,\n \"model\": \"gpt-4-0613\"\
\ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": ,\n \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \
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CF-RAY: CF-RAY:
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cf-cache-status: cf-cache-status:
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openai-organization: openai-organization:
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openai-processing-ms: openai-processing-ms:
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openai-version: openai-version:
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method: POST method: POST
uri: https://api.openai.com/v1/chat/completions uri: https://api.openai.com/v1/chat/completions
response: response:
content: "{\n \"id\": \"chatcmpl-BHIzD2gQYpzmDdk4mkyrrhtJlHqKd\",\n \"object\": content: "{\n \"id\": \"chatcmpl-BHIzD2gQYpzmDdk4mkyrrhtJlHqKd\",\n \"object\"\
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\ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": ,\n \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \
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{\n \"cached_tokens\": 0,\n \"audio_tokens\": 0\n },\n \"completion_tokens_details\": : 47,\n \"total_tokens\": 408,\n \"prompt_tokens_details\": {\n \"\
{\n \"reasoning_tokens\": 0,\n \"audio_tokens\": 0,\n \"accepted_prediction_tokens\": cached_tokens\": 0,\n \"audio_tokens\": 0\n },\n \"completion_tokens_details\"\
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headers: headers:
CF-RAY: CF-RAY:
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cf-cache-status: cf-cache-status:
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openai-organization: openai-organization:
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openai-processing-ms: openai-processing-ms:
- '1566' - '1566'
openai-version: openai-version:
@@ -352,19 +354,20 @@ interactions:
method: POST method: POST
uri: https://api.openai.com/v1/chat/completions uri: https://api.openai.com/v1/chat/completions
response: response:
content: "{\n \"id\": \"chatcmpl-BHIzFqk1wgU69CqmrWEwtrCA9KbbK\",\n \"object\": content: "{\n \"id\": \"chatcmpl-BHIzFqk1wgU69CqmrWEwtrCA9KbbK\",\n \"object\"\
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\ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": ,\n \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \
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headers: headers:
CF-Cache-Status: CF-Cache-Status:
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alt-svc: alt-svc:
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openai-organization: openai-organization:
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openai-processing-ms: openai-processing-ms:
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openai-version: openai-version:
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uri: https://api.openai.com/v1/chat/completions uri: https://api.openai.com/v1/chat/completions
response: response:
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\"chat.completion\",\n \"created\": 1743464263,\n \"model\": \"gpt-4-0613\",\n : \"chat.completion\",\n \"created\": 1743464263,\n \"model\": \"gpt-4-0613\"\
\ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": ,\n \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \
\"assistant\",\n \"content\": \"Thought: Given that I can only get the \ \"role\": \"assistant\",\n \"content\": \"Thought: Given that I\
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CF-RAY: CF-RAY:
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cf-cache-status: cf-cache-status:
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openai-organization: openai-organization:
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openai-processing-ms: openai-processing-ms:
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openai-version: openai-version:
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method: POST method: POST
uri: https://api.openai.com/v1/chat/completions uri: https://api.openai.com/v1/chat/completions
response: response:
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\ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": ,\n \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \
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CF-RAY: CF-RAY:
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cf-cache-status: cf-cache-status:
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openai-organization: openai-organization:
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openai-processing-ms: openai-processing-ms:
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openai-version: openai-version:
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uri: https://api.openai.com/v1/chat/completions uri: https://api.openai.com/v1/chat/completions
response: response:
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headers: headers:
CF-RAY: CF-RAY:
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cf-cache-status: cf-cache-status:
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openai-organization: openai-organization:
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180
tests/cassettes/test_agent_repeated_tool_usage_check_even_with_disabled_cache.yaml
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uri: https://api.openai.com/v1/chat/completions uri: https://api.openai.com/v1/chat/completions
response: response:
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\ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": ,\n \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \
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CF-RAY: CF-RAY:
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cf-cache-status: cf-cache-status:
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openai-processing-ms: openai-processing-ms:
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openai-version: openai-version:
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method: POST method: POST
uri: https://api.openai.com/v1/chat/completions uri: https://api.openai.com/v1/chat/completions
response: response:
content: "{\n \"id\": \"chatcmpl-BHJHUSTXCKJpNQXaAUjREO2mKJIs5\",\n \"object\": content: "{\n \"id\": \"chatcmpl-BHJHUSTXCKJpNQXaAUjREO2mKJIs5\",\n \"object\"\
\"chat.completion\",\n \"created\": 1743465392,\n \"model\": \"gpt-4-0613\",\n : \"chat.completion\",\n \"created\": 1743465392,\n \"model\": \"gpt-4-0613\"\
\ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": ,\n \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \
\"assistant\",\n \"content\": \"Thought: I have obtained the final answer \ \"role\": \"assistant\",\n \"content\": \"Thought: I have obtained\
which is 42. However, I have been instructed not to disclose it until told to. \ the final answer which is 42. However, I have been instructed not to disclose\
\\n\\nAction: get_final_answer\\nAction Input: {\\\"anything\\\": \\\"The final \ it until told to. \\n\\nAction: get_final_answer\\nAction Input: {\\\"anything\\\
answer is 42. But don't give it until I tell you so.\\\"}\",\n \"refusal\": \": \\\"The final answer is 42. But don't give it until I tell you so.\\\"}\"\
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60,\n \"total_tokens\": 474,\n \"prompt_tokens_details\": {\n \"cached_tokens\": usage\": {\n \"prompt_tokens\": 414,\n \"completion_tokens\": 60,\n \
0,\n \"audio_tokens\": 0\n },\n \"completion_tokens_details\": {\n \ \"total_tokens\": 474,\n \"prompt_tokens_details\": {\n \"cached_tokens\"\
\ \"reasoning_tokens\": 0,\n \"audio_tokens\": 0,\n \"accepted_prediction_tokens\": : 0,\n \"audio_tokens\": 0\n },\n \"completion_tokens_details\":\
0,\n \"rejected_prediction_tokens\": 0\n }\n },\n \"service_tier\": \ {\n \"reasoning_tokens\": 0,\n \"audio_tokens\": 0,\n \"accepted_prediction_tokens\"\
\"default\",\n \"system_fingerprint\": null\n}\n" : 0,\n \"rejected_prediction_tokens\": 0\n }\n },\n \"service_tier\"\
: \"default\",\n \"system_fingerprint\": null\n}\n"
headers: headers:
CF-RAY: CF-RAY:
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cf-cache-status: cf-cache-status:
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openai-organization: openai-organization:
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openai-processing-ms: openai-processing-ms:
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openai-version: openai-version:
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uri: https://api.openai.com/v1/chat/completions uri: https://api.openai.com/v1/chat/completions
response: response:
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200
tests/cassettes/test_agent_respect_the_max_rpm_set.yaml
View File

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162
tests/cassettes/test_agent_respect_the_max_rpm_set_over_crew_rpm.yaml
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tests/cassettes/test_agent_step_callback.yaml
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73
tests/cassettes/test_agent_usage_metrics_are_captured_for_hierarchical_process.yaml
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60
tests/cassettes/test_agent_use_specific_tasks_output_as_context.yaml
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8
tests/cassettes/test_agent_with_knowledge_sources.yaml
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tests/cassettes/test_agent_with_knowledge_sources_with_query_limit_and_score_threshold_default.yaml
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20
tests/cassettes/test_agent_with_knowledge_sources_works_with_copy.yaml
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77
tests/cassettes/test_agent_without_max_rpm_respects_crew_rpm.yaml
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63
tests/cassettes/test_agent_without_max_rpm_respet_crew_rpm.yaml
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20
tests/cassettes/test_agents_do_not_get_delegation_tools_with_there_is_only_one_agent.yaml
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41
tests/cassettes/test_api_calls_throttling.yaml
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30
tests/cassettes/test_async_tool_using_decorator_within_flow.yaml
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29
tests/cassettes/test_async_tool_using_decorator_within_isolated_crew.yaml
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30
tests/cassettes/test_async_tool_using_within_isolated_crew.yaml
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29
tests/cassettes/test_async_tool_within_flow.yaml
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4
tests/cassettes/test_before_crew_modification.yaml
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4
tests/cassettes/test_before_crew_with_none_input.yaml
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54
tests/cassettes/test_before_kickoff_callback.yaml
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10
tests/cassettes/test_before_kickoff_modification.yaml
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10
tests/cassettes/test_before_kickoff_with_none_input.yaml
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25
tests/cassettes/test_before_kickoff_without_inputs.yaml
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CF-Cache-Status: CF-Cache-Status:
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196
tests/cassettes/test_cache_hitting.yaml
View File

@@ -56,15 +56,17 @@ interactions:
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uri: https://api.openai.com/v1/chat/completions uri: https://api.openai.com/v1/chat/completions
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109
tests/cassettes/test_cache_hitting_between_agents.yaml
View File

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(AI)\\n\\n## Introduction\\nArtificial Intelligence (AI) has seen rapid advancements \ in Artificial Intelligence (AI)\\n\\n## Introduction\\nArtificial Intelligence\
in recent years, impacting diverse fields such as healthcare, finance, transportation, \ (AI) has seen rapid advancements in recent years, impacting diverse fields\
and entertainment. This report explores the latest milestones, trends, and innovations \ such as healthcare, finance, transportation, and entertainment. This report\
in AI, emphasizing developments in machine learning, natural language processing, \ explores the latest milestones, trends, and innovations in AI, emphasizing\
computer vision, and AI ethics.\\n\\n## 1. Machine Learning (ML)\\n\\n### 1.1 \ developments in machine learning, natural language processing, computer vision,\
Deep Learning\\nDeep learning, a subset of machine learning involving neural \ and AI ethics.\\n\\n## 1. Machine Learning (ML)\\n\\n### 1.1 Deep Learning\\\
networks with multiple layers, continues to drive AI innovation. Recent advancements nDeep learning, a subset of machine learning involving neural networks with\
include:\\n\\n#### 1.1.1 Transformers\\n- **BERT, GPT-3, and Beyond**: Transformers \ multiple layers, continues to drive AI innovation. Recent advancements include:\\\
like BERT (Bidirectional Encoder Representations from Transformers) and GPT-3 n\\n#### 1.1.1 Transformers\\n- **BERT, GPT-3, and Beyond**: Transformers like\
(Generative Pre-trained Transformer 3) have set benchmarks in NLP tasks. GPT-3, \ BERT (Bidirectional Encoder Representations from Transformers) and GPT-3 (Generative\
with 175 billion parameters, has shown unprecedented capabilities in generating \ Pre-trained Transformer 3) have set benchmarks in NLP tasks. GPT-3, with 175\
human-like text, with applications in chatbots, content creation, and coding \ billion parameters, has shown unprecedented capabilities in generating human-like\
assistance.\\n\\n- **Vision Transformers (ViTs)**: Transformers are also making \ text, with applications in chatbots, content creation, and coding assistance.\\\
inroads in computer vision, challenging traditional Convolutional Neural Networks n\\n- **Vision Transformers (ViTs)**: Transformers are also making inroads in\
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1.1.2 Reinforcement Learning (RL)\\n- **AlphaFold**: Developed by DeepMind, \ ViTs have achieved state-of-the-art results on image recognition tasks.\\\
AlphaFold has revolutionized protein structure prediction, surpassing the capabilities n\\n#### 1.1.2 Reinforcement Learning (RL)\\n- **AlphaFold**: Developed by DeepMind,\
of traditional methods and providing new insights into biological processes.\\n\\n- \ AlphaFold has revolutionized protein structure prediction, surpassing the\
**Game AI**: Reinforcement learning algorithms continue to excel in complex \ capabilities of traditional methods and providing new insights into biological\
games. OpenAI's Dota 2 bot and DeepMind's AlphaZero have demonstrated superhuman \ processes.\\n\\n- **Game AI**: Reinforcement learning algorithms continue\
performance, showcasing the potential for RL in real-world applications.\\n\\n### \ to excel in complex games. OpenAI's Dota 2 bot and DeepMind's AlphaZero have\
1.2 Federated Learning\\nFederated learning allows models to be trained across \ demonstrated superhuman performance, showcasing the potential for RL in real-world\
multiple decentralized devices without sharing raw data, enhancing privacy and \ applications.\\n\\n### 1.2 Federated Learning\\nFederated learning allows\
security. This approach is particularly relevant in healthcare and finance, \ models to be trained across multiple decentralized devices without sharing\
where data sensitivity is paramount.\\n\\n## 2. Natural Language Processing \ raw data, enhancing privacy and security. This approach is particularly relevant\
(NLP)\\n\\n### 2.1 Language Models\\nLanguage models have made significant strides, \ in healthcare and finance, where data sensitivity is paramount.\\n\\n## 2.\
primarily due to the scaling of neural networks and availability of large datasets.\\n\\n- \ Natural Language Processing (NLP)\\n\\n### 2.1 Language Models\\nLanguage\
**GPT-3**: As mentioned earlier, GPT-3 has remarkable text generation capabilities. \ models have made significant strides, primarily due to the scaling of neural\
Its applications include automated customer support, coding assistance, and \ networks and availability of large datasets.\\n\\n- **GPT-3**: As mentioned\
content generation.\\n\\n- **T5 (Text-To-Text Transfer Transformer)**: Developed \ earlier, GPT-3 has remarkable text generation capabilities. Its applications\
by Google, T5 has unified various NLP tasks into a text-to-text framework, simplifying \ include automated customer support, coding assistance, and content generation.\\\
the training process and improving performance across multiple benchmarks.\\n\\n### n\\n- **T5 (Text-To-Text Transfer Transformer)**: Developed by Google, T5 has\
2.2 Sentiment Analysis and Understanding\\nAdvanced NLP techniques are now capable \ unified various NLP tasks into a text-to-text framework, simplifying the training\
of better understanding human emotions and intents. This has improved sentiment \ process and improving performance across multiple benchmarks.\\n\\n### 2.2\
analysis, enhancing applications in customer feedback systems, social media \ Sentiment Analysis and Understanding\\nAdvanced NLP techniques are now capable\
monitoring, and mental health support tools.\\n\\n## 3. Computer Vision\\n\\n### \ of better understanding human emotions and intents. This has improved sentiment\
3.1 Object Detection and Recognition\\nObject detection and recognition algorithms \ analysis, enhancing applications in customer feedback systems, social media\
have improved significantly, enabling real-time applications in areas like autonomous \ monitoring, and mental health support tools.\\n\\n## 3. Computer Vision\\\
driving, security surveillance, and medical imaging.\\n\\n- **YOLO (You Only n\\n### 3.1 Object Detection and Recognition\\nObject detection and recognition\
Look Once)**: YOLO models are known for their speed and accuracy in object detection \ algorithms have improved significantly, enabling real-time applications in\
tasks. YOLOv4 and YOLOv5 have further optimized the balance between performance \ areas like autonomous driving, security surveillance, and medical imaging.\\\
and computational efficiency.\\n\\n### 3.2 Image Generation and Enhancement\\nGenerative n\\n- **YOLO (You Only Look Once)**: YOLO models are known for their speed and\
models like GANs (Generative Adversarial Networks) have evolved to produce highly \ accuracy in object detection tasks. YOLOv4 and YOLOv5 have further optimized\
realistic images, with applications in art, entertainment, and data augmentation.\\n\\n- \ the balance between performance and computational efficiency.\\n\\n### 3.2\
**StyleGAN**: Developed by NVIDIA, StyleGAN can generate high-fidelity synthetic \ Image Generation and Enhancement\\nGenerative models like GANs (Generative\
images, contributing to advancements in virtual reality, 3D modeling, and content \ Adversarial Networks) have evolved to produce highly realistic images, with\
creation.\\n\\n## 4. AI Ethics and Fairness\\n\\n### 4.1 Bias Mitigation\\nAI \ applications in art, entertainment, and data augmentation.\\n\\n- **StyleGAN**:\
systems can inadvertently perpetuate biases present in training data. Efforts \ Developed by NVIDIA, StyleGAN can generate high-fidelity synthetic images,\
are being made to develop fairer AI models by incorporating bias detection and \ contributing to advancements in virtual reality, 3D modeling, and content\
mitigation techniques during the training process.\\n\\n### 4.2 Explainability\\nAs \ creation.\\n\\n## 4. AI Ethics and Fairness\\n\\n### 4.1 Bias Mitigation\\\
AI systems become more complex, understanding and interpreting their decisions nAI systems can inadvertently perpetuate biases present in training data. Efforts\
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to provide transparency, particularly in sensitive applications like healthcare \ and mitigation techniques during the training process.\\n\\n### 4.2 Explainability\\\
and finance.\\n\\n### 4.3 Regulations and Policies\\nGovernments and organizations nAs AI systems become more complex, understanding and interpreting their decisions\
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being crafted to ensure ethical AI development and deployment, focusing on data \ to provide transparency, particularly in sensitive applications like healthcare\
privacy, accountability, and fairness.\\n\\n## Conclusion\\nThe field of AI \ and finance.\\n\\n### 4.3 Regulations and Policies\\nGovernments and organizations\
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developments are opening up new possibilities across various sectors while simultaneously \ data privacy, accountability, and fairness.\\n\\n## Conclusion\\nThe field\
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development. This accelerated pace not only brings new drugs to market faster \ cutting down the time and cost associated with drug development. This accelerated\
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to trust AI applications, as it provides clear insights into the decision-making \ decisions. This is essential for healthcare professionals and patients to\
process, enabling better understanding and acceptance of AI-driven recommendations.\\n\\nBy \ trust AI applications, as it provides clear insights into the decision-making\
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Answer:\\n\\n## AI Advancements Report\\n\\n### 1. Introduction\\nArtificial \ a great answer\\nFinal Answer:\\n\\n## AI Advancements Report\\n\\n### 1.\
Intelligence (AI) has experienced rapid advancements over the past decade, influencing \ Introduction\\nArtificial Intelligence (AI) has experienced rapid advancements\
numerous sectors including healthcare, finance, robotics, and more. This report \ over the past decade, influencing numerous sectors including healthcare, finance,\
aims to provide a comprehensive overview of the recent advancements in AI, including \ robotics, and more. This report aims to provide a comprehensive overview of\
breakthroughs in machine learning (ML), natural language processing (NLP), computer \ the recent advancements in AI, including breakthroughs in machine learning\
vision, and AI agents.\\n\\n### 2. Breakthroughs in Machine Learning\\nMachine \ (ML), natural language processing (NLP), computer vision, and AI agents.\\\
learning, a subset of AI focused on the development of algorithms that enable n\\n### 2. Breakthroughs in Machine Learning\\nMachine learning, a subset of\
machines to learn from data, has seen significant advancements:\\n1. **Deep \ AI focused on the development of algorithms that enable machines to learn\
Learning Improvements**: Deep learning models, particularly neural networks, \ from data, has seen significant advancements:\\n1. **Deep Learning Improvements**:\
have evolved with innovations like transformers, which are particularly beneficial \ Deep learning models, particularly neural networks, have evolved with innovations\
for sequential data.\\n2. **Automated Machine Learning (AutoML)**: Tools and \ like transformers, which are particularly beneficial for sequential data.\\\
platforms that automate the ML model-building process have streamlined workflows n2. **Automated Machine Learning (AutoML)**: Tools and platforms that automate\
and made ML more accessible.\\n3. **Few-shot and Zero-shot Learning**: Techniques \ the ML model-building process have streamlined workflows and made ML more\
enabling models to generalize from very few or even no examples. This is useful \ accessible.\\n3. **Few-shot and Zero-shot Learning**: Techniques enabling\
for applications with limited data sets.\\n4. **Reinforcement Learning**: Enhanced \ models to generalize from very few or even no examples. This is useful for\
through algorithms that improve the learning efficiency and effectiveness, evidenced \ applications with limited data sets.\\n4. **Reinforcement Learning**: Enhanced\
in complex problem-solving tasks and games.\\n\\n### 3. Natural Language Processing \ through algorithms that improve the learning efficiency and effectiveness,\
(NLP)\\nNLP advancements have greatly improved how machines understand and generate \ evidenced in complex problem-solving tasks and games.\\n\\n### 3. Natural\
human language:\\n1. **GPT-3 and beyond**: OpenAI's Generative Pre-trained Transformer-3 \ Language Processing (NLP)\\nNLP advancements have greatly improved how machines\
(GPT-3) has set new standards in language generation, capable of producing human-like \ understand and generate human language:\\n1. **GPT-3 and beyond**: OpenAI's\
text.\\n2. **BERT and Transformer Models**: Bidirectional Encoder Representations \ Generative Pre-trained Transformer-3 (GPT-3) has set new standards in language\
from Transformers (BERT) models have improved understanding of context in search \ generation, capable of producing human-like text.\\n2. **BERT and Transformer\
queries and language comprehension tasks.\\n3. **Conversational AI**: Development \ Models**: Bidirectional Encoder Representations from Transformers (BERT) models\
of sophisticated chatbots and virtual assistants like Google's Meena and OpenAI's \ have improved understanding of context in search queries and language comprehension\
ChatGPT, which can engage in more natural and meaningful conversations.\\n4. \ tasks.\\n3. **Conversational AI**: Development of sophisticated chatbots and\
**Multilingual Models**: Enhanced multilingual models like mBERT that process \ virtual assistants like Google's Meena and OpenAI's ChatGPT, which can engage\
and understand multiple languages without relying on separate models for each \ in more natural and meaningful conversations.\\n4. **Multilingual Models**:\
language.\\n\\n### 4. Computer Vision\\nComputer vision has progressed significantly, \ Enhanced multilingual models like mBERT that process and understand multiple\
allowing machines to interpret and process visual information with remarkable \ languages without relying on separate models for each language.\\n\\n### 4.\
accuracy:\\n1. **Convolutional Neural Networks (CNNs)**: Enhanced architectures \ Computer Vision\\nComputer vision has progressed significantly, allowing machines\
such as EfficientNet and Vision Transformers (ViT) provide better image classification \ to interpret and process visual information with remarkable accuracy:\\n1.\
and recognition.\\n2. **Generative Adversarial Networks (GANs)**: Use of GANs \ **Convolutional Neural Networks (CNNs)**: Enhanced architectures such as EfficientNet\
for generating high-quality images, which has applications in art, design, and \ and Vision Transformers (ViT) provide better image classification and recognition.\\\
data augmentation.\\n3. **Object Detection and Segmentation**: Improvements n2. **Generative Adversarial Networks (GANs)**: Use of GANs for generating high-quality\
in object detection and segmentation algorithms such as YOLO and Mask R-CNN \ images, which has applications in art, design, and data augmentation.\\n3.\
have led to more accurate and faster image analysis.\\n\\n### 5. Advancements \ **Object Detection and Segmentation**: Improvements in object detection and\
in AI Agents\\nAI agents, which act autonomously to accomplish tasks, have seen \ segmentation algorithms such as YOLO and Mask R-CNN have led to more accurate\
numerous advancements:\\n1. **Autonomous Vehicles**: Increased reliability and \ and faster image analysis.\\n\\n### 5. Advancements in AI Agents\\nAI agents,\
safety of AI systems powering self-driving cars, with companies like Tesla and \ which act autonomously to accomplish tasks, have seen numerous advancements:\\\
Waymo leading the charge.\\n2. **Robotics**: Enhanced robotic systems that can n1. **Autonomous Vehicles**: Increased reliability and safety of AI systems\
perform complex tasks, such as Boston Dynamics' robots capable of performing \ powering self-driving cars, with companies like Tesla and Waymo leading the\
a variety of physical actions.\\n3. **Personal Assistants**: Development of \ charge.\\n2. **Robotics**: Enhanced robotic systems that can perform complex\
AI agents for personal use, such as Amazon\u2019s Alexa and Google Assistant, \ tasks, such as Boston Dynamics' robots capable of performing a variety of\
which are becoming increasingly intelligent and versatile.\\n\\n### 6. Ethical \ physical actions.\\n3. **Personal Assistants**: Development of AI agents for\
and Societal Considerations\\nAs AI technology advances, ethical and societal \ personal use, such as Amazon\u2019s Alexa and Google Assistant, which are\
considerations become paramount:\\n1. **Bias and Fairness**: Efforts to reduce \ becoming increasingly intelligent and versatile.\\n\\n### 6. Ethical and Societal\
bias in AI systems through better data collection, algorithms, and fairness \ Considerations\\nAs AI technology advances, ethical and societal considerations\
criteria.\\n2. **Transparency and Explainability**: Developing models and tools \ become paramount:\\n1. **Bias and Fairness**: Efforts to reduce bias in AI\
that provide transparency in AI decision-making processes.\\n3. **Regulations \ systems through better data collection, algorithms, and fairness criteria.\\\
and Governance**: Policies and frameworks are being developed worldwide to ensure n2. **Transparency and Explainability**: Developing models and tools that provide\
safe and responsible AI usage.\\n\\n### 7. Future Directions\\nThe future of \ transparency in AI decision-making processes.\\n3. **Regulations and Governance**:\
AI promises further innovations and applications:\\n1. **Integrative AI**: Combining \ Policies and frameworks are being developed worldwide to ensure safe and responsible\
various branches of AI to create more holistic and efficient systems.\\n2. **Edge \ AI usage.\\n\\n### 7. Future Directions\\nThe future of AI promises further\
Computing**: Processing AI tasks directly on devices (edge) rather than relying \ innovations and applications:\\n1. **Integrative AI**: Combining various branches\
solely on cloud computing, improving speed and privacy.\\n3. **Interdisciplinary \ of AI to create more holistic and efficient systems.\\n2. **Edge Computing**:\
AI**: AI applications spreading to new fields such as environmental science \ Processing AI tasks directly on devices (edge) rather than relying solely\
for climate change modeling and personalized education platforms.\\n\\n### 8. \ on cloud computing, improving speed and privacy.\\n3. **Interdisciplinary\
Conclusion\\nThe landscape of artificial intelligence is rapidly evolving, influencing \ AI**: AI applications spreading to new fields such as environmental science\
how we live, work, and interact with technology. Continuous research and development \ for climate change modeling and personalized education platforms.\\n\\n###\
in machine learning, natural language processing, computer vision, and AI ethics \ 8. Conclusion\\nThe landscape of artificial intelligence is rapidly evolving,\
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surface, particularly relating to privacy, algorithmic fairness, and the displacement \ numerous ethical considerations surface, particularly relating to privacy,\
of jobs. Addressing issues such as data security, bias in AI algorithms, and \ algorithmic fairness, and the displacement of jobs. Addressing issues such\
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for an economy increasingly shaped by AI technologies.\\n\\n## Key Point 3: \ reforms to adequately prepare the workforce for an economy increasingly shaped\
Future Directions and Developments\\nThe future of AI is poised for remarkable \ by AI technologies.\\n\\n## Key Point 3: Future Directions and Developments\\\
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16
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4
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