![\"Large](\"https:\/\/blogs.mathworks.com\/deep-learning\/files\/2024\/12\/llms_with_matlab_repo.png\")
\r\n<\/h6>\r\nFile Exchange Repository: Large Language Models (LLMs) with MATLAB<\/em>\r\n<\/h6>\r\n \r\n<\/h6>\r\nThe\u00a0Large Language (LLMs) with MATLAB repository<\/a> was created to provide you with the code you need to access and interact with LLMs using MATLAB. You can connect MATLAB to the OpenAI\u00ae Chat Completions API (which powers ChatGPT\u2122), Ollama\u2122 (for local LLMs), and Azure\u00ae OpenAI services.\r\n<\/h6>\r\nBy interfacing the OpenAI API from your MATLAB environment, you can use models (such as GPT-4 and GPT-4 Turbo) for a variety of NLP tasks, including building your own chatbot and sentiment analysis<\/a>. To learn more, read this blog post<\/a>.\r\n<\/h6>\r\nBy connecting MATLAB to a local Ollama server, you have access to popular local LLMs, such as llama3, mistral, and gemma. Local LLMs are great for NLP tasks, such as retrieval-augmented generation (RAG) that can enhance the LLM accuracy by using your own data. To learn more, read this blog post<\/a>.\r\n<\/h6>\r\n \r\n<\/h6>\r\n
Enhanced Integration with Python<\/strong><\/p>\r\n![\"Interoperation](\"https:\/\/blogs.mathworks.com\/deep-learning\/files\/2024\/12\/interop.png\")
\r\n<\/h6>\r\nConversion of deep learning models between MATLAB, PyTorch, TensorFlow, and ONNX<\/em>\r\n<\/h6>\r\n \r\n<\/h6>\r\nTo facilitate cross-platform and cross-team collaboration when designing AI-enabled systems, Deep Learning Toolbox integrates with PyTorch\u00ae, TensorFlow\u2122, and other Python\u00ae-based frameworks. With every release this integration is enhanced and improved.\r\n<\/h6>\r\nWith MATLAB R2024b, you can now import deep learning models from PyTorch 2.0 by using the importNetworkFromPyTorch function. To learn more about interoperation, see\u00a0Convert Deep Learning Models between PyTorch, TensorFlow, and MATLAB<\/a>.\r\n<\/h6>\r\nAnother exciting enhancement is that you can now simulate and test PyTorch, TensorFlow, ONNX, and custom Python models within systems by using the Simulink co-execution blocks<\/a>. This allows you to quickly iterate on your design, assess model behavior, and test system performance.\r\n<\/h6>\r\n \r\n<\/h6>\r\n
Verification and Validation for AI<\/strong><\/p>\r\n![\"W-shaped](\"https:\/\/blogs.mathworks.com\/deep-learning\/files\/2024\/12\/w-shaped-dev.png\")
\r\n<\/h6>\r\nW-shaped development process. Credit: EASA, Daedalean<\/em>\r\n<\/h6>\r\n \r\n<\/h6>\r\nAI-enabled engineered systems are being increasingly adopted in safety-critical industries like aerospace, automotive, and manufacturing, where ensuring reliability and safety is vital. In 2024, Lucas Garc\u00eda completed a blog post series<\/a> on \u201cVerification and Validation for AI\u201d. This series delves into the meticulous process of ensuring AI models are both reliable and robust, especially in the context of safety-critical applications.\r\n<\/h6>\r\nIn 2024, important tools were introduced in MATLAB to help you build confidence in AI. Two of these are a function for explainability for object detection<\/a> and a repository for performing constrained deep learning<\/a>.\r\n<\/h6>\r\n \r\n<\/h6>\r\n
The Year Ahead<\/strong><\/p>\r\nHappy New Year! Let\u2019s look forward to a year with more exciting AI tools and AI system integration, bridging the gap between innovation and real-world applications.\r\n<\/h6>","protected":false},"excerpt":{"rendered":"<\/div>\r\nToday is the last day of 2024. Most of you are busy with non-AI, non-programming, non-thinking-about-work preparations for the new year. Nevertheless, I wanted to wrap up this year by looking back... read more >><\/a><\/p>","protected":false},"author":194,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[63,9,66,12,39],"tags":[],"_links":{"self":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715"}],"collection":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/users\/194"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/comments?post=16715"}],"version-history":[{"count":11,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715\/revisions"}],"predecessor-version":[{"id":16760,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715\/revisions\/16760"}],"wp:attachment":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/media?parent=16715"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/categories?post=16715"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/tags?post=16715"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
<\/h6>\r\nThe\u00a0Large Language (LLMs) with MATLAB repository<\/a> was created to provide you with the code you need to access and interact with LLMs using MATLAB. You can connect MATLAB to the OpenAI\u00ae Chat Completions API (which powers ChatGPT\u2122), Ollama\u2122 (for local LLMs), and Azure\u00ae OpenAI services.\r\n<\/h6>\r\nBy interfacing the OpenAI API from your MATLAB environment, you can use models (such as GPT-4 and GPT-4 Turbo) for a variety of NLP tasks, including building your own chatbot and sentiment analysis<\/a>. To learn more, read this blog post<\/a>.\r\n<\/h6>\r\nBy connecting MATLAB to a local Ollama server, you have access to popular local LLMs, such as llama3, mistral, and gemma. Local LLMs are great for NLP tasks, such as retrieval-augmented generation (RAG) that can enhance the LLM accuracy by using your own data. To learn more, read this blog post<\/a>.\r\n<\/h6>\r\n \r\n<\/h6>\r\n
Enhanced Integration with Python<\/strong><\/p>\r\n\r\n<\/h6>\r\nConversion of deep learning models between MATLAB, PyTorch, TensorFlow, and ONNX<\/em>\r\n<\/h6>\r\n \r\n<\/h6>\r\nTo facilitate cross-platform and cross-team collaboration when designing AI-enabled systems, Deep Learning Toolbox integrates with PyTorch\u00ae, TensorFlow\u2122, and other Python\u00ae-based frameworks. With every release this integration is enhanced and improved.\r\n<\/h6>\r\nWith MATLAB R2024b, you can now import deep learning models from PyTorch 2.0 by using the importNetworkFromPyTorch function. To learn more about interoperation, see\u00a0Convert Deep Learning Models between PyTorch, TensorFlow, and MATLAB<\/a>.\r\n<\/h6>\r\nAnother exciting enhancement is that you can now simulate and test PyTorch, TensorFlow, ONNX, and custom Python models within systems by using the Simulink co-execution blocks<\/a>. This allows you to quickly iterate on your design, assess model behavior, and test system performance.\r\n<\/h6>\r\n \r\n<\/h6>\r\n
Verification and Validation for AI<\/strong><\/p>\r\n\r\n<\/h6>\r\nW-shaped development process. Credit: EASA, Daedalean<\/em>\r\n<\/h6>\r\n \r\n<\/h6>\r\nAI-enabled engineered systems are being increasingly adopted in safety-critical industries like aerospace, automotive, and manufacturing, where ensuring reliability and safety is vital. In 2024, Lucas Garc\u00eda completed a blog post series<\/a> on \u201cVerification and Validation for AI\u201d. This series delves into the meticulous process of ensuring AI models are both reliable and robust, especially in the context of safety-critical applications.\r\n<\/h6>\r\nIn 2024, important tools were introduced in MATLAB to help you build confidence in AI. Two of these are a function for explainability for object detection<\/a> and a repository for performing constrained deep learning<\/a>.\r\n<\/h6>\r\n \r\n<\/h6>\r\n
The Year Ahead<\/strong><\/p>\r\nHappy New Year! Let\u2019s look forward to a year with more exciting AI tools and AI system integration, bridging the gap between innovation and real-world applications.\r\n<\/h6>","protected":false},"excerpt":{"rendered":"<\/div>\r\nToday is the last day of 2024. Most of you are busy with non-AI, non-programming, non-thinking-about-work preparations for the new year. Nevertheless, I wanted to wrap up this year by looking back... read more >><\/a><\/p>","protected":false},"author":194,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[63,9,66,12,39],"tags":[],"_links":{"self":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715"}],"collection":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/users\/194"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/comments?post=16715"}],"version-history":[{"count":11,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715\/revisions"}],"predecessor-version":[{"id":16760,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715\/revisions\/16760"}],"wp:attachment":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/media?parent=16715"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/categories?post=16715"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/tags?post=16715"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
<\/h6>\r\nBy connecting MATLAB to a local Ollama server, you have access to popular local LLMs, such as llama3, mistral, and gemma. Local LLMs are great for NLP tasks, such as retrieval-augmented generation (RAG) that can enhance the LLM accuracy by using your own data. To learn more, read this blog post<\/a>.\r\n<\/h6>\r\n \r\n<\/h6>\r\n
Enhanced Integration with Python<\/strong><\/p>\r\n\r\n<\/h6>\r\nConversion of deep learning models between MATLAB, PyTorch, TensorFlow, and ONNX<\/em>\r\n<\/h6>\r\n \r\n<\/h6>\r\nTo facilitate cross-platform and cross-team collaboration when designing AI-enabled systems, Deep Learning Toolbox integrates with PyTorch\u00ae, TensorFlow\u2122, and other Python\u00ae-based frameworks. With every release this integration is enhanced and improved.\r\n<\/h6>\r\nWith MATLAB R2024b, you can now import deep learning models from PyTorch 2.0 by using the importNetworkFromPyTorch function. To learn more about interoperation, see\u00a0Convert Deep Learning Models between PyTorch, TensorFlow, and MATLAB<\/a>.\r\n<\/h6>\r\nAnother exciting enhancement is that you can now simulate and test PyTorch, TensorFlow, ONNX, and custom Python models within systems by using the Simulink co-execution blocks<\/a>. This allows you to quickly iterate on your design, assess model behavior, and test system performance.\r\n<\/h6>\r\n \r\n<\/h6>\r\n
Verification and Validation for AI<\/strong><\/p>\r\n\r\n<\/h6>\r\nW-shaped development process. Credit: EASA, Daedalean<\/em>\r\n<\/h6>\r\n \r\n<\/h6>\r\nAI-enabled engineered systems are being increasingly adopted in safety-critical industries like aerospace, automotive, and manufacturing, where ensuring reliability and safety is vital. In 2024, Lucas Garc\u00eda completed a blog post series<\/a> on \u201cVerification and Validation for AI\u201d. This series delves into the meticulous process of ensuring AI models are both reliable and robust, especially in the context of safety-critical applications.\r\n<\/h6>\r\nIn 2024, important tools were introduced in MATLAB to help you build confidence in AI. Two of these are a function for explainability for object detection<\/a> and a repository for performing constrained deep learning<\/a>.\r\n<\/h6>\r\n \r\n<\/h6>\r\n
The Year Ahead<\/strong><\/p>\r\nHappy New Year! Let\u2019s look forward to a year with more exciting AI tools and AI system integration, bridging the gap between innovation and real-world applications.\r\n<\/h6>","protected":false},"excerpt":{"rendered":"<\/div>\r\nToday is the last day of 2024. Most of you are busy with non-AI, non-programming, non-thinking-about-work preparations for the new year. Nevertheless, I wanted to wrap up this year by looking back... read more >><\/a><\/p>","protected":false},"author":194,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[63,9,66,12,39],"tags":[],"_links":{"self":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715"}],"collection":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/users\/194"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/comments?post=16715"}],"version-history":[{"count":11,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715\/revisions"}],"predecessor-version":[{"id":16760,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715\/revisions\/16760"}],"wp:attachment":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/media?parent=16715"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/categories?post=16715"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/tags?post=16715"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
<\/h6>\r\n
\r\n<\/h6>\r\nConversion of deep learning models between MATLAB, PyTorch, TensorFlow, and ONNX<\/em>\r\n<\/h6>\r\n \r\n<\/h6>\r\nTo facilitate cross-platform and cross-team collaboration when designing AI-enabled systems, Deep Learning Toolbox integrates with PyTorch\u00ae, TensorFlow\u2122, and other Python\u00ae-based frameworks. With every release this integration is enhanced and improved.\r\n<\/h6>\r\nWith MATLAB R2024b, you can now import deep learning models from PyTorch 2.0 by using the importNetworkFromPyTorch function. To learn more about interoperation, see\u00a0Convert Deep Learning Models between PyTorch, TensorFlow, and MATLAB<\/a>.\r\n<\/h6>\r\nAnother exciting enhancement is that you can now simulate and test PyTorch, TensorFlow, ONNX, and custom Python models within systems by using the Simulink co-execution blocks<\/a>. This allows you to quickly iterate on your design, assess model behavior, and test system performance.\r\n<\/h6>\r\n \r\n<\/h6>\r\n
Verification and Validation for AI<\/strong><\/p>\r\n\r\n<\/h6>\r\nW-shaped development process. Credit: EASA, Daedalean<\/em>\r\n<\/h6>\r\n \r\n<\/h6>\r\nAI-enabled engineered systems are being increasingly adopted in safety-critical industries like aerospace, automotive, and manufacturing, where ensuring reliability and safety is vital. In 2024, Lucas Garc\u00eda completed a blog post series<\/a> on \u201cVerification and Validation for AI\u201d. This series delves into the meticulous process of ensuring AI models are both reliable and robust, especially in the context of safety-critical applications.\r\n<\/h6>\r\nIn 2024, important tools were introduced in MATLAB to help you build confidence in AI. Two of these are a function for explainability for object detection<\/a> and a repository for performing constrained deep learning<\/a>.\r\n<\/h6>\r\n \r\n<\/h6>\r\n
The Year Ahead<\/strong><\/p>\r\nHappy New Year! Let\u2019s look forward to a year with more exciting AI tools and AI system integration, bridging the gap between innovation and real-world applications.\r\n<\/h6>","protected":false},"excerpt":{"rendered":"<\/div>\r\nToday is the last day of 2024. Most of you are busy with non-AI, non-programming, non-thinking-about-work preparations for the new year. Nevertheless, I wanted to wrap up this year by looking back... read more >><\/a><\/p>","protected":false},"author":194,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[63,9,66,12,39],"tags":[],"_links":{"self":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715"}],"collection":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/users\/194"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/comments?post=16715"}],"version-history":[{"count":11,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715\/revisions"}],"predecessor-version":[{"id":16760,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715\/revisions\/16760"}],"wp:attachment":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/media?parent=16715"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/categories?post=16715"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/tags?post=16715"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
<\/h6>\r\nTo facilitate cross-platform and cross-team collaboration when designing AI-enabled systems, Deep Learning Toolbox integrates with PyTorch\u00ae, TensorFlow\u2122, and other Python\u00ae-based frameworks. With every release this integration is enhanced and improved.\r\n<\/h6>\r\nWith MATLAB R2024b, you can now import deep learning models from PyTorch 2.0 by using the importNetworkFromPyTorch function. To learn more about interoperation, see\u00a0Convert Deep Learning Models between PyTorch, TensorFlow, and MATLAB<\/a>.\r\n<\/h6>\r\nAnother exciting enhancement is that you can now simulate and test PyTorch, TensorFlow, ONNX, and custom Python models within systems by using the Simulink co-execution blocks<\/a>. This allows you to quickly iterate on your design, assess model behavior, and test system performance.\r\n<\/h6>\r\n \r\n<\/h6>\r\n
Verification and Validation for AI<\/strong><\/p>\r\n\r\n<\/h6>\r\nW-shaped development process. Credit: EASA, Daedalean<\/em>\r\n<\/h6>\r\n \r\n<\/h6>\r\nAI-enabled engineered systems are being increasingly adopted in safety-critical industries like aerospace, automotive, and manufacturing, where ensuring reliability and safety is vital. In 2024, Lucas Garc\u00eda completed a blog post series<\/a> on \u201cVerification and Validation for AI\u201d. This series delves into the meticulous process of ensuring AI models are both reliable and robust, especially in the context of safety-critical applications.\r\n<\/h6>\r\nIn 2024, important tools were introduced in MATLAB to help you build confidence in AI. Two of these are a function for explainability for object detection<\/a> and a repository for performing constrained deep learning<\/a>.\r\n<\/h6>\r\n \r\n<\/h6>\r\n
The Year Ahead<\/strong><\/p>\r\nHappy New Year! Let\u2019s look forward to a year with more exciting AI tools and AI system integration, bridging the gap between innovation and real-world applications.\r\n<\/h6>","protected":false},"excerpt":{"rendered":"<\/div>\r\nToday is the last day of 2024. Most of you are busy with non-AI, non-programming, non-thinking-about-work preparations for the new year. Nevertheless, I wanted to wrap up this year by looking back... read more >><\/a><\/p>","protected":false},"author":194,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[63,9,66,12,39],"tags":[],"_links":{"self":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715"}],"collection":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/users\/194"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/comments?post=16715"}],"version-history":[{"count":11,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715\/revisions"}],"predecessor-version":[{"id":16760,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715\/revisions\/16760"}],"wp:attachment":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/media?parent=16715"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/categories?post=16715"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/tags?post=16715"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
<\/h6>\r\nAnother exciting enhancement is that you can now simulate and test PyTorch, TensorFlow, ONNX, and custom Python models within systems by using the Simulink co-execution blocks<\/a>. This allows you to quickly iterate on your design, assess model behavior, and test system performance.\r\n<\/h6>\r\n \r\n<\/h6>\r\n
Verification and Validation for AI<\/strong><\/p>\r\n\r\n<\/h6>\r\nW-shaped development process. Credit: EASA, Daedalean<\/em>\r\n<\/h6>\r\n \r\n<\/h6>\r\nAI-enabled engineered systems are being increasingly adopted in safety-critical industries like aerospace, automotive, and manufacturing, where ensuring reliability and safety is vital. In 2024, Lucas Garc\u00eda completed a blog post series<\/a> on \u201cVerification and Validation for AI\u201d. This series delves into the meticulous process of ensuring AI models are both reliable and robust, especially in the context of safety-critical applications.\r\n<\/h6>\r\nIn 2024, important tools were introduced in MATLAB to help you build confidence in AI. Two of these are a function for explainability for object detection<\/a> and a repository for performing constrained deep learning<\/a>.\r\n<\/h6>\r\n \r\n<\/h6>\r\n
The Year Ahead<\/strong><\/p>\r\nHappy New Year! Let\u2019s look forward to a year with more exciting AI tools and AI system integration, bridging the gap between innovation and real-world applications.\r\n<\/h6>","protected":false},"excerpt":{"rendered":"<\/div>\r\nToday is the last day of 2024. Most of you are busy with non-AI, non-programming, non-thinking-about-work preparations for the new year. Nevertheless, I wanted to wrap up this year by looking back... read more >><\/a><\/p>","protected":false},"author":194,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[63,9,66,12,39],"tags":[],"_links":{"self":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715"}],"collection":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/users\/194"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/comments?post=16715"}],"version-history":[{"count":11,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715\/revisions"}],"predecessor-version":[{"id":16760,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715\/revisions\/16760"}],"wp:attachment":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/media?parent=16715"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/categories?post=16715"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/tags?post=16715"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
<\/h6>\r\n
\r\n<\/h6>\r\nW-shaped development process. Credit: EASA, Daedalean<\/em>\r\n<\/h6>\r\n \r\n<\/h6>\r\nAI-enabled engineered systems are being increasingly adopted in safety-critical industries like aerospace, automotive, and manufacturing, where ensuring reliability and safety is vital. In 2024, Lucas Garc\u00eda completed a blog post series<\/a> on \u201cVerification and Validation for AI\u201d. This series delves into the meticulous process of ensuring AI models are both reliable and robust, especially in the context of safety-critical applications.\r\n<\/h6>\r\nIn 2024, important tools were introduced in MATLAB to help you build confidence in AI. Two of these are a function for explainability for object detection<\/a> and a repository for performing constrained deep learning<\/a>.\r\n<\/h6>\r\n \r\n<\/h6>\r\n
The Year Ahead<\/strong><\/p>\r\nHappy New Year! Let\u2019s look forward to a year with more exciting AI tools and AI system integration, bridging the gap between innovation and real-world applications.\r\n<\/h6>","protected":false},"excerpt":{"rendered":"<\/div>\r\nToday is the last day of 2024. Most of you are busy with non-AI, non-programming, non-thinking-about-work preparations for the new year. Nevertheless, I wanted to wrap up this year by looking back... read more >><\/a><\/p>","protected":false},"author":194,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[63,9,66,12,39],"tags":[],"_links":{"self":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715"}],"collection":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/users\/194"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/comments?post=16715"}],"version-history":[{"count":11,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715\/revisions"}],"predecessor-version":[{"id":16760,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715\/revisions\/16760"}],"wp:attachment":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/media?parent=16715"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/categories?post=16715"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/tags?post=16715"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
<\/h6>\r\nAI-enabled engineered systems are being increasingly adopted in safety-critical industries like aerospace, automotive, and manufacturing, where ensuring reliability and safety is vital. In 2024, Lucas Garc\u00eda completed a blog post series<\/a> on \u201cVerification and Validation for AI\u201d. This series delves into the meticulous process of ensuring AI models are both reliable and robust, especially in the context of safety-critical applications.\r\n<\/h6>\r\nIn 2024, important tools were introduced in MATLAB to help you build confidence in AI. Two of these are a function for explainability for object detection<\/a> and a repository for performing constrained deep learning<\/a>.\r\n<\/h6>\r\n \r\n<\/h6>\r\n
The Year Ahead<\/strong><\/p>\r\nHappy New Year! Let\u2019s look forward to a year with more exciting AI tools and AI system integration, bridging the gap between innovation and real-world applications.\r\n<\/h6>","protected":false},"excerpt":{"rendered":"<\/div>\r\nToday is the last day of 2024. Most of you are busy with non-AI, non-programming, non-thinking-about-work preparations for the new year. Nevertheless, I wanted to wrap up this year by looking back... read more >><\/a><\/p>","protected":false},"author":194,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[63,9,66,12,39],"tags":[],"_links":{"self":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715"}],"collection":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/users\/194"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/comments?post=16715"}],"version-history":[{"count":11,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715\/revisions"}],"predecessor-version":[{"id":16760,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715\/revisions\/16760"}],"wp:attachment":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/media?parent=16715"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/categories?post=16715"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/tags?post=16715"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
<\/h6>\r\n \r\n<\/h6>\r\n
The Year Ahead<\/strong><\/p>\r\nHappy New Year! Let\u2019s look forward to a year with more exciting AI tools and AI system integration, bridging the gap between innovation and real-world applications.\r\n \r\nToday is the last day of 2024. Most of you are busy with non-AI, non-programming, non-thinking-about-work preparations for the new year. Nevertheless, I wanted to wrap up this year by looking back... read more >><\/a><\/p>","protected":false},"author":194,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[63,9,66,12,39],"tags":[],"_links":{"self":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715"}],"collection":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/users\/194"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/comments?post=16715"}],"version-history":[{"count":11,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715\/revisions"}],"predecessor-version":[{"id":16760,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/posts\/16715\/revisions\/16760"}],"wp:attachment":[{"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/media?parent=16715"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/categories?post=16715"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/deep-learning\/wp-json\/wp\/v2\/tags?post=16715"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}<\/h6>","protected":false},"excerpt":{"rendered":"
<\/div>