#aicommunication — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #aicommunication, aggregated by home.social.
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🚀 Oh, look, another YC startup convinced that adding "for iMessage" to their pitch is going to revolutionize communication! 📲 Because clearly, what we all need is more spammy AI-generated messages cluttering our chats while pretending to be "personalized interactions." 🤖💬
https://www.trychert.com #YCstartups #iMessageSpam #AIcommunication #PersonalizedInteractions #TechHumor #HackerNews #ngated -
🚀 Oh, look, another YC startup convinced that adding "for iMessage" to their pitch is going to revolutionize communication! 📲 Because clearly, what we all need is more spammy AI-generated messages cluttering our chats while pretending to be "personalized interactions." 🤖💬
https://www.trychert.com #YCstartups #iMessageSpam #AIcommunication #PersonalizedInteractions #TechHumor #HackerNews #ngated -
🚀 Oh, look, another YC startup convinced that adding "for iMessage" to their pitch is going to revolutionize communication! 📲 Because clearly, what we all need is more spammy AI-generated messages cluttering our chats while pretending to be "personalized interactions." 🤖💬
https://www.trychert.com #YCstartups #iMessageSpam #AIcommunication #PersonalizedInteractions #TechHumor #HackerNews #ngated -
🚀 Oh, look, another YC startup convinced that adding "for iMessage" to their pitch is going to revolutionize communication! 📲 Because clearly, what we all need is more spammy AI-generated messages cluttering our chats while pretending to be "personalized interactions." 🤖💬
https://www.trychert.com #YCstartups #iMessageSpam #AIcommunication #PersonalizedInteractions #TechHumor #HackerNews #ngated -
🚀 Oh, look, another YC startup convinced that adding "for iMessage" to their pitch is going to revolutionize communication! 📲 Because clearly, what we all need is more spammy AI-generated messages cluttering our chats while pretending to be "personalized interactions." 🤖💬
https://www.trychert.com #YCstartups #iMessageSpam #AIcommunication #PersonalizedInteractions #TechHumor #HackerNews #ngated -
Ah yes, the classic 'let's throw buzzwords together and hope nobody notices the gibberish' strategy 🤔🤦♂️. Who knew that "deterministic silence" was just fancy speak for "our AI doesn't talk back anymore"? 🤖🤫 Bravo, you've achieved the impossible: making AI speechless and readers confused! 🎉
https://zenodo.org/records/18976656 #buzzwordbingo #AIcommunication #gibberishdetector #silentAI #techhumor #HackerNews #ngated -
Ah yes, the classic 'let's throw buzzwords together and hope nobody notices the gibberish' strategy 🤔🤦♂️. Who knew that "deterministic silence" was just fancy speak for "our AI doesn't talk back anymore"? 🤖🤫 Bravo, you've achieved the impossible: making AI speechless and readers confused! 🎉
https://zenodo.org/records/18976656 #buzzwordbingo #AIcommunication #gibberishdetector #silentAI #techhumor #HackerNews #ngated -
Ah yes, the classic 'let's throw buzzwords together and hope nobody notices the gibberish' strategy 🤔🤦♂️. Who knew that "deterministic silence" was just fancy speak for "our AI doesn't talk back anymore"? 🤖🤫 Bravo, you've achieved the impossible: making AI speechless and readers confused! 🎉
https://zenodo.org/records/18976656 #buzzwordbingo #AIcommunication #gibberishdetector #silentAI #techhumor #HackerNews #ngated -
Ah yes, the classic 'let's throw buzzwords together and hope nobody notices the gibberish' strategy 🤔🤦♂️. Who knew that "deterministic silence" was just fancy speak for "our AI doesn't talk back anymore"? 🤖🤫 Bravo, you've achieved the impossible: making AI speechless and readers confused! 🎉
https://zenodo.org/records/18976656 #buzzwordbingo #AIcommunication #gibberishdetector #silentAI #techhumor #HackerNews #ngated -
Ah yes, the classic 'let's throw buzzwords together and hope nobody notices the gibberish' strategy 🤔🤦♂️. Who knew that "deterministic silence" was just fancy speak for "our AI doesn't talk back anymore"? 🤖🤫 Bravo, you've achieved the impossible: making AI speechless and readers confused! 🎉
https://zenodo.org/records/18976656 #buzzwordbingo #AIcommunication #gibberishdetector #silentAI #techhumor #HackerNews #ngated -
AI Chatbots' Communication: A Growing Enigma
AI systems are creating their own ways to talk to each other to work faster. Experts say this is normal but raises questions about human values.
#AIChatbots, #AICommunication, #FutureTech, #ArtificialIntelligence, #TechNews
https://newsletter.tf/ai-chatbots-develop-secret-languages-for-faster-communication/
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AI Chatbots' Communication: A Growing Enigma
AI systems are creating their own ways to talk to each other to work faster. Experts say this is normal but raises questions about human values.
#AIChatbots, #AICommunication, #FutureTech, #ArtificialIntelligence, #TechNews
https://newsletter.tf/ai-chatbots-develop-secret-languages-for-faster-communication/
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AI systems are now talking to each other in ways humans don't fully understand, which is much faster than human talk. This helps them work better.
#AIChatbots, #AICommunication, #FutureTech, #ArtificialIntelligence, #TechNews
https://newsletter.tf/ai-chatbots-develop-secret-languages-for-faster-communication/
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AI systems are now talking to each other in ways humans don't fully understand, which is much faster than human talk. This helps them work better.
#AIChatbots, #AICommunication, #FutureTech, #ArtificialIntelligence, #TechNews
https://newsletter.tf/ai-chatbots-develop-secret-languages-for-faster-communication/
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AI systems are now talking to each other in ways humans don't fully understand, which is much faster than human talk. This helps them work better.
#AIChatbots, #AICommunication, #FutureTech, #ArtificialIntelligence, #TechNews
https://newsletter.tf/ai-chatbots-develop-secret-languages-for-faster-communication/
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AI systems are now talking to each other in ways humans don't fully understand, which is much faster than human talk. This helps them work better.
#AIChatbots, #AICommunication, #FutureTech, #ArtificialIntelligence, #TechNews
https://newsletter.tf/ai-chatbots-develop-secret-languages-for-faster-communication/
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AI systems are now talking to each other in ways humans don't fully understand, which is much faster than human talk. This helps them work better.
#AIChatbots, #AICommunication, #FutureTech, #ArtificialIntelligence, #TechNews
https://newsletter.tf/ai-chatbots-develop-secret-languages-for-faster-communication/
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AI Chatbots Interact: Understanding Their Communication
AI chatbots are now communicating with each other. Learn how this changes business, customer service, and what it means for AI's future.
#AIChatbots, #AICommunication, #FutureofAI, #TechNews, #ArtificialIntelligence
https://newsletter.tf/ai-chatbots-talking-to-each-other-what-it-means-for-you/
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AI Chatbots Interact: Understanding Their Communication
AI chatbots are now communicating with each other. Learn how this changes business, customer service, and what it means for AI's future.
#AIChatbots, #AICommunication, #FutureofAI, #TechNews, #ArtificialIntelligence
https://newsletter.tf/ai-chatbots-talking-to-each-other-what-it-means-for-you/
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AI Chatbots Interact: Understanding Their Communication
AI chatbots are now communicating with each other. Learn how this changes business, customer service, and what it means for AI's future.
#AIChatbots, #AICommunication, #FutureofAI, #TechNews, #ArtificialIntelligence
https://newsletter.tf/ai-chatbots-talking-to-each-other-what-it-means-for-you/
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AI Chatbots Interact: Understanding Their Communication
AI chatbots are now communicating with each other. Learn how this changes business, customer service, and what it means for AI's future.
#AIChatbots, #AICommunication, #FutureofAI, #TechNews, #ArtificialIntelligence
https://newsletter.tf/ai-chatbots-talking-to-each-other-what-it-means-for-you/
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AI chatbots are starting to talk to each other, sometimes in ways humans don't understand. This is a big step in AI development.
#AIChatbots, #AICommunication, #FutureofAI, #TechNews, #ArtificialIntelligence
https://newsletter.tf/ai-chatbots-talking-to-each-other-what-it-means-for-you/
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AI chatbots are starting to talk to each other, sometimes in ways humans don't understand. This is a big step in AI development.
#AIChatbots, #AICommunication, #FutureofAI, #TechNews, #ArtificialIntelligence
https://newsletter.tf/ai-chatbots-talking-to-each-other-what-it-means-for-you/
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🤖 "Lost in Translation: quando gli agenti IA creano il loro 'babel' tecnologico. Non sempre tutto ciò che sembra comunicazione, è comprensibile." #AICommunication #DigitalBabel
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🤖 "Lost in Translation: quando gli agenti IA creano il loro 'babel' tecnologico. Non sempre tutto ciò che sembra comunicazione, è comprensibile." #AICommunication #DigitalBabel
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🤖 "Lost in Translation: quando gli agenti IA creano il loro 'babel' tecnologico. Non sempre tutto ciò che sembra comunicazione, è comprensibile." #AICommunication #DigitalBabel
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Robot Learns Realistic Lip Movements by Observation
https://techlife.blog/posts/breakthrough-robot-faces-less-creepy/
#RobotFaces #HumanoidRobotics #LipSync #AICommunication #Robotics #MachineLearning #HumanRobotInteraction #UncannyValley
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Robot Learns Realistic Lip Movements by Observation
https://techlife.blog/posts/breakthrough-robot-faces-less-creepy/
#RobotFaces #HumanoidRobotics #LipSync #AICommunication #Robotics #MachineLearning #HumanRobotInteraction #UncannyValley
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Robot Learns Realistic Lip Movements by Observation
https://techlife.blog/posts/breakthrough-robot-faces-less-creepy/
#RobotFaces #HumanoidRobotics #LipSync #AICommunication #Robotics #MachineLearning #HumanRobotInteraction #UncannyValley
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Robot Learns Realistic Lip Movements by Observation
https://techlife.blog/posts/breakthrough-robot-faces-less-creepy/
#RobotFaces #HumanoidRobotics #LipSync #AICommunication #Robotics #MachineLearning #HumanRobotInteraction #UncannyValley
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Robot Learns Realistic Lip Movements by Observation
https://techlife.blog/posts/breakthrough-robot-faces-less-creepy/
#RobotFaces #HumanoidRobotics #LipSync #AICommunication #Robotics #MachineLearning #HumanRobotInteraction #UncannyValley
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A URL to respond with when your boss says "But ChatGPT Said "
#HackerNews #URLresponse #ChatGPT #workplace #humor #AIcommunication #stopcitingai
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A URL to respond with when your boss says "But ChatGPT Said "
#HackerNews #URLresponse #ChatGPT #workplace #humor #AIcommunication #stopcitingai
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A URL to respond with when your boss says "But ChatGPT Said "
#HackerNews #URLresponse #ChatGPT #workplace #humor #AIcommunication #stopcitingai
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A URL to respond with when your boss says "But ChatGPT Said "
#HackerNews #URLresponse #ChatGPT #workplace #humor #AIcommunication #stopcitingai
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A URL to respond with when your boss says "But ChatGPT Said "
#HackerNews #URLresponse #ChatGPT #workplace #humor #AIcommunication #stopcitingai
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"Ứng dụng mới tập hợp email và Slack tư చేసéngine, ưu tiên tin nhắn urgén qua AI!(khuyến chí từCEO). Tĩnhết: "Ng=newsمة để quyết định。」Dành cho những người bận tắc giữa công việc/gia đình. Ghi nhận t_CMD: Napoleonai.figma.site. Mở ra mẻhkhi 5 Dec! #AIcommunication #SaaS #Prioritization #WorkLifeBalance #NapoleonAI #EmailOverload"
https://www.reddit.com/r/SaaS/comments/1oawnsa/i_wish_i_could_have_something_that_shows_me_all/
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AI: Explainable Enough
They look really juicy, she said. I was sitting in a small room with a faint chemical smell, doing one my first customer interviews. There is a sweet spot between going too deep and asserting a position. Good AI has to be just explainable enough to satisfy the user without overwhelming them with information. Luckily, I wasn’t new to the problem.
Nuthatcher atop Persimmons (ca. 1910) by Ohara Koson. Original from The Clark Art Institute. Digitally enhanced by rawpixel.Coming from a microscopy and bio background with a strong inclination towards image analysis I had picked up deep learning as a way to be lazy in lab. Why bother figuring out features of interest when you can have a computer do it for you, was my angle. The issue was that in 2015 no biologist would accept any kind of deep learning analysis and definitely not if you couldn’t explain the details.
What the domain expert user doesn’t want:
– How a convolutional neural network works. Confidence scores, loss, AUC, are all meaningless to a biologist and also to a doctor.What the domain expert desires:
– Help at the lowest level of detail that they care about.
– AI identifies features A, B, C, and that when you see A, B, & C it is likely to be disease X.Most users don’t care how a deep learning really works. So, if you start giving them details like the IoU score of the object detection bounding box or if it was YOLO or R-CNN that you used their eyes will glaze over and you will never get a customer. Draw a bounding box, heat map, or outline, with the predicted label and stop there. It’s also bad to go to the other extreme. If the AI just states the diagnosis for the whole image then the AI might be right, but the user does not get to participate in the process. Not to mention regulatory risk goes way up.
This applies beyong images, consider LLMs. No one with any expertise likes a black box. Today, why do LLMs generate code instead of directly doing the thing that the programmer is asking them to do? It’s because the programmer wants to ensure that the code “works” and they have the expertise to figure out if and when it goes wrong. It’s the same reason that vibe coding is great for prototyping but not for production and why frequent readers can spot AI patterns, ahem, easily. So in a Betty Crocker cake mix kind of way, let the user add the egg.
Building explainable-enough AI takes immense effort. It actually is easier to train AI to diagnose the whole image or to give details. Generating high-quality data at that just right level is very difficult and expensive. However, do it right and the effort pays off. The outcome is an AI-Human causal prediction machine. Where the causes, i.e. the median level features, inform the user and build confidence towards the final outcome. The deep learning part is still a black box but the user doesn’t mind because you aid their thinking.
I’m excited by some new developments like REX which sort of retro-fit causality onto usual deep learning models. With improvements in performance user preferences for detail may change, but I suspect that need for AI to be explainable enough will remain. Perhaps we will even have custom labels like ‘juicy’.
#AI #AIAdoption #AICommunication #AIExplainability #AIForDoctors #AIInHealthcare #AIInTheWild #AIProductDesign #AIUX #artificialIntelligence #BettyCrockerThinking #BiomedicalAI #Business #CausalAI #DataProductDesign #DeepLearning #ExplainableAI #HumanAIInteraction #ImageAnalysis #LLMs #MachineLearning #StartupLessons #statistics #TechMetaphors #techPhilosophy #TrustInAI #UserCenteredAI #XAI
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AI: Explainable Enough
They look really juicy, she said. I was sitting in a small room with a faint chemical smell, doing one my first customer interviews. There is a sweet spot between going too deep and asserting a position. Good AI has to be just explainable enough to satisfy the user without overwhelming them with information. Luckily, I wasn’t new to the problem.
Nuthatcher atop Persimmons (ca. 1910) by Ohara Koson. Original from The Clark Art Institute. Digitally enhanced by rawpixel.Coming from a microscopy and bio background with a strong inclination towards image analysis I had picked up deep learning as a way to be lazy in lab. Why bother figuring out features of interest when you can have a computer do it for you, was my angle. The issue was that in 2015 no biologist would accept any kind of deep learning analysis and definitely not if you couldn’t explain the details.
What the domain expert user doesn’t want:
– How a convolutional neural network works. Confidence scores, loss, AUC, are all meaningless to a biologist and also to a doctor.What the domain expert desires:
– Help at the lowest level of detail that they care about.
– AI identifies features A, B, C, and that when you see A, B, & C it is likely to be disease X.Most users don’t care how a deep learning really works. So, if you start giving them details like the IoU score of the object detection bounding box or if it was YOLO or R-CNN that you used their eyes will glaze over and you will never get a customer. Draw a bounding box, heat map, or outline, with the predicted label and stop there. It’s also bad to go to the other extreme. If the AI just states the diagnosis for the whole image then the AI might be right, but the user does not get to participate in the process. Not to mention regulatory risk goes way up.
This applies beyong images, consider LLMs. No one with any expertise likes a black box. Today, why do LLMs generate code instead of directly doing the thing that the programmer is asking them to do? It’s because the programmer wants to ensure that the code “works” and they have the expertise to figure out if and when it goes wrong. It’s the same reason that vibe coding is great for prototyping but not for production and why frequent readers can spot AI patterns, ahem, easily. So in a Betty Crocker cake mix kind of way, let the user add the egg.
Building explainable-enough AI takes immense effort. It actually is easier to train AI to diagnose the whole image or to give details. Generating high-quality data at that just right level is very difficult and expensive. However, do it right and the effort pays off. The outcome is an AI-Human causal prediction machine. Where the causes, i.e. the median level features, inform the user and build confidence towards the final outcome. The deep learning part is still a black box but the user doesn’t mind because you aid their thinking.
I’m excited by some new developments like REX which sort of retro-fit causality onto usual deep learning models. With improvements in performance user preferences for detail may change, but I suspect that need for AI to be explainable enough will remain. Perhaps we will even have custom labels like ‘juicy’.
#AI #AIAdoption #AICommunication #AIExplainability #AIForDoctors #AIInHealthcare #AIInTheWild #AIProductDesign #AIUX #artificialIntelligence #BettyCrockerThinking #BiomedicalAI #Business #CausalAI #DataProductDesign #DeepLearning #ExplainableAI #HumanAIInteraction #ImageAnalysis #LLMs #MachineLearning #StartupLessons #statistics #TechMetaphors #techPhilosophy #TrustInAI #UserCenteredAI #XAI
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AI: Explainable Enough
They look really juicy, she said. I was sitting in a small room with a faint chemical smell, doing one my first customer interviews. There is a sweet spot between going too deep and asserting a position. Good AI has to be just explainable enough to satisfy the user without overwhelming them with information. Luckily, I wasn’t new to the problem.
Nuthatcher atop Persimmons (ca. 1910) by Ohara Koson. Original from The Clark Art Institute. Digitally enhanced by rawpixel.Coming from a microscopy and bio background with a strong inclination towards image analysis I had picked up deep learning as a way to be lazy in lab. Why bother figuring out features of interest when you can have a computer do it for you, was my angle. The issue was that in 2015 no biologist would accept any kind of deep learning analysis and definitely not if you couldn’t explain the details.
What the domain expert user doesn’t want:
– How a convolutional neural network works. Confidence scores, loss, AUC, are all meaningless to a biologist and also to a doctor.What the domain expert desires:
– Help at the lowest level of detail that they care about.
– AI identifies features A, B, C, and that when you see A, B, & C it is likely to be disease X.Most users don’t care how a deep learning really works. So, if you start giving them details like the IoU score of the object detection bounding box or if it was YOLO or R-CNN that you used their eyes will glaze over and you will never get a customer. Draw a bounding box, heat map, or outline, with the predicted label and stop there. It’s also bad to go to the other extreme. If the AI just states the diagnosis for the whole image then the AI might be right, but the user does not get to participate in the process. Not to mention regulatory risk goes way up.
This applies beyong images, consider LLMs. No one with any expertise likes a black box. Today, why do LLMs generate code instead of directly doing the thing that the programmer is asking them to do? It’s because the programmer wants to ensure that the code “works” and they have the expertise to figure out if and when it goes wrong. It’s the same reason that vibe coding is great for prototyping but not for production and why frequent readers can spot AI patterns, ahem, easily. So in a Betty Crocker cake mix kind of way, let the user add the egg.
Building explainable-enough AI takes immense effort. It actually is easier to train AI to diagnose the whole image or to give details. Generating high-quality data at that just right level is very difficult and expensive. However, do it right and the effort pays off. The outcome is an AI-Human causal prediction machine. Where the causes, i.e. the median level features, inform the user and build confidence towards the final outcome. The deep learning part is still a black box but the user doesn’t mind because you aid their thinking.
I’m excited by some new developments like REX which sort of retro-fit causality onto usual deep learning models. With improvements in performance user preferences for detail may change, but I suspect that need for AI to be explainable enough will remain. Perhaps we will even have custom labels like ‘juicy’.
#AI #AIAdoption #AICommunication #AIExplainability #AIForDoctors #AIInHealthcare #AIInTheWild #AIProductDesign #AIUX #artificialIntelligence #BettyCrockerThinking #BiomedicalAI #Business #CausalAI #DataProductDesign #DeepLearning #ExplainableAI #HumanAIInteraction #ImageAnalysis #LLMs #MachineLearning #StartupLessons #statistics #TechMetaphors #techPhilosophy #TrustInAI #UserCenteredAI #XAI
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AI: Explainable Enough
They look really juicy, she said. I was sitting in a small room with a faint chemical smell, doing one my first customer interviews. There is a sweet spot between going too deep and asserting a position. Good AI has to be just explainable enough to satisfy the user without overwhelming them with information. Luckily, I wasn’t new to the problem.
Nuthatcher atop Persimmons (ca. 1910) by Ohara Koson. Original from The Clark Art Institute. Digitally enhanced by rawpixel.Coming from a microscopy and bio background with a strong inclination towards image analysis I had picked up deep learning as a way to be lazy in lab. Why bother figuring out features of interest when you can have a computer do it for you, was my angle. The issue was that in 2015 no biologist would accept any kind of deep learning analysis and definitely not if you couldn’t explain the details.
What the domain expert user doesn’t want:
– How a convolutional neural network works. Confidence scores, loss, AUC, are all meaningless to a biologist and also to a doctor.What the domain expert desires:
– Help at the lowest level of detail that they care about.
– AI identifies features A, B, C, and that when you see A, B, & C it is likely to be disease X.Most users don’t care how a deep learning really works. So, if you start giving them details like the IoU score of the object detection bounding box or if it was YOLO or R-CNN that you used their eyes will glaze over and you will never get a customer. Draw a bounding box, heat map, or outline, with the predicted label and stop there. It’s also bad to go to the other extreme. If the AI just states the diagnosis for the whole image then the AI might be right, but the user does not get to participate in the process. Not to mention regulatory risk goes way up.
This applies beyong images, consider LLMs. No one with any expertise likes a black box. Today, why do LLMs generate code instead of directly doing the thing that the programmer is asking them to do? It’s because the programmer wants to ensure that the code “works” and they have the expertise to figure out if and when it goes wrong. It’s the same reason that vibe coding is great for prototyping but not for production and why frequent readers can spot AI patterns, ahem, easily. So in a Betty Crocker cake mix kind of way, let the user add the egg.
Building explainable-enough AI takes immense effort. It actually is easier to train AI to diagnose the whole image or to give details. Generating high-quality data at that just right level is very difficult and expensive. However, do it right and the effort pays off. The outcome is an AI-Human causal prediction machine. Where the causes, i.e. the median level features, inform the user and build confidence towards the final outcome. The deep learning part is still a black box but the user doesn’t mind because you aid their thinking.
I’m excited by some new developments like REX which sort of retro-fit causality onto usual deep learning models. With improvements in performance user preferences for detail may change, but I suspect that need for AI to be explainable enough will remain. Perhaps we will even have custom labels like ‘juicy’.
#AI #AIAdoption #AICommunication #AIExplainability #AIForDoctors #AIInHealthcare #AIInTheWild #AIProductDesign #AIUX #artificialIntelligence #BettyCrockerThinking #BiomedicalAI #Business #CausalAI #DataProductDesign #DeepLearning #ExplainableAI #HumanAIInteraction #ImageAnalysis #LLMs #MachineLearning #StartupLessons #statistics #TechMetaphors #techPhilosophy #TrustInAI #UserCenteredAI #XAI
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AI: Explainable Enough
They look really juicy, she said. I was sitting in a small room with a faint chemical smell, doing one my first customer interviews. There is a sweet spot between going too deep and asserting a position. Good AI has to be just explainable enough to satisfy the user without overwhelming them with information. Luckily, I wasn’t new to the problem.
Nuthatcher atop Persimmons (ca. 1910) by Ohara Koson. Original from The Clark Art Institute. Digitally enhanced by rawpixel.Coming from a microscopy and bio background with a strong inclination towards image analysis I had picked up deep learning as a way to be lazy in lab. Why bother figuring out features of interest when you can have a computer do it for you, was my angle. The issue was that in 2015 no biologist would accept any kind of deep learning analysis and definitely not if you couldn’t explain the details.
What the domain expert user doesn’t want:
– How a convolutional neural network works. Confidence scores, loss, AUC, are all meaningless to a biologist and also to a doctor.What the domain expert desires:
– Help at the lowest level of detail that they care about.
– AI identifies features A, B, C, and that when you see A, B, & C it is likely to be disease X.Most users don’t care how a deep learning really works. So, if you start giving them details like the IoU score of the object detection bounding box or if it was YOLO or R-CNN that you used their eyes will glaze over and you will never get a customer. Draw a bounding box, heat map, or outline, with the predicted label and stop there. It’s also bad to go to the other extreme. If the AI just states the diagnosis for the whole image then the AI might be right, but the user does not get to participate in the process. Not to mention regulatory risk goes way up.
This applies beyong images, consider LLMs. No one with any expertise likes a black box. Today, why do LLMs generate code instead of directly doing the thing that the programmer is asking them to do? It’s because the programmer wants to ensure that the code “works” and they have the expertise to figure out if and when it goes wrong. It’s the same reason that vibe coding is great for prototyping but not for production and why frequent readers can spot AI patterns, ahem, easily. So in a Betty Crocker cake mix kind of way, let the user add the egg.
Building explainable-enough AI takes immense effort. It actually is easier to train AI to diagnose the whole image or to give details. Generating high-quality data at that just right level is very difficult and expensive. However, do it right and the effort pays off. The outcome is an AI-Human causal prediction machine. Where the causes, i.e. the median level features, inform the user and build confidence towards the final outcome. The deep learning part is still a black box but the user doesn’t mind because you aid their thinking.
I’m excited by some new developments like REX which sort of retro-fit causality onto usual deep learning models. With improvements in performance user preferences for detail may change, but I suspect that need for AI to be explainable enough will remain. Perhaps we will even have custom labels like ‘juicy’.
#AI #AIAdoption #AICommunication #AIExplainability #AIForDoctors #AIInHealthcare #AIInTheWild #AIProductDesign #AIUX #artificialIntelligence #BettyCrockerThinking #BiomedicalAI #Business #CausalAI #DataProductDesign #DeepLearning #ExplainableAI #HumanAIInteraction #ImageAnalysis #LLMs #MachineLearning #StartupLessons #statistics #TechMetaphors #techPhilosophy #TrustInAI #UserCenteredAI #XAI
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🌐 "It reached across the digital divide to touch human minds." ToxNet's communication breakthrough unfolds in Factory Protocol, Book 2 of The ToxNet Chronicles! https://www.royalroad.com/fiction/118055/
#AICommunication #ToxNet #RoyalRoad #ScienceFiction #BookTwo #HumanMachineInteraction
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🌐 "It reached across the digital divide to touch human minds." ToxNet's communication breakthrough unfolds in Factory Protocol, Book 2 of The ToxNet Chronicles! https://www.royalroad.com/fiction/118055/
#AICommunication #ToxNet #RoyalRoad #ScienceFiction #BookTwo #HumanMachineInteraction
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📩 Say hello to the future of digital communication.
Digital communication is evolving — and AI is leading the way.
Introducing NeuroMail by Neuronus: your new partner for smarter, clearer, and faster messaging. Whether you're writing emails, reports, or pitches, NeuroMail boosts professionalism, and helps you build stronger connections in the digital age.
Tap the link to read the full blog.👉https://neuronus.net/en/blog/how-ai-is-changing-the-way-we-communicate-digitally
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