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Teaching AI to Think Like Humans and Make Trade-Offs will Transform Healthcare with Fadi Micaelian Sparkdit TRANSCRIPT

 
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コンテンツは Karen Jagoda によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、Karen Jagoda またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal

Fadi Micaelian, CEO of Sparkdit, teaches machines to think like humans by understanding trade-offs. AI is not good at finding nuance and capturing trade-offs, which is where Sparkdit comes in. They have developed a technology that can teach computers to make trade-offs like humans and put humans at the center of the technology rather than replacing them. Incorporating AI into patient-centered decision formats can revolutionize healthcare, improve the way doctors interact with patients, and address issues like ageism, sexism, and racism.

Fadi explains, "We have been working in AI for years. And AI is magnificent when the data is in abundance. However, we felt that AI fell short in a series of areas, and the main one is to teach machines to think like humans. Because AI, at the end of the day, does not think like humans. AI thinks like neurons. But we humans think very differently. Our thinking is universal. Whether you are an Eskimo, or you are in Paris, or you are in Russia, or whether you’re in South Africa, we all think the same way. The way we think is by trade-offs, and AI does not understand trade-offs. So we set our mission to teach machines to think like humans, by trade-offs."

"To do that, we needed to create a platform, and that platform was based on trade-offs. It was based on the way we think. We’re trying to mimic the way humans think, and that’s doing trade-off. How do we think? When we have a decision to make, we take a set of criteria into account. Then, we apply to each criterion a certain logic - how we think about that criterion. Then we overlay that with a set of trade-offs that says really what is the relative importance of the criteria, which ones are important, and which ones are not."

#Sparkdit #AI #AppliedAI #AIinHealthcare #TrainingAI

Sparkdit.com

Listen to the podcast here

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1873 つのエピソード

Artwork
iconシェア
 
Manage episode 431296446 series 99915
コンテンツは Karen Jagoda によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、Karen Jagoda またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal

Fadi Micaelian, CEO of Sparkdit, teaches machines to think like humans by understanding trade-offs. AI is not good at finding nuance and capturing trade-offs, which is where Sparkdit comes in. They have developed a technology that can teach computers to make trade-offs like humans and put humans at the center of the technology rather than replacing them. Incorporating AI into patient-centered decision formats can revolutionize healthcare, improve the way doctors interact with patients, and address issues like ageism, sexism, and racism.

Fadi explains, "We have been working in AI for years. And AI is magnificent when the data is in abundance. However, we felt that AI fell short in a series of areas, and the main one is to teach machines to think like humans. Because AI, at the end of the day, does not think like humans. AI thinks like neurons. But we humans think very differently. Our thinking is universal. Whether you are an Eskimo, or you are in Paris, or you are in Russia, or whether you’re in South Africa, we all think the same way. The way we think is by trade-offs, and AI does not understand trade-offs. So we set our mission to teach machines to think like humans, by trade-offs."

"To do that, we needed to create a platform, and that platform was based on trade-offs. It was based on the way we think. We’re trying to mimic the way humans think, and that’s doing trade-off. How do we think? When we have a decision to make, we take a set of criteria into account. Then, we apply to each criterion a certain logic - how we think about that criterion. Then we overlay that with a set of trade-offs that says really what is the relative importance of the criteria, which ones are important, and which ones are not."

#Sparkdit #AI #AppliedAI #AIinHealthcare #TrainingAI

Sparkdit.com

Listen to the podcast here

  continue reading

1873 つのエピソード

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