The @mitsmr
“AI in Action” column series dives deep into successful use cases that can help other organizations accelerate their #AI progress https://
sloanreview.mit.edu/series/ai-in-a
ction/
… #MachineLearning #DataScience #serverless #100DaysofCode #womenwhocode @FmFrancoise @CatherineAdenle @Shi4Tech
MACHINE LEARNING
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MIT Sloan AI in Action: Real-World Use Cases
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Data Growth and Machine Learning: The Human Question Challenge
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The rise of machines! Data predicted to grow 5x by 2025 >>> yet, humans need to ask and action the right questions >>> chart by @Applied4Tech via @MikeQuindazzi >>> #DataScience #MachineLearning #Sensors #DataAnalytics #AI #IoT
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Real-time ML Feature Store: Merge Data Pipelines and Deploy AI Models
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@abacusai
's Real-time ML feature store lets you merge batch and streaming data pipelines, build features in SQL or Python, and create advanced nested and time travel features for your models. You can deploy your AI models at scale with enterprise-class security and governance. -
Chinchilla shows scaling laws matter more than model size
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Y es que este año DeepMind con Chinchilla demostró que no hacía falta escalar tanto a estos Enormes Modelos del Lenguaje. Que GPT-3 todavía tenía margen de mejora para ser entrenado más y con más datos sin necesidad de hacerlos MÁS GRANDES. Así que el tamaño no es tan importante.
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Future AI Models: 5x Larger than GPT-3 Predicted
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Rumores hablan de esto: un modelo 500 VECES más grande que GPT-3. …pues no lo creo Si así fuera tendría miedo de tremenda bestia Pero creo que lo que vamos a ver es algo más "modesto" quizás un modelo 5 VECES mayor que llegue al BILLÓN de parámetros
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GPT-4 scaling potential beyond GPT-3’s 175 billion parameters
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Si algo impresionó de GPT-3 fue cómo OpenAI logró escalar el tamaño de su modelo frente a sus predecesores. Respecto a GPT-2, la nueva versión aumentó su número de parámetros en >100 veces, hasta los 175 mil millones de parámetros. ¿Cuánto podría escalar GPT-4? ¿Qué pensáis?
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Gradient Descent: Essential Optimization Algorithm for Machine Learning Models
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Gradient descent – a crucial tool for anyone working in #machinelearning – is an optimization algorithm commonly used to train models and neural networks. IBM Master Inventor @martinrtp
, illustrates its utility: https://
ibm.co/3hH8zSm —-
#IBM #datascience #AI #ML -
Google’s Teachable Machine: Free Web-Based ML Model Creator
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Teachable Machine by @google is a free web-based tool that makes creating Machine Learning models easier! https://
futurepedia.io/tool/teachable
-machine
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Next-Generation Bionic Limbs: AI-Powered Innovation for Amputees
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I wish 2023 will bring us many more life-changing innovations…
— Pascal Bornet (@pascal_bornet) 26 décembre 2022
Such as this next generation of bionic limbs. It leverages 3D printing, sensors and machine learning, and provides superpowers to five million amputees globally!
Credit: Open Bionics#innovation #tech #ml #ai pic.twitter.com/AgbIvOPReGI wish 2023 will bring us many more life-changing innovations… Such as this next generation of bionic limbs. It leverages 3D printing, sensors and machine learning, and provides superpowers to five million amputees globally! Credit: Open Bionics
#innovation #tech #ml #ai -
Saving the Titanic Using Azure AutoML
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Saving the Titanic Using Azure AutoML!: This article was published as a part of the Data Science Blogathon. Source:
http://
pixabay.com Introduction State-of-the-art machine learning models and artificially intelligent machines are made of complex… https://
analyticsvidhya.com/blog/2022/11/s
aving-the-titanic-using-azure-automl/?utm_source=dlvr.it&utm_medium=twitter
…