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Why artificial intelligence stands at odds with the goals of cutting greenhouse emissions


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Yves Herman/ Reuters
This month, Google forced out a prominent AI ethics researcher after she voiced frustration with the company for making her withdraw a research paper. The paper pointed out the risks of language-processing artificial intelligence, the type used in Google Search and other text analysis products.
Among the risks is the large carbon footprint of developing this kind of AI technology. By some estimates, training an AI model generates as much carbon emissions as it takes to build and drive five cars over their lifetimes.
I am a researcher who studies and develops AI models, and I am all too familiar with the skyrocketing energy and financial costs of AI research. Why have AI models become so power-hungry, and how are they different from traditional data centre computation? ....

New York , United States , San Francisco , University Of Massachusetts Amherst , Google Search , Bidirectional Encoder Representations , Massachusetts Amherst , புதியது யார்க் , ஒன்றுபட்டது மாநிலங்களில் , சான் பிரான்சிஸ்கோ , பல்கலைக்கழகம் ஆஃப் மாசசூசெட்ஸ் மஹேர்ஸ்ட , கூகிள் தேடல் , மாசசூசெட்ஸ் மஹேர்ஸ்ட ,

Google India: Google aims to help researchers, startups better understand Indian languages


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On Thursday, Google has unveiled a machine learning tool for Indian languages to help researchers, students, and startups keen on building local language technologies with a common framework across several languages in the country.
Called Multilingual Representations for Indian Languages (MuRIL), the model aims to address concerns around Indian language understanding of computer systems, including all of its complexities like transliteration, spelling variations, mixed languages and other specific use cases that emerge in the Indian context. It also supports transliterated text such as writing Hindi using Roman script.
MuRIL was developed at Google s India research unit and currently supports 16 local languages and English, which the company says is the highest coverage for Indian languages among other publicly available machine learning models of its kind. ....

Sundar Pichai , Partha Talukdar , Research Scientist , Google Research India , Multilingual Representations , Indian Languages , Bidirectional Encoder Representations , Google Research , Homework Help , பார்த்தா தாலுக்தார் , ஆராய்ச்சி விஞ்ஞானி , கூகிள் ஆராய்ச்சி இந்தியா , இந்தியன் மொழிகள் , கூகிள் ஆராய்ச்சி , வீட்டு பாடம் உதவி ,

Google's new open-source AI model understands Indic languages better


Google’s new open-source AI model understands Indic languages better
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Google’s various products, such as Search and Assistant, are already available in India in multiple local languages. The company is now turning to a new AI to potentially make more of its offerings accessible to Indic language speakers — more specifically, it’s using a technology called MuRIL.
At its virtual event today, the Big G unveiled a new language model called Multilingual Representations for Indian Languages (MuRIL). This is the first model to support interoperation between 16 different Indic languages.  
That includes Assamese, Bengali, English, Gujarati, Hindi, Kannada, Kashmiri, Malayalam, Marathi, Nepali, Oriya, Punjabi, Sanskrit, Sindhi, Tamil, Telugu, and Urdu. ....

Partha Talukdar , Multilingual Representations , Indian Languages , Bidirectional Encoder Representations , Google India , பார்த்தா தாலுக்தார் , இந்தியன் மொழிகள் , கூகிள் இந்தியா ,

It takes a lot of energy for machines to learn - Why AI is so power-hungry?


This month, Google forced out a prominent AI ethics researcher after she voiced frustration with the company for making her withdraw a research paper. The paper pointed out the risks of language-processing artificial intelligence, the type used in Google Search and other text analysis products.
Among the risks is the large carbon footprint of developing this kind of AI technology. By some estimates, training an AI model generates as much carbon emissions as it takes to build and drive five cars over their lifetimes.
I am a researcher who studies and develops AI models, and I am all too familiar with the skyrocketing energy and financial costs of AI research. Why have AI models become so power hungry, and how are they different from traditional data center computation? ....

New York , United States , San Francisco , Kate Saenko , University Of Massachusetts Amherst , Boston University , Google Search , Bidirectional Encoder Representations , Massachusetts Amherst , Associate Professor , Computer Science , Creative Commons , புதியது யார்க் , ஒன்றுபட்டது மாநிலங்களில் , சான் பிரான்சிஸ்கோ , பல்கலைக்கழகம் ஆஃப் மாசசூசெட்ஸ் மஹேர்ஸ்ட , போஸ்டன் பல்கலைக்கழகம் , கூகிள் தேடல் , மாசசூசெட்ஸ் மஹேர்ஸ்ட , இணை ப்ரொஃபெஸர் , கணினி அறிவியல் , படைப்பு காமன்ஸ் ,

It takes a lot of energy for machines to learn – here's why AI is so power-hungry -- GCN


By Kate Saenko
Dec 15, 2020
This month, Google forced out a prominent AI ethics researcher after she voiced frustration with the company for making her withdraw a research paper. The paper pointed out the risks of language-processing artificial intelligence, the type used in Google Search and other text analysis products.
Among the risks is the large carbon footprint of developing this kind of AI technology. By some estimates, training an AI model generates as much carbon emissions as it takes to build and drive five cars over their lifetimes.
I am a researcher who studies and develops AI models, and I am all too familiar with the skyrocketing energy and financial costs of AI research. Why have AI models become so power hungry, and how are they different from traditional data center computation? ....

New York , United States , San Francisco , Kate Saenko , University Of Massachusetts Amherst , Google Search , Bidirectional Encoder Representations , Massachusetts Amherst , புதியது யார்க் , ஒன்றுபட்டது மாநிலங்களில் , சான் பிரான்சிஸ்கோ , பல்கலைக்கழகம் ஆஃப் மாசசூசெட்ஸ் மஹேர்ஸ்ட , கூகிள் தேடல் , மாசசூசெட்ஸ் மஹேர்ஸ்ட ,