Difference between revisions of "Energy"
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| − | |keywords=artificial, intelligence, machine, learning, models | + | |keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools |
| − | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | + | |
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| − | [ | + | [https://www.youtube.com/results?search_query=energy+consumption+policy+considerations+Efficient+Deep+Learning+ YouTube search...] |
| − | [ | + | [https://www.google.com/search?q=energy+consumption+policy+considerations+Efficient+deep+machine+learning+ML+artificial+intelligence ...Google search] |
| + | * [[Energy-based Model (EBN)]] | ||
* [[Case Studies]] | * [[Case Studies]] | ||
** [[Power (Management)]] | ** [[Power (Management)]] | ||
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** [[Chemistry]] | ** [[Chemistry]] | ||
* [[Other Challenges]] in Artificial Intelligence | * [[Other Challenges]] in Artificial Intelligence | ||
| − | * [ | + | * [https://drive.google.com/file/d/1v3TxkqPuzvRfiV_RVyRTTFbHl1pZq7Ab/view Energy and Policy Considerations for Deep Learning in NLP | E. Strubell, A. Ganesh, and A. McCallum - College of Information and Computer Sciences & University of Massachusetts Amherst] |
| − | * [ | + | * [https://www.google.com/search?q=Energy+Efficient+Machine+Learning+and+Cognitive+Computing Energy Efficient Machine Learning and Cognitive Computing] |
| − | * [ | + | * [https://www.wired.com/story/ai-is-throwing-battery-development-into-overdrive/ AI Is Throwing Battery Development Into Overdrive | Daniel Oberhaus - Wired] ... Improving batteries has always been hampered by slow experimentation and discovery processes. Machine learning is speeding it up by orders of magnitude. |
| − | * [ | + | * [https://www.jhunewsletter.com/article/2021/11/spiral-center-uses-artificial-intelligence-to-make-solar-energy-cheaper SPIRAL Center uses artificial intelligence to make solar energy cheaper | Zachary Bahar - Johns Hopkins News-Letter] |
| − | * [ | + | * [https://www.livescience.com/ai-controls-hydrogen-plasmas-nuclear-fusion Nuclear fusion is one step closer with new AI breakthrough | Tom Metcalfe - Livescience] |
* [https://techxplore.com/news/2023-03-deep-efficiently-electric-grids.html Using deep learning to develop a forecasting model for efficiently managing electric grids | Chung-Ang University] | * [https://techxplore.com/news/2023-03-deep-efficiently-electric-grids.html Using deep learning to develop a forecasting model for efficiently managing electric grids | Chung-Ang University] | ||
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<youtube>8Qa0E1jdkrE</youtube> | <youtube>8Qa0E1jdkrE</youtube> | ||
<b>Energy-Efficient Deep Learning: Challenges and Opportunities | <b>Energy-Efficient Deep Learning: Challenges and Opportunities | ||
| − | </b><br>This talk will describe methods to enable energy-efficient processing for deep learning, specifically convolutional neural networks (CNN), which is the cornerstone of many deep-learning algorithms. Deep learning plays a critical role in extracting meaningful information out of the zetabytes of sensor data collected every day. For some applications, the goal is to analyze and understand the data to identify trends (e.g., surveillance, portable/wearable electronics); in other applications, the goal is to take immediate action based the data (e.g., robotics/drones, self-driving cars, smart Internet of Things). For many of these applications, local embedded processing near the sensor is preferred over the cloud due to privacy or latency concerns, or limitations in the communication bandwidth. However, at the sensor there are often stringent constraints on energy consumption and cost in addition to throughput and accuracy requirements. Furthermore, flexibility is often required such that the processing can be adapted for different applications or environments (e.g., update the weights and model in the classifier). We will give a short overview of the key concepts in CNNs, discuss its challenges particularly in the embedded space, and highlight various opportunities that can help to address these challenges at various levels of design ranging from architecture, implementation-friendly algorithms, and advanced technologies (including memories and sensors). [ | + | </b><br>This talk will describe methods to enable energy-efficient processing for deep learning, specifically convolutional neural networks (CNN), which is the cornerstone of many deep-learning algorithms. Deep learning plays a critical role in extracting meaningful information out of the zetabytes of sensor data collected every day. For some applications, the goal is to analyze and understand the data to identify trends (e.g., surveillance, portable/wearable electronics); in other applications, the goal is to take immediate action based the data (e.g., robotics/drones, self-driving cars, smart Internet of Things). For many of these applications, local embedded processing near the sensor is preferred over the cloud due to privacy or latency concerns, or limitations in the communication bandwidth. However, at the sensor there are often stringent constraints on energy consumption and cost in addition to throughput and accuracy requirements. Furthermore, flexibility is often required such that the processing can be adapted for different applications or environments (e.g., update the weights and model in the classifier). We will give a short overview of the key concepts in CNNs, discuss its challenges particularly in the embedded space, and highlight various opportunities that can help to address these challenges at various levels of design ranging from architecture, implementation-friendly algorithms, and advanced technologies (including memories and sensors). [https://www.rle.mit.edu/eems/wp-content/uploads/2018/04/Energy-Efficient-Deep-Learning-SSCS-DL-Sze.pdf Slides] |
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<youtube>WN7VI0h7kbU</youtube> | <youtube>WN7VI0h7kbU</youtube> | ||
<b>Advances in Energy Efficiency Through Cloud and Machine Learning | <b>Advances in Energy Efficiency Through Cloud and Machine Learning | ||
| − | </b><br>Main Speaker - Urs Hölzle Today, the IT Industry accounts for about 2 percent of total greenhouse gas emissions, comparable to the footprint of air travel. Will IT emission eclipse air travel one day soon? Urs Hölzle thinks the clear answer is “no”: he says IT energy will decrease, and perhaps decrease significantly, over the next decade. Find out why. Hölzle is Senior Vice President of Technical Infrastructure & Google Fellow and oversees the design and operation of the servers, networks, and data centers that power Google's services, as well as the development of the software infrastructure used by Google’s applications. Recorded on 10/20/2017. Series: "Institute for Energy Efficiency" [3/2018] [Show ID: 33271] | + | </b><br>Main Speaker - Urs Hölzle Today, the IT Industry accounts for about 2 percent of total greenhouse gas emissions, comparable to the footprint of air travel. Will IT emission eclipse air travel one day soon? Urs Hölzle thinks the clear answer is “no”: he says IT energy will decrease, and perhaps decrease significantly, over the next decade. Find out why. Hölzle is Senior Vice President of Technical Infrastructure & Google Fellow and oversees the design and operation of the servers, networks, and data centers that power Google's services, as well as the [[development]] of the software infrastructure used by Google’s applications. Recorded on 10/20/2017. Series: "Institute for Energy Efficiency" [3/2018] [Show ID: 33271] |
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<youtube>ePCT9d8HI0Y</youtube> | <youtube>ePCT9d8HI0Y</youtube> | ||
<b>Deep Learning Montréal @ Autodesk – Deeplite Faster, smaller, energy-efficient Deep Neural Networks | <b>Deep Learning Montréal @ Autodesk – Deeplite Faster, smaller, energy-efficient Deep Neural Networks | ||
| − | </b><br>Ehsan Saboori, Technical Co-founder, Deeplite | + | </b><br>Ehsan Saboori, Technical Co-founder, Deeplite https://www.deeplite.ai/ [https://www.meetup.com/Deep-Learning-Montreal/events/248338612/ Full details here] |
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<youtube>qIZOhK2Ywz0</youtube> | <youtube>qIZOhK2Ywz0</youtube> | ||
<b>AI Is Helping Supply 1 Billion People in India with Renewable Energy | <b>AI Is Helping Supply 1 Billion People in India with Renewable Energy | ||
| − | </b><br>World Economic Forum India is changing the way energy happens by bringing together the power of hardware with software. Watch to see how. | + | </b><br>World Economic Forum India is changing the way energy happens by bringing together the power of hardware with software. Watch to see how. https://www.weforum.org/ |
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<youtube>VzyzKv_LBRw</youtube> | <youtube>VzyzKv_LBRw</youtube> | ||
<b>Saving Energy Consumption With Deep Learning | <b>Saving Energy Consumption With Deep Learning | ||
| − | </b><br>Discover how big data, GPUs, and deep learning, can enable smarter decisions on making your building more energy-efficient with AI startup, Verdigris. Explore more about AI & Deep Learning: | + | </b><br>Discover how big data, GPUs, and deep learning, can enable smarter decisions on making your building more energy-efficient with AI startup, Verdigris. Explore more about AI & Deep Learning: https://nvda.ws/2sbWvNm |
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<youtube>A3p_w7ENefs</youtube> | <youtube>A3p_w7ENefs</youtube> | ||
<b>Energy-Efficient AI | <b>Energy-Efficient AI | ||
| − | </b><br>Carlos Macian, senior director of innovation for eSilicon EMEA, talks with Semiconductor Engineering about how to improve the efficiency of AI operations by focusing on the individual operations, including data transport, computation and memory. | + | </b><br>Carlos Macian, senior director of innovation for eSilicon EMEA, talks with Semiconductor Engineering about how to improve the efficiency of AI operations by focusing on the individual operations, including data transport, computation and [[memory]]. |
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Latest revision as of 21:31, 2 March 2024
YouTube search... ...Google search
- Energy-based Model (EBN)
- Case Studies
- Other Challenges in Artificial Intelligence
- Energy and Policy Considerations for Deep Learning in NLP | E. Strubell, A. Ganesh, and A. McCallum - College of Information and Computer Sciences & University of Massachusetts Amherst
- Energy Efficient Machine Learning and Cognitive Computing
- AI Is Throwing Battery Development Into Overdrive | Daniel Oberhaus - Wired ... Improving batteries has always been hampered by slow experimentation and discovery processes. Machine learning is speeding it up by orders of magnitude.
- SPIRAL Center uses artificial intelligence to make solar energy cheaper | Zachary Bahar - Johns Hopkins News-Letter
- Nuclear fusion is one step closer with new AI breakthrough | Tom Metcalfe - Livescience
- Using deep learning to develop a forecasting model for efficiently managing electric grids | Chung-Ang University
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Solar Energy
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Reducing Energy Consumption of AI
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