Definitely bringing along @kadironureren with me. Give him and @use_covalent a follow if you haven't – you'll be hearing about them lots as the web3/climate space grows. And join us! Weather should be… better than it is now… it was sunny last year
SUSTAINABILITY
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VC Workshop: No-Code AI Building at Climate Web3 Event
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How many hyped up industries can I include in one marketing sentence? This #vc is hosting a #nocode/#lowcode workshop to build with #ai at this #climate #web3 event! In Seattle. In person!
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Methodology flaw in AI emissions estimation across power sources
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As an extreme version: taking work that was actually run on EfficientAcceleratorsA in SolarPowerDataCenterB & then estimating what happens if you were to run it on WarehouseOfIBMPCs powered by CoalPowerPlant & saying work produces lots of emissions doesn't seem reasonable to me.
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Carbon emissions computation carbon footprint AI model training
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Because carbon emissions is highly dependent on which data center you run in, which electricity grid it is powered by, etc. If you want to talk about the amount of computation needed to train one model architecture vs. another, that seems reasonable to compare abstractly.
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Wave Energy System Converts Ocean Waves Into Clean Electricity
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This system rotates and convert waves into electricity #cleanenergy #innovation #gigadgets #TechForGood #ESG pic.twitter.com/CoF9N2XvkY
— Helen Yu (@YuHelenYu) 14 mars 2023This system rotates and convert waves into electricity #cleanenergy #innovation #gigadgets #TechForGood #ESG
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NAS Cost Methodology: Distinguishing One-Time Versus Recurring Expenses
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Presenting the one-time cost of a NAS that has already been conducted in an energy efficient environment (and the best architecture from it open sourced) as if it was an every problem cost is pretty confusing to the reader. "Counting building the factory for every car produced."
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GM Data Scientist Ken Black Explores Climate Change Data Analysis
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It's been a long journey. @GM
's Sr. #DataScientist Ken Black's not done fighting the good fight. Read our #AlteryxImpact story featuring #AlteryxAce Ken diving deep into #ClimateChange data. See what he uncovered: http://
ow.ly/bpgX50Ng0Sp #AlteryxCommunity #DataScience -
EV Development Skills: Motor Control Battery Management Systems
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Upskill your #EV development abilities in motor control, battery management, fuel cells, electrical systems, and system simulation. Get ahead of the game here #ElectrificationWeek #Upskill
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NAS Results Reduce Language Model Training Emissions by 1.3X
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It's actually more ironic. The results of one-time NAS (which took 88X less CO2 than the external paper estimated) are open-sourced and make training language models 1.3X faster & produce 1.3X less emissions (see figure 4 in https://
arxiv.org/abs/2104.10350) https://
github.com/tensorflow/ten
sor2tensor/blob/master/tensor2tensor/models/evolved_transformer.py
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Neural Network Search Emissions Metric Accuracy Questioned
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Fwiw, "lifetime emissions of five cars" metric is wrong & based on inaccurate data & confusion about the one-time vs. every problem misunderstanding of neural architecture search (NAS), proxy tasks, &HW used. It's really a factor of 19X, 88X, 3261X, or 118,000X less than that.