Using artificial intelligence to track solar power

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What they did: Cape Analytics analyzed visual data on tens of millions of homes in major metro areas nationwide by working with partners like the location data company Nearmap. That enabled a fine-grain analysis of residential solar power at a neighborhood level. Why it matters: The firm intends its localized data to help policymakers better understand where solar power is being adopted and why -- and help homeowners understand if they can get state-specific incentives for going solar. What they found: Every "super solar" neighborhood in the U.S. -- those with over 500 homes and solar systems -- is in California, except for one in Saint Petersburg, Florida, which is 13.2% solar. The big picture: Cape Analytics examined the entire U.S., Farzaneh tells Axios.


#FinServ_2019-11-13_11-31-13.xlsx

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The graph represents a network of 2,353 Twitter users whose tweets in the requested range contained "#FinServ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 13 November 2019 at 19:32 UTC. The requested start date was Monday, 11 November 2019 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 5-day, 13-hour, 33-minute period from Tuesday, 05 November 2019 at 11:26 UTC to Monday, 11 November 2019 at 01:00 UTC.


5 Lessons on the Power of AI, Language, and Emotion to Transform Sales Outcomes

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Without emotional bonding, there is no flow. People don't remember what you say, they remember how you made them feel. Naturally, the words we choose influence the outcomes. "The single biggest problem in communication is the illusion that it has taken place." How often do we struggle to clearly communicate our message to someone?


An Introduction to Meta-Learning

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At Walmart Labs, we utilize meta-learning every day -- whether it's in our robust item catalog or item recommendations. This article will walk through what meta-learning is and how it is being used to solve practical industry problems. Meta-learning is an exciting area of research that tackles the problem of learning to learn. The goal is to design models that can learn new skills or rapidly adapt to new environments with minimal training examples. Not only does this dramatically speed up and improve the design of Machine learning (ML) pipelines or neural architectures, but it also allows us to replace hand-engineered algorithms with novel approaches learned in a data-driven way (Vanschoren, 2018).


My journey to become an international Tech-speaker- Leapcode

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Should a speaker prepare content for beginner level or advanced? What present of participants would be having some-what technical background? How to engage every single attendee in your 90 minutes of session who might not be a native English speaker? All of these questions were never in my mind before MozFest, but MozFest taught me to think, prepare and deliver content which person of any age group, any educational and working background understand and get started with. I had my session on Decentralized & Personalized AI, with Privacy by Design which was divided into two phases, Learning Phase and Practicing Phase.


Inside the the World's First Mainstream Album Made With AI

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This article is part of New York's Future Issue, a collection of predictions about the near future as seen through the recent past. Click here to read more. On June 21, 2017, electronic musician Holly Herndon and her husband, writer/philosopher/teacher Mat Dryhurst, welcomed a new addition to their family. "She's an inhuman child," Herndon tells me one afternoon, while seated in the offices of her record label, 4AD. Spawn is nascent machine intelligence, or AI.


Recurrent Neural Networks (RNN) Explained -- the ELI5 way

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Sequence Modeling is the task of predicting what word/letter comes next. Sequence models compute the probability of occurrence of a number of words in a particular sequence. In the sequence model, the length of the input is not fixed. Citation Note: The content and the structure of this article is based my understand of the deep learning lectures from One-Fourth Labs -- PadhAI. Recurrent Neural Networks(RNN) are a type of Neural Network where the output from the previous step is fed as input to the current step.


Recurrent Neural Networks (RNN) Explained -- the ELI5 way

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Sequence Modeling is the task of predicting what word/letter comes next. Sequence models compute the probability of occurrence of a number of words in a particular sequence. In the sequence model, the length of the input is not fixed. Citation Note: The content and the structure of this article is based my understand of the deep learning lectures from One-Fourth Labs -- PadhAI. Recurrent Neural Networks(RNN) are a type of Neural Network where the output from the previous step is fed as input to the current step.


Woman seeking treatment for dizziness finds out she's missing her cerebellum

FOX News

Despite being born without this essential part, the woman learned to walk and talk, although her mother reported she learned these actions around age 6 and 7. (iStock) In 2014, a Chinese woman in her 20's sought treatment for recurring problems with balance and dizziness, reports a case study published in the journal Brain. But when doctors looked at brain imaging via a CT scan and MRI, they discovered their patient was living without her cerebellum. This young woman falls within a small group of nine people diagnosed with cerebellar agenesis, the study reports. Despite being born without this essential part, the woman learned to walk and talk, although her mother reported she learned these actions around age 6 and 7. However, the young woman had always struggled with walking steadily and had some trouble pronouncing words, according to the study.


Blue Pujiang discusses AI in finance

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In finance, machine learning, computer vision and knowledge graphs are the most widely used. "From overseas practices, AI promotes digital finance, …