GNY's Machine Learning engine went head-to-head with the U.S. Energy Information Administration and outperformed it in predicting energy demand for California. The GNY team are building a larger vision for how GNY can support the work of scientists and advocates fighting for a sustainable and green planet long-term.

March 4, 2021--Artificial intelligence is present in most facets of American digital life, but experts are in a constant race to identify and address potential dangers before they impact consumers. From making a simple search on Google to listening to music on Spotify to streaming Tiger King on Netflix, AI is everywhere. Predictive algorithms learn from a consumer's viewing habits and attempt to direct consumers to other content an algorithm thinks a consumer will be interested in. While this can be extremely convenient for consumers, it also raises many concerns. Jaisha Wray, associate administrator for international affairs at the National Telecommunications and Information Administration, was a panelist at a conference hosted Tuesday by the Federal Communications Bar Association.

Chatbots are used in many service industries to answer customers' questions and help them navigate through a company's website. They provide a way for customers to further engage with your company. Chatbots are projected to be a continuing trend in meeting those expectations. AI technology is constantly developing and progressing, which suggests that chatbots will also be in a constant state of change. New Year's is right around the corner.

A few days before racing in the 36th match for the America's Cup, the covers have been lifted on the testing and development process, using Artificial Intelligence employed by Emirates Team New Zealand, and developed in conjunction with one of worlds most prestigious consulting firms McKinsey & Company. While the team's use of simulators has been widely discussed, and one is on display at the America's Cup Village. The team has been working with McKinsey subsidiary Quantum Black to develop a "digital twin" of the team's AC75 that used a process of machine learning to perform many more iterations of a sailing situation than was possible using human crew, and to come up with options that were faster than the crew was currently achieving. AI Bots work particularly well when there is large volume of data. The Bot is programmed to self-learn from its own analysis.

When the Covid-19 pandemic emerged last year, physician Lara Jehi and her colleagues at the Cleveland Clinic were running blind. Who were the patients likely to get sicker? What kinds of care will they need? "The questions were endless," says Jehi, the clinic's chief research information officer. "We didn't have the luxury of time to wait and see what's going to evolve over time."

Washington – The Mars rover Perseverance has successfully conducted its first test drive on the red planet, the U.S. space agency NASA said Friday. The six-wheeled rover traveled about 6.5 meters (21.3 feet) in 33 minutes on Thursday, NASA said. It drove 4 meters forward, turned in place 150 degrees to the left, and then backed up 2.5 meters, leaving tire tracks in the Martian dust. "This was our first chance to'kick the tires' and take Perseverance out for a spin," said Anais Zarifian, Perseverance mobility test bed engineer at NASA's Jet Propulsion Laboratory in Pasadena, California. Zarifian said the test drive went "incredibly well" and represented a "huge milestone for the mission and the mobility team."

Artificial intelligence can now gauge human emotions, and it's being used in everything from education to marketing, experts say. Your emotions could potentially be tracked using your Wi-Fi router and analyzed by AI, according to a new study from London's Queen Mary University. Researchers used radio waves like those used in Wi-Fi to measure heart and breathing rate signals, which could determine how a person is feeling. The study shows just how pervasive emotion-monitoring could become. "In education, AI could be used in adapting content to serve the needs of each child best," Kamilė Jokubaitė, CEO and founder of Attention Insight, who was not involved in the study, said in an email interview.

The ancient Silk Road, once the longest overland trade route, ran over 4,000 miles long. Today nestled in Eurasia's heart along the Silk Road is the national railway company – Kazakhsthan Temir Zholy (KTZ). Though traversing across this historic pathway sounds rather cool, the complex geography and harsh weather conditions propose serious challenges in operating cargo and passenger transportation. KTZ is a crucial part of Kazakhstan – the world's ninth-largest country – and its economy. This railway company knew the importance of having a seamless operating model in order to maintain its current operations, both on and off the track.

This article is based on an in-depth study of the data science efforts in three large, private-sector Indian banks with collective assets exceeding $200 million. The study included onsite observations; semistructured interviews with 57 executives, managers, and data scientists; and the examination of archival records. The five obstacles and the solutions for overcoming them emerged from an inductive analytical process based on the qualitative data. More and more companies are embracing data science as a function and a capability. But many of them have not been able to consistently derive business value from their investments in big data, artificial intelligence, and machine learning.1

Being a data scientist by profession and a part-time crypto trader by passion, I have been very interested in creating a Deep Learning model that could help me predict Bitcoin price. This article is based on the experimentation I did to create such a model. Long short term memory, or more popularly known as LSTM's, is a type of Recurrent Neural network that helps the model learn long-term sequences in the data set. Since my focus here is more on their usage, if you are interested in knowing more details about what LSTM's are and how they work, you can check out this great article that goes in-depth to explain all that. I imported the data onto my local drive and read it as a CSV using pandas. For this model I created fields to track the hour of the day and the weekday.