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Is artificial intelligence the next tool to fight wildfires?

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With wildfires becoming bigger and more destructive as the western part of the United States dries out and heats up, agencies and officials tasked with preventing and battling the blazes could soon have a new tool to add to their arsenal of prescribed burns, pick axes, chainsaws and aircraft. The high-tech help could come from an area not normally associated with fighting wildfires: artificial intelligence (AI). Lockheed Martin Space, based in Jefferson County, is tapping decades of experience in managing satellites, exploring space and providing information to the US military to offer more accurate data quicker to ground crews. It is talking to the US Forest Service, university researchers, and a Colorado state agency about how their technology could help. By generating more timely information about on-the-ground conditions and running computer programs to process massive amounts of data, Lockheed Martin representatives say they can map fire perimeters in minutes rather than the hours it can take now.


Guest Blog – Machine Learning In Talent Management - AI Summary

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AI & Machine Learning Applications in the Real World According to the latest trends of AI-based solutions, there is hardly any decisive sector or industry that does not rely on smart algorithms and automation to perform highly advanced tasks that would be impossible for most humans. Many companies use Machine Learning and Artificial Intelligence to identify and sort through the best possible candidates for a position. With a few Machine Learning courses that are specially designed for regular people, without advanced technical knowledge, it's easy to understand why there are so many applications of advanced technologies in the real world. Luckily, this situation can now be avoided by training machine learning algorithms to take over the task. According to a case study performed at Canada's largest bookstore chain (Indigo), the use of AI and machine learning algorithms to screen job candidates and decide who to hire has led to an increase in overall productivity.


ML for Algorithmic Trading, with Stefan Jansen

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Listen to this episode on Anchor FM. Stefan has been a partner in an investment firm where he assisted in building data infrastructure and predictive analytics practice. He accomplished this when data science was only beginning to be taken seriously in the investment industry. You won't want to miss this opportunity to learn from Stefan's experiences. Machines learning from data will continually improve in achieving performance measures.



About

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His Excellency Omar Sultan Al Olama has been appointed as Minister of State for Artificial Intelligence in October 2017, and then was appointed as Minister of State for Artificial Intelligence, Digital Economy and Remote Work Applications in July 2020. His responsibilities include enhancing the government performance levels by investing in the latest technologies and tools of artificial intelligence and applying them in various sectors. His Excellency Omar Sultan Al Olama is Spearheading UAE efforts to be positioned as a global leader in Digital economy, With the focus to enhance UAEs digital economy contribution to the GDP. His Excellency is also focused on strengthening the UAE's position as a global reference in remote work applications. His Excellency Omar Sultan Al Olama is currently the Managing Director of the World Government Summit.


How deep learning took so much time to take off

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Maybe the worse thing that can happen to an idea is being born on the wrong moment, or/and even wrong place. Take the case of YouTube, was is the first video streaming platform? But it was born on the right moment! "In 1999–2000 it was too hard to watch online content you had to put codecs in your browser and do all this stuff [about company that failed two years before YouTube]" Bill Gross It was somehow similar with deep learning, since the act adding more hidden-layers is not new, and it is even straightfoward: anyone with a outside thinking could try that out, and have succeeded if we had the proper tools. What made deep learning just now? Indeed, it is amazing how fast hardware evolved, in special for personal usage.


Why AI is everywhere except your company

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Not a day goes by without reports of a new achievement, investment or national plan powered by artificial intelligence. AI is embedded in many of the apps and the software we use, and it is making functions such as voice interaction a reality. Yet the adoption of AI itself is largely absent from most of the organisations with which we directly interact or work. While applications that were just a dream only a few years ago are now widespread, their development is still restricted to a handful of savvy companies. For instance, Meta (formerly Facebook) is building the world's largest supercomputer.


Self-Driving Car Startup Wayve Taps Microsoft For 'Supercomputer Muscle'

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British startup Wayve said on Wednesday it will use supercomputer infrastructure designed for the firm by its investor Microsoft to process vast amounts of data as it develops machine learning-based models for self-driving cars. Wayve's technology relies on machine learning using camera sensors fitted on the outside of the vehicle, where the system learns from traffic patterns and the behaviour of other drivers, instead of the conventional method of relying on detailed digital maps and coding to tell vehicles how to operate. "Microsoft is providing supercomputing muscle," Wayve Chief Executive Alex Kendall told Reuters. "What we're looking to do goes beyond the bounds of what's possible for commercial cloud offerings today." Kendall said Microsoft will be able to process the terabyte of data - 1 trillion bytes, or equivalent to around an hour of consumer video - that Wayve's cars generate every minute.


GATO: Google's Generalized AI

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Note: The entire model is trained in a purely supervised fashion as opposed to any form of reinforcement learning. The first question you may ask is how the model takes different types of inputs like tabular data, images, sound, audio, video, etc. The answer to this is that everything is first converted to the same format, i.e. After converting data into tokens, they use the following canonical sequence ordering. The goal here is to put everything in the same format with a particular ordering depending upon the task.


Best Examples Of Python Programming Jobs

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A dedicated Python Developer will be expected to understand the language at a higher level and be capable of using Python to accomplish any number of tasks, including but not limited to data collection and analytics, database creation, web development, design scripting, and automation. A Python Developer frequently collaborates with data collection and analytics to provide valuable answers and insight. Python is used in web development, machine learning, artificial intelligence, scientific computing, and academic research. Its growing popularity can be attributed to the data science community's embrace of artificial intelligence and machine learning. Machine-learning applications are being used to innovate organizations in education, healthcare, and finance.