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Artificial intelligence could soon enhance real-time police surveillance🤔🤔🤔
Several technology companies are working with police departments across the U.S. to develop the capability to add artificial intelligence to video surveillance and body cameras that could identify faces in real time, potentially expanding the reach of police surveillance.
The body-camera technology, expected to be ready by the fall, hasn’t yet been purchased by police departments and is still in the development stage. Police departments, including the New York Police Department, already use facial recognition to review surveillance footage after a crime has occurred.
Several technology companies are working with police departments across the U.S. to develop the capability to add artificial intelligence to video surveillance and body cameras that could identify faces in real time, potentially expanding the reach of police surveillance.
The body-camera technology, expected to be ready by the fall, hasn’t yet been purchased by police departments and is still in the development stage. Police departments, including the New York Police Department, already use facial recognition to review surveillance footage after a crime has occurred.
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Is Artificial Intelligence Dangerous? 6 AI Risks Everyone Should Know About?
Should we be scared of artificial intelligence (AI)?
Some notable individuals such as legendary physicist Stephen Hawking and Tesla and SpaceX leader and innovatorElon Musk suggest AI could potentially be very dangerous; Musk at one point was comparing AI to the dangers of the dictator of North Korea. Microsoft co-founder Bill Gates also believes there’s reason to be cautious, but that the good can outweigh the bad if managed properly. Since recent developments have made super-intelligent machines possible much sooner than initially thought, the time is now to determine what dangers artificial intelligence poses.
Should we be scared of artificial intelligence (AI)?
Some notable individuals such as legendary physicist Stephen Hawking and Tesla and SpaceX leader and innovatorElon Musk suggest AI could potentially be very dangerous; Musk at one point was comparing AI to the dangers of the dictator of North Korea. Microsoft co-founder Bill Gates also believes there’s reason to be cautious, but that the good can outweigh the bad if managed properly. Since recent developments have made super-intelligent machines possible much sooner than initially thought, the time is now to determine what dangers artificial intelligence poses.
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🤖🤖Why China is going ahead in AI then USA?🤖🤖
decided to leave Benito Mussolini’s Italy and emigrate to the United States, he changed the global balance of power. After arriving in the US, Fermi led the world’s first self-sustaining nuclear reaction at the University of Chicago and played an indispensable role in the Manhattan Project, which led to the end of World War II in the Pacific and laid the groundwork for a new world order and America’s prominent role.😱😱
So it is not surprising that some Americans think the same should be true with AI. Emigrant AI researchers like Geoff Hinton, Yann LeCun, Yoshua Bengio, Andrew Ng, and Fei-Fei Li are the Enrico Fermis of AI and should secure an American (and Canadian) hegemony in AI. Indeed, the US and Canada have 100 percent of the top 10 AI researchers, and 68 percent of the world’s best 1,000 or so researchers.🤓🤓
But technological revolutions are not only driven by big discoveries. Often, once a fundamental breakthrough has been published, the center of gravity quickly shifts from a handful of elite researchers to an army of tinkerers—engineers with enough expertise to apply the technology to different real-world problems and customer needs. This is particularly true when the payoff of a breakthrough is diffused throughout society rather than just concentrated in a few labs.👮👮
decided to leave Benito Mussolini’s Italy and emigrate to the United States, he changed the global balance of power. After arriving in the US, Fermi led the world’s first self-sustaining nuclear reaction at the University of Chicago and played an indispensable role in the Manhattan Project, which led to the end of World War II in the Pacific and laid the groundwork for a new world order and America’s prominent role.😱😱
So it is not surprising that some Americans think the same should be true with AI. Emigrant AI researchers like Geoff Hinton, Yann LeCun, Yoshua Bengio, Andrew Ng, and Fei-Fei Li are the Enrico Fermis of AI and should secure an American (and Canadian) hegemony in AI. Indeed, the US and Canada have 100 percent of the top 10 AI researchers, and 68 percent of the world’s best 1,000 or so researchers.🤓🤓
But technological revolutions are not only driven by big discoveries. Often, once a fundamental breakthrough has been published, the center of gravity quickly shifts from a handful of elite researchers to an army of tinkerers—engineers with enough expertise to apply the technology to different real-world problems and customer needs. This is particularly true when the payoff of a breakthrough is diffused throughout society rather than just concentrated in a few labs.👮👮
More than half of the 100 million patients of a US-based hospital interacted with their physicians online last year. IT services major Cognizant's internet-of-things (IoT) solution made this happen using information from smartphones and other devices such as glucose meters.
Video games have become a proving ground for AIs and Uber has shown how its new type of reinforcement learning has succeeded where others have failed.
Some of mankind’s most complex games, like Go, have failed to challenge AIs from the likes of DeepMind. Reinforcement learning trains algorithms by running scenarios repeatedly with a ‘reward’ given for successes, often a score increase.
Two classic games from the 80s – Montezuma’s Revenge and Pitfall! – have thus far been immune to a traditional reinforcement learning approach. This is because they have little in the way of notable rewards until later in the games
Some of mankind’s most complex games, like Go, have failed to challenge AIs from the likes of DeepMind. Reinforcement learning trains algorithms by running scenarios repeatedly with a ‘reward’ given for successes, often a score increase.
Two classic games from the 80s – Montezuma’s Revenge and Pitfall! – have thus far been immune to a traditional reinforcement learning approach. This is because they have little in the way of notable rewards until later in the games
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Best Data Science Resource for You!💪
1. Codementor - Best Data Science Tutorial❤️
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3. Kaggle - Data Science News
4. Best Data Science Course (Recommended💥)- https://www.udemy.com/data-science-complete-course/?couponCode=T10SPCLTST
Best Data Science Resource for You!💪
1. Codementor - Best Data Science Tutorial❤️
2. Data Camp - Best Paid site for Data Scientist🗣️
3. Kaggle - Data Science News
4. Best Data Science Course (Recommended💥)- https://www.udemy.com/data-science-complete-course/?couponCode=T10SPCLTST
Udemy
Data Science Masterclass With R! 4 Projects+8 Case Studies
Data Science by IITian -Data Science+R Programming ,Data analysis, Data Visualization, Data Science: Data Pre-processing