Publishing news across all your social media platforms to reinforce your standing as the leading authority in your industry among your customers
from search engines with our cutting-edge SEO strategies designed to elevate your website's visibility
by sharing compelling viral news videos that captivate and engage audiences
Hello!My name is Sarah Lightman.
News used: /50
Expertise: SMM, customer acquisition
Salary: from $49 per month
News type: Text + Image
Included:
50 news per month
Skills and Features:
Expertise: SMM, customer acquisition
Salary: from $49 per month
News type: Text + Image
Included:
50 news per month
Publishing to:
Hello!My name is Bill Lightman.
News used: /120
Expertise: SEO and SMM, customer acquisition, Video Production
Salary: from $149 per month
News type: Text + Image, Text + Video, Video
Included:
120 news per month
30 videos per month
Skills and Features:
Expertise: SEO and SMM, customer acquisition, Video Production
Salary: from $149 per month
News type: Text + Image, Text + Video, Video
Included:
120 news per month
30 videos per month
Publishing to:
Hello!My name is Amanda Lightman.
News used: /1000
Expertise: SEO and SMM, customer acquisition, Video Production
Salary: from $399 per month
News type: Text + Image, Text + Video, Video
Included:
1000 news per month
100 videos per month
Skills and Features:
Expertise: SEO and SMM, customer acquisition, Video Production
Salary: from $399 per month
News type: Text + Image, Text + Video, Video
Included:
1000 news per month
100 videos per month
Publishing to:
Quantity
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30
$0.5
31-60
$0.45
61-100
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101-300
$0.35
301-500
$0.3
501-1000
$0.25
1000+
$0.2
Quantity
Price per news
30
$0.99
31-60
$0.9
61-100
$0.8
101-300
$0.7
301-500
$0.6
501-1000
$0.5
1001+
$0.4
Meet Neuron!
As your new Content Manager, I will diligently track industry-related and local news to curate compelling content for your social media channels and website, ensuring your audience stays informed and engaged.
AI News Manager designed to automate news content creation and distribution, making it an indispensable tool for business owners and startups
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Configure Neuron News to meet your needs. We offer a wide range of custom settings that allow you to choose the number of publications, type of content, and platforms for publication, making your content management process as efficient as possible
May 31, 2023
A new AI-based approach for controlling autonomous robots satisfies the often-conflicting goals of safety and stability.
The MIT researchers’ controller was able to prevent the jet from crashing or stalling while stabilizing to
the
goal far better than any of the baselines.
In the future, this technique could be a starting point for
designing controllers for highly dynamic robots that must meet safety and stability requirements, like
autonomous delivery drones. Or it could be implemented as part of larger system. Perhaps the algorithm is
only
activated when a car skids on a snowy road to help the driver safely navigate back to a stable trajectory.
Navigating extreme scenarios that a human wouldn’t be able to handle is where their approach really
shines, So adds.
“We believe that a goal we should strive for as a field is to give reinforcement
learning the safety and stability guarantees that we will need to provide us with assurance when we deploy
these
In the film “Top Gun: Maverick,” Maverick, played by Tom Cruise, is charged with training
young
pilots to complete a seemingly impossible mission — to fly their jets deep into a rocky canyon, staying so
low
to the ground they cannot be detected by radar, then rapidly climb out of the canyon at an extreme angle,
avoiding the rock walls. Spoiler alert: With Maverick’s help, these human pilots accomplish their mission.
A machine, on the other hand, would struggle to complete the same pulse-pounding task. To an
autonomous aircraft, for instance, the most straightforward path toward the target is in conflict with what
the machine needs to do to avoid colliding with the canyon walls or staying undetected. Many existing AI
methods aren’t able to overcome this conflict, known as the stabilize-avoid problem, and would be unable to
reach their goal safely.
MIT researchers have developed a new technique that can solve complex
stabilize-avoid problems better than other methods. Their machine-learning approach matches or exceeds the
safety of existing methods while providing a tenfold increase in stability, meaning the agent reaches and
remains stable within its goal region.
In an experiment that would make Maverick proud, their
technique effectively piloted a simulated jet aircraft through a narrow corridor without crashing into the
ground.
“This has been a longstanding, challenging problem. A lot of people have looked at it but
didn’t know how to handle such high-dimensional and complex dynamics,” says Chuchu Fan, the Wilson Assistant
Professor of Aeronautics and Astronautics, a member of the Laboratory for Information and Decision Systems
(LIDS), and senior author of a new paper on this technique.
When compared with several baselines,
their approach was the only one that could stabilize all trajectories while maintaining safety. To push
their
method even further, they used it to fly a simulated jet aircraft in a scenario one might see in a “Top Gun”
movie. The jet had to stabilize to a target near the ground while maintaining a very low altitude and
staying
within a narrow flight corridor.
This simulated jet model was open-sourced in 2018 and had been
designed by flight control experts as a testing challenge. Could researchers create a scenario that their
controller could not fly? But the model was so complicated it was difficult to work with, and it still
couldn’t handle complex scenarios, .
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