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Tag Archives: YouTube

Cedric The Entertainer Launches YouTube Channel: Channel Will Feature Behind The Scenes Looks

May 6, 2020   Humor
 Cedric The Entertainer Launches YouTube Channel: Channel Will Feature Behind The Scenes Looks

World-renowned actor, comedian and host Cedric The Entertainer, is pleased to announce the highly-anticipated launch of his YouTube channel. With his brand new YouTube channel – built in partnership with GC Studios – the icon and Hollywood Walk of Famer will be beginning a new digital journey, uploading exclusive new content and will also be giving viewers a peek behind the curtain as he shares about his daily life. 

Since the 90’s, Cedric has dominated the comedy space, from his iconic stints as a host on BET’s ComicView and on Def Comedy Jam, to recognizable roles on The Steve Harvey Show and Barbershop. Cedric is also the star and executive producer of CBS’ strongest comedy series The Neighborhood, now in its second season. The launch of his YouTube channel marks a brand new era of content, one that allows Cedric to engage directly with his audience.

This is not his first rodeo at exploring new digital formats. Earlier in April, he hosted a star-studded, marathon fundraiser called Def Comedy Jam: Healing Through Laughter, alongside big names like Usher, Tiffany Hadish and Marlon Wayans. The live-stream drew more than one million viewers across Facebook and Twitch, and raised over US$ 92,000 and counting.

“The entertainment world has completely changed from when I first started out. With my new channel, I will now be able to create content more regularly, and also communicate directly with my fans – something I’m incredibly excited about,” said Cedric. 

Fans can expect weekly uploads on his channel, where Cedric will provide exclusive, behind the scenes coverage of his shows and projects, travel, family time, and other fun activities. Viewers will also be treated to Cedric’s take on current events and pop-culture news which will be delivered with his signature style and humor. Finally, this will also be the platform where Cedric will update his fans about the projects he’s working on and passionate about as well as exciting collaborations with other YouTube personalities or celebrities doing challenges, skits, and games.

Tune in to watch Cedric The Entertainer’s first Youtube video that will premiere on the 6th of May, 2020 at 18:00 PST

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DeepMind’s AI learns to generate realistic videos by watching YouTube clips

July 20, 2019   Big Data

Perhaps you’ve heard of FaceApp, the mobile app that taps AI to transform selfies, or This Person Does Not Exist, which surfaces computer-generated photos of fictional people. But what about an algorithm whose videos are wholly novel? One of the newest papers from Google parent company Alphabet’s DeepMind (“Efficient Video Generation on Complex Datasets”) details recent advances in the budding field of AI clip generation. Thanks to “computationally efficient” components and techniques and a new custom-tailored data set, researchers say their best-performing model — Dual Video Discriminator GAN (DVD-GAN) — can generate coherent 256 x 256-pixel videos of “notable fidelity” up to 48 frames in length.

“Generation of natural video is an obvious further challenge for generative modeling, but one that is plagued by increased data complexity and computational requirements,” wrote the coauthors. “For this reason, much prior work on video generation has revolved around relatively simple data sets, or tasks where strong temporal conditioning information is available. We focus on the tasks of video synthesis and video prediction … and aim to extend the strong results of generative image models to the video domain.”

The team built their system around a cutting-edge AI architecture and introduced video-specific tweaks that enabled it to train on Kinetics-600, a data set of natural videos “an order of magnitude” larger than commonly used corpora.  Specifically, the researchers leveraged scaled-up generative adversarial networks, or GANs — two-part AI systems consisting of generators that produce samples and discriminators that attempt to distinguish between the generated samples and real-world samples — that have historically been applied to tasks like converting captions to scene-by-scene storyboards and generating images of artificial galaxies. The flavor here was BigGANs, which are distinguished by their large batch sizes and millions of parameters.

 DeepMind’s AI learns to generate realistic videos by watching YouTube clips

Above: A set of four-second synthesized video clips trained on 12 128×128 frames from Kinetics-600.

Image Credit: DeepMind

DVD-GAN contains dual discriminators: a spatial discriminator that critiques a single frame’s content and structure by randomly sampling full-resolution frames and processing them individually, and a temporal discriminator that provides a learning signal to generate movement. A separate module — a Transformer — allowed learned information to propagate across the entire AI model.

As for the training data set (Kinetics-600), which was compiled from 500,000 10-second high-resolution YouTube clips originally curated for human action recognition, the researchers describe it as “diverse” and “unconstrained,” which they claim obviated concerns about overfitting. (In machine learning, overfitting refers to models that correspond too closely to a particular set of data and as a result fail to predict future observations reliably.)

The team reports that after being trained on Google’s AI-accelerating third-generation Tensor Processing Units for between 12 and 96 hours, DVD-GAN managed to create videos with object composition, movement, and even complicated textures like the side of an ice rink. It struggled to create coherent objects at higher resolutions where movement consisted of a much larger number of pixels, but the researchers note that, evaluated on UCF-101 (a smaller data set of 13,320 videos of human actions), DVD-GAN produced samples with a state-of-the-art Inception Score of 32.97.

 DeepMind’s AI learns to generate realistic videos by watching YouTube clips

“We further wish to emphasize the benefit of training generative models on large and complex video data sets, such as Kinetics-600,” wrote the coauthors. “We envisage the strong baselines we established on this data set with DVD-GAN will be used as a reference point by the generative modeling community moving forward. While much remains to be done before realistic videos can be consistently generated in an unconstrained setting, we believe DVD-GAN is a step in that direction.”

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YouTube took down 58 million videos in Q3 2018

December 14, 2018   Big Data
 YouTube took down 58 million videos in Q3 2018

(Reuters) — YouTube took down more than 58 million videos and 224 million comments during the third quarter based on violations of its policies, the unit of Alphabet’s Google said on Thursday in an effort to demonstrate progress in suppressing problem content.

Government officials and interest groups in the United States, Europe, and Asia have been pressuring YouTube, Facebook, and other social media services to quickly identify and remove extremist and hateful content that critics have said incite violence.

The European Union has proposed online services should face steep fines unless they remove extremist material within one hour of a government order to do so.

An official at India’s Ministry of Home Affairs speaking on the condition of anonymity on Thursday said social media firms had agreed to tackle authorities’ requests to remove objectionable content within 36 hours.

This year, YouTube began issuing quarterly reports about its enforcement efforts.

As with past quarters, most of the removed content was spam, YouTube said.

Automated detection tools help YouTube quickly identify spam, extremist content and nudity. During September, 90 percent of the nearly 10,400 videos removed for violent extremism or 279,600 videos removed for child safety issues received fewer than 10 views, according to YouTube.

But YouTube faces a bigger challenge with material promoting hateful rhetoric and dangerous behavior.

Automated detection technologies for those policies are relatively new and less efficient, so YouTube relies on users to report potentially problematic videos or comments. This means that the content may be viewed widely before being removed.

Google added thousands of moderators this year, expanding to more than 10,000, in hopes of reviewing user reports faster. YouTube declined to comment on growth plans for 2019.

It has described pre-screening every video as unfeasible.

The third-quarter removal data for the first time revealed the number of YouTube accounts Google disabled for either having three policy violations in 90 days or committing what the company found to be an egregious violation, such as uploading child pornography.

YouTube removed about 1.67 million channels and all of the 50.2 million videos that were available from them.

Nearly 80 percent of the channel takedowns related to spam uploads, YouTube said. About 13 percent concerned nudity, and 4.5 percent child safety.

YouTube said users post billions of comments each quarter. It declined to disclose the overall number of accounts that have uploaded videos, but said removals were also a small fraction.

In addition, about 7.8 million videos were removed individually for policy violations, in line with the previous quarter.

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YouTube says machine learning is making progress against violent extremist content

October 18, 2017   Big Data
 YouTube says machine learning is making progress against violent extremist content

Four months after launching a program to fight violent extremist content, YouTube says it has become far more efficient at identifying and removing such videos, thanks to its machine learning technology.

In June, YouTube detailed four steps it would take to fight the rising tide of such objectionable content on its platform. Its announcement came amid a general backlash against the tech industry for its role in enabling hate-fueled and terrorist-related messages.

YouTube pledged to deploy machine learning to tackle the issue. In addition, it created a program to enlist the help of third-party experts, toughen standards for controversial videos, and support voices that are “speaking out against hate and extremism.”

The company published an update on these efforts on its blog today. Most notably, it said the investment in machine learning seemed to be paying dividends.

The company wrote that it “always used a mix of human flagging and human review together with technology” to help it spot violent content. The program introduced in June added machine learning to flag violent extremist content, which would then be reviewed by humans.

YouTube said that over the last month, 83 percent of violent extremist videos it removed were spotted without a human flag, up 8 percent from August. The company said its human teams have reviewed more than a million videos since June, adding new context and information to continue improving the machine learning efforts.

It also acknowledged that “as we have increased the volume of videos for review by our teams, we have made some errors. We know we can get better and we are committed to making sure our teams are taking action on the right content.”

“Terrorist and violent extremist material should not be spread online,” the blog post reads. “We will continue to heavily invest to fight the spread of this content, provide updates to governments, and collaborate with other companies through the Global Internet Forum to Counter Terrorism.”

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YouTube passes 1 billion videos with automatic captions

February 16, 2017   Big Data
origin 3606295240 780x485 YouTube passes 1 billion videos with automatic captions

Google today announced a big YouTube milestone: 1 billion videos with automatic captions. And these videos are popular: YouTube users watch videos with automatic captions more than 15 million times per day.

Captions are critical for the more than 300 million people who are deaf or hard of hearing, but they’re also useful for anyone looking to read instead of listen. Whether the audio quality is poor, it’s loud wherever you are, the person is mumbling, you want to practice a different language, or you simply want to hear and read simultaneously, automatic captions are a game-changer.

Google first launched YouTube’s video captions in September 2006, then took the feature to a whole new level with automatic captions in November 2009. It therefore took just over seven years to pass the 1 billion mark. That said, automatic captions are far from perfect and a constant work in progress thanks to mispronunciations, accents, dialects, and background noise.

Over the years, Google has been working to improve the accuracy of automatic captions, which is not an easy feat given YouTube’s size and diversity of content. By improving its speech recognition, refining the machine learning algorithms, and expanding the training data, the company has managed to boost accuracy for automatic captions in English by 50 percent. In fact, the company today declared that automatic captioning in English is getting “closer and closer to human transcription error rates.”

YouTube is looking to extend that to all its 10 supported languages. In addition to English, that means Dutch, French, German, Italian, Japanese, Korean, Portuguese, Russian, and Spanish. This is only possible if the site’s creators and viewers participate.

One day, every YouTube video could have an automatic caption track generated and reviewed by the creator. The technology isn’t quite there yet, so until then, you can do your part by reviewing, editing, or unpublishing automatic captions.

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How To Make the Most of the YouTube Content Marketing Customer Journey

August 18, 2016   CRM News and Info
How To Make the Most of the YouTube Content Marketing Customer Journey fI How To Make the Most of the YouTube Content Marketing Customer Journey

This type of content marketing, where companies give away helpful information for free, is very common on company blogs. But sadly, only a small percentage of companies have unlocked the potential of content marketing on YouTube. There are great advantages for those who can dominate in their niche, any niche. This is particularly true for brands who can produce evergreen or long-tail content – videos that prove helpful to potential customers for months or even years.

Focus on your specialty

Now you’re convinced you should make videos for your company, but about what, exactly? You can decide by asking yourself these questions:

1) What does your business know how to do that many others do not?

There’s something you do in your business that you know better than most other people, or do better than most other companies. Content marketing is about giving away some (but not all) of those secrets to increase your authority, with a soft sell or no sell at all. When people think about your service, they’ll come to you because you have given them something and demonstrated your expertise.

2) What questions are people specifically asking about your products?

People are always asking you questions about your business. They may do this in person to your salespeople, or via email or social media. Why not answer these questions in a video?

3) What are people asking about your business sector?

If you can help people with general tips and tactics they can learn about in your field of business in general – that don’t necessarily have to do with your products – they will be very appreciative and see you as a trustworthy expert, in it to be helpful.

4) In what way can you best demonstrate this on video?

Content marketing videos don’t have to be exciting. They just have to be helpful.

5) Who on your team would best demonstrate this?

Sometimes the best person on the team to do videos is the CEO. Sometimes it’s the intern in the back room. Sometimes it’s both. Experiment with different approaches.

6) How can you shoot and edit these videos as quickly and cheaply as possible?

YouTube content marketing doesn’t have to have high production values. It just has to communicate how to do something helpful. If it takes a lot of time or money to shoot and edit, you probably should think about another approach.

7) Do you have the resources to launch one video every week, on the same day and time? I repeat – every week?

You should be able to release at least one video per week, ideally on the same day. Make it a habit. If it’s too hard or you don’t have enough time or it’s not a priority, this approach may not be for you. The YouTube algorithm rewards consistency.

8) Can you learn about video optimization and social sharing?

You’ve got to learn about video optimization, or get someone to manage your channel for you. The job is only half done when the video is uploaded. The rest is optimization and promotion in the other areas of your marketing funnel.

You can do it!

Do you have comments or questions about content marketing for YouTube? I will respond in the comments section below.

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How to Increase ROI and Conversion from YouTube Ads

June 27, 2016   CRM News and Info
How to Get Results from YouTube Ads Hint Its Not Advertising  How to Increase ROI and Conversion from YouTube Ads

Breeze says that while impressions and frequency are great metrics for television, this approach doesn’t resonate on YouTube, and particularly not for eCommerce, where businesses are looking for direct sales resulting from the ads.

“I think the big problem that brands and eCommerce companies – the thing they forget – is that YouTube is more of a community, and people go there with specific intent. They’re looking for videos about certain topics that interest them, they’re looking for how-to videos, or they’re looking at review videos about what product to buy.”

Breeze recommends going further than just understanding intent. He says that advertisers need to master the moment, arriving at the right time for the right person.

“Be there when your customers are looking for you,” he says. “Nine times out of ten, brands aren’t appearing when they should be appearing. When their customers are there, the brands aren’t there. That’s a big missed opportunity.”

For instance, while it’s good to have an ad for a coffee maker show up when someone has done a search for “What’s the best coffee machine?,” it’s better if marketers can dive deeper into a predicted human experience.

Breeze gives a personal example of when his young son wakes him up in the early hours of the morning by bouncing on Breeze’s bed. To get his boy to settle down for a few minutes while Breeze gets up, he’ll do a quick search on his iPad for a children’s cartoon and pass the device to his son, which gives dad a few precious moments to get organized.

That, he says, would be a perfect moment in time for an ad about a coffee maker targeted to him as a parent, which would say something like, “Don’t you hate when you get woken up by your kids? Wouldn’t you just love for a coffee to be waiting for you? This is what our coffee machine does.”

Advertisers can build into their strategy predictions of specific types of moments in the human experience, then reverse-engineer the video content to apply to those moments. Furthermore, they can utilize YouTube’s extensive targeting options, such as a viewer’s age and gender, the day of the week, the time of day, and even whether they have young children or not.

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The Top 5 YouTube Analytics Every Marketer Should Know

February 24, 2016   CRM News and Info
youtube featured image The Top 5 YouTube Analytics Every Marketer Should Know

But like Google Analytics, YouTube Analytics has so much data that it can be overwhelming. So today we’re going to give you just five key features that will help you track the audience of your business videos. And please note that while there are also many great measurement tools available with Google AdWords for Video, aka TrueView (where any business can advertise on YouTube), we won’t be covering them in this post. Also there are excellent third-party solutions for YouTube Analytics, including Tubular Labs, TubeBuddy, VidIQ and others, but today we will just cover the basic YouTube Analytics features from a mostly organic traffic point of view.

Take a look: This is your own YouTube Analytics home page here (you must be signed in to your channel).

Debunking a Myth: It’s Not About The Views

Importantly, YouTube doesn’t rank its videos based on how many views they get. YouTube ranks them mostly based on the more opaque metric of “session time,” which is how long a user spends watching a series of YouTube videos, yours being one of them, during a given viewing session. But unfortunately Google/YouTube does not show this session time metric to users – it’s a closely held secret, just like the exact algorithm by which Google ranks websites in search. So we have to use other metrics that, when used together, give us general indicators of the quality of our session time.

Tracking By Channel, Video, And Date Range

As we begin, it’s important to know how the features near the top of the page work. It’s very easy to toggle between looking at overall channel analytics and individual video analytics. If you want to look at an individual video’s metrics, click in the “Search for Content” field (highlighted in yellow above) and type in the name of the video. To look at the channel overall, just clear that field. Towards the top right of the page, you can select the date range (highlighted in pink above) that you want to measure.

The Top 5 YouTube Analytics features every marketer should know:

1) The Subscriber Count

The subscriber count is easy to find on any of your videos – you don’t even need to go into YouTube Analytics for this one. This may be basic, but your subscriber count offers a predictive analytic of how many organic views you’ll get on a given future video. As a brand, if you do no additional amplification of the video via social media, email or paid promotion, you can reasonably expect about 5% of your subscriber count to transfer to views. So if you have 1000 subscribers, you can expect around 50 views. Of course, your video still has to be interesting and relevant to the audience. But, you may be asking, shouldn’t every video on YouTube get viewed millions of times? Of course not. It’s the same on Facebook or Twitter – if you don’t have the likes or followers, don’t expect to get your posts read. Same goes for YouTube videos.

Often you will see a business YouTube channel that has many more views than subscribers. These views have most likely been driven as part of a paid campaign. But the important question is: Are those paid views properly targeted and engaging the viewer? Without proper targeting of engaging videos, the views lose their value.

2) Average View Duration and Percentage Viewed

Matt Gielen, director of programming and audience development for Frederator Networks, is one of the top thinkers in the YouTube Analytics space. Gielen says that “Average View Duration” and “Average Percentage Viewed” are his most valuable metrics. By looking at the average view duration you can get a pretty good idea of how long people want to watch videos on your channel. If you can keep viewers watching longer, your channel will generally rank higher in YouTube search and suggested videos.

“Watch Time, as YouTube refers to it,” Gielen says, “is the primary factor in YouTube algorithm optimization. As a result, focusing on the average view duration and percentage of video viewed are the two most valuable metrics to get more organic viewership to your channel and videos.”

Check out your channel’s average view duration here.

3) Traffic Sources, Especially Suggested Video Views

Just as in website analytics, you should know where your YouTube traffic is coming from. And importantly, the “Traffic Sources” section shows a value for “Suggested Videos.” Suggested videos are referrals to other videos that come up on the right hand side of each page when you’re watching a video (on mobile, they’re below the video). Suggested videos are the top source of organic traffic for most videos, and they’re ranked by session time and relevance to the playing video. You can increase the likelihood of your video being clicked on here by having enticing thumbnails that look good small and are relevant to the topic your video covers.

Check out your channel’s traffic sources here.

4) Subscribers Driven By Videos

If people are interested in your videos and products, one way to show that interest is by subscribing to your channel. If your videos are very engaging, you will be able to track, for each video, day by day, how many subscribers that video is bringing you.

According to Jeremy Vest of the YouTube marketing agency VidPow, if you’re driving more than 1% subscribers per view, you’re on the right track. Meaning for every 1,000 views, your video should drive 10 new subscribers to your channel.

“By understanding the percentage of people watching videos who then subscribe to your channel,” Vest said, “you can gauge how many people value your message. If this metric is low, you might not be providing value or entertainment.”

Check out how many new subscribers you’re driving on your channel.

5) Video Engagement Reports

Again it comes back to engagement. How engaging are your videos to your viewers? Well, if people value a video, they will like it, comment on it, and/or share it. According to VidPow, if you can get more than 1% engagement on a video, you’re doing OK. It’s a little tricky to total up all the engagement points by hand, but as a shortcut you can add the comments and likes and estimate that as the engagement. So if you have three engagements (two likes and one comment) on a video with about 300 views, your engagement is on the right track.

Check out your how many likes your YouTube videos are getting.

Alright, now you’re equipped with your YouTube Analytics tools, and en route to a more engaging, and hopefully more prosperous, YouTube channel. Go forth and conquer! If you have questions, feel free to ask them in the comments section.

Video is taking content marketing by storm as people are spending more and more time watching videos online. Brands are able to create strong relationships with their audiences, build brand loyalty, and drive quantifiable customer engagement. Despite this trend, few companies know how to utilize video content marketing. Watch Act-On’s free webinar to learn 3 Building Blocks to Get You Started with Video Marketing.

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YouTube will livestream Google’s AI playing Go superstar Lee Sedol in March

February 6, 2016   Big Data

Google will provide YouTube livestreams of its artificial intelligence (AI) software playing games of the ancient Chinese board game of Go against Lee Sedol, the highest ranked Go player in the world. Demis Hassabis, head of the Google DeepMind lab behind its AlphaGo AI system, announced the news today in a tweet.

Match days: 9, 10, 12, 13, 15 March – will be livestreamed on YouTube. More details soon. We are very excited to be coming to South Korea!

— Demis Hassabis (@demishassabis) February 4, 2016

This news that there will be a broadcast of the five-game match scheduled to take place in Seoul — and for which there is a $ 1 million prize — is interesting for a few reasons.

Google did not publicize the matches that its AI played against French Go champion Fan Hui — even though the software ended up sweeping the human 5-0, which lead to a paper in the prominent scientific journal Nature.

Sedol, of South Korea, is much better known than Hui — and it does make sense to build on all of the media attention that Google received as a result of the Nature paper.

Google is clearly jazzed about its progress in AI, which is already used inside many products at the company. On the Alphabet earnings call earlier this week, Google CEO Sundar Pichai boasted about AlphaGo’s victory over Hui, almost as if it was a business highlight for the quarter. AI isn’t a business segment at Alphabet, or even a product. But as Google advances its technology in the area, AI could lead to business advantages — like greater user engagement and satisfaction with speech recognition on mobile devices.

It is possible that AlphaGo could lose to Sedol.

“I have heard that Google DeepMind’s AI is surprisingly strong and getting stronger, but I am confident that I can win at least this time,” Sedol said in a statement.

A Sedol victory — put another way, a loss for AlphaGo — would be something thousands or perhaps millions of people would see in real time, and something that would persist on the Internet for years, right alongside all the other videos on Go. A loss televised on YouTube like that could be an embarrassment to the herd of AI researchers inside Google, which is competing with companies like Facebook and Microsoft to pick up talent and technology.

But a victory would be a truly excellent coup for Google. It would be the kind of thing that would validate its considerable research spending on AI. People watched IBM’s Watson beat Jeopardy contestants on television. Now Google’s vastly more complex AlphaGo system could be in for its 15 minutes — or hours, really — of fame.

“If we win the match in March, then that’s sort of the equivalent of beating Kasparov in chess,” Hassabis told reporters in a press briefing on the Nature paper last month. “Lee Sedol is the greatest player of the past decade. I think that would mean AlphaGo would be better than any human at playing Go. Go is the ultimate game in AI research.”

Google’s innovative search technologies connect millions of people around the world with information every day. Founded in 1998 by Stanford Ph.D. students Larry Page and Sergey Brin, Google today is a top web property in all major glob… read more »

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Robots can now learn to cook just like you do: by watching YouTube videos

January 4, 2015   Big Data

Researchers have come up with a new way to teach robots how to use tools simply by watching videos on YouTube.

The researchers, from the University of Maryland and the Australian research center NICTA, have just published a paper on their achievements, which they will present this month at the 29th annual conference of the Association for the Advancement of Artificial Intelligence.

The demonstration is the latest impressive use of a type of artificial intelligence called deep learning. A hot area for acquisitions as of late, deep learning entails training systems called artificial neural networks on lots of information derived from audio, images, and other inputs, and then presenting the systems with new information and receiving inferences about it in response.

The researchers employed convolutional neural networks, which are now in use at Facebook, among other companies, to identify the way a hand is grasping an item, and to recognize specific objects. The system also predicts the action involving the object and the hand.

To train their model, researchers selected data from 88 YouTube videos of people cooking. From there, the researchers generated commands that a robot could then execute.

“We believe this preliminary integrated system raises hope towards a fully intelligent robot for manipulation tasks that can automatically enrich its own knowledge resource by “watching” recordings from the World Wide Web,” the researchers concluded.

Read their full paper, “Robot Learning Manipulation Action Plans by ‘Watching’ Unconstrained Videos
from the World Wide Web,” here (PDF).


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