Using Artificial Intelligence with Social Media

With social media usage at an all-time high and the amount of information being shared astronomical, it’s no surprise that some of the content is bogus. Half of U.S. adults claim that they get their news via social media channels, but not everything that is shared is factual and legitimate. To weed out some of the incorrect information, social media platforms are using artificial intelligence.

This article by Venture Beat talks about using it as a spam filter:

How AI is becoming essential for social media | VentureBeat | Bots | by Eli Israel, Meshfire

The best analogy for the use of artificial intelligence in social media is to think of it like spam filtering for email. We use some of the same techniques to

Image from Pixabay

help email users find the most important messages and ignore the junk. But we would never have our anti-spam systems write our emails for us or try to tell us what they mean.

The same applies in social media: AI will help us find the most valuable, most important interactions to engage in, but humans will have to do all the actual engagement.

The final lesson to learn from the analogy with anti-spam tools is that AI for finding and prioritizing social media interactions is now an essential tool for community managers, marketers, and customer service teams.

Read the full post here:  How AI is becoming essential for social media | VentureBeat | Bots | by Eli Israel, Meshfire

If you haven’t been keeping up with the advances in artificial intelligence, it is getting closer to the original definition of AI in a 1955 proposal by Dartmouth College. (pictured below) According to the video below, the first six aspects have been somewhat mastered, but randomness and creativity is just beginning to be discovered.

This video by Cold Fusion goes into a deeper explanation of AI and where we are at today. The difference between artificial intelligence, machine learning, deep learning are analyzed.  I love the nonsensical script that was written using AI at 2:43:

How are the big social platforms using artificial intelligence to help make their product better?  This is a brief version of what each is doing:

Facebook:  Using it to sort through large databases helps adjust suggestions, news feed filters, figure out trending topics, and tag the appropriate friends in photos.

Google:  DeepMind is working on making AI faster, more efficient learning to save operational memory and accomplish tasks more efficiently.

LinkedIn:  Using Bright.com to help offer better job-candidate matches for both employers and job seekers.

Pinterest:  Using Visual Graph for object recognition to boost Pin and product recommendations,ad performance and relevance prediction, and to detect spam.

This statement in a post by Hootsuite sums up the need for artificial intelligence with social media:

Artificial Intelligence in Social Media: What AI Knows About You, and What You Need to Know

Image from Pixabay

Almost every major player in the social media arena has invested internal resources, or established third-party collaborations with teams focused on artificial intelligence. Don’t worry, we’re still quite far from SkyNet, but that last good product recommendation you got online may have been the work of an AI technology. To help you figure out what deep learning really does for major social networks, here’s the skinny on artificial intelligence in social media.

Read the original post here:   Artificial Intelligence in Social Media: What AI Knows About You, and What You Need to Know

Seen originally on S&S Pro Blog

How Google is Using Machine Learning to Lower Energy Usage

In a time period when global warming and climate change are on everyone’s mind and a rather important topic of discussion, Google is stepping up to do their part. It’s no surprise that by using machine learning, they are helping the environment. This basically means that they are using artificial intelligence (AI) to cut their energy usage as opposed to humans doing the same job.

The amount of energy that is saved in their massive data centers across the globe is 15% so far.  By using DeepMind technology, an AI company that Google purchased in 2014, they are able to anticipate incoming higher-than-normal data loads and then cool the system before it gets past the point of needing a big surge of energy to do that.

How many jobs were lost?  That isn’t mentioned in the following article from The Guardian, but it is fascinating how Google is using machine learning to lower energy usage. It sounds pretty futuristic, but Google never fails to amaze me with their integration of AI technology.

Google uses AI to cut data centre energy use by 15% | Environment | The Guardian

Google says it has cut its vast data centres’ energy use by 15% by applying artificial intelligence to manage them more efficiently than humans.

The servers that power billions of web searches, streamed films and social media accounts are estimated to account for approximately 2% of global greenhouse gas emissions. Google is believed to have one of the biggest fleets of them in the world.

On Wednesday, Google said it had proved it could cut total energy use at its data centres by 15% by deploying machine learning from DeepMind, the British AI company it bought in 2014 for about £400m. Such centres require significant energy for cooling, as well as constant adjustments to air temperature, pressure and humidity, to run as efficiently as possible.

Mustafa Suleyman, DeepMind’s co-founder, said that the level of complexity and number of variables meant the job of managing data centres was one where an algorithm could outperform a human.

He said: “It’s one of those perfect examples of a setting where humans have a really good intuition they’ve developed over time but the machine-learning algorithm has so much more data that describes real-world conditions [five years in this case].

“It’s much more than any human has ever been able to experience, and it’s able to learn from all sorts of niche little edge cases seen in the data that a human wouldn’t be able to identify. So it’s able to tune the settings much more subtly and much more accurately.”

Suleyman said the reduction in energy use was achieved through a combination of DeepMind more accurately predicting the incoming computational load – ie when people were mostly likely to request data-hungry YouTube videos – and match that prediction very quickly to the cooling load required. “It’s about tweaking all of the knobs simultaneously,” he said.

The environmental impact of the online world has come under increasing scrutiny in recent years, as data centres’ share of global emissions has risen to be on a par with those from aviation. Google first disclosed its carbon footprint in 2011 – it was roughly equivalent to Laos’s annual emissions – and says that since then its data centres have improved so that they get 3.5 times the computing power for the same amount of energy.

The trial using machine learning to further cut those data centres’ energy – and carbon emissions – began two years ago, and was tested on “more than 1%” of its servers, Suleyman said. It is now being used across a “double-digit percentage” of all Google’s data centres globally and will be applied across all of them by the end of the year.

Google does not disclose exactly how much energy its data centres use, but says as a company it’s responsible for 0.01% of global electricity use, and much of that is data centres. DeepMind has cut Google’s energy use for cooling by 40%, and total energy use by 15%.

“I really think this is just the beginning. There are lots more opportunities to find efficiencies in data centre infrastructure,” said Suleyman. “One of the most exciting things is the kind of algorithms we develop are inherently general … that means the same machine-learning system should be able to perform well in a wide variety of environments [such as power generation facilities and energy networks].”

See more here:  Google uses AI to cut data centre energy use by 15% | Environment | The Guardian

Google is also using renewable energy to save money. This interview with  Google’s Head of Energy Strategy explains that the deals made with renewable energy companies are long term and will help save a significant amount of money. She noted that the cost of solar has come down 80% in the last six years and the price of wind energy has come down 60% in the same amount of time:

In this Tweet, which is linked to another similar article by Bloomberg, the process that DeepMind uses is compared to that of playing a video game, where the end goal is to achieve the “highest score” of saving energy. It also mentions that although Google is helping the environment, they are also helping their own bottom line with big savings in electricity costs at their data centers.

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Josh Whitney @joshcwhitney

cheap trick @Google's Deep Mind AI games its data centers into saving energy http://bloom.bg/29LtWKm  #datacenter #sustainability#green

3:44 PM - 2 Aug 2016

Google Cuts Its Giant Electricity Bill With DeepMind-Powered AI

Google just paid for part of its acquisition of DeepMind in a surprising way.

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Artificial Intelligence and the Semantic Web

If you are in the Baby Boomer era, you grew up watching all kinds of Sci-Fi shows about futuristic robots, crazy technological inventions, and space-time travel. It seems so ironic to me how quickly “make believe” has caught up with reality.  We as humans have been advancing for one hundred thousand years, but in the last 2 decades, technology has exploded exponentially.

The movie Back To The Future has surpassed the date of October 2015 that Marty McFly made his leap ahead in time to save his and his family’s existence. Space Odyssey 2001, an epic movie where HAL, their on board space computer, takes control of the ship, is now part of history, and the movie 1984 with Big Brother and the totalitarian society has long come and gone.

With many of these older Sci-Fi films, artificial intelligence is a recurring theme.   Amazingly, AI has been around for longer than Marty McFly. The term artificial intelligence was first coined in 1956 at Dartmouth College. However, achieving artificial intelligence was more difficult than coming up with the name. After a rocky start with up and down funding, it wasn’t until the 1990s that research began really moving forward.

Infographic:  History of Artificial Intelligence

Source: LiveScience

Fast forward to February 2016 when Google’s own Amit Singhal, head of the search engine department, decided to announce his retirement from the company. Google chose to replace him with John Giannandrea who was previously in charge of Google’s artificial intelligence research. Now the word is that Google will be heavily integrating AI into the way its search engine functions.

Since early 2015, Google was using a deep learning system called RankBrain to help generate responses to a small part of the search queries, but Singhal was known for having a resistance to machine learning and preferred using computerized algorithms to do the work instead. It looks as though change is on the horizon now that Singhal is gone and their artificial intelligence guru has taken his place, as you can read about in this article by wired.com.

======================================================

AI Is Transforming Google Search. The Rest of the Web Is Next

• AUTHOR: CADE METZ  BUSINESS
• DATE OF PUBLICATION: 02.04.16.02.04.16
• TIME OF PUBLICATION: 7:00 AM.7:00 AM

YESTERDAY, THE 46-YEAR-OLD Google veteran who oversees the company’s search engine, Amit Singhal, announced his retirement. And in short order, Google revealed that Singhal’s rather enormous shoes would be filled by a man named John Giannandrea. On one level, these are just two guys doing something new with their lives. But you can also view the pair as the ideal metaphor for a momentous shift in the way things work inside Google—and across the tech world as a whole.

Giannandrea, you see, oversees Google’s work in artificial intelligence. This includes deep neural networks, networks of hardware and software that approximate the web of neurons in the human brain. By analyzing vast amounts of digital data, these neural nets can learn all sorts of useful tasks, like identifying photos, recognizing commands spoken into a smartphone, and, as it turns out, responding to Internet search queries. In some cases, they can learn a task so well that they outperform humans. They can do it better. They can do it faster. And they can do it at a much larger scale.

This approach, called deep learning, is rapidly reinventing so many of the Internet’s most popular services, from Facebook to Twitter to Skype. Over the past year, it has also reinvented Google Search, where the company generates most of its revenue. Early in 2015, as Bloomberg recently reported, Google began rolling out a deep learning system called RankBrain that helps generate responses to search queries. As of October, RankBrain played a role in “a very large fraction” of the millions of queries that go through the search engine with each passing second.

As Bloomberg says, it was Singhal who approved the roll-out of RankBrain. And before that, he and his team may have explored other, simpler forms of machine learning. But for a time, some say, he represented a steadfast resistance to the use of machine learning inside Google Search. In the past, Google relied mostly on algorithms that followed a strict set of rules set by humans. The concern—as described by some former Google employees—was that it was more difficult to understand why neural nets behaved the way it did, and more difficult to tweak their behavior.

These concerns still hover over the world of machine learning. The truth is that even the experts don’t completely understand how neural nets work. But they do work. If you feed enough photos of a platypus into a neural net, it can learn to identify a platypus. If you show it enough computer malware code, it can learn to recognize a virus. If you give it enough raw language—words or phrases that people might type into a search engine—it can learn to understand search queries and help respond to them. In some cases, it can handle queries better than algorithmic rules hand-coded by human engineers. Artificial intelligence is the future of Google Search, and if it’s the future of Google Search, it’s the future of so much more.

Read more here:  http://www.wired.com/2016/02/ai-is-changing-the-technology-behind-google-searches

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