Daily Archives: February 7, 2017

AI For Matching Images With Spoken Word Gets A Boost From MIT – Fast Company

Posted: February 7, 2017 at 8:15 am

Children learn to speak, as well as recognize objects, people, and places, long before they learn to read or write. They can learn from hearing, seeing, and interacting without being given any instructions. So why shouldnt artificial intelligence systems be able to work the same way?

That's the key insight driving a research project under way at MIT that takes a novel approach to speech and image recognition: Teaching a computer to successfully associate specific elements of images with corresponding sound files in order to identify imagery (say, a lighthouse in a photographic landscape) when someone in an audio clip says the word "lighthouse."

Though in the very early stages of what could be a years-long process of research and development, the implications of the MIT project, led by PhD student David Harwath and senior research scientist Jim Glass, are substantial. Along with being able to automatically surface images based on corresponding audio clips and vice versa, the research opens a path to creating language-to-language translation without needing to go through the laborious steps of training AI systems on the correlation between two languages words.

That could be particularly important for deciphering languages that are dying because there aren't enough native speakers to warrant the expensive investment in manual annotation of vocabulary by bilingual speakers, which has traditionally been the cornerstone of AI-based translation. Of 7,000 spoken languages, Harwath says, speech recognition systems have been applied to less than 100.

It could even eventually be possible, Harwath suggested, for the system to translate languages with little to no written record, a breakthrough that would be a huge boon to anthropologists.

"Because our model is just working on the level of audio and images," Harwath told Fast Company, "we believe it to be language-agnostic. It shouldnt care what language its working on."

t-SNE analysis of the 150 lowest-variance audio pattern cluster centroids for k = 500. Displayed is the majority-vote transcription of the each audio cluster. All clusters shown contained a minimum of 583 members and an average of 2482, with an average purity of .668.

The MIT project isnt the first to consider the idea that computers could automatically associate audio and imagery. But the research being done at MIT may well be the first to pursue it at scale, thanks to the "renaissance" in deep neural networks, which involve multiple layers of neural units that mimic the way the human brain solves problems. The networks require churning through massive amounts of data, and so theyve only taken off as a meaningful AI technique in recent years as computers processing power has increased.

Thats led just about every major technology company to go on hiring sprees in a bid to automate services like search, surfacing relevant photos and news, restaurant recommendations, and so on. Many consider AI to be perhaps the next major computing paradigm.

"It is the most important computing development in the last 20 years," Jen-Hsun Huang, the CEO of Nvidia, one of the worlds largest makers of the kinds of graphics processors powering many AI initiatives, told Fast Company last year, "and [big tech companies] are going to have to race to make sure that AI is a core competency."

Now that computers are powerful enough to begin utilizing deep neural networks in speech recognition, the key is to develop better algorithms, and in the case of the MIT project, Harwath and Glass believe that by employing more organic speech recognition algorithms, they can move faster down the path to truly artificial intelligent systems along the line of what characters like C-3PO have portrayed in Star Wars movies.

To be sure, were many years away from such systems, but the MIT project is aiming to excise one of the most time-consuming and expensive pieces of the translation puzzle: requiring people to train models by manually labeling countless collections of images or vocabularies. That laborious process involves people going through large collections of imagery and annotating them, one by one, with descriptive keywords.

Harwath acknowledges that his team spent quite a lot of time starting in late 2014 doing that kind of manual, or supervised, learning on sound files and imagery, and that afforded them a "big collection of audio."

Now, theyre on to the second version of the project, which is to build algorithms that can both learn language as well as the real-world concepts the language is grounded in, and to do so utilizing very unstructured data.

Heres how it works: The MIT team sets out to train neural networks on what amounts to a game of "which one of these things is not like the other," Harwath explains.

They want to teach the system to understand the difference between matching pairsan image of a dog with a fluffy hat and an audio clip with the caption "dog with a fluffy hat"and mismatched pairs like the same audio clip and a photo of a cat.

Matches get a high score and mismatches get a low score, and when the goal is for the system to learn individual objects within an image and individual words in an audio stream, they apply the neural network to small regions of an image, or small intervals of the audio.

Right now the system is trained on only about 500 words. Yet its often able to recognize those words in new audio clips it has never encountered. The system is nowhere near perfect, for some word categories, Harwath says, the accuracy is in the 15%-20% range. But in others, its as high as 90%.

"The really exciting thing," he says, "is its able to make the association between the acoustic patterns and the visual patterns. So when I say lighthouse, Im referring to a particular [area] in an image that has a lighthouse, [and it can] associate it with the start and stop time in the audio where you says, lighthouse."

A different task that they frequently run the system through is essentially an image retrieval task, something like a Google image search. They give it a spoken query, say, "Show me an image of a girl wearing a blue dress in front of a lighthouse," and then wait for the neural network to search for an image thats relevant to the query.

Heres where its important not to get too excited about the technology being ready for prime time. Harwath says the team considers the results of the query accurate if the appropriate image comes up in the top 10 results from a library of only about 1,000 images. The system is currently able to do that just under 50% of the time.

The number is improving, though. When Harwath and Glass wrote a paper on the project for an upcoming conference in France, it was 43%. Still, he believes that although there are regular improvements and increased accuracy every time they train a new model, theyre held back by the available computational power. Even with a set of eight powerful GPUs, it can still take two weeks to train a single model.

An example of our grounding method. The left image displays a grid defining the allowed start and end coordinates for the bounding box proposals. The bottom spectrogram displays several audio region proposals drawn as the families of stacked red line segments. The image on the right and spectrogram on the top display the final output of the grounding algorithm. The top spectrogram also displays the time-aligned text transcript of the caption, so as to demonstrate which words were captured by the groundings. In this example, the top three groundings have been kept, with the colors indicating the audio segment that is grounded to each bounding box.

Perhaps the most exciting potential of the research is in breakthroughs for language-to-language translation.

"The way to think about it is this," Harwath says. "If you have an image of a lighthouse, and if we speak different languages but describe the same image, and if the system can figure out the word Im using and the word youre using, then implicitly, it has a model for translating my word to your word . . . It would bypass the need for manual translations and a need for someone whos bilingual. It would be amazing if we could just completely bypass that."

To be sure, that is entirely theoretical today. But the MIT team is confident that at some point in the future, the system could reach that goal. It could be 10 years, or it could be 20. "I really have no idea," he says. "Were always wrong when we make predictions."

In the meantime, another challenge is coming up with enough quality data to satisfy the system. Deep neural networks are very hungry models.

Traditional machine learning models were limited by diminishing returns on additional data. "If you think of a machine learning algorithm as an engine, data is like the gasoline," he says. "Then, traditionally, the more gas you pour into the engine, the faster it runs, but it only works up to a point, and then levels off.

"With deep neural networks, you have a much higher capacity. The more data you give it, the faster and faster it goes. It just goes beyond what older algorithms were capable of."

But he thinks no ones sure of the outer limits of deep neural networks capacities. The big question, he says, is how far will a deep neural network scale? Will they saturate at some point and stop learning, or will it just keep going?

"We havent reached this point yet," Harwath says, "because people have been consistently showing that the more data you give them, the better they work. We dont know how far we can push it."

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Artificial intelligence: How to build the business case – ZDNet

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"The acceptance of AI in the business is going to involve an evolution."

There's plenty of excitement around artificial intelligence: analyst Gartner places it at the top of its top 10 strategic technology trends for 2017. The analyst says the technology has reached a tipping point and AI is beginning to extend its tentacles into every service, thing, or application, and that it will become the primary battleground for technology vendors looking to make money through 2020.

Interim CIO Christian McMahon, who is managing director at transformation specialist three25, acknowledges interest in AI has exploded recently, but he also voices a word of caution.

AI and the Future of Business

Machine learning, task automation and robotics are already widely used in business. These and other AI technologies are about to multiply, and we look at how organizations can best take advantage of them.

"All the major corporates, accelerators and venture capitalists are desperate to find a foothold," he says. "However, I don't think the current AI market is at a stage where breakthrough technologies are about to be unveiled. Rather, it's a vibrant market which seems more conceptual than one of tangible substance."

It is a sentiment that chimes with Omid Shiraji, interim CIO at Camden Council. His organisation holds a huge amount of data and aims to use its knowledge to help people with complex needs. AI could provide a breakthrough in data insight, yet Shiraji says CIOs must focus on value creation.

"The business case for these projects is not easy -- you can take a step into the unknown," says Shiraji. "You sometimes have to rely on intuition rather than ROI to place your investments in these types of projects."

Gartner suggests executives who take a risk on AI projects will be rewarded and should consider experiments in one or two high-impact scenarios. So how will pioneering organisations build a business case for AI? Two IT leaders -- one each from the private and public sectors -- give us their take.

Sizing up the opportunity

Matt Peers, CIO of global law firm Linklaters, draws a parallel between the use of big data and the growing importance of AI. Peers says success in big data is all about being able to make the best use of the information you possess -- and Linklaters, a 175-year-old firm, is a business with more knowledge than most.

Peers says his organisation should be able to turn its history into a competitive advantage. Lawyers need knowledge about legal precedent, previous projects, and internal skills specialisms. He believes advances in AI will help his firm to create more sophisticated approaches to search.

"The key to success is getting the right information to people quickly," he says. "Some of the tools that are being developed for AI will help us search big data. Most of the technologies on the market today are good at clustering and reading contracts, and enabling you to search vast volumes of data for legal themes."

He expects the ability to digitise and search contracts for key legal themes to become commonplace very quickly. Linklaters has already created an AI working group to help analyse services in the marketplace and to work out how these technologies might impact the business.

"Firms in some key sectors are already making a move," says Peers. "We've spent a lot of time in the past 18 months sizing up the opportunities by talking to people, seeing demonstrations, and running proof of concept studies."

Peers recognises AI could also help change the way lawyers work, yet he also expects a cultural challenge. Senior partners trust their associates to spend hours considering the details of legal documents. Trusting computers to undertake the same task in seconds presents a different form of dependence.

"It's a big shift because the reputation of that lawyer and firm is on the line," he says. "The acceptance of AI in the business is going to involve an evolution. It's important to remember that there are many matters in the legal world where AI is not going to be useful for quite a long time. It's going to take a while for computers to provide trusted advice and opinion."

Using data to save lives

Toby Clarke, interim head of IT at Moorfields Eye Hospital NHS Foundation Trust, says AI will have a huge impact on the work of publicly-funded organisations. Moorfields has been working closely with DeepMind Research, a project that involves the Trust sharing a set of one million anonymised eye scans.

The project between Moorfields and DeepMind relies on historic scans, meaning that while the results of the research might be used to improve future care, they will not affect patients today. However, the hope is that discoveries through the initiative will lead to earlier detection and help reduce preventable eye disease.

"What they're doing with that information is truly amazing," says Clarke, referring to the DeepMind project. "It's cutting edge and will make a significant difference."

He says the key to long-term change through AI is being able to use information to inform patient care. And that use presents challenges, particularly in terms of data security and confidentiality. "The real value will come from using non-anonymised data," he says.

"If you have a large repository of information, and you can add big data from demographics, you can start to take make predictions about patient healthcare. You could potentially say when people should be coming in for tests in terms of early warnings."

The current project uses anonymous data. "It has to be that way," says Clarke. "In terms of healthcare, there will always be issues around how you commercialise data, and how you deliver value back to the host organisation and its patients."

Clarke, however, is keen to point out that similar projects could sponsor significant change. "It's difficult for humans to understand the impact of AI right now but the potential is huge," he says. "The technology self-learns and I find it exceptionally exciting. AI is different and new, and it's something everyone involved in IT should be investigating."

In contrast to reports that automation simply leads to job cuts, Clarke says AI - particularly in the role of predictive medicine - could lead to a whole new range of data science roles. "It's not about removing jobs but it is potentially about saving lives," he says.

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Artificial Intelligence-Driven Robots: More Brains Than Brawn – Forbes

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Artificial Intelligence-Driven Robots: More Brains Than Brawn
Forbes
Automation and robots for manufacturing have come a long way since Unimate was introduced in the 1960's. The machines that manufacturers are using today are smaller, safer and able to perform more than a single task without expensive programming.

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Why C-Levels Need To Think About eLearning And Artificial Intelligence – Forbes

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Why C-Levels Need To Think About eLearning And Artificial Intelligence
Forbes
... proprietary Artificial Intelligence to analyze each learner's behavior, cognition, engagement and performance to predict learning and future performance, optimize learning content and to create a deep personalized individual and social learning ...

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Artificial Intelligence Correctly Predicted the Patriots’ 34-28 Super … – Digital Trends

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Home > Cool Tech > Swarm AI correctly predicted the outcome of Super

Why it matters to you

If you believe it was impossible to predict the outcome of this year's Super Bowl, you would be wrong. You may want to consult with the AI before your next sporting wager.

The New England Patriots win over the Atlanta Falcons was nothing short of amazing. The Pats rallied back from a 25-point deficit to tie the game in the final minutes of regulation and secured the win with a decisive touchdown drive in overtime. You may still be reeling from the comeback, but heres something else that will blow your mind: Even before the first ball was snapped, an artificial intelligence platformaccurately predicted the outcome of the game, right down to the 34-28 win by the Patriots.

Created by Unanimous, Swarm AI is a prediction engine that combines swarming algorithms with human input. The companys AI software allows real, live human users to gather in artificial swarms, The software monitored the conversations in these swarms and collected group intelligence data that is used to make predictions. The company has a string of success accurately predicting the top four winning horses in a recent Kentucky Derby, the last two Stanley Cup winners, and nine out of 10 NFLplayoff games.

Being able to predict the final score of the Super Bowl is not an easy task, though. Of the 1,641 Super Bowl final score predictions published by Scripps Howard over the past 19 years, only two have been correct regarding Super Bowl final score predictions. For the Super Bowl, Unanimous took on this challenge by creating wars with 40 football fans and connected them online. The platform then used the swarms group intelligence to make its own predictions abouthow many points each team would score.

More:Suggestic wants to use artificial intelligence to help you stick to your diet

So when the Patriots ended up winning the Super Bowl with the predicted 34-28 score, the folks at Unanimous (and everywhere else) were blown away by this uncanny prediction. Now that the Super Bowl is out of the way, Unanimous is eyeing the NHLs Stanley Cup and the NCAA March Madness tournament.

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Forget lessons, these smart skis are loaded with artificial intelligence – Mashable

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Forget lessons, these smart skis are loaded with artificial intelligence
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Forget lessons, these smart skis are loaded with artificial intelligence. 790. Shares. Share. Tweet. Share. What's This? Image: Piq. 2016%2f09%2f16%2f8f%2fhttpsd2mhye01h4nj2n.cloudfront.netmediazgkymde1lza3.c1888 By Karissa Bell 2017-02-07 ...

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RealDoll Creating Artificial Intelligence System, Robotic Sex Dolls … – Breitbart News

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Harmony AI, which is set to be released on April 15, will be a smartphone app andis reported tofeature a range of traits for customers to choose fortheir sex dolls,while the dolls will also be able to learn about their ownersand respond in different ways accordingly.

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We are developing the Harmony AI system to add a new layer to the relationships people can have with a RealDoll, said CEO Matt McMullen to Digital Trends. Many of our clients rely on their imaginations to a great degree to impose imagined personalities on their dolls. With the Harmony AI, they will be able to actually create these personalities instead of having to imagine them.

They will be able to talk to their dolls, and the AI will learn about them over time through these interactions, thus creating an alternative form of relationship, he continued. The scope of conversations possible with the AI is quite diverse, and not limited to sexual subject matter. We feel that this system, and this technology, will appeal to a segment of the population that struggles with forming intimate connections with other people, whether by choice or circumstance. Furthermore, it will likely attract those who seek to explore uncharted and new territory where relationships and sex are concerned.

Harmony AI will be the first product in a range of next-generation technologies coming from RealDoll over the next few years.

Other planned releases include robotic head systems, which are set to be released by the end of the year, followed by a virtual reality platform in 2018.

RealDoll isnt the first company to recognize the potential connection between sex and AI. This happens because people are lonely and bored It is a symptom of our society, said Robin Labs chief executive Ilya Eckstein, who claims that his companys virtual assistant Robin is used by teenagers and truckers without girlfriends for up to 300 conversations a day.

As well as the people who want to talk dirty, there are men who want a deeper sort of relationship or companionship, hecontinued, adding that some people wanted to talk for no particular reason and were just lonely or bored.

In an interview with Breitbart Tech last year, Futurologist Dr. Ian Pearson also predicted that sex with robots would be fully emotional in the future, addingthat people will eventually spendabout the same as they do today on a decent family-size car.

Artificial intelligence is reaching human levels and also becoming emotional as well, claimed Dr. Pearson. So people will actually have quite strong emotional relationships with their own robots. In many cases that will develop into a sexual one because theyll already think that the appearance of the robot matches their preference anyway, so if it looks nice and it has a superb personality too its inevitable that people will form very strong emotional bonds with their robots and in many cases that will lead to sex.

Charlie Nash is a reporterforBreitbart Tech. You can follow himon Twitter@MrNashingtonorlike his page at Facebook.

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Is America Prepared for Meme Warfare? – Motherboard

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Memes, as any alt-right Pepe sorcerer will tell you, are not just frivolous entertainment. They are magic, the stuff by which reality is made and manipulated. What's perhaps surprising is that this view is not so far off from one within the US defense establishment, where a growing body of research explores how memes can be used to win wars.

This recent election proved that memes, some of which have been funded by politically motivated millionaires and foreign governments, can be potent weapons, but they pose a particular challenge to a superpower like the United States.

Memes appear to function like the IEDs of information warfare. They are natural tools of an insurgency; great for blowing things up, but likely to sabotage the desired effects when handled by the larger actor in an asymmetric conflict. Just think back to the NYPD's hashtag boondoggle for an example of how quickly things can go wrong when big institutions try to control messaging on the internet. That doesn't mean research should be abandoned or memes disposed of altogether, but as the NYPD case and other examples show, the establishment isn't really built for meme warfare.

For a number of reasons, memetics are likely to become more important in the new White House.

To understand this issue, we first have to define what a meme is because that is a subject of some controversy and confusion in its own right. We tend to think of memes from their popular use on the internet as iterative single panel illustrations with catchy tag lines, Pepe and Lolcats being two well known known examples of that type. But in its scientific and military usage a meme refers to something far broader. In his 2006 essay Evolutionary Psychology, Memes and the Origin of War, the American transhumanist writer Keith Henson defined memes as "replicating information patterns: ways to do things, learned elements of culture, beliefs or ideas."

Memetics, the study of meme theory and application, is a kind of grab bag of concepts and disciplines. It's part biology and neuroscience, part evolutionary psychology, part old fashioned propaganda, and part marketing campaign driven by the same thinking that goes into figuring out what makes a banner ad clickable. Though memetics currently exists somewhere between science, science fiction, and social science, some enthusiasts present it as a kind of hidden code that can be used to reprogram not only individual behaviors but entire societies.

For a number of reasons, memetics are likely to become more important in the new White House. Jeff Giesea is a former employee of tech giant and Trump donor Peter Thiel, and an influential organizer within the alt right who was prominently featured in recent profiles on the movement and its ties to the Trump administration. Giesea is also the author of an article published in an official NATO strategic journal in late 2015just as the Trump campaign was really building steamentitled "It's Time to Embrace Memetic Warfare."

"It's time to drive towards a more expansive view of Strategic Communications on the social media battlefield," Giesea said in his essay on the power of memes. "It's time to adopt a more aggressive, proactive, and agile mindset and approach. It's time to embrace memetic warfare."

Giesea was far from the first to suggest this. Some forward thinkers within the US military were interested in how memes might be used in warfare years before the killing and digital resurrection of Harambe dominated popular culture. Public records indicate that the military's interest in memes picked up after 2001, spurred by the wars against jihadist terrorist groups and the parallel "War of ideas" with Islamist ideology.

Despite the government research and interest inside the military for applying memes to war, it seemed to be insurgent groups that used them most effectively.

"Memetics: A Growth Industry in US Military operations" was published in 2005 by Michael B. Prosser, then a Major and now a Lieutenant Colonel in the Marine Corps. Written as an assignment for the Marine Corps' School of Advanced Warfighting, Prosser's paper includes a disclaimer clarifying that it represents only his own views and not those of the military or US government. In it, he lays out a vision for both weaponizing and diffusing memes, defined as "units of cultural transmission" and "bits of cultural information transmitted and replicated throughout populations and/or societies" in order to "understand and defeat an enemy ideology and win over the masses of undecided noncombatants."

Prosser's paper includes a detailed proposal for the development of a "Meme Warfare Center." The center's function is to "advise the Commander on meme generation, transmission, coupled with a detailed analysis on enemy, friendly and noncombatant populations." Headed by a senior civilian or military leader known as a "Meme Management Officer" or "Meme and Information Integration Advisor," Prosser writes, "the MWC is designed to advise the commander and provide the most relevant meme combat options within the ideological and nonlinear battle space."

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A year after the Meme Warfare Center proposal was published, DARPA, the Pentagon agency that develops new military technology, commissioned a four-year study of memetics. The research was led by Dr. Robert Finkelstein, founder of the Robotic Technology Institute, and an academic with a background in physics and cybernetics.

Finkelstein's study of "Military Memetics" centered on a basic problem in the field, determining "whether memetics can be established as a science with the ability to explain and predict phenomena." It still had to be proved, in other words, that memes were actual components of reality and not just a nifty concept with great marketing.

Finkelstein's work tries to bring memetics closer to hard science by providing a "meme definition for Military Memetics," that is "information which propagates, has impact, and persists (Info-PIP)." Classifying memes according to this definition, and separating them out from all the ideas that don't count as memes, he offers metrics like "persistence" to measure their effectiveness.

Despite the government research and interest inside the military for applying memes to war, it seemed to be insurgent groups that used them most effectively. During the early stages of ISIS' war in Iraq and Syria, for instance, the group used memes to captivate an international audience and broadcast its message both to enemies and potential recruits.

One of the first public applications of the research into memetics and social media propaganda was the State Department's 2013 "Think Again Turn Away" initiative. The campaign's attempts to counteract ISIS social media propaganda did not turn out well. The program, according to director of the SITE Intelligence Group Rita Katz, was "not only ineffective, but also provides jihadists with a stage to voice their arguments." Similar to how ISIS supporters hijacked the government's platform, a year later activists used the NYPD's own hashtag to highlight police abuse.

"Look at their fancy memes compared to what we're not doing," said Sen. Cory Booker to other members of the Homeland Security Committee during a 2015 hearing on "Jihad 2.0." Booker's assessment has become increasingly common but some critics question whether focusing on a "meme gap" is an effective way to combat groups like ISIS.

"I've never seen a military program in that area that was effective," John Robb, a former Air Force pilot involved in special operations and author of Brave New War: The Next Stage of Terrorism and the End of Globalization, told Motherboard. As he sees it, the US military will always be at a structural disadvantage when it comes to applying memetics in war because, "the most effective types of manipulation all yield disruption." According to Robb, "the broad manipulation of public sentiment is really not in [the military's] wheelhouse," and that is largely because, "all the power is in the hands of the people on the outside doing the disruption."

Meme wars seem to favor insurgencies because, by their nature, they weaken monopolies on narrative and empower challenges to centralized authority. A government could use memes to increase disorder within a system, but if the goal is to increase stability, it's the wrong tool for the job.

"Stuff like this is perennial," Robb said about the new interest in meme warfare. "Every couple of years a new program comes out, people spend money for a couple of years then it goes away. Then people forget about that failure and they do it again."

We've just witnessed a successful meme insurgency in America. Donald Trump's campaign was founded as an oppositional movementagainst the Republican establishment, Democrats, the media, and "political correctness." It used memes successfully precisely because, as an opposition, it benefited by increasing disorder. Every meme about "Sick Hillary," "cucks," or "draining the swamp" chipped away at the wall built around institutional authority.

Trump's win shocked the world, but if we all read alt-right power broker Jeff Giesea's paper about memetic warfare in 2015, we might have seen it coming.

"For many of us in the social media world, it seems obvious that more aggressive communication tactics and broader warfare through trolling and memes is a necessary, inexpensive, and easy way to help destroy the appeal and morale of our common enemies," he said.

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The scientific controversy behind memes – Varsity Online

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Bethan Clark investigates the surprising academics behind memeticsm the field behind the humble internet meme

Memes currently dominate Facebook homepages and Twitter feeds. Indeed, even before their relatively recent rise to ubiquity, they had a home on niche sites for sharing image macros, early meme flagships, and other more popular platforms such as Tumblr. Recently, the rise of Memebridge has already prompted discussion on the pervasiveness and influence of memes on social media.

An interesting point is often overlooked however. There is an oft-omitted fact about the origin of internet memes: that they are not what the term was originally intended to mean. Tracing back the evolution of the term is a gateway to the surprisingly controversial field of science that inspired memes as we know them.

It was Richard Dawkins who coined the term meme, proposing to define it as the cultural version of a gene in his well-known book, The Selfish Gene. Understanding human cultural evolution as being comparable to the biological evolution of species, this makes the meme a unit of culture, just as the gene is a unit of genetic inheritance.

Like genes existing in individuals cells and being passed down through generations, memes reside in individuals and can replicate themselves. Memes are hosted in the mind and reproduce by jumping between individuals when one influences another to adopt a belief. What makes the meme such a useful idea is the framework it provides to describe cultural evolution.

In the academic world, as well as across our Facebook feeds, the meme war rages on

What counts as a meme? Almost anything, according to Dawkins. His examples include tunes, ideas, catch-phrases, clothes fashions, ways of making pots or of building arches and even the idea of God. The spin-off that we are more familiar with nowadays, internet memes, is clearly a rather more limited category.

The internet meme as a concept was first suggested by Mike Godwin in Wired in June 1993 and 20 years later, Dawkins made clear their distinction from his original. This distinction lies in their distribution, altered deliberately by human creativity as opposed to random mutation and selection processes.

However, the original version of the meme is still discussed in academia. It led to the creation of a whole field, that of memetics, where memes are used as an approach to evolutionary models of cultural information transfer. Extending the analogy with genes, if the three conditions of variation, replication, and differential fitness are met, then meme evolution willnaturally occur, and with it, cultural evolution.

Memetics is simply the study of this process, applied to culture: the analysis of the spread of ideas based on their success instead of the more traditional concern for their truth. However, it is a hotly-contested field, full of internal warring as well as external attacks.

Criticism of meme theory comes from many angles, ranging from quibbles about terminology to queries of theresearch status of the meme. Its been labelled a pseudoscience by critics, with the concept of a meme being called into question at every stage. At the level of terminology, semiotic theorists claim the meme is a simplified version of the semiotic concept of the sign, and evolutionary biology Ernst Mayr declared it an unnecessary synonym for concept.

The usefulness of memetics has also been criticised. Mary Midgley, an English moral philosopher, argued that as culture is pattern-like, a reductionist approach is limited. Its an interesting parallel to emerging criticism about internet memes, though many would reject this as taking them too seriously.

Memebridge: dank memes or dark feelings?

Not even the application of memes within the field itself is free from quarrelling. Some memeticists see memes as a useful philosophical perspective to guide inquiry, whereas others focus on developing an empirical grounding for the field to be respected.

Not everyone is convinced this is possible, however. Midgley has highlighted the reliance of memetics on producing knowledge through metaphors, something she asserts is a questionable research approach. The use of metaphor, in this case the analogy between cultural phenomena and genes, can overlook effects that do not fit neatly into the comparison.

Memeticists defend their position, pointing to the ability of metaphors to reveal insights that would otherwise have been missed, but its a debate that is unlikely to be decisively concluded any time soon. The mirror criticism of the reliance of internet memes on relatability, and the corresponding alienation of individuals who do not identify with the subject of the memes, is currently just as unresolved.

It seems the criticism and confusion in the field of memetics is unlikely to abate any time soon. In the academic world, as well as across our Facebook feeds, the meme war rages on

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The scientific controversy behind memes - Varsity Online

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Cognitive science: Dennett rides again – Nature.com

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Daniel C. Dennett W. W. Norton: 2017. ISBN: 9780393242072

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Bryce Vickmark/NYT/eyevine

Cognitive scientist Daniel Dennett in 2013.

In Joel and Ethan Coen's 2009 film A Serious Man, physics professor Larry Gopnik is in the middle of an existential crisis. In a dream, he gives a lecture on Heisenberg's uncertainty principle; Sy Ableman, the older man with whom Gopnik's wife is having an affair, stays on after the students disperse. In a condescending drawl, he addresses Gopnik and his equation-covered chalkboard: I'll concede that it's subtle, clever but at the end of the day, is it convincing?

Philosopher and cognitive scientist Daniel Dennett has been hearing variants of this riposte for decades. If history is a guide, his latest book, From Bacteria to Bach and Back, will elicit similar responses. It is a supremely enjoyable, intoxicating work, tying together 50 years of thinking about where minds come from and how they work. Dennett's path from the origins of life to symphonies is long and winding, but you couldn't hope for a better guide. Walk with him and you'll learn a lot.

The book's backbone is Charles Darwin's theory of natural selection. That replaced the idea of top-down intelligent design with a mindless, mechanical, bottom-up process that guides organisms along evolutionary trajectories into ever more complex regions of design space. Dennett also draws heavily on the idea of 'competence without comprehension', best illustrated by mathematician Alan Turing's proof that a mechanical device could do anything computational. Natural selection has created, through genetic evolution, a world rich in competence without comprehension the bacteria, trees and termites that make up so much of Earth's biomass.

Yet, as Dennett and others argue, genetic evolution is not enough to explain the skills, power and versatility of the human mind. Over the past 10,000 years, human behaviour and our ability to manipulate the planet have changed too quickly for biological evolution to have been the driving force. In Dennett's view, our brains turned into fully fledged modern minds thanks to cultural memes: 'ways of behaving' pronouncing a word this way, dancing like so that can be copied, remembered and passed on.

Some memes are better than others at getting passed on. This drives natural selection, fashioning memetic design without a designer. The first memes, Dennett argues, were words, the lifeblood of cultural evolution, which act as virtual DNA for the richly cumulative cultural evolution that marks out our species. At first, he writes, words evolved to better fit the brains they had to colonize. Only later did brains start evolving genetically to better accommodate words, beginning a co-evolutionary process that turned us into voluble creatures.

More generally, Dennett sees memetic evolution as akin to how software has co-evolved with hardware. Memes are like apps that add a talent, a bit of know-how, slowly building up the repertoire of human competences and ever-greater degrees of comprehension. This, he avers, kicked off an incremental process that led to self-monitoring, reflection and the emergence of new things to think about: words and other memes.

Later, inventions from writing to clocks gave us memorable things to do things with. Step by small step, he argues, we moved away from bottom-up cultural evolution towards consciously directed, top-down explorations, giving birth to genuinely intelligent design. This has enabled us to wipe out smallpox, put people on the Moon and ask questions about our own minds.

Perhaps none are bigger than the problem of consciousness. Dennett reprises his long-held counter-intuitive idea that consciousness is a 'user illusion' similar to the interface of an app, through which people interact with the program without understanding how it works. Memetic apps in our brains, Dennett argues, create a 'user interface' that renders the memes 'visible' to the 'self', authoring both words and deeds.

Critics often quip that Dennett doesn't explain consciousness so much as explain it away, or duck the challenge entirely, and this chapter is unlikely to bring them around. When it comes to plugging the hole of subjective experience, sceptics are likely to see his solution as barely touching the sides. Dennett might well reply that a lack of imagination prevents them from seeing how his theory supports a version of consciousness devoid of over-inflation. For the philosophical background to these hard-to-swallow ideas, see Dennett's Consciousness Explained (Little, Brown, 1991).

Although From Bacteria to Bach and Back covers territory that Dennett has explored before, it is no mere rehash. Over the past couple of decades, many psychologists, linguists and philosophers have developed ideas that extend and deepen Dennett's contributions, and he draws on these in consolidating and refining his arguments.

Dennett has earned his reputation as one of today's most readable, intellectually nimble and scientifically literate philosophers, as this subtle, clever book shows. But at the end of the day, is it convincing? It's not an open-and-shut case, as he acknowledges. Many may find the earlier chapters more persuasive than the later ones, in which memetics shoulders so much weight and human consciousness looms large. Even scholars who embrace Dennett's account of how Darwinian processes fashion cultural design may stop short of hitching their wagon to his claims. But a virtue of his broad perspective is that it can tolerate disagreements over fine details while still hewing to the spirit of his vision.

Dennett's is not the only game in town, as he well knows, but it is immensely instructive and pleasurable to see this game played with such skill, verve and wit.

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