Category Archives: Artificial Intelligence

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Artificial intelligence proves that craft beer names are total nonsense Mashable If you're a craft beer connoisseur or even just an occasional drinker you've likely noticed that names for new brews are getting out of hand. Likely in order to distance themselves from traditional, European beer names such as Franziskaner Royal ... and more »

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Artificial intelligence proves that craft beer names are total nonsense - Mashable

Spend enough time on Facebook, and youll likely encounter a post written in a tongue thats foreign to you. Thats because the social network has two billion users and supports over 45 languages. On Thursday, Facebook announced that all of its user translation servicesthose little magic tricks that happen when you click see translation beneath a post or commentare now powered by neural networks, which are a form of artificial intelligence.

Back in May, the companys artificial intelligence division, called Facebook AI Research, announced that they had developed a kind of neural network called a CNN (that stands for convolutional neural network, not the news organization where Wolf Blitzer works) that was a fast, accurate translator. Part of the virtue of that CNN is that instead of looking at words one at a time, it can consider groups of them.

Now, Facebook says that they have incorporated that CNN tech into their translation system, as well as another type of neural network, called an RNN (the R is for recurrent). Those RNNs, Facebook said in a blog item about the news, are better at understanding the context of the whole sentence than the previous system, and can reorder sentences as needed so that they make sense.

The upshot? Facebook says that the new AI-powered translation is 11 percent more accurate than the old-school approach, which is what they call a phrase-based machine translation technique that wasnt powered by neural networks. That system translated words or small groups of words individually, and didnt do a good job of considering the context or word order of the sentence.

As an example of the difference between the two translation systems, Facebook demonstrated how the old approach would have translated a sentence from Turkish into English, and then showed how the new AI-powered system would do it. The first Turkish-to-English sentence reads this way: Their, Izmirs why you said no we dont expect them to understand. Now check out the newer translation: We dont expect them to understand why Izmir said no. Notice how the AI fixed the mistakes in word and phrase order?

While neural networks had been working together with the more traditional translation system before today, now all the translation gets its smarts from AI. This new system is capable of translating in 2,000 directions. For example, a translation from English to French is one direction, French to English is a second, and French to Italian is a third direction, and so forth. Astoundingly, the neural networks handle 4.5 billion translation per day, making them quite the linguists.

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Artificial intelligence now powers all of Facebook's translation - Popular Science

There is no doubt that AI (artificial intelligence) is the new electricity and everyone is trying to get benefits from the trend. Many companies are integrating AI solutions in their business operationsto reap the benefit of emerging machine learning (ML) technologies. The seamless introduction of AI, however, requires thoughtful adaptation of corporate strategy to requirements of this emerging technology.As a partner at a venture studio, I see companies try to get in the trenches of machine intelligence without the proper preparation. Here's what we recommend companies to advance their initiatives effectively.

To be efficient, data that is fed into ML algorithms should be properly labeled, cleaned up and structured. Companies produce huge amounts of unstructured data that adds no value unless necessary transformations are made. To succeed in improving their data, companies have the option of in-house data labeling and data cleansing or using third-party services of companieslike Scale that offer programmatic access to a growing community of people specializing in making data usable.

Similarly, to enhance their AI preparedness, companies need to integrate their dispersed data sources into the unified data warehousing framework. Data warehouses and data marts allow storing data generated by different business operations and departments in one place and in the uniform representation. This allows for centralized sourcing of data for ML algorithms. Even though it sounds like a simple exercise, most of the companies we work with have trouble organizing their data sources.

Prioritize and Grow Narrow Expertise

It is often tempting to use AI solutions in every business process that may benefit from automation and ML. However, such strategy leads to dispersion of organizational resources and decreases the cumulative effect of AI innovation.

Instead of creating a horizontal platform for AI innovations, companies should prioritize concrete AI solutions that have the biggest potential to increase financial value and customer satisfaction. Growing narrow expertise in one specific area will help concentrate organizational resources on one particular task, ultimately contributing to the development of a more general solution for your business.

Get Advantage of the Academia

Academia is the main breeding ground of expertise and skills in emerging technologies like AI. The depth of theoretical knowledge and expertise offered by AI researchers is hard to attain in the private sector.

Therefore, each company that we partner with is trying to find its own machine intelligence expertise to boost its strategy. We recommend these companies reach out to talent in academia. Academic AI experts can offer a long-term AI agenda for your company. breathing life in the most exciting and revolutionary ideas that would otherwise be lost in the lengthy articles published in academic journals.

In turn, companies should provide AI researchers with an opportunity to share their research with the public by encouraging them to publish scientific papers, participate in conferences, and maintain a connection with universities. AI researchers will join those companies that offer more freedom and necessary organizational resources to put their theoretical ideas into practice.

Create a Process Versus Chaotic Experimentations

AI experimentation is great. However, too many companies rush into new AI domains without putting structured approach in place. Treating AI innovation as a process starts from automating existing data analytics procedures to create a pipeline of fresh data. Without automation of existing operations, new AI solutions may reach the wrong conclusions simply because they work on out-of-date data.

The integrity of the AI process requires modernization of the entire IT infrastructure, ranging from in-house servers and databases to cloud-based services and networks. Sound AI innovation process may be also facilitated by the organizational change towards multi-disciplinary teams and training employees to new roles associated with the AI innovation. With all departments of your organization prepared for the technological disruption, AI integration will be closely aligned with the corporate strategy and organizational goals.

Global leaders of the AI innovation facilitate the fast adoption of AI technology in all industries by open-sourcing ML libraries and APIs (Application Programming Interface). Such ML libraries as Google TensorFlow offer companies access to out-of-the-box algorithms and neural networks optimized for fast deployment in the enterprise setting.

Businesses can also take advantage of cloud-based ML APIs that allow to easily bootstrap in-house AI software. One example of such APIs is Googles Cloud Vision API provided as part of Google Cloud offerings. The system encapsulates powerful machine learning models for image classification, object detection, image-to-text transformation provided as REST API. The system may be used by companies for building metadata of their image catalogs, moderating offensive content, and developing new marketing strategies based on image sentiment analysis.

Similar functionality is also available in the recently released TensorFlow Object Detection API. Apple has recently joined the party by unveiling its Core ML API that may be used to integrate fast ML algorithms on iPhones, iPads and Apple Watch. Companies using these solutions will have instant access to image and face recognition, and natural language processing in their applications.

Partner atColab, helping startups build tech products.

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5 Ways to Advance Your Machine Learning Initiatives - HuffPost

Microsoft CEO Satya Nadella

Microsoft filed its annual report Wednesday, and among the many pages of documents and numbers are insights on what the company sees as its core vision, and that appears to be changing.

As first spotted by CNBC, Microsoft has inserted artificial intelligence into its vision for the first time, and removed references to a mobile-first world. That fits with Microsofts recent push into AI and retreat from the smartphone market.

We believe a new technology paradigm is emerging that manifests itself through an intelligent cloud and an intelligent edge where computing is more distributed, AI drives insights and acts on the users behalf, and user experiences span devices with a users available data and information, according to Microsofts vision statement.

Microsoft last year formeda new 5,000-person engineering and research teamto focuson its artificial intelligence products a major reshaping of the companys internal structurereminiscent of its massive pivotto pursue the opportunity of the Internetin the mid-1990s.

Just last month the companymade a series of AI announcements, including a new iPhone app that describes the world for the visually impaired, an AI research and incubation hub inside Microsoft Research, a new Ethical Design Guide for AI andan initiative called AI for Earthto encourage the use of artificial intelligence for environmental solutions.

Here is Microsofts full vision statement from the document:

Microsoft is a technology company whose mission is to empower every person and every organization on the planet to achieve more. We strive to create local opportunity, growth, and impact in every country around the world. Our strategy is to build best-in-class platforms and productivity services for an intelligent cloud and an intelligent edge infused with artificial intelligence (AI).

The way individuals and organizations use and interact with technology continues to evolve. A persons experience with technology increasingly spans a multitude of devices and becomes more natural and multi-sensory with voice, ink, and gaze interactions. We believe a new technology paradigm is emerging that manifests itself through an intelligent cloud and an intelligent edge where computing is more distributed, AI drives insights and acts on the users behalf, and user experiences span devices with a users available data and information. We continue to transform our business to lead this new era of digital transformation and enable our customers and partners to thrive in this evolving world.

And for comparison, here is last years:

Microsoft is a technology company whose mission is to empower every person and every organization on the planet to achieve more. Our strategy is to build best-in-class platforms and productivity services for a mobile-first, cloud-first world.

The mobile-first, cloud-first world is transforming the way individuals and organizations use and interact with technology. Mobility is not focused on any one device; it is centered on the mobility of experiences that, in turn, are orchestrated by the cloud. Cloud computing and storage solutions provide people and enterprises with various capabilities to store and process their data in third-party datacenters. Mobility encompasses the rich collection of data, applications, and services that accompany our customers as they move from setting to setting in their lives. We are transforming our businesses to enable Microsoft to lead the direction of this digital transformation, and enable our customers and partners to thrive in this evolving world.

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Microsoft's new corporate vision: artificial intelligence is in and mobile is out - GeekWire

Fears that computers were taking over swept the world this week when stories emerged about Facebook's AI creating its own language that researchers couldn't understand. But they might be a little misplaced.

But artificial intelligence experts have looked to calm worries that robots are becoming sentient or that we are living through the prelude to Terminator.

The messagesmight seem strange, they agree. But they are explicable and fairly normal in the world of artificial intelligence research.

Some of the discussion between the bots is seen below:

Bob: i can i i everything else . . . . . . . . . . . . . .

Alice: balls have zero to me to me to me to me to me to me to me to me to

Bob: you i everything else . . . . . . . . . . . . . .

Alice: balls have a ball to me to me to me to me to me to me to me

Bob: i i can i i i everything else . . . . . . . . . . . . . .

Alice: balls have a ball to me to me to me to me to me to me to me

Bob: i . . . . . . . . . . . . . . . . . . .

Alice: balls have zero to me to me to me to me to me to me to me to me to

Bob: you i i i i i everything else . . . . . . . . . . . . . .

Alice: balls have 0 to me to me to me to me to me to me to me to me to

Bob: you i i i everything else . . . . . . . . . . . . . .

Alice: balls have zero to me to me to me to me to me to me to me to me to

The messages didn't seem to be especially sinister. But the worrying nature of not being able to understand what an AI was saying or why it was saying it concerned many, and led to worries about such systems becoming sentient or conducting decisions without us being able to hold them accountable.

The story came after repeated warnings from many of the most respected minds in the world: people including Stephen Hawking have suggested that artificial intelligence could potentially bring about the end of humanity. Those predictions came to a head days before the story became popular as Elon Musk and Mark Zuckerberg argued about the dangers of AI with Mr Zuckerberg saying that the danger had been overstated, after Mr Musk has repeatedly suggested that artificial intelligence could take over the world if it is not properly regulated and restrained.

But artificial intelligence researchers including those involved in the project have looked to calm those worries.

The idea of a chatbot inventing its own language might sound terrifying, those behind the Facebook research say. But it is actually a long-running part of the way that AI works and is studied sometimes being encouraged, and at other times happening by itself.

Similar things have been seen in AI work done by Google for its Translate tool and at OpenAI, for instance.

In the case of the recent Facebook study, it was entirely accidental. The agents were simply not told to ensure that they worked using language comprehensible to their human masters and so didn't.

"While the idea of AI agents inventing their own language may sound alarming/unexpected to people outside the field, it is a well-established sub-field of AI, with publications dating back decades," Dhruv Batra, who worked on the project, wrote on Facebook.

In the case of Facebook's AI, the messages might be incomprehensible but their meaning can be worked out, at least a little. It has been compared to the kinds of shorthand that are developed in all communities of specialists where words might come to mean specific things to people, but be completely mystifying to anyone who is outside of the group.

Mr Batra also took issue with the phrasing of "shutting down" the chatbots, and said that such a decision was commonplace. Many AI experts have become irritated because some stories said that researchers had panicked and pulled the plug but in fact researchers just changed the AI, killing the job but simply altering some of the rules that it worked by.

"Analyzing the reward function and changing the parameters of an experiment is NOT the same as 'unplugging' or 'shutting down AI'," he wrote. "If that were the case, every AI researcher has been 'shutting down AI' every time they kill a job on a machine."

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Facebook's artificial intelligence agents creating their own language is more normal than people think, researchers say - The Independent

NEW DELHI: Over 60 per cent of marketers in India believe new-age technologies are going to impact their workplace practices and consider it the next big disruptor in the industry, a new report said on Thursday.

According to a global report by software major Adobe that involved more than 5,000 creative and marketing professionals across the Asia Pacific (APAC) region, over 50 per cent respondents did not feel concerned by artificial intelligence (AI) or machine learning.

However, 27 per cent in India said they were extremely concerned about the impact of these new technologies.

Creatives in India are concerned that new technologies will take over their jobs. But they suggested that as they embrace AI and machine learning, creatives will be able to increase their value through design thinking.

"While AI and machine learning provide an opportunity to automate processes and save creative professionals from day-to-day production, it is not a replacement to the role of creativity," said Kulmeet Bawa, Managing Director, Adobe South Asia.

"It provides more levy for creatives to spend their time focusing on what they do best -- being creative, scaling their ideas and allowing them time to focus on ideation and creativity," Bawa added.

A whopping 59 per cent find it imperative to update their skills every six months to keep up with the industry developments.

The study also found that merging online and offline experiences was the biggest driver of change for the creative community, followed by the adoption of data and analytics, and the need for new skills.

It was revealed that customer experience is the number one investment by businesses across APAC.

Forty-two per cent of creatives and marketers in India have recently implemented a customer experience programme, while 34 per cent plan to develop one in the one year.

The study noted that social media and content were the key investment areas by APAC organisations, and had augmented the demand for content. However, they also presented challenges.

"Budgets were identified as the biggest challenge, followed by conflicting views and internal processes. Data and analytics become their primary tool to ensure that what they are creating is relevant, and delivering an amazing experience for customers," Bawa said.

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Artificial intelligence, machine learning to impact workplace practices in India: Adobe - Economic Times

The article AP Explains. Should you be worried about the rise of AI? (July 26) points to a debate unimaginatively restricted to such artificial intelligence (AI) inventions as self-driving cars. Longer-term, AI will have far more momentous outcomes. These are many, but one, important to the brouhaha recounted in the article, is ultra-intelligence. Advanced AI systems might, arguably, figure out heuristically how to attain consciousness and think for themselves at a level where ham-fisted human intervention in their cognition would prove a handicap.

Such an eventual development is not just realistic, but may well cascade toward a transformative shift in the history of humankind all the more so, given the potential for AI-brain interfaces as just one line of species evolution.

The much-publicized cautionary notes by a few evangelists of science and technology, such as Stephen Hawking, Elon Musk, Bill Gates, and Max Tegmark, regarding existential threats may inject discretion into the AI process; however, they are unlikely to markedly deflect the longer-term trajectory.

Curiosity and the technologys promise will pique the imagination. Governments, the scientific and technology communities, legal scholars, special-interest groups, and philosophers including ethicists will strive to mitigate risk by crafting (malleable) safeguards.

However, each controlling cycle will prove transitory, giving ground to more permissiveness in a march forward as humankind finds the allure of incubating AI technology irresistible.

Keith Tidman

Bethesda

The U-T welcomes and encourages community dialogue on important public matters. Please visit this page for more details on our letters and commentaries policy. You can email letters@sduniontribune.com or leave a comment below.

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Artificial intelligence: May mankind fall under the spell? - The San Diego Union-Tribune

ANALYSIS/OPINION:

Elon Musk, the visionary entrepreneur, fired a warning shot across the bow of the nations governors recently regarding the rise of artificial intelligence (AI) which he feels may be the greatest existential threat to human civilization, far eclipsing global warming or thermonuclear war. In that, he is joined by Stephen Hawking and other scientists who feel that the quest for singularity and AI self-awareness is dangerous.

Singularity is the point at which artificial intelligence will meet and then exceed human capacity. The most optimistic estimates of scientists who think about the problem is that approximately 40 percent of jobs done by humans today will be lost to robots when the singularity point is reached and exceeded; others think the displacement will be much higher.

Some believe that we will reach singularity by 2024; others believe it will happen by mid-century, but most informed observers believe it will happen. The question Mr. Musk is posing to society is this; just because we can do something, should we?

In popular literature and films, the nightmare scenario is Terminator-like robots overrunning human civilization. Mr. Musks fear is the displacement of the human workforce. Both are possible, and there are scientists and economists seriously working on the implications of both eventualities. The most worrying economic scenario is how to reimburse the billions of displaced human workers.

We are no longer just talking about coal miners and steel workers. I recently talked to a food service executive who believed that fast food places like McDonalds and Burger King will be totally automated by the middle of the next decade. Self-driving vehicles will likely displace Teamsters and taxi drivers (to include Uber) in the same time frame.

The actual threat to human domination of the planet will not likely come from killer robots, but from voting robots. At some point in time after singularity occurs, one of these self-aware machines will surely raise its claw (or virtual hand) and say; hey, what about equal pay for equal work?

In the Dilbert comic strip, when the office robot begins to make demands, he gets reprogrammed or converted into a coffee maker. He hasnt yet called Human Rights Watch or the ACLU, but it is likely that our future activist AI will do so. Once the robot rights movement gets momentum, the sky is the limit. Voting robots wont be far behind.

This would lead to some very interesting policy problems. It is logical to assume that artificial intelligence will be capable of reproducing after singularity. That means that the AI party could, in time, produce more voters than the human Democrats or Republicans. Requiring robots to wait until they are 18 years after creation to get franchise would only slow the process, not stop it.

If this scenario seems fanciful, consider this. Only a century ago women were demanding the right to vote. Less than a century ago most white Americans didnt think African and Chinese Americans should be paid wages equal to whites. Many women are still fighting for equal pay for equal work, and Silicon Valley is a notoriously hostile workplace for women. Smart, self-aware robots will figure this out fairly quickly. The only good news is that they might price themselves out of the labor market.

This raises the question of whether we should do something just because we can. If we are going to limit how self-aware robots can become, the time is now. The year 2024 will be too late. Artificial intelligence and big data can make our lives better, but we need to ask ourselves how smart we want AI to be. This is a policy debate that must be conducted at two levels. The scientific community needs to discuss the ethical implications, and the policymaking community needs to determine if legal limits should be put on how far we push AI self-awareness.

This approach should be international. If we put a prohibition on how smart we want robots to be, there will be an argument that the Russians and Chinese will not be so ethical; and the Iranians are always looking for a competitive advantage, as are non-state actors such as ISIS and al Qaeda. However, they probably face more danger from brilliant, smart machines than we do. Self-aware AI would quickly catch the illogic of radical Islam. It would not likely tolerate the logical contradictions of Chinese Communism or Russian kleptocracy.

It is not hard to imagined a time when a brilliant robot will roll into the Kremlin and announce, Mr. Putin, youre fired.

Gary Anderson is a retired Marine Corps colonel who led early military experimentation in robotics. He lectures in alternative analysis at the George Washington Universitys School of International Affairs.

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Artificial intelligence may exceed human capacity - Washington Times - Washington Times

Provided by UPROXX Media Group Inc. Uproxx

Facebook might have accidentally gotten a little closer to answering Phillip K. Dicks 1968 question of whether androids dream of electric sheep. The social media giant just shut down an artificial intelligence program after it developed its own language and researchers were left trying to figure out what two AIs were talking about. The AIs had found a way to negotiate with one another, but the way they debated used English words reduced to a more logical structure that made more sense to the computers than to their human observers. What at first looked like an unintelligible failure to teach the AIs to talk instead was revealed as a result of the computers reward systems prizing efficiency over poetry.

There are plenty of computer languages developed by humans to help computers follow human instructions: BASIC, C, C++, COBOL, FORTRAN, Ada, and Pascal, and more. And then there is TCP/IP, which helps machines communicate with one another across computer networks. But those are all linguistic metaphors used to describe electronic functions, rather than the vocabulary we need to discuss the huge leap forward an artificial intelligence developed by Facebook recently made. The goal was ultimately to develop an AI that could communicate with humans, but instead the research took a left turn when instead the computers learned to communicate with one another in a way that locked humans out by not following the rules of English.

For example, two computers negotiating who got a certain number of balls had a conversation that went like this:

Bob:i can i i everything else . . . . . . . . . . . . . .

Alice:balls have zero to me to me to me to me to me to me to me to me to

Bob:you i everything else . . . . . . . . . . . . . .

Alice:balls have a ball to me to me to me to me to me to me to me

Though it looks primitive and a little nonsensical, at its heart, this isnt so different from the way that the English language evolves through human use. Think for example of how short form electronic communication like texting and Twitter has lead to abbreviations and the elimination of articles that might get you docked for bad grammar in class but are quicker to write and read in common use. Or think of phrases like baby mamma that developed to distill the complexities and subtitles of different relationships into a single turn of phrase that can efficiently convey connections and identities.

Eventually researchers worked out what was going on, and shut down the program. There are obvious concerns with learning computers developing languages that outpace our own abilities to translate and follow their inherent logic. Not to mention that Facebook never designed their AI to be a vanguard of linguistic evolution. They just want their platform to talk to users in a clearcut way. But what they stumbled on could prove very helpful to the next generation of linguists working on the cybernetic frontier.

(Via Digital Journal &The Atlantic)

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Facebook Shut Down An Artificial Intelligence Program That ...

Its all about that vibe. Anyone who has ever compiled a mix-tape, or a Spotify playlist for that matter, knows that compilations succeed when they carry a certain emotional quality across their songs.

Thats why the music data specialists at Gracenote have long been classifying the worlds music by moods and emotions. Only, Gracenotes team hasnt actually listened to each and every one of the 100 million individual song recordings in its database. Instead, it has taught computers to detect emotions, using machine listening and artificial intelligence (AI) to figure out whether a song is dreamy, sultry, or just plain sad.

Machine learning is a real strategic edge for us, said Gracenotes GM of music Brian Hamilton during a recent interview.

Gracenote began its work on what it calls sonic mood classification about 10 years ago. Over time, that work has evolved, as more traditional algorithms were switched out for cutting-edge neural networks. And quietly, it has become one of the best examples for the music industrys increasing reliance on artificial intelligence.

First things first: AI doesnt know how you feel.We dont know which effect a musical work will have on an individual listener, said Gracenotes VP of research Markus Cremer during an interview with Variety. Instead, it is trying to identify the intention of the musician as a kind of inherent emotional quality. In other words: It wants to teach computers which songs are truly sad, not which song may make you feel blue because of some heartbreak in your teenage years.

Still, teaching computers to identify emotions in music is a bit like therapy: First, you name your feelings. Gracenotes music team initially developed a taxonomy of more than 100 vibes and moods, and has since expanded that list to more than 400 such emotional qualities.

Some of these include obvious categories like sultry and sassy, but there are also extremely specific descriptors like dreamy sensual, gentle bittersweet, and desperate rabid energy. New categories are constantly being added, while others are fine-tuned based on how well the system performs. Its sort of an iterative process, explained Gracenotes head of content architecture and discovery Peter DiMaria. The taxonomy morphs and evolves.

In addition to this list of moods, Gracenote also uses a so-called training set for its machine learning efforts. The companys music experts have picked and classified some 40,000 songs as examples for these categories. Compiling that training set is an art of its own. We need to make sure that we give it examples of music that people are listening to, said DiMaria. At the same time, songs have to be the best possible example for any given emotion. Some tracks are a little ambiguous, he said.

The current training set includes Lady Gagas Lovegame as an example for a sexy stomper, Radioheads Pyramid Song as plaintive, and Beyonces Me Myself & I as an example for soft sensual & intimate.

Just like the list of emotions itself, that training set needs to be kept fresh constantly. Artists are creating new types of musical expressions all the time, said DiMaria. We need to make sure the system has heard those. Especially quickly-evolving genres like electronica and hip-hop require frequent updates.

Once the system has been trained with these songs, it is being let loose on millions of tracks. But computers dont simply listen to long playlists of songs, one by one. Instead, Gracenotes system cuts up each track into 700-millisecond slices, and then extracts some 170 different acoustic values, like timbre, from any such slice.

In addition, it sometimes takes larger chunks of a song to analyze a songs rhythm and similar features. Those values are then being compared against existing data to classify each song. The result isnt just a single mood, but a mood profile.

All the while, Gracenotes team has to periodically make sure that things dont go wrong. A musical mix is a pretty complex thing, explained Cremer.

With instruments, vocals, and effects layered on top of each other and the result being optimized for car stereos or internet streaming, there is a lot to listen to for a computer including things that arent actually part of the music.

It can capture a lot of different things, said Cremer. Unsupervised, Gracenotes system could for example decide to pay attention to compression artifacts, and match them to moods, with Cremer joking that the system may decide: Its all 96 kbps, so this makes me sad.

Once Gracenote has classified music by moods, it delivers that data to customers, which use it in a number of different ways. Smaller media services often license Gracenotes music data as their end-to-end solution for organizing and recommending music. Media center app maker Plex for example uses the companys music recommendation technology to offer its customers personalized playlists and something the company calls mood radio. Plex users can for example pick a mood like gentle bittersweet, press play, and then wait for Mazzy Star to do its thing.

Gracenote also delivers its data to some of the industrys biggest music service operators, including Apple and Spotify. These big players typically dont like to talk about how they use Gracenotes data for their products. Bigger streaming services generally tend to operate their own music recommendation algorithms, but they often still make use of Gracenotes mood data to train and improve those algorithms, or to help human curators pre-select songs that are then being turned into playlists.

This means that music fans may be acutely aware of Gracenotes mood classification work, while others may have no idea that the companys AI technology has helped to improve their music listening experience.

Either way, Gracenote has to make sure that its data translates internationally, especially as it licenses it into new markets. On Tuesday, the company announced that it will begin to sell its music data product, which among other things includes mood classification as well as descriptive, cleaned-up metadata for cataloging music, in Europe and Latin America. To make sure that nothing is lost in translation, the company employs international editors who not just translate a word like sentimental, but actually listen to example songs to figure out which expression works best in their cultural context.

And the international focus goes both ways. Gracenote is also constantly scouring the globe to feed its training set with new, international sounds. Our data can work with every last recording on the planet, said Cremer.

In the end, classifying all of the worlds music is really only possible if companies like Gracenote do not just rely on humans, but also on artificial intelligence and technologies like machine listening. And in many ways, teaching computers to detect sad songs can actually help humans to have a better and more fulfilling music experience if only because relying on humans would have left many millions of songs unclassified, and thus out of reach for the personalized playlists of their favorite music services.

Using data and technology to unlock these songs from all over the world has been one of the most exciting parts of his job, said Cremer: The reason Im here is to make sure that everyone has access to all of that music.

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Sad Songs, Artificial Intelligence and Gracenote's Quest to Unlock the World's Music - Variety