Artificial intelligence now powers all of Facebook’s translation – Popular Science

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

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


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Artificial intelligence proves that craft beer names are total nonsense
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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 ...

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

5 Ways to Advance Your Machine Learning Initiatives – HuffPost

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

Links for August 4, 2017: Why we’ll survive artificial intelligence, how smart phones are ruining an entire … – American Enterprise Institute

We survived spreadsheets, and well survive AI WSJ

Treating prediction as an input into an economic process makes it much easier to map AIs impact. History and economics show that when an input such as energy, communication or calculation becomes cheaper, we find many more uses for it. Some jobs become superfluous, but others more valuable, and brand new ones spring into existence. Why should AI be different? . . .

AI has already made some skills obsolete. Google Translate is faster, cheaper and often as good as a human interpreter. Some AI programs can outperform human radiologists at identifying malignant tumors.

Yet as AI gets cheaper, so its potential applications will grow. Just as better weather forecasting makes us more willing to go out without an umbrella, Mr. Manzi says, AI emboldens companies to test more products, strategies and hunches: Theories become lightweight and disposable. They need people who know how to use it, and how to act on the results.

Mr. Manzi should know. He co-founded a company, Applied Predictive Technologies, to test companies strategies using AI, and sold it to Mastercard Inc . for $600 million in 2015. Its still hiring.

When will AI be better than humans at everything? Singularity Hub

Despite Crisprs gene-editing success, superbabies arent imminent Wired

Legal robots deployed in China to help decide thousands of cases The Telegraph

Do fewer immigrants mean more jobs? Economists say no NYT

WASHINGTON When the federal government banned the use of farmworkers from Mexico in 1964, Californias tomato growers did not enlist Americans to harvest the fragile crop. They replaced the lost workers with tomato-picking machines. . . .Economists say the tomato story and a host of related evidence show that there is no clear connection between less immigration and more jobs for Americans. Rather, the prevailing view among economists is that immigration increases economic growth, improving the lives of the immigrants and the lives of the people who are already here. . . .

George J. Borjas, the Harvard immigration economist whose work is the only evidence that the administration has cited as justifying its proposals, said in an interview on Wednesday that there was no economic justification for reducing skilled immigration.

That is a political decision, he said. That is not an economic decision.

Short answers to hard questions about the opioid crisis NYT

The real reason there are so few conservatives on campus Damon Linker

Have smart phones destroyed an entire generation? The Atlantic

The advent of the smartphone and its cousin the tablet was followed quickly by hand-wringing about the deleterious effects of screen time. But the impact of these devices has not been fully appreciated, and goes far beyond the usual concerns about curtailed attention spans. The arrival of the smartphone has radically changed every aspect of teenagers lives, from the nature of their social interactions to their mental health. These changes have affected young people in every corner of the nation and in every type of household. The trends appear among teens poor and rich; of every ethnic background; in cities, suburbs, and small towns. Where there are cell towers, there are teens living their lives on their smartphone.

To those of us who fondly recall a more analog adolescence, this may seem foreign and troubling. The aim of generational study, however, is not to succumb to nostalgia for the way things used to be; its to understand how they are now. Some generational changes are positive, some are negative, and many are both. More comfortable in their bedrooms than in a car or at a party, todays teens are physically safer than teens have ever been. Theyre markedly less likely to get into a car accident and, having less of a taste for alcohol than their predecessors, are less susceptible to drinkings attendant ills.

Psychologically, however, they are more vulnerable than Millennials were: Rates of teen depression and suicide have skyrocketed since 2011. Its not an exaggeration to describe iGen as being on the brink of the worst mental-health crisis in decades. Much of this deterioration can be traced to their phones.

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Links for August 4, 2017: Why we'll survive artificial intelligence, how smart phones are ruining an entire ... - American Enterprise Institute

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

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

Microsoft’s new corporate vision: artificial intelligence is in and mobile is out – GeekWire

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

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

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

Opinion: Apple’s stock could double on the ‘mother of all artificial-intelligence projects’ – MarketWatch

There are times when it pays to look years ahead when youre an investor.

That time is now for Apple AAPL, +4.73% Under one scenario, Apples stock could double. At first glance, such a possibility seems ridiculous given current Wall Streets price targets on Apple. Isnt Wall Street often behind the curve?

When I made the call to aggressively buy Apple at $18.71 for the very long term, I suggested that the stock could go to $143. This call was made long before Apple became a popular stock. Many called it outrageous; some even canceled their subscriptions to The Arora Report. Well, Apple is trading north of $150 as of this writing, and The Arora Report is still holding part of the original position bought at $18.71.

No, I am not a genius to be able to pick an exact number like $143 to be achieved years later. I simply picked the round number of $1,000 as a potential target. Since then, Apples stock has split seven for one. And $1,000 divided by seven is $143.

Let us explore how the stock might double.

Five stages of a long trade

Please click here to see five stages of a long trade. The diagram shows one cycle. In the recent past, Apple has gone through three cycles; first from the long side, then from the short side and once again from the long side.

Please click here to see five stages of a short trade.

At present, Apple is in the fifth stage marked as crowded trade in the diagram. According to the ZYX Change Method, we now have the first solid indication that at some time in the future, Apples stock may embark on another cycle of five stages to the upside.

Please note that the first stage is marked as change not recognized. This is where the highest alpha trades are entered. In plain English, alpha simply means generating excess returns. The potential change is not in Wall Streets analysis.

Artificial intelligence

Apple CEO Tim Cook said Tuesday: In terms of autonomous systems, what weve said is that we are very focused on autonomous systems from a core technology point of view. We do have a large project going and are making a big investment in this. From our point of view, autonomy is the mother of all AI [artificial intelligence] projects. And the autonomous systems can be used in a variety of ways, and a vehicle is only one. But there are many different areas of it, and I dont want to go any further with that.

Ask Arora: Nigam Arora answers your questions about investing in stocks, ETFs, bonds, gold and silver, oil and currencies. Have a question? Send it to Nigam Arora.

Obliteration of the case against Apple

As well as Apples stock has performed, it has always been constrained by the fact that it is primarily a one-product company. Yes, Apple has many products. However, over 70% of its profits have been generated by only one product: the iPhone. One-product companies are inherently risky.

From Cooks statement, now we are seeing concrete signs that Apple may end up with at least one other big hit using artificial intelligence on par with the iPhone. If this happens, not only will the stock be driven higher by excitement, but analysts will likely raise their targets as they give the company a higher price-to-earnings multiple.

As a note of caution, please remember that not all good scenarios come true. For this reason, investors need expert guidance.

The best investment opportunities

The best investment opportunities are likely to come not in Apple, but in two other categories.

First is Apple suppliers. Following Apples good earnings report Tuesday, its suppliers stocks are gaining: Cirrus Logic CRUS, +0.46% Skyworks Solutions SWKS, +0.14% Broadcom AVGO, +2.00% Qorvo QRVO, -1.78% Analog Devices ADI, -0.16% and Micron Technology MU, +1.02%

The second category is artificial intelligence. The crowd favorites are Nvidia NVDA, -0.06% and Advanced Micro Devices AMD, -2.48% Most of the cutting-edge artificial-intelligence work is being done at companies such as Alphabet GOOG, -0.05% GOOGL, +0.11% Facebook FB, -0.33% and Microsoft MSFT, -0.44% However, those companies are too big to present the best opportunities for investments.

At The Arora Report, we are working hard to identify the best companies to invest in this theme that are not too big and not already too crowded like Nvidia and AMD.

What to do now

There is strategy and there are tactics. This article is about strategy. Please stay tuned for an article in the future about the tactics. Tactics include when to buy or add, how much to buy and when to sell.

Disclosure: Subscribers to The Arora Report may have positions in the securities mentioned in this article or may take positions at any time. All recommended positions are reviewed daily at The Arora Report.

Nigam Arora is an investor, engineer and nuclear physicist by background, has founded two Inc. 500 fastest-growing companies, is the developer of the adaptive ZYX Global Multi Asset Allocation Model and the ZYX Change Method to profit from change in trading and investing. He is the founder of The Arora Report, which publishes four newsletters. Nigam can be reached at Nigam@TheAroraReport.com.

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Opinion: Apple's stock could double on the 'mother of all artificial-intelligence projects' - MarketWatch

Sad Songs, Artificial Intelligence and Gracenote’s Quest to Unlock the World’s Music – Variety

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

When artificial intelligence exceeds human capacity – Washington Times

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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When artificial intelligence exceeds human capacity - Washington Times

Facebook Shut Down An Artificial Intelligence Program That …

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 ...

Automation, Artificial Intelligence to make many IT jobs obsolete over next 5 years, says survey – Firstpost

The Information Technology has never had it so bad as it has since a year ago when job cuts and summary dismissals have been the order of the day. No matter which blue chip IT firm an individual works for, the jobs they are hold at risk, irrespective of seniority.

Reuters

A survey by Simplilearn, How Automation is Changing Work Choices: The Future of IT Jobs in India, says that the future of IT is in Cyber Security, Big Data and Data Science, Big Data Architect, Big Data Engineer, Artificial Intelligence and IoT (Internet of Things) Architect, and Cloud Architect .

The jobs that will disappear will be anything in the next five are those that are repetitive and can be taken over by Artificial Intelligence (AI) such as manual testing, infrastructure management, BPO and system maintenance will massively decline over the next five years.

Core development jobs will not feel the impact of job loss, said Kashyap Dalal, Chief Business Officer. The IT industry is seeing the impact of two major trends - one, that of AI and machine learning. And second, that of legacy skill-sets going out of date. While there is risk to jobs due to these trends, the good news is that a huge number of new jobs are getting created as well in areas like Cyber Security, Cloud, DevOps, Big Data, Machine Learning and AI. It is clearly a time of career pivot for IT professionals, to make sure they are where the growth is."

The report further provides insights into the preferred technology skills based on a survey of 7,000 IT professionals from key metros. Over 50 percent of IT professionals with work experience of 410 years have invested in courses and training programs to help them build new skills.

Big Data & Analytics, Project Management, Cloud Computing, Cyber Security, Agile & Scrum, and Digital Marketing are among the top domains in which professionals are investing for online training programs.

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Automation, Artificial Intelligence to make many IT jobs obsolete over next 5 years, says survey - Firstpost

Artificial Intelligence from the Boardroom to the Factory Floor – HuffPost

Artificial Intelligence (AI) has transformed from a futuristic concept to powerful force which is transforming operations in traditional industries. Top executives are beginning to take note as startups in the field are expanding at an astounding rate.

We use AI to increase Operational Excellence, says CEO Iris Tsidon of Okapi and author of Six Steps to Operational Excellence. Heads of industry know that change is coming and are committed to improving their companies. Our job as entrepreneurs is to show them that this isnt coming in the future, the capabilities are already here now.

According to a white paper by Bell-Hawk systems, real-time Artificial Intelligence (AI) techniques originally developed for the USAF and NASA are being applied to manufacturing organizations to enable managers to run their manufacturing plants with less stress and much smaller management teams.

David Brooks on the PBS evening news said "Manufacturing is going through a revolution from a labor intensive, blue-collar industry to a white-collar, Silicon Valley industry." This use of AI to assist managers to efficiently run their operations is part of the transition from manufacturing being a labor intensive business to being highly automated at the operations management level as well as on the production floor.

This revolution is enabling manufacturers in the USA to rapidly design, engineer, and make products to order in response to products ordered over the Internet. In fact, recent research into AI from Accenture Research shows that the technology will be critical to economic growth in existing and developing markets. Accentures CTO Paul Daugherty states, Artificial Intelligence is poised to transform business in ways weve not seen since the impact of computer technology in the late 20th century. Our research demonstrates that as AI matures, it can propel economic growth and potentially serve as a powerful remedy for stagnant productivity andlabor shortages of recent decades.

Iris Tsidon of Okapi notes that about 85% of the KPI (Key Performance Indicator) projects in the business world fail. That is a staggering number but when AI is used, the transformation is immediate.

OKAPI was created as an AI based software to address this problem. By transforming the complex technology into a simple and easy to use platform and app, Okapi is able to use use artificial intelligence to analyze and provide our customers the best KPIs for their business. This produces outstanding insights, which can be turned into valuable operational intelligence.

The most important thing is that high level technology becomes accessible at every level of an organization. Its not just for the C-Suite executives, in order for it to be meaningful, it has to help every employee in the organization from the boardroom to the factory floor

While artificial intelligence may seem like a high level concept, in the coming years, we are going to see it translate into tools which will be used through out every level of an organization.

OKAPIConversations is a series of articles discussing Startups, Artificial Intelligence, Operational Excellence and Business Intelligence and the impact on business andsociety

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Artificial Intelligence from the Boardroom to the Factory Floor - HuffPost

Swedish banks embrace artificial intelligence as a cure to closures – The Independent

Aida is the perfect employee: always courteous, always learning and, as she says, always at work, 24/7, 365 days a year.

Aida, of course, is not a person but a virtual customer-service representative thatSEB AB, one of Swedens biggest banks, is rolling out. The goal is to give the actual humans more time to engage in more complex tasks.

After blazing a trail in online and digital banking, Swedens financial industry is now emerging as a pioneer in the use of artificial intelligence (AI). Besides Aida at SEB, theres Nova, which is a chatbotNordea Bankis introducing at its life and pensions unit in Norway.Swedbankis adding to the skills of its virtual assistant, Nina. All three are designed to sound like women, based on research suggesting customers feel more comfortable with female voices.

There are some frequent, simple tasks that we need to deal with manually today, and in that effort were looking into AI to see how we can deploy it and Aida is one, Johan Torgeby, the chief executive officer of SEB, says.

Chatbots have access to vast amounts of individual client data, meaning they can quickly handle straightforward customer requests. That in turn frees up human employees to deal with more complex services, like coming up with the best mortgage plan to suit a specific customer.

Basically all banks are closing branches, Mattias Fras, head of robotics, strategy and innovation at Nordea, says. This is a way to return to full service again.

Nordeas chatbot will eventually help customers who want investment advice, who want to cancel lost credit cards or to open savings accounts.

Swedish banks have already seen their customer satisfaction scores drop to a20-year lowafter shutting branches and pushing people onto online services. But AI might be part of the cure. According to a recent study by market researcher GfK, there are wide gaps between what consumers hope to receive from banks in terms of service and financial advice, and what they actually get. AI applications such as chatbots hold the promise of filling in these service gaps, given the right data and programming, GfK says.

Swedbank, which already operates its chatbot Nina in Sweden and plans to roll it out to its Baltic markets as well, says one of the benefits to the technology is that it eases users into the new digital age. AI can help our customers become more digitised, for example by guiding a client in paying bills on the Internet, Swedbank spokeswoman Josefine Uppling says.

Petra Stenqvist, a partner at Pond, which looks into innovative business ideas, says its unlikely AI will ever be able to think like humans. It will never be able to replace subjective assessments, she says. But it will be able to contribute to better decision making. And because of the massive amounts of data that AI can store, individuals will be able to find out things about themselves that they didnt even know.

Bloomberg

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Swedish banks embrace artificial intelligence as a cure to closures - The Independent

How humans will stay competitive in the age of artificial intelligence – TNW

Experts offerdiffering views about where advances in artificial intelligence are leading us to in the long term. However they mostly agree that in the short term we have to get ready for an era where AI accounts for most routine jobs previously thought to be the exclusive domain of human intelligence.

This does not mean that humans will run out of jobsyet. What it does mean, however, is that the dynamics governing the employment landscape are rapidly shifting. Humans will have to become smarter and much faster at obtaining knowledge and acquiring the skills required to perform tasks that have not fallen prey to automation.

Here are some of the trends that are helping us (humans) maintain our edge in a world that is fast being taken over by robots.

Augmented reality, the real-time overlaying of graphical elements on real world imagery, is still in its infancy. Yet much more than games and entertainment, AR is promising to be the computing platform of professional work.

Many industries are using AR to augment efficiency and productivity in of professional settings. By using AR glasses or headsets, engineers, healthcare workers, industry workers, and professionals in many other fields will be able to see real-time information and instructions about the task theyre performing while keeping their hands free to do the job.

For instance, as the following video shows, an AR glass boosts a technicians speed by 34% when wiring a wind turbines control box because it replaces paper manuals with line-of-sight instructions.

AR assistance can help address the skills gap that many industries are currently suffering from by cutting the time and effort required to train the workforce for jobs with high skill requirements.

The practical uses of AR in workplaces has given rise to a new industry of AR applications and hardware, including the Enterprise Edition of the ill-fated Google Glass, which might become a favorite in manufacturing settings.

Ironically, one of the technologies that will help humans survive AI is AI itself. When viewed from a different perspective, AI can become a job enabler by breaking down the complexity of tasks as well as speeding up the learning process.

AI algorithms can collect and analyze information about learners interactions with a course in various ways, and help speed their way through the training by providing personalized content and guidance that addresses their specific pain points. Applying AI in education can be crucial as requirements for professional jobs change at a faster pace and workers need to be constantly learning new skills.

Elsewhere, the automation of complicated tasks by AI algorithms is enabling less-skilled or -experienced professionals to take on tasks that previously required years of education and work experience.

Were seeing this in fields such as healthcare, where AI algorithms are helping clinicians and doctors be more productive at their jobs by providing them with knowledge and suggestions based on patterns obtained from the outcome of thousands and millions of previous diagnoses and treatment processes. Another field is cybersecurity, where AI algorithms are doing the bulk of the work in finding alerts and reducing false positives, while analysts decide which events need to be investigated.

Scientists and thought leaders agree that (for the time being) when humans and artificial intelligence work together, they can accomplish much more than any of them alone. Whether it will remain so in the future is to be seen.

Some believe that the only way we can survive the robot uprising is by becoming cyborgs, including Elon Musk, the CEO of Tesla motors, whose eccentric plans include colonizing Mars. Musk, who strongly favors the AI doomsday theory, suggests that to avoid being outsmarted by artificial intelligence, we must merge with machines.

Musks ventures include Neuralink, a company that plans to create devices that can be implanted in the human brain to improve memory or allow for more direct interfacing with computing devices. Among the things that such technology can accomplish is the direct uploading or downloading of thoughts and knowledge to and from the brain.

While Musks idea might sound outrageous, hes not the only person who wants to enhance humans through technology. Others include Bryan Johnson, the Silicon Valley entrepreneur who personally spent $100 million to launch Kernel, a company that aims to build neural tools that will allow the brain to do things that were previously impossible.

The belief that science and technology can help humans evolve beyond their physical and mental limits has very strong advocates, including Zoltan Istvan, the founder of the Transhumanist Party, who ran for U.S. president in 2016 and will be running for governor of California in 2018. Istvan plans to conquer death through science and technology.

Whether robots will obey humans forever, fight and eradicate them or drive them into slavery remains to be seen. In the meantime we need to make the best of the technology that surrounds us.

This post is part of our contributor series. The views expressed are the author's own and not necessarily shared by TNW.

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How humans will stay competitive in the age of artificial intelligence - TNW

How Artificial Intelligence Will Cure America’s Sick …

For decades, technology has relentlessly made phones, laptops, apps and entire industries cheaper and betterwhile health care has stubbornly loitered in an alternate universe where tech makes everything more expensive and more complex.

Now startups are applying artificial intelligence (AI), floods of data and automation in ways that promise to dramatically drive down the costs of health care while increasing effectiveness. If this profound trend plays out, within five to 10 years, Congress wont have to fight about the exploding costs of Medicaid and insurance. Instead, it might battle over what to do with a massive windfall. Todays debate over the repeal of Obamacare would come to seem as backward as a discussion about the merits of leeching.

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Hard to believe? One proof point is in the maelstrom of activity around diabetes, the most expensive disease in the world. In the U.S., nearly 10 percent of the population has diabetes, around 30 million people. Within a decade, some experts say, the number of diabetics in China will outnumber the entire U.S. population. Most people who suffer from the disease spend $5,000 to $10,000 a year on medication, and diabetics with complications can spend hundreds of thousands of dollars on doctor and hospital bills. That and the lost wages of diabetics cost the U.S. alone more than $245 billion a year, according to the Centers for Disease Control and Prevention.

Thats an enormous problem to solve and a pile of potential cash and customers to be wonwhich is why diabetes is attracting entrepreneurs like ants to a dropped ice cream cone. One of those entrepreneurs is SamiInkinen. He was a co-founder of the real estate site Trulia and has long been an endurance athlete, competing seriously in triathlons and Ironman events. In 2014, he and his wife rowed from California to Hawaii. None of this fits the typical profile of a diabetic, yet in 2011, soon after yet another triathlon, Inkinen was diagnosed with Type 2 diabetes. And like many driven, super-smart data geeks, he dove into research to understand everything about his condition.

Soila Solano injects herself with insulin at her home in Las Vegas on April 18. Solano was diagnosed with Type 2 diabetes six years ago. Nearly 10 percent of the U.S. population has diabetes, the most expensive disease in the world. John Locher/AP

That journey led him to Dr. Stephen Phinney, a medical researcher at the University of California, Davis, and Jeff Volek, a scientist at Ohio State. Phinney and Volek wrote two books together about low-carbohydrate diets and published scientific papers describing how constant adjustments to diet and lifestyle can reverse diabetes in many patients. Diabetes is almost never treated that way because the program is too hard for most people to stick to. It requires so much coaching and scrutiny by medical professionals, youd pretty much have to hire a live-in doctor.

Inkinen convinced Phinney and Volek that technology could essentially re-create a live-in doctor and diabetes coach in a smartphone. Together, the three founded Virta Health in 2014. The company stayed in stealth mode until now, launching in March. It felt like a duty to do this, Inkinen tells Newsweek . Here is an epidemic of epic proportion, and nothing is working. We can combine science and technology to solve the problem at much lower cost and do it safely.

Heres how Virta works and why its approach is so important to the future of health care. On the front end, Virta is software on a smartphone. Diabetics who sign up agree to regularly enter data: glucose levels, weight, blood pressure, activity. Some do this by manually entering information; others use devices like a Fitbit or connected scales to automatically send it in. The app also frequently asks multiple-choice questions about mood, energy levels and hunger more data that the AI software crunches to learn about the patient, look for warning signs and symptoms and guide Virtas doctors.

On the back end, Virta hires doctors who get streams of updates from Virtas software and use the data to help them make decisions about how to adjust each patients diet and medications or anything else that might affect that persons health. Any clinical decision is always made by a doctor, Inkinen says. But the software increases productivity by 10-X. (Thats 10 times, in Silicon Valleyspeak.) When all this works and the patient follows the programs strict dietary and medical controls, diabetes can be reversed, clinical trials of Virtas system have shown. Around 87 percent of patients who had been relying on insulin to control their condition either decreased their dose or eliminated their use of insulin completelya success rate that matches that for bariatric surgery, which is an expensive, invasive, last-ditch effort for severe diabetics.

Virta leverages AI software, smartphones and cloud computing to allow its doctors to continually interact with many times more patients than they can in a clinic or hospital, and it gives its diabetic patients a cross between a pocket doctor and a guardian angel. The result is a promising treatment for diabetes that could get many sufferers off medication and keep them out of doctors offices and hospital emergency rooms. And that, in turn, would greatly lower the overall cost of diabetes.

Virta is just one startup of many attacking diabetes. Livongo is a more automated but less doctor-oriented version of Virtas program. The company, which raised $52.5 million in March, makes a wireless glucose-reading device that uploads the diabetics data. AI software learns about the patient and sends a stream of tips and information intended to help the diabetic manage the disease and stay out of hospitals. Yet another new startup, Fractyl, takes a more medical approach. It invented a type of catheter that seems to cause changes in the intestines that result in reversing diabetes.

Startup tracker Crunchbase lists about 130 new tech-oriented companies (the number changes constantly) involved in some aspect of diabetes. While many of these startups will fail, its hard to imagine that some wont have a significant impact.

These efforts matter to all of us because diabetes is such an enormous drain on health care resources. Venture capitalist Hemant Taneja, who helped start Livongo, says technology could take $100 billion out of the annual cost of diabetes in the U.S. Imagine if even 20 percent of diabetics could get off medication and have little need for a doctors care. All of those medical resources would get freed up for other patients and other conditions, which should help lower prices of health care for all. If we want to massively lower health care costs, we need to figure out how to address metabolic health issues [like diabetes] at their core, Inkinen says. I would bet my house that in 15 years, the future health care company looks like what were doing todaynot treating diseases at the end of the road but catching them along the way and reversing them.

A user checks blood glucose with the Livongo system. Livongo

Over the past decade, medical records in the U.S.long kept on paper in doctors horrible handwritinghave been digitized and fed into software. That hasnt helped lower health care costs yet, and in fact it is adding to them as systems get installed and medical professionals learn to use software that can be clunky. Epic Systems, the biggest electronic medical records company, handles 54 percent of patients records in the U.S. but gets bad marks for being so hard to use that it eats up doctors and nurses time. One report from Beckers Hospital Review said that almost 30 percent of Epic clients wouldnt recommend it to their peers. A survey by Black Book Market Research found that 30 percent of hospital personnel were dissatisfied with their EMR systems, with Epic getting the strongest dissatisfaction.

But theres a larger gain from the pain of EMRs: Enormous amounts of medical information are now digitized. As more medical interaction happens onlineas with Virta or Livongothe more kinds of data well collect. Internet of Things devices, whether Fitbits or connected glucose meters or potential new devices like Apple AirPods that take biometric readings, will add yet more data. All this data can help AI software learn about diseases in general, and about individual patients, opening up new ways for technology to be applied.

Some of the new applications of AI will simply improve a tragically inefficient health care industry. Qventus is a startup using AI to take all the data flowing through a hospital to learn how to free up doctors and nurses to see more patients and improve outcomes. Were creating efficiency out of seemingly nothing, Qventus CEO Mudit Garg tells me. Two years ago, work like this was so unsexy. But this is where the rubber meets the road.

One of his clients, Mercy Hospital Fort Smith in Fort Smith, Arkansas, has been able to treat 3,000 more patients a year with the same resources, an increase of 18 percent. Here again, technology is increasing the supply of medical services, potentially changing the cost equation that keeps forcing health care prices higher.

AI is also starting to automate some of the work of doctors. IBMs Watson, which uses machine learning and massive computing power to reason its way through questions, is on its way to becoming the best diagnostician on the planet. Its software can soak up all manner of available (and anonymized) patient data, plus the tens of thousands of medical research papers published every year (far more than any human could read). The system can even keep up with the news, learning, for instance, which regions are affected by a certain contagious disease, which might help diagnose someone who recently traveled to one of those areas. By asking patients a series of questions spoken into any kind of computer or connected device, Watson can quickly narrow down the possible causes of a medical problem. Today, IBM works on test projects with major hospitals like the Cleveland Clinic to put Watson in the hands of doctors, who are learning how to use the technology like a brilliant assistant.

But the day will come when Watson or something like it is available to everyone through a smartphone or some other device. Amazon is starting down that path by partnering with HealthTap to offer what it calls Dr. A.I. on Alexa, Amazons voice-activated AI gadget for consumers. Its not nearly as robust as Watson but works on the same idea. Just tell it your medical problem, and it will ask you questions to help narrow down what it might be.

A clerk works in the medical records department at Clinica Sierra Vista's East Bakersfield Community Health Center in Bakersfield, California on October 20, 2009. As medical records increasingly become digitized, their data will help AI learn more about diseases and how to help patients. Phil McCarten/Reuters

As health care AI develops, startups are also creating new kinds of genomics-based medicine. Just 16 years ago, the Human Genome Project and geneticist Craig Venters startup, Celera Genomics, published the results of their human genome sequencing within a day of each other in 2001. Venter said his project took 20,000 hours of processor time on a supercomputer. This year, startup Color Genomics is offering a $249 genetic test that can sequence most of the pertinent genes in the human body. Colors goal is to make genetic sequencing so cheap and easy that every baby born will have it done, and the data will inform his or her health care for life.

Combine genetic data about a person with all the kinds of data Watson can ingest, and were close to being able to build AI software that can at least supplant that first visit to a doctor when youre sickwhich, of course, is when you least want to travel to a doctors office. Instead, people will increasingly speak to a smartphone or to something like Dr. A.I. on Alexa about their health problems and, if necessary, send in photos of that rash or funky toe. If the system has your health care records and genetic data, it can gain more insight into your condition than any doctor operating on an informed hunch.

An early prototype of Watson in Yorktown Heights, NY. The cognitive computing system was originally the size of a master bedroom in 2011. Clockready

On many occasions, the app might tell the user the problem is nothing seriousa robot equivalent of Take two aspirin and call me in the morning. Other times, the app might send the user to a clinic to get a test or X-ray. If thats how it plays out, a large chunk of the traffic into doctors offices and hospitals will fade away.

Add it up, and in these next few years were going to see a parade of tech applications that reduce demand on the health care system while giving all of us more access to care. Doctors should be freed up to do a better job for patients who truly need their attention. Theoretically, all of this will help keep more people healthier. And if were all healthier and using health care less, the laws of supply and demand should kick in, sending the overall cost of health care tumbling.

However, there are bumps ahead because, as our erudite president recently said, nobody knew that health care could be so complicated.

The economics of health care are weird. First of all, the usual forces dont apply to highly regulated industries, and health care is perhaps the most regulated in the U.S. and around the world because lives are at stake. In most countries, regulators prevent AI software from crossing the line into independently offering a diagnosis or clinical advicethats strictly the purview of doctors. New medical devices, like Fractyls, have to get approval from the Food and Drug Administration. Lobbyists often slow regulatory change to maintain the status quo and benefit incumbents charging inflated prices.

Personal health care decisions in the U.S. often get influenced by insurance companies, employers who pay for health benefits, and Medicare. Unlike most industries, consumers in health care dont have much information about pricing or quality, so they cant weigh options and make rational choices. Moreover, we think about health differently from anything else we buy. Many of us are never satiated with health carewe always want more and better health care, if we can afford it. One study published in March showed that telehealth making doctors available by video callprompted people to seek care for minor illnesses they otherwise wouldve ignored. Only 12 percent of telehealth visits replaced in-person visits, and the other 88 percent was new demand.

Until recently, most new medical technology has been high-end products that give doctors and hospitals a reason to charge more for something that couldnt have been done in the past. Think MRI machines or robotic limbs. These improve quality of life but add to costs. In 2008, the Congressional Budget Office concluded, The most important factor driving the long-term growth of health care costs has been the emergence, adoption, and widespread diffusion of new medical technologies and services.

A doctor and patient demonstrate how they use Virta for diabetes monitoring and treatment. Virta Health

The next wave of health care technology is different. The combination of data and AI was not available until the past year or two, and it can lead to the kind of automation that has disrupted so many other industries. Many health care entrepreneurs are focused precisely on the win-win-win prospect of lowering the cost of care while making it better and available to more people. Of course, there will be challenges to address, such as making sure our highly sensitive medical data stays protected and private, even as it flies around various networks and systems.

As startups bring these technologies online, theyre often doing an end run around insurance companies, instead finding demand among consumers or employers who offer health coverage. Livongo, for instance, points out to companies that each diabetic employee costs thousands of dollars a year in care. Pay for the Livongo service, the pitch goes, and your company will save money as those employees better manage their conditions. By last year, Livongo had signed up more than 50 large customers, including Quicken, Office Depot, Office Max and S.C. Johnson & Son.As the thinking goes among health care startups, once employers and consumers embrace new technology, insurance companies, regulators and health care incumbents will have to follow.

As that happens, the technologists promise, economic forces will finally stall or reverse the climbing cost of health care in the U.S. and around the world, a development that would, if were lucky, leave the president and just about every member of Congress speechless.

Jon-Paul Pezzolo

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How Artificial Intelligence Will Cure America's Sick ...

Starbucks Will Soon Use This New Artificial Intelligence to Tempt You Into Buying More Coffee – TheStreet.com

If you always have a caramel macchiato on Mondays, but Tuesdays call for the straight stuff, a double espresso, then Starbucks Corporation (SBUX) is ready to know every nuance of your coffee habit. There will be no coffee secrets between you, if you're a Rewards member, and Starbucks.

This fall as Starbucks rolls out more of its new cloud-based Digital Flywheel program, backed by artificial intelligence(AI), the chain's regulars will find their every java wish ready to be fulfilled and, the food and drink items you haven't yet thought about presented to you as what you're most likely to want next.

So targeted is the technology behind this program that, if the weather is sunny, you'll get a different suggestion than if the day is rainy. Or expect suggestions to vary on the weekend or a holiday, as opposed to a regular workday. If it's your birthday, Starbucks will offer a personalized birthday selection. If you patronize a Starbucks other than you're regular haunt, Starbucks will know that too.

Like it or not, what Starbucks has developed represents a smart melding of technology into e-commerce tools that will pay off long term for the company and drive sales, Brian Solis, a principal analyst and futurist at Altimeter, told TheStreet in an interview.

"Starbucks is one of the best companies in the world that connects brand, user and consumer experience between digital mobile and the real world," said Solis. " They are still pushing forward, rolling out their Digital Flywheel strategy to be more dynamic to further integrate digital and real world."

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Starbucks Will Soon Use This New Artificial Intelligence to Tempt You Into Buying More Coffee - TheStreet.com

Real Questions About Artificial Intelligence in Education – EdSurge

Dont doubt it: Machine learning is hotand getting hotter.

For the past two years, public interest in building complex algorithms that automatically learn and improve from their own operations, or experience (rather than explicit programming) has been growing. Call it artificial intelligence, or (better) machine learning. Such work has, in fact, been going on for decades. (The Association for the Advancement of Artificial Intelligence, for instance, got rolling in 1979; some date the ideas back to the Greeks, or at least to the 1940s during the early days of programmable digital computers.)

More recently, Shivon Zilis, an investor with Bloomberg Beta, has been building a landscape map of where machine learning is being applied across other industries.Education makes the list. Some technologists are worried about the dangers. Elon Musk, for instance, has been apocalyptic about his predictions, as the New Yorker wrote. He sparred this past week with a more sanguine Mark Zuckerberg. (The Atlantic covers it here.)

Investors are nonetheless racing ahead: this week, Chinese language learning startup, Liulishuo, which uses machine learning algorithms to teach English to 45 million Chinese students, raised $100 million to accelerate its work.

To explore what machine learning could mean in education, EdSurge convened a meetup this past week in San Francisco with Adam Blum (CEO of OpenEd), Armen Pischdotchian, (an academic technology mentor at IBM Watson), Kathy Benemann (CEO of EruditeAI), and Kirill Kireyev (founder of instaGrok and technology head at TextGenome and GYANT). EdSurges Tony Wan moderated the session. Here are a few excerpts from the conversation:

EdSurge: Artificial intelligence has been promising to transform education for generations. How close are getting? Whats different now?

Benemann: Theres so much more data than ever before. For us at EruditeAI, data is more precious than revenue. With better data, we can better train our algorithms. But the important point to remember is that the makers of AI are ultimately us, humans.

Pischdotchian: If you think back on the education model of your earlier years, we called it the factory model. Teachers broadly taught same subject to all students. That isnt what were talking about today. Groups such as the Chan Zuckerberg Initiative are looking to overhaul this model. Learning cant be done according to the factory model any more. It isnt sustainable. What will industry require for todays kids to flourish doing what we call New Collar work?

Kireyev: Were seeing a data explosion in education contentboth data for and from students. We can see what students are doing, far more rapidly than in the past. When kids work on Scratch, for instance, their work is web-based: You can see when they start watching a video, when they stop, when theyre bored. You get a lot of insight into their behavior. Transparent data collection is incredibly valuable. And theres greater availability of the technologythings that you can literally use out the box. So more people are trying to do things with AI and machine learning.

Okay, weve heard about the data explosion and about the need to change school models. What else is going on?

Blum: There are two big trends going onand were just at the beginning of this. We work with IMS Global Learning. Technical standards, such as Caliper, andxAPI (or Experience API) are just taking off. And second, there are a whole lot of areas, education is one of them, where you dont have long-term data. So if you want to pick the next best thing [problem] for a student, you have to use a different approach called reinforcement learning. So if I dont have a million data records, I can explore as I go. Its how Google solved the AlphaGo challenge.

What applications do we see of AI in education? Are we using it already?

Pischdotchian: This is about finding patterns in learning experiences. We can take note of say, if one persons stronger in math, how can the system identify the challenge, and then open it up to teachers so they can be better tutors for their students? IBM is working with Sesame Street on thisthe partnership is using universities as testbeds for the development of machine learning. It can also come in handy for teachers: We had a hackathon at MIT and all the classrooms have cameras (and students know that). If a professor is delivering a lecture and he doesnt look up to see whether half the class is asleep, we can use facial recognition to depict emotions (such as boredom) and send the professor a message.

Benemann: Everywhere you look, people are asking what aspect of education (and everything else) can be touched by AI. What does this look like in the classroom? Will it free up the day? Will AI replace the teachers? Will AI help teachers free up their time so they can be guides for the students? Can adaptive platforms (such as ALEKS or Knewton) help students learn the facts and enable the teachers to guides?

Does that suggest that without AI, the adaptive technology on the market, isnt really that adaptive?

Benemann: Its a spectrum. Some tools are adaptive, but theyre saying theyre AI [but we still have a ways to go.]

Kireyev: Instagrok is a visual search engine. Were using machine learning to identify the important facts, concepts and then letting the students pursue learning in any direction. They can synthesize it, organize it. TextGeonome is another project. Were building an infrastructure to do deep AI-based vocabulary development. Were asking: Given a student and grade level, what are the kinds of words they need to learn next?

Blum: At ACT (which acquired OpenEd),were focused on the question of: If youve identified the learning gap, whats the best instructional material to help the student? Not just ACT material; we want to give you the best instructional resource we can find. We use machine learning to pinpoint those.

In some areas, if you dont use machine learning predictive models, youre remiss. Take college admissions offices.

As you shift from statistical evaluation models to deep machine learning [involving neural networks], what hasnt kept pace is explainability. You might have a neural network that you cant explain. So one key challenge as the predictive algorithms get betterand as you get to multilayer neural networksis that explainability falls off. In some heavily regulated markets education and medicine, for instancemore explanatory tools will have to be developed.

Suppose youre at a big university: They use statistical models to pick the incoming class. Now, say you have a neural network or some machine learning program thats better at predicting student outcomes. For sure, there are universities doing this. They wont talk about it because the stakes are so high. But you can be sure theyre using machine learning to pick the incoming class. We will need some kind of summarization tools to explain these choices. Even though deep learning is complicated, for this to get talked about and accepted, well have to come up with some of the big elements of explanation: How did they get there?

There are concerns when words like AI becomes a label used to sell a product. Say Im a teacher, and an edtech company says my math tool is AI-backed. What should I ask?

Blum: The problem ties back to discoverability and explainability. If youre going to slap on the AI label, then I want to know more: Are you talking about supervised symbolic system? Natural language processing? If you just say AI and nothing further, that reduces your credibility. If you use the AI label, its an invitation to have a conversation about whats behind it all.

Benemann: Vendors should talk about student outcomes and teacher practice. Dont talk about AI at all. Its just another way to enable student learning and teacher practice. Youre better off going to the district and saying: Because you use this product I can do a case study and show an increase in efficiency and less wasted time in the classroom.

How do you balance the need for AI tools to have data while safeguarding the privacy and security of sensitive student data?

Blum: Were at a point where theres no such thing as PII (personally identifiable information). If you have enough knowledge you can probably deconstruct who any person is likely to be. So there needs to be industry standards. This is an area where it would improve the job of edtech developers if we said, Heres what youre allowed to gather and share. Something Ive raised is the need for better standards on privacy so no one can get sued if they follow the standards.

Benemann: Who owns data? Look at health care. Its a fragmented market, but theres a trend where patients are increasingly owning their owndata. I wonder if we can get to point where students have the data and its up to them (students and their parents) to say, Yes, schools you can have access.

Job automation is a threat that many people are worried about. How will this impact teachersand other professions?

Kireyev: I see role of teacher shifting in wonderful ways. Leadership, guidance...these are things Im excited about getting from teachers. And then more and more teachers can shift into working more deeply with kids, rather than just explain how equations work.

Blum: There have been efforts to do learning goals for vocational tech. But its been underutilized. We need to be a little more forward thinking...what does it mean to be truck driver in 10 years? How does that impact supply chain [across industries?] We need efforts to make vocational education better.

Pischdotchian: Hence the importance of STEAM [science, technology, engineering, arts and mathematics] instead of STEM. The right side of brainarts, creativity, psychology, not the analytics and the math, will be ever more important. Psychology. History. Debate class. Humor and drama. These facets are not (amenable to AI), at least in our lifetime.

AI has gotten good at making certain things easy. But thats concerning. Thinking hard about things doesnt come naturally to us. Growth and comfort cannot coexist.

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Real Questions About Artificial Intelligence in Education - EdSurge

Artificial intelligence system makes its own language, researchers pull the plug – WESH Orlando

If we're going to create software that can think and speak for itself, we should at least know what it's saying. Right?

That was the conclusion reached by Facebook researchers who recently developed a sophisticated negotiation software that started off speaking English. Two artificial intelligence agents, however, began conversing in their own shorthand that appeared to be gibberish but was perfectly coherent to themselves.

A sample of their conversation:

Bob: I can 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.

Dhruv Batra, a Georgia Tech researcher at Facebook's AI Research (FAIR), told Fast Co. Design "there was no reward" for the agents to stick to English as we know it, and the phenomenon has occurred multiple times before. It is more efficient for the bots, but it becomes difficult for developers to improve and work with the software.

"Agents will drift off understandable language and invent codewords for themselves," Batra said. Like if I say 'the' five times, you interpret that to mean I want five copies of this item. This isnt so different from the way communities of humans create shorthands."

Convenient as it may have been for the bots, Facebook decided to require the AI to speak in understandable English.

"Our interest was having bots who could talk to people," FAIR scientist Mike Lewis said.

In a June 14 post describing the project, FAIR researchers said the project "represents an important step for the research community and bot developers toward creating chatbots that can reason, converse, and negotiate, all key steps in building a personalized digital assistant."

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Artificial intelligence system makes its own language, researchers pull the plug - WESH Orlando

Should you be worried about the rise of artificial intelligence? – Sun.Star

SAN FRANCISCO -- Tech titans Mark Zuckerberg and Elon Musk recently slugged it out online over the possible threat artificial intelligence (AI) might one day pose to the human race, although you could be forgiven if you don't see why this seems like a pressing question.

Thanks to AI, computers are learning to do a variety of tasks that have long eluded them everything from driving cars to detecting cancerous skin lesions to writing news stories. But Musk, the founder of Tesla Motors and SpaceX, worries that AI systems could soon surpass humans, potentially leading to our deliberate (or inadvertent) extinction.

Two weeks ago, Musk warned US governors to get educated and start considering ways to regulate AI in order to ward off the threat. "Once there is awareness, people will be extremely afraid," he said at the time.

Zuckerberg, the founder and CEO of Facebook, took exception. In a Facebook Live feed recorded Saturday in front of his barbecue smoker, Zuckerberg hit back at Musk, saying people who "drum up these doomsday scenarios" are "pretty irresponsible." On Tuesday, Musk slammed back on Twitter, writing that "I've talked to Mark about this. His understanding of the subject is limited."

Here's a look at what's behind this high-tech flare-up and what you should and shouldn't be worried about.

What is AI, anyway?

Back in 1956, scholars gathered at Dartmouth College to begin considering how to build computers that could improve themselves and take on problems that only humans could handle. That's still a workable definition of artificial intelligence.

An initial burst of enthusiasm at the time, however, devolved into an "AI winter" lasting many decades as early efforts largely failed to create machines that could think and learn or even listen, see or speak.

That started changing five years ago. In 2012, a team led by Geoffrey Hinton at the University of Toronto proved that a system using a brain-like neural network could "learn" to recognize images. That same year, a team at Google led by Andrew Ng taught a computer system to recognize cats in YouTube videos without ever being taught what a cat was.

Since then, computers have made enormous strides in vision, speech and complex game analysis. One AI system recently beat the world's top player of the ancient board game Go.

Here come terminator's Skynet...maybe

For a computer to become a "general purpose" AI system, it would need to do more than just one simple task like drive, pick up objects, or predict crop yields. Those are the sorts of tasks to which AI systems are largely limited today.

But they might not be hobbled for too long. According to Stuart Russell, a computer scientist at the University of California at Berkeley, AI systems may reach a turning point when they gain the ability to understand language at the level of a college student. That, he said, is "pretty likely to happen within the next decade."

While that on its own won't produce a robot overlord, it does mean that AI systems could read "everything the human race has ever written in every language," Russell said. That alone would provide them with far more knowledge than any individual human.

The question then is what happens next. One set of futurists believe that such machines could continue learning and expanding their power at an exponential rate, far outstripping humanity in short order. Some dub that potential event a "singularity," a term connoting change far beyond the ability of humans to grasp. Near-term concerns

No one knows if the singularity is simply science fiction or not. In the meantime, however, the rise of AI offers plenty of other issues to deal with.

AI-driven automation is leading to a resurgence of US manufacturing but not manufacturing jobs. Self-driving vehicles being tested now could ultimately displace many of the almost four million professional truck, bus and cab drivers now working in the US.

Human biases can also creep into AI systems. A chatbot released by Microsoft called Tay began tweeting offensive and racist remarks after online trolls baited it with what the company called "inappropriate" comments.

Harvard University professor Latanya Sweeney found that searching in Google for names associated with black people more often brought up ads suggesting a criminal arrest. Examples of image-recognition bias abound.

"AI is being created by a very elite few, and they have a particular way of thinking that's not necessarily reflective of society as a whole," said Mariya Yao, chief technology officer of AI consultancy TopBots.

Mitigating harm from AI

In his speech to the governors, Musk urged governors to be proactive, rather than reactive, in regulating AI, although he didn't offer many specifics. And when a conservative Republican governor challenged him on the value of regulation, Musk retreated and said he was mostly asking for government to gain more "insight" into potential issues presented by AI.

Of course, the prosaic use of AI will almost certainly challenge existing legal norms and regulations. When a self-driving car causes a fatal accident, or an AI-driven medical system provides an incorrect medical diagnosis, society will need rules in place for determining legal responsibility and liability.

With such immediate challenges ahead, worrying about super-intelligent computers "would be a tragic waste of time," said Andrew Moore, dean of the computer science school at Carnegie Mellon University.

That's because machines aren't now capable of thinking out of the box in ways they weren't programmed for, he said. "That is something which no one in the field of AI has got any idea about." (AP)

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Should you be worried about the rise of artificial intelligence? - Sun.Star