{"id":6892,"date":"2016-05-26T06:00:34","date_gmt":"2016-05-26T13:00:34","guid":{"rendered":"http:\/\/editor.thinkapps.com\/blog\/?p=6892"},"modified":"2016-05-25T15:42:46","modified_gmt":"2016-05-25T22:42:46","slug":"machine-intelligence-affectiva-interview","status":"publish","type":"post","link":"https:\/\/thinkapps.com\/blog\/development\/machine-intelligence-affectiva-interview\/","title":{"rendered":"Affectiva Helps Machines Understand Human Emotions [Interview]"},"content":{"rendered":"

This is part of a series on machine intelligence companies<\/a><\/span>. We interviewed\u00a0Beagle<\/a><\/span>,\u00a0Mariana<\/a><\/span>,\u00a0Beyond Verbal<\/a><\/span>,\u00a0Preteckt<\/span>,<\/a>\u00a0and\u00a0Eigen Innovations<\/a><\/span>. Now we’re featuring\u00a0Affectiva<\/a><\/span>, which\u00a0analyzes human emotions.<\/em><\/p>\n

Machines don\u2019t know what it\u2019s like to have feelings. Yet for most of us, emotions are the prism through which we view our lives. <\/span><\/p>\n

What seems like a benign request when we\u2019re in a good mood can seem like nagging when we\u2019re stressed. We tolerate jokes from our loved ones that we\u2019d never suffer from someone we can\u2019t stand. <\/span><\/p>\n

Emotions are core to the so-called human experience.<\/strong><\/span><\/p>\n

Affectiva<\/a>, an emotional intelligence company spun out of the illustrious MIT Media Lab<\/a> research laboratory, is teaching artificial intelligence systems to view the world through that same prism. <\/span><\/p>\n

In the process, it\u2019s making it easier than ever for developers to build their products around a deep understanding of how people feel at any given moment, thanks to a series of public SDKs<\/a>.<\/span><\/p>\n

\"Machine<\/a><\/p>\n

We interviewed Gabi Zijderveld<\/a>, Affectiva\u2019s Vice President of Marketing and Product Strategy, to learn more about how the company plans to teach machines to understand our wily emotions.\u00a0<\/strong><\/p>\n

Jay Turcot<\/a>, Affectiva’s\u00a0Director of Applied Science, also provided insight into the company’s science and use of machine learning.<\/strong><\/p>\n

[Editor\u2019s note: The interview questions and responses below have been edited for clarity and length.]<\/em><\/p>\n

What’s your role at Affectiva\u00a0and what does that\u00a0entail?<\/h2>\n

My role is VP of Marketing and Product Strategy.\u00a0<\/span><\/p>\n

At the highest level, what that entails is all the marketing functions \u2014 including PR<\/a>, media relations<\/a>, communications, events, messaging, positioning, digital marketing campaigns, website, and content development.\u00a0<\/span><\/p>\n

I\u2019m also involved in\u00a0product strategy, so basically I lead the product management function.<\/p>\n

We\u2019re a startup, and we have a relatively small team. So many of the people who work here wear a variety of\u00a0different hats.<\/strong><\/span><\/p>\n

\"Machine<\/p>\n

What did you do before you joined\u00a0Affectiva?<\/h2>\n

I\u2019ve worked in high tech for over 20 years in a variety of roles encompassing product management, marketing, and channel management.<\/span><\/p>\n

I\u2019ve done a lot to help US-based software companies grow their customer base outside the US. I\u2019ve worked predominantly at software companies. But I\u2019ve also been at IBM, and I worked on the hardware side<\/a> at their house.<\/span><\/p>\n

I\u2019ve been around the block in\u00a0high tech for quite some time. That diversity of experience is especially important when you\u2019re at a startup and you\u2019re a small team, because effectively what we did a few years ago was set out to build a completely new company. <\/span><\/p>\n

We\u2019re building a new market, defining a new product category, and building new technology. And\u00a0my personal role hovers from the extremely strategic (what is the vision for the future and how do you define emotion AI?) \u00a0to the inanely tactical. <\/strong><\/p>\n

Sometimes my workday can be ridiculously tactical. Right now I don\u2019t have an intern who\u2019s doing social media for me. So if I want a tweet to go out, I\u2019m the one tweeting it.<\/span><\/p>\n

I have a lot of responsibility. I\u2019m basically setting the future but also getting my hands very dirty every single day, which can be tough, but which actually I really, really like.<\/p>\n

What’s the most difficult thing about your role at Affectiva?<\/h2>\n

The most difficult thing \u2014 and I don\u2019t mean it in a cliche way \u2014\u00a0is balancing the workload. <\/span><\/p>\n

There are so many moving parts and so many requests;\u00a0I\u2019m permanently backlogged on email. We\u2019re a very small team and there\u2019s basically too much going on for us to handle. <\/span><\/p>\n

It\u2019s a mix of prioritizing and time management, while knowing you never have enough time.<\/strong> <\/span><\/p>\n

That\u2019s a good problem to have, and over time it\u00a0will get rectified. But right now that\u2019s a daily challenge.<\/span><\/p>\n

\"Machine<\/a><\/p>\n

What is “emotional intelligence”? How is it quantified and analyzed?<\/h2>\n

In a nutshell,\u00a0our software measures facial expressions of emotion. <\/span><\/p>\n

In order to do that, all we need is our software running either in the cloud or on the device and access to a web cam or device camera.\u00a0<\/span>We\u2019re all about opt-in and consent, of course; we want people to know we\u2019re recording their faces and to say they\u2019re okay with that. <\/span><\/p>\n

Our algorithms identify the face and pinpoint key landmarks on the face \u2014 tip of your nose, corner of your eyebrows, chin \u2014 and then measure and analyze gradations at the pixel level to measure facial muscle movement. <\/span><\/p>\n

From there our software measures different facial expressions, which\u00a0are then mapped to emotions. Our algorithms are tested and trained using our extensive emotion database.<\/p>\n

\"Machine<\/a><\/p>\n

Our database has information from over 4 million faces from 75 countries, all of which have been measured and analyzed frame-by-frame, second-by-second.<\/p>\n

We have over 50 billion data points.\u00a0We are the only company with an emotional data repository of that size.<\/strong><\/p>\n

This allows us to build norms and benchmarks. In media and advertising, for example, these norms let our clients compare performance of their ads to that of their competitors.<\/p>\n

Our emotion database also gives us some really unique insight into how people respond emotionally in a digital context.<\/p>\n

What are the benefits of analyzing someone’s emotional state?<\/h2>\n

Some of our clients use our software to do advertising testing. <\/span><\/p>\n

Say they have a panel of paid analysts who are asked to watch a video online, wherever they are in the world. They consent to having their webcam turned on, and then our software analyzes frame-by-frame how they respond to what they\u2019re seeing.<\/p>\n

This data gives these firms the opportunity to optimize, change, and improve their ads, and also determine how to allocate their media spend.<\/p>\n

\"Affdex<\/p>\n

We also license our tech to developers through free SDKs (which are accessible here<\/a>) so their products can sense and analyze emotion in real time. Our tools are available for iOS, Android, Windows, Linux, and OS X, and anyone is welcome to use them.<\/span><\/p>\n

That\u2019s extremely powerful because it allows us to enter into new markets. <\/span><\/p>\n

We are never going to be healthcare experts<\/a> or education experts or automotive experts. But there are plenty of companies in those spaces that can integrate our tech into their own tools to augment what they\u2019re doing.<\/span><\/p>\n

On a technical level, what’s the hardest aspect of emotion analysis?<\/h2>\n

One of the hardest parts about emotion analysis is that there’s so little real-world emotion data.\u00a0<\/span><\/p>\n

Affectiva had to be a trailblazer, not just in emotion technology but also in understanding how things like cross-cultural differences occur. <\/strong><\/p>\n

As we learn more about human emotion, we hope to use that knowledge to help all the users of our SDK and our partners.<\/span><\/p>\n

What might people expect from Affectiva in the future?<\/h2>\n

Just yesterday, we announced<\/a> $14M in growth capital in addition to $20M raised prior.\u00a0This was a round led by Fenox Venture Capital, and investors include IT Services giant CAC Holdings, Bandai Namco, and Sega.<\/p>\n

We will continue to make our technology easily accessible to any developer that wants to “emotion-enable” their own apps, digital experiences, or whatever solution they’re building. <\/span><\/p>\n

This will enable us to rapidly enter into new verticals (like education, healthcare, automotive, robotics, HR, etc.), in which\u00a0<\/span>our technology will be\u00a0used to build engaging, authentic experiences. \u00a0<\/span><\/p>\n

We also believe that in the near future we will see the emotion chip, where our technology is embedded in device chips (very much like the GPS locator is available today).<\/span><\/p>\n

\"Machine<\/a><\/p>\n

What’s the most exciting trend in machine learning\/AI from Affectiva’s perspective?<\/h2>\n

The most exciting trend has been the mainstream uses of machine learning that have emerged in recent years built on top of deep networks.<\/p>\n

It seems that the applications of machine learning have advanced drastically and are being applied to change so many industries. The move of machine learning into mainstream products signals the beginning of a very exciting time.<\/strong><\/p>\n

Right now, what\u2019s really interesting is that in general the narrative around AI has picked up again because there\u2019s a lot of really practical applications on the market today. There are a number of companies and vendors that are developing really interesting capabilities. <\/span><\/p>\n

But our premise is that, if there are artificial intelligence systems, especially those which are designed to interface with humans, they have to have an appreciation and understanding of human emotions.<\/strong> <\/span><\/p>\n

They have to be able to sense human emotion and be able to adapt their responses and reactions to them. We believe that, at least as of today, these artificial intelligence systems are missing these abilities.<\/span><\/p>\n

What advances in machine learning have benefitted Affectiva the most?<\/h2>\n

Methods that are able to leverage large amounts of data. <\/span><\/p>\n

At Affectiva, we have a large and growing database\u00a0that\u00a0we can leverage to achieve increased accuracy, which has really let us improve our technology.<\/span><\/p>\n

\"Machine<\/a><\/p>\n

Are there any limitations on machine learning that Affectiva would like to see removed?<\/h2>\n

The reliance on large amounts of labeled (or annotated) data. <\/span><\/p>\n

In many tasks, large amounts of data are used to make learned models robust and generalized to all the variations that occur in the real world.<\/span><\/p>\n

When you compare this to the ability of children and their ability to generalize and learn, you see that there is still much more that is possible. <\/span><\/p>\n

For example, a child will be able to recognize a giraffe in a zoo despite having only seen a handful of cartoon depictions of giraffes. Machines would require thousands of photographs of giraffes to learn what a giraffe looks like. Even then, a machine would not be able to recognize a cartoon giraffe with what it had learned.<\/p>\n

Does Affectiva consider itself a machine learning company?<\/h2>\n

Absolutely; the core of Affectiva’s technology relies heavily on machine learning. <\/span><\/p>\n

In situations\u00a0where machines perform human-like tasks, inferences, or behaviors,\u00a0the underlying technology is almost always machine learning.<\/span><\/p>\n

What are the differences between machine learning, deep learning, and AI?<\/h2>\n

AI is the broadest area of research\u00a0that\u00a0covers machines capable of demonstrating intelligence. <\/span><\/p>\n

This area includes\u00a0fields of research such as computer vision (understanding images\/video), natural language processing (understanding language), and machine learning.<\/span><\/p>\n

In some cases, the term AI actually refers to AGI (Artificial General Intelligence), which is more akin to the kind of sentient machines you see in movies.<\/span><\/p>\n

Machine learning is the science of writing algorithms that can learn from examples, in order to perform a task. The alternative would be to create rule-based algorithms based on rules, whether those rules are grounded by intuition or by science (i.e. physics). <\/span><\/p>\n

Many subfields of AI, such as computer vision and natural language processing, now rely heavily on machine learning.<\/span><\/p>\n

Deep learning is an area of research within machine learning in which the algorithms that are used create models that have many layers. <\/span><\/p>\n

Typically these are neural networks, where each layer is relatively simple. However, when you use many of these layers, one on top of the other, you can start modeling things that are very complex. A network with just one layer would be considered shallow.<\/span><\/p>\n

\"Machine<\/a><\/p>\n

We recently featured Beyond Verbal<\/a><\/span>, which also works on emotional intelligence. Do you see it as a competitor?<\/h2>\n

We know Beyond Verbal well. We\u2019re quite friendly with them. They actually participated in our first-ever hackathon just a few months ago, during which we made our tech available to a whole bunch of people over a single weekend. <\/span><\/p>\n

Ultimately we tackle some of the same problems, in terms of automating and quantifying how people respond emotionally. But they do \u201cvoice\u201d and we do \u201cface.\u201d <\/span><\/p>\n

There are cases where \u201cface\u201d is more accurate than \u201cvoice.\u201d But there are also times when you can\u2019t analyze someone\u2019s face, so analyzing their voice is the only option. <\/span><\/p>\n

We don\u2019t really see it as a competition because typically the use cases are still quite different. There\u2019s a bit of overlap, but to the best of my knowledge, we haven\u2019t directly competed with them. <\/span><\/p>\n

Right now we see our products more as adjacent to each other, and we think that there might be opportunities for us to partner with companies like Beyond Verbal in the future.<\/span><\/p>\n

 <\/p>\n

This post is part of a\u00a0series of\u00a0interviews with\u00a0machine intelligence companies<\/a><\/em>\u00a0that\u00a0are harnessing the power of machine learning and artificial intelligence in innovative ways. Stay tuned for more!<\/em><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

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