Artificial Intelligence is transforming so many aspects of our lives, but in order to work, it needs large volumes of data that is free of errors and biases. I spoke to Affectiva CEO Rana el Kaliouby and Synthesis.AI CEO Yashar Behzadi to explore how synthetic data can help.

1. How To Solve AI’s Bias Problem,
Democratize With Synthetic Data
Create Emotional Ais, And

2. © 2020 Bernard Marr, Bernard Marr & Co. All rights reserved
Title
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Introduction
Introduction
Artificial Intelligence is transforming so many aspects of our lives, but in
order to work, it needs large volumes of data that is free of errors and
biases. I spoke to Affectiva CEO Rana el Kaliouby and Synthesis. AI CEO
Yashar Behzadi to explore how synthetic data can help.
How To Solve AI’s Bias Problem, Create Emotional AIs, And
Democratize AI With Synthetic Data

3. © 2020 Bernard Marr, Bernard Marr & Co. All rights reserved
AI has the potential to change the world in many amazing ways. But like every revolution,
it requires fuel. It’s long been said that “data is the oil of the information age," and that's
certainly true in many ways. But while data is a less finite resource than actual oil, it does
come with some challenges.
People are (rightly) protective of their personal data, and there are compliance and
regulatory responsibilities that must be upheld if we're using that personal data (often
the most valuable kind of data) to power AI and generate predictions. Additionally,
although data is in abundance everywhere – pretty much everything we do generates
data – getting the right sort of data, at the time you need it, isn't always straightforward.
Generating or collecting specific types of data, when you have specialist requirements,
can be expensive, time-consuming, and tricky.

4. © 2020 Bernard Marr, Bernard Marr & Co. All rights reserved
As an example, we can look to the work of Affectiva – a leader in the field of “emotional”
artificial intelligence. It builds systems that help machines understand the emotional or
cognitive states of human beings. A function of one of its core business units helps vehicle
manufacturers to create smart in-cabin systems – one such system is designed to detect when
we might be feeling drowsy or in danger of falling asleep at the wheel.
It does this by using cameras to track our facial expressions, and analyzing the data with
machine learning algorithms designed to keep track as we get fatigued the course of long
journeys. Because these systems have to work for anyone who uses them, they need to study a
huge number of faces in order for it to be able to recognize the signs that a person it has never
come across before is getting sleepy.
Until recently, Affectiva gathered this data by hiring humans to sit in a driving simulator for
periods of up to six hours. When many thousands of faces are needed, clearly this is an
expensive undertaking – not to mention very dull for the data subjects themselves!

5. © 2020 Bernard Marr, Bernard Marr & Co. All rights reserved
This is where the concept of synthetic data comes in. Computers are now capable of
generating images of faces that are virtually impossible to tell apart from photographs of
real people, that can be made to behave in a completely realistic manner. To solve this data
problem, Affectiva partnered with Synthesis AI, a startup that recently closed a $4.5 million
funding round based on its ability to create synthetic data, including faces of humans who
have never existed. Synthesis’s mission is to reduce the cost of AI by billions per year by
reducing the need for companies to collect, store and label “real” data, in a legally and
morally compliant fashion.
The use cases for synthetic data are practically unlimited. AI algorithms used to pilot self-
driving cars can be “driven” in simulated conditions, creating hours of driving experience
without any risk to real-world road users. Likewise, algorithms that diagnose illness from
medical images can learn to become increasingly accurate in their assessments, from
studying computer-generated images of human bodies and organs.

6. © 2020 Bernard Marr, Bernard Marr & Co. All rights reserved
CEO of Synthesis AI, Yashar Behzadi, told me that his business incorporates
technology and methods pioneered in the movie industry to create very lifelike
images and move them in a realistic way.
These assets are then “supercharged” with generative AI modeling that allows
any number of variations to be created very easily. Essentially, this can include
any combination of age groups, genders, and ethnicities, and all these variables
can be tailored to ensure that the result is a dataset that is truly diverse and
representative. This has the potential to help iron out some of the issues around
AI caused by bias – often a major challenge in AI development, with often
alarming consequences when it goes wrong.

7. © 2020 Bernard Marr, Bernard Marr & Co. All rights reserved
“We make fair AI systems,” Behzadi tells me. “By being able to declare distributions of
data, you make sure you’re representing all the classes you’re interested in, and you’re
able to create well-balanced datasets … [and] you can do it in a very privacy-compliant
way. With synthetic data, you don't have to worry about breaching GDPR or regulations.
This … democratizes access; smaller companies can compete and win. This has always
been fundamentally what’s driven us to build these systems.”
Even seemingly minor details like hairstyles and sunglasses can be modeled in this way so
computers can learn to understand how they might impact their ability to understand
humans. Camera angles can quickly be changed, too – which was helpful for Affectiva
when, having captured a lot of data from cameras facing drivers head-on, from the
steering column, it realized that images taken from a rear-view mirror position were
actually much more insightful. Rather than having to re-do thousands of photographs,
the computer images can be very quickly re-rendered from a different point of view.

8. © 2020 Bernard Marr, Bernard Marr & Co. All rights reserved
Rana el Kaliouby, CEO of Affectiva, told me, "Humans are very complex; we're interested
in the nuanced, complex emotional states, like frustration, confusion, or fatigue. What
does fatigue look like? We can combine that with something like object detection – you
have a phone in your hand – to augment all of this and to detect activities and behaviors,
and it becomes really powerful. The goal is to combine these multiple modalities to get a
very holistic understanding of what the person’s state is, then have the technology
respond in real-time.“
Synthetic data has the potential to help machines understand human emotions and
nuances in many other ways, too. Human-to-machine communication will likely play an
increasingly big role in human society as time goes on, and building machines that are
capable of developing a deeper understanding of us, beyond merely the buttons we
press or the words we use, will help to make that relationship more productive.

9. © 2020 Bernard Marr, Bernard Marr & Co. All rights reserved
“I’m super-excited about the applications in social robotics, conversational
interfaces, and the internet of things," el Kaliouby says. “One of my favorite
examples is a smart fridge, and it knows you are stressed because it has a 'mood
chip,' and it says, ‘you know what, you’re about to have your third tub of ice-cream
and I’m not going to let you do that. Now that's going to require human perception
AI; human emotion AI to understand your state.”
Greater access to synthetic data is also likely to lower the barrier-to-entry of AI to
smaller businesses, many of which may have the vision and innovation to create
truly new applications. Without the expensive production and compliance
requirements, the doors are open for new entrants to get involved.

10. © 2020 Bernard Marr, Bernard Marr & Co. All rights reserved
"Synthetic data is a very democratizing force; you can log in and start creating
loads of images and train your system," says Behzadi.
“That really changes who can access, who can benefit and who can contribute
to the AI space, and I think that will break down some walls that have existed,
where the larger technology companies have built these data models …
ultimately to sell advertising. And I think there’re so many better benefits out
there if we can unlock those use cases.”

11. Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a
strategic business & technology advisor to governments and companies. He helps
organisations improve their business performance, use data more intelligently, and
understand the implications of new technologies such as artificial intelligence, big data,
blockchains, and the Internet of Things.
LinkedIn has ranked Bernard as one of the world’s top 5 business influencers. He is a frequent
contributor to the World Economic Forum and writes a regular column for Forbes. Every day
Bernard actively engages his 1.5 million social media followers and shares content that
reaches millions of readers.
Visit The
Website
© 2020 Bernard Marr , Bernard Marr & Co. All rights reserved
© 2017 Bernard Marr , Bernard Marr & Co. All rights reserved
© 2020 Bernard Marr, Bernard Marr & Co. All rights reserved
Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a
strategic business & technology advisor to governments and companies. He helps
organisations improve their business performance, use data more intelligently, and
understand the implications of new technologies such as artificial intelligence, big data,
blockchains, and the Internet of Things.
LinkedIn has ranked Bernard as one of the world’s top 5 business influencers. He is a frequent
contributor to the World Economic Forum and writes a regular column for Forbes. Every day
Bernard actively engages his 1.5 million social media followers and shares content that
reaches millions of readers.
Visit The
Website

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