Tagging.tech presents an audio interview with Nikolai Buwalda about image recognition

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Transcript:

Henrik de Gyor:  This is Tagging.tech. I’m Henrik de Gyor. Today, I’m speaking with Nikolai Buwalda. Nikolai, who are you, and what do you do?

Nikolai Buwalda:  I support organizations with product strategy, and I’ve being doing that for the last 15 years. My primary focus is products that have social networking components, and whenever you have social networking and user‑generated content, there is a lot of content moderation that’s a part of that workflow.

Recently, I’ve been working with a French company, who’s launched their large social network in Europe, and as a part of that, we’ve spun up a startup that I’m the Founder of called moderatecontent.com, uses artificial intelligence to handle some of the edge cases when moderating content.

Henrik:  Nikolai, what are the biggest challenges and successes you’ve seen with image recognition?

Nikolai:  2015 was really an amazing year with image recognition. A lot of forces really came to maturity and so you’ve seen a lot of organizations deploy products and feature sets in the cloud that used or depend heavily on image recognition. It probably started about 20 years ago with experiments using neural networks.

In 2012, a team from the University of Toronto came forward with a real radical development in how neural networks are used for image recognition. Based on that, there was quite a few open source projects, a lot of video card makers also developed hardware that supported it, and in 2014 you saw another big leap by Google in image recognition.

Those products really matured in 2015, and that’s really allowed for a lot of enterprises to have a very cost effective ability now to integrate image recognition into the work that they do. So 2015 really has seen, in the $1000 range, the ability to buy a video card, use an open source platform, and very quickly have image recognition technology available to your workflow.

In terms of challenges, I continue to see two of the very same challenges existing in the industry. One is the risk to a company’s brand, and that still continues.

Even though image recognition is widely accepted as a technology that can surpass humans in a lot of cases for detecting patterns and understanding content, when you go back to your legal and to your privacy departments, they still want to have an element of humans reviewing content in the process.

It really helps them with their audit, and their ability to represent the organization when an incident does occur. Despite companies like Google going with an image recognition first passing the Turing test, you still end up with these parts of the organization who want human review.

I think it’s still another five years before these groups are going to be swayed to have an artificial intelligence machine‑learning first approach.

The second major issue is context. Machine learning or image recognition is really great at matching patterns in content and understanding these are all the different elements that make up some content, but they are not great at understanding the context ‑‑ the metadata that goes along with a piece of content ‑‑ and making assumptions about how all the elements work together.

To illustrate this, it’s probably a very good use case that’s commonly talked about, which is having a person pouring a glass of wine. Now, in all kinds of different contexts, this content could be recognized as something that you don’t want associated with your brand versus not being an issue at all.

If you think about somebody pouring a glass of wine, say at a cafe in France versus somebody pouring a glass of wine in Saudi Arabia. Between the two, there’s very different context there, but very difficult for machine to draw conclusion about the appropriateness of that.

Another very common edge case that people like to use as example is the bicycle example where machines are great at detecting bicycles. They can do amazing things, far surpass the ability of people to detect this type of object, but if that bicycle was a few seconds away from being into some sort of accident, machines are very difficult at detecting this.

That’s where human review ‑‑ human escalations comes into play for these types of issues and still represent a large portion of the workflow and the cost in moderating content. So, mitigating risk within your organization to have some sort of person review of content.

Then to also really understand the context are two things that I think, in the next five years, will be solved by artificial intelligence and will really put these challenges for image recognition behind them.

Henrik:  As of March 2016, how much of image recognition is completed by people versus machines?

Nikolai:  This is a natural stat to ask about, but I think, with all the advancements in 2015, I really like to talk about a different stat. Right now, anybody developing a platform that has user‑generated content has gone with Computer Vision Machine learning approach first.

They’ll have a 100 percent of their content initially reviewed with this technology and then, depending on the use case and the risk profile, a certain percentage gets flagged and moved on to a human workflow. I really like to think about it in terms of, “What is the number of people globally working in the industry?”

We know today that about 100,000 to 200,000 people worldwide are working at terminals moderating content. That’s a pretty large cost and a pretty staggering human cost. We know these jobs are quite stressful. We know they have high turnover and have long‑term effects on the people doing these jobs.

The stat I like to think about is, “How do we reduce the number of people who have to do this and move that task over to computers?” We also know that it’s about a thousand times less expensive to use a computer to moderate this. It’s about a tenth of a cent per piece of content versus about 10 cents per content to have a piece of content reviewed with human escalation.

In terms of really understanding how far we’ve a