Babak Hodjat, Sentient Technologies
Babak Hodjat is a Co-founder and the Chief Executive Officer of Sentient. A serial entrepreneur, Babak has started numerous Silicon Valley companies as main inventor and technologist.
Prior to co-founding Sentient, he was senior director of engineering at Sybase iAnywhere, where he led mobile solutions engineering. Babak was also a co-founder, CTO and board member of Dejima Inc., which was acquired by Sybase in April 2004. He is the primary inventor of Dejima’s patented, agent-oriented technology applied to intelligent interfaces for mobile and enterprise computing – the technology behind Apple’s Siri.
He has 21 granted or pending patents to his name and holds a PhD in Machine Intelligence from Kyushu University, in Fukuoka, Japan. Capgemini’s Digital Transformation Institute spoke to Babak to understand specific applications of AI across industries.
Building disruptive AI that delivers today
Can you tell us a bit about Sentient and your background?
I’m the co-founder and CEO of Sentient Technologies, where we scale AI to disrupt industries. We build products that disrupt industries and we’re organized into three business units. One is in asset management, investment management or trading. The second is ‘intelligent commerce’ – the AI-enablement of the shopper journey. The third is digital media optimization – effectively building websites that adapt themselves to different users.
I have a PhD in Machine Intelligence from Kyushu University in Japan. In a past life, I set up a startup and I was the main inventor behind the National Language Conversational Technology, which ended up being the underlying technology behind Siri. A big chunk of my team went on to work at Siri, then became part of Apple, and now we have started this.
Are you already seeing some early benefits where you have deployed Sentient AI for clients?
Clients don’t put money into this because it’s some sort of ‘magical AI’. For example, when we sell AI enablement for retailers, we are measured by indicators such as the increase in conversion rates or the average order value. Our business model is based on that approach and we are incentivized by the performance of our AI-enabled products. The same thing goes for digital media optimization. The user defines the measure – such as conversion rates online – and that’s what we’re optimizing, and we show where we actually improved on that. AI is doing that, but how we get paid is a function of that improvement.
Can you tell us more about how AI delivers benefit in commerce?
E-commerce has really not changed significantly in the past decade or more. If you want to buy something online, your experience is more or less the same. You go online you see pages upon pages of seemingly randomly ordered images of what you’re looking for. You then have to scroll through it until you find what you want, or you have to put in lots of filters and so forth. It’s cumbersome, difficult and it’s been a failure. The reality is that online conversion rates are at around 3%, which translates to a 97% failure rate.
So how do you change that? An AI-enabled shopping assistant interacts with you and through that interaction, you can quickly find exactly what you’re looking for. We took AI and we had it learn what there is for sale so that it has a full understanding of everything that this e-commerce site offers. The AI then sits in-between that understanding it has developed and the user. Within a few clicks, users get to exactly what they’re looking for.
We have deployments for example with Sunglass Hut or with Skechers, and in these cases, we have seen 30 or 40% improvements in conversion rates. Not just that, we’ve also been able to increase the average order value which is an indication that the users are actually finding what they’re looking for and therefore, the order value is going up. There’s this anecdote that users at any given point in time, are only seeing about 10% of the inventory. Post our AI deployment, 75% to 80% of the inventory is actually seen by the users because the users are directed and are helped by the AI to navigate the sites.
Can you explain how AI-based trading works?
The AI makes all the decisions from what instruments, how to trade it, whether to go long or short, how long to hold, how much to buy and when to cover or sell. All of these decisions are made by the AI. The AI-based desks have certain risk and return profiles. That depends on the appetite of the investor in the fund.
The AI can very much optimize for different outcomes. These are problems that we call multi-objective problems, which means that we’re actually solving for more than one objective rather than only solving for making money. The AI is solving for making money while reducing risk and, by doing so, there are multiple solutions to a multi-objective problem. You could have a high-risk but high-return solution that is acceptable for a certain class of your customers, and you could have another solution that is much more conservative with, of course, lower returns. So, when you give the AI a multi-objective problem, rather than coming back with a single solution it gives you a spectrum of possible, acceptable solutions. Based on that, you can actually market different offerings depending on the appetite of your customers.
Getting AI off the ground
How do you measure the ROI from an AI deployment?
In the case of digital media or intelligent commerce, it’s pretty straightforward, because organizations already have the cost of acquisition for a user. So, when a user hits a website in e-commerce, every click is a cost and bringing the user to the page is a cost. So, the larger the percentage of people that convert, the less the cost of conversion. And, of course, the increase in conversion – or an increase in Average Order Value – mean more revenue for the organization. We measure that improvement as reflected in dollars. In the case of digital media optimization, there’s also the traffic. Here, our business model is currently called a traffic-based business model. Depending on the traffic on the site, you have a different value to that site. So, if we can improve the value of the conversion relative to the traffic, then we can improve on that particular KPI.
In trading, it is very straightforward and just like any other hedge fund. We’re measuring consistency of returns through time and it depends on the appetite for risk with the different desks. Our model is very similar to a hedge fund model, where there is a commission and percentage of the returns. It’s commonly known as the 2-20, but it could be variations of that, so 2% management fee, 20% of the returns.
How do you train the AI and how much time does it take?
Building an AI product is not trivial. It takes a lot of effort and work and we’re getting better and better at it. Today, it’s a matter of weeks to get from signing an agreement to actual deployment. The actual custom work is maybe 5% or lower, because for most of these categories that are being sold online, we already have models. The whole thing is very much industrialized so that we can manage multiple customers at the same time as on-boarding them and taking new inventory and changes on a daily basis.
How should a large organization go about deploying AI?
I don’t think of AI as a one-size-fits-all kind of a solution. I don’t think a big organization would ever say, “We’re going to standardize and AI-enable everything”. This is because the AI-enablement for different aspects of the organization is going to manifest itself very differently. So, you’re going to have multiple AI-enabled products, each one applied to a different aspect of an organization. There are companies that are starting to think strategically about AI. The usual way is to identify the two or three different areas where it’s fastest to deploy an AI solution and then move to a wider-scale deployment.
A bright future for AI
Which sectors do you think are conducive to AI?
We believe that AI has very broad applicability. For example, there’s a lot of AI being applied in healthcare these days. but not too many people looking at agriculture. But we have been working with OpenAg at MIT Media Lab and undertaking some projects where the AI would discover recipes for how to grow plants and environmentally control situations. For example, we see that the AI is discovering how to grow plants – deciding which spectrum of light to shine, the humidity, water levels, and minerals to be added. You can actually optimize for taste, believe it or not. I wouldn’t say there’s an industry in which AI is not right for disruption.
How do you see the future of AI?
I think AI is going to become more and more ubiquitous in products across many different industries. We will see a lot of AI though in many instances we will not recognize it as AI. It’s just going to be natural – you will interface with it without knowing it is AI. I think AI has a bright future in things like the IoT, in agriculture, healthcare, and across many different enterprises such as banking and insurance. AI will also manifest itself in what we more commonly know as interfaces for robots. Robotics is an industry that’s very much hardware-originated and the interface, therefore, has always been kind of neglected. I think we will come to a point where the best interface to robots are going to be conversational and augmented so that the robot can actually perceive its surroundings. There is a very bright future for AI.