Marc Sloan is the Co-Founder & CEO of Scout, the world’s first web browser chatbot, a digital assistant for getting anything done online. Scout suggests useful things it can do for you based on what you’re doing online.
What initially attracted you to AI?
My first experience of working on AI was during a gap year I spent working in the natural language processing research team at GCHQ during my Bachelor’s degree. I got to see first-hand the impact machine learning could have on real world problems and the difference it makes.
It flipped a switch in my mind about how computers can be used to solve problems: software engineering teaches you to create programs that take data and produce results, but machine learning lets you take data and describe the results you want to produce a program. Meaning you can use the same framework to solve thousands of different problems. To me this felt far more impactful than having to write a program for each problem.
I was already studying optimisation problems in mathematics alongside computer science, so once I got back to university I focused on AI and completed my dissertation on speech processing before applying for a PhD in Information Retrieval at UCL.
You researched reinforcement learning in web search under supervision of David Silver, the founder of AlphaGo. Could you discuss some of this research?
My PhD was on the topic of applying reinforcement learning to learning to rank problems in information retrieval, a field I helped create called Dynamic Information Retrieval. I was supervised by Prof Jun Wang and Prof David Silver, both experts in agent-based reinforcement learning.
Our research looked at how search engines could learn from user behaviour to improve search results autonomously over time. Using a Multi-Armed Bandit approach, our system would attempt different search rankings and collect click behaviour to determine if they were effective or not. It could also adapt to individual users over time and was particularly effective in handling ambiguous search queries. At the time, David was focusing deeply on the Go problem and he helped me determine the appropriate reinforcement learning setup of states and value function for this particular problem.
What are some of the entrepreneur lessons that you learned from working with David Silver?
Research at UCL is often entrepreneurial. David had previously founded Elixir studios with Demis Hassabis and then of course joined DeepMind to work on Alpha Go. But other members of our Media Futures research group also ended up spinning out a range of different startups: Jun founded Mediagamma (applying RL to online ad spend), Simon Chan started prediction.io (acquired by SalesForce) and Jagadeesh Gorla started Jaggu (a recommendation service for e-commerce). Our team often discussed the commercial impact our research could have, I think perhaps because UCL’s base in London makes it a natural starting point for creating a business.
You recently launched Scout, the world’s first web browser chatbot. What was the inspiration behind launching Scout?
The idea naturally evolved from my PhD research. I went straight from finishing my PhD to joining Entrepreneur First where I started to think about how I could turn my research into a product.
Before I started this, I completed an internship at Microsoft Research where I applied my research to Bing. At the time, the main thing I learned from my research was that information finding could be predicted based on online user behaviour. But I became frustrated that the only real way to surface these predictions in a search engine was by making auto-suggest better. So I started to think about how the user’s entire online experience could be improved using these predictions, not just the search experience.
It was this thinking that led me and my new co-founder on Entrepreneur First to create a browser add-on that observes user behaviour, predicts what information the user is likely to need next online, and fetches it for them. After a few years of experiments and prototypes, this evolved into a chatbot interface where the browser ‘chats’ to you about what you’re up to online and tries to help you along the way.
Which web browsers will Scout be compatible with?
We’re focusing on Chrome at the moment due to it being the most popular web browser and having a mature add-on architecture, but we have prototypes working on Firefox and Safari and even a mobile app.
The Scout shopping assistant functionality sounds like it could save users both time and money. Assuming someone is researching a product on Amazon, what happens in the backend, and how does Scout interact with the user?
The idea is that once you have Scout installed, you just continue using the web as normal. If you’re shopping, you may visit Amazon to look at products. At this point, Scout recognises that you’re shopping on Amazon, and the product you’re looking at, and it will say “Hello”. It pops up as a chat widget on the webpage, kind of like how Intercom works, except Scout can appear on potentially any webpage. You can see what it looks like on my website.
Because you’re shopping, it’ll start to suggest ways it can help. It’ll ask you if you want to see reviews online, other prices, YouTube videos of the product and more. You interact by pressing buttons and the chatbot tailors the experience to what you want it to do. Whenever it finds information (like a YouTube video), it will embed it within the chat thread, just like how a friend might share media with you on WhatsApp. Over time, you end up having a dialogue with the browser about what you are doing online, with the browser helping you along the way.
The webpage processing happens within the browser itself. The only information our backend sees is the chat thread, meaning that the privacy implications are minimal.
We have a bespoke architecture for understanding online browsing behaviour and managing dialogues with the user. We use machine learning to identify what tasks we can help with online and how we should help. Originally, we used reinforcement learning to adapt to user preferences over time. However, one of the biggest lessons I’ve learned from running an AI startup is to keep processes simple and to try to only use machine learning to optimise an existing process. So instead, we now have a sophisticated rules engine for handling tasks over time that can be managed by reinforcement learning once we need to scale.
What are some examples of how Scout can assist with event planning?
We realised that event planning (and travel booking) are not so different from shopping online. You’re still looking at products, reading reviews and committing to purchase/attend. So a lot of what we’ve built for shopping also applies here.
The biggest difference is that time and location are now important. So for instance, if you’re looking at concert tickets on Ticketmaster, Scout can identify the address of the venue and suggest finding you directions from your current location to it, or find the price of an Uber, or suggest what time you should leave. If you’ve connected Scout into your calendar, then Scout can check to see if you’re available at the time of the event and add it to your calendar for you.
In the future, we foresee Scout users being able to communicate to their friends through the platform to discuss the things they’re doing online such as event planning, shopping, work etc.
Dialogue triggers will be used for Scout to initiate communications. What are some of these triggers?
By default, Scout won’t disturb you unless it encounters a trigger that tells it you may need help. There are several types of trigger:
- Visiting a specific website.
- Visiting a type of website (such as news, shopping etc.).
- Visiting a website containing a certain type of information (i.e. an address, a video etc.).
- Clicking links or buttons on webpages.
- Interacting with Scout by pressing buttons
- Scout retrieving certain types of media such as videos, music, tweets etc.
We plan to allow users to fine-tune what type of triggers they want Scout to respond to, and eventually, learn their preference automatically.
Can you discuss some of the difficulties behind ensuring that Scout is genuinely helpful when it decides to interact with a user without becoming annoying?
We take user engagement very seriously and try to measure whether interactions led to positive or negative outcomes. We try to maintain a good ratio for how often Scout tries to start a conversation and how often it’s used. However, it’s a tricky balance to get right and we’re always trying to improve.
Because of the intrusive nature of this product, getting the interface and UX right is critical. We’ve spent a lot of time trying completely different interfaces and user interaction methods. This work has led us to the current, chatbot style interface, which we find gives us the greatest flexibility in the help we can provide, coupled with user familiarity and minimal user effort for interactions.
Can you provide other scenarios of how Scout can assist end users?
Our focus at the moment is in market-testing specific applications for Scout. Shopping and event planning have already been mentioned, but we’re also looking at how Scout can help academics (with finding research papers, author details and reference networks) and even guitarists (finding guitar sheet music, playing music and videos alongside sheet music online and helping to tune a guitar). We’ve also spent some time exploring professional scenarios such as online recruitment, financial analysis and law.
Ultimately, Scout can potentially work on any website and help in any scenario, which is what makes the technology incredibly exciting, but also makes it difficult to get started.
Is there anything else that you would like to share about Scout?
If you’d like to see what it’s like if your browser could talk to you, you can read more on Scout’s blog.
Thank you for the fascinating take on designing a unite type of chatbot. We are excited to follow this project. Please visit Marc Sloan’s website to learn more.
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