Since the end of Google IO 2016, a new narrative has been looming around the messaging industry: “AI is the new black”. Although the hype has been growing earlier for Privacy-by-Default, Google said: “No, we’re choosing AI-by-default”. Artificial intelligence, according to the company, will revolutionize how you perceive messaging. Accordingly, Google’s new app, Allo, will be listening to all your conversations, analyzing your photos via computer vision, and applying natural language processing to your texts. The goal is to provide you with smart replies and to suggest services that you might need without leaving the app. It’s all about saving time, and giving you the “aha” moment that you’d never experience in traditional messaging.

Smart replies to text and images in Allo

Google still gave users an option: “if you want end-to-end encryption, simply click that button to switch to incognito mode, and our servers won’t see anything. Of course, your experience will suffer. No more aha moments; only plain-old messaging for you privacy-seeking users.”

Google Allo’s incognito mode

However, the danger we face in the path towards the future of messaging is that we are giving Google and Facebook a free pass: “as long as you keep these new features coming, we will continue to stick to your platforms, regardless of how you handle our data”.

But the reality is that we do have another track. Encryption and AI are not oil and water. And a lot of the arguments that have been diverting us from marrying these two concepts are not technologically-grounded. How is that? Let’s try answering three main questions in that regard:

1- Are servers needed to analyze messages and suggest replies?

No, this is why On-device AI is a hot topic nowadays. A lot of complex machine learning tasks that previously relied on powerful servers are achievable nowadays on smartphones. At WWDC 2016, Apple announced that all its algorithms for face, object, and scene recognition in the Photos app will be run on the device itself. Of course, messaging requires more real-time analysis compared to indexing photos. But it won’t take Apple a long time to bring such features to iMessage. In fact, the state of the art techniques for image processing enable processing multiple images per second, which should be enough for most users. Even more, chip makers, such as Movidius and Qualcomm are helping push towards offline data analysis by equipping their chips with cognitive capabilities, such as visual perception, speech recognition, and always-on awareness.

2- Can we still benefit from suggested services without Google reading our conversations?

Yes, you can. For example, when someone in your group chat says: “Let’s get Italian food”, on-device AI can be used to detect your intent and your goal. You can then get a suggestion to search for “nearby Italian food”. At this stage, nothing should have left your device. Only when you click that suggestion should the app search and obtain results from Google. This search is similar to any Google search you issue and does not need any of your conversations’ content or even metadata. The same applies to almost every other service that you can think of in this context.

Suggestions of smart services in Google Allo

3- Won’t end-to-end encryption hurt the progress of AI by preventing Google from gathering data?

The answer to this question is “A little, for now, but eventually, it might not.” First, these companies gather data from other sources, like social networking posts, comments, etc. Still, it’s obvious that Google used chat messages, sent via its services, to train Allo to suggest smart replies. Facebook is also improving its bot engine and its M digital assistant via analyzing users’ chats on Messenger. If such messages were encrypted, this wouldn’t have been possible. In the future, however, this might totally change when advances in privacy technologies allow advanced computing with encrypted data. Apple’s usage of differential privacy has set a precedent, in the sense that privacy-preserving technologies can make their way to the masses if companies intend to do so. Accordingly, CryptoNets, which are neural networks that enable object detection in encrypted images, might become the next hashtag after WWDC 2025. Or you might see someone from Apple confirming their commitment to users’ protection through privacy preserving deep learning.

The Curse of the Web

There is more to the story, though. From day one, Google and Apple have each approached messaging differently. Google has taken a web-focused approach, with Hangouts beating Skype into web video calls that don’t need any app installed. Apple, on the other hand, has taken an app-focused approach, with no dedicated iMessage web app available till this day.

Web apps themselves are still limited, both in terms of encryption and in terms of AI. While a mobile app can afford to reach 100’s of MBs in size, which makes space for several complex machine learning models, a web app is expected to be orders of magnitude smaller. For the moment, this precludes almost any attempt for on-device AI that happens completely in the browser. Hence, Google’s commitment to web apps implies that it’s not looking back. The company has taken a one-way ticket to the server-based AI land. And machine learning folks usually love all this freedom, which enables them, for example, to deploy new models and algorithms on the fly without requiring app updates.

Is there a way out of this loop? Well, WhatsApp has done a good job at giving users access to its app on the web, without sacrificing end-to-end encryption. This required pairing the web app with a mobile device, though, which can be a setback for those used to Google’s previous messaging offerings. However, this might be a minor inconvenience that is significantly outweighed by the advantages of a cross-platform, secure, and rich messaging experience.

Looking Forward

Evidently, the transition to privacy-by-default will not happen overnight. For Google and the likes, foregoing the wealth of data that can be collected from users’ chats is also against what they have been striving for as data-first companies. That is a reason why Google tried to set the bar high with AI in Allo. It actually bought itself time as others cannot yet provide the same offering with end-to-end encryption in place. This kept the discussion topic as: “would you prioritize AI or privacy?”

However, at some point in the future, I believe that the discussion will be again about AI and privacy on one side vs. data collection on the other. Recall the story of WhatsApp, which took around 6 years before transitioning towards default end-to-end encryption. But what made the transition that fast was groups like Open Whisper Systems which took the initiative to implement their own protocol that was later adopted by the major players. Hence, the next big question is: which company will pave the road now, by taking the initiative to bring us privacy-first messaging that is enriched with AI? Will Apple continue to take the lead? Or will we see open-source initiatives, like Open Whisper Systems and OpenAI, joining efforts to do that? Exciting, but intriguing, times are definitely ahead!