The previous year was very exciting in terms of technology. A lot of new concepts, tools and frameworks were introduced, laying the ground for software developers and entrepreneurs to introduce cool new innovative products in the following years. 2018 would be even more exciting, with lots of improvements in the already existing areas, that will (hopefully) make our lives better and more efficient. In this post, we will see what I believe would be the trendy topics for 2018.
Last year, $1.7 billion dollars were invested in AR technologies. And that’s only the beginning. It’s expected that in the next few years, it will be a $120 billion dollars industry.
What’s really cool about AR is that it’s not mostly for gaming, like Virtual Reality (VR). Available both via the mobile phones and special devices like Microsoft’s Hololens, AR will be used in our everyday lives. There are real use-cases, that will help people and businesses in improving their tasks. Starting from decorating your home with the cool Ikea Place app, AR can be used in online shopping, tourism, medicine and much more. Check the AugmentIT website from Netcetera, for some cool use-cases that are developed at the company I work at.
This year, Apple and Google have released their frameworks ARKit and ARCore, that will provide developers an easier way to develop AR apps. There are already a lot of cool concepts and apps that developers have done with these frameworks, which give a glimpse of the power and possibilities of these new technologies.
2018 will be a huge year for Augmented reality and it will be really exciting to see the innovative products and technologies in this area. It’s still not too late to get started, check my blog post about ARKit, to dive into Apple’s brand new framework.
Conversational Interfaces offer an easier and more natural way for people to use their devices. Will people use conversational interfaces? That’s the biggest question. Most people feel strange walking around the streets and talking with their voice assistants. A recent survey has shown that high 98% have at least tried Siri, but only 3% of them have used it in public. The reason why they haven’t used it in public is that they felt uncomfortable talking to their device in public. But when they are not surrounded by other people, like when they are in a car or at home, people use the voice assistants.
Usage at home makes sense, since we are usually more comfortable at home (and more lazy). With the rise of the smart home and the Internet of Things, Conversational Interfaces are needed more than ever. For example, if the light switch is far away from the bed, who wouldn’t just say “turn off the light”, instead of getting up and doing this by themselves.
Grocery shopping can be transformed with these technologies, both for managing the lists that need to be bought and for paying the items in the list. Check the ShoppieTalkie website, a PoC product we have developed in our company that will make shopping easier for the users.
Another place where Conversational Interfaces would be used more in the future are technical support businesses. People tend to ask the same set of questions when calling the support people, which makes the introduction of a chatbot a good solution, at least as a filter for the standard questions. If the chatbot can’t resolve the issue by themselves, the support people can take over.
These technologies can also be used in the service industries. Imagine people inspecting machines or trains or anything else that could be damaged and just saying “oh, the window is broken here” and their assistant writes everything down and uploads it (along with the location) to the cloud. The repairman can just see where’s the damage and what’s the problem and solve it with less communication and paperwork. That will work well with AR technologies too.
There are a lot of platforms that can be used by developers to provide conversational interfaces or chatbots. Google’s Dialogflow, Amazon’s Lex, Facebook’s Wit.ai, IBM’s Watson, to name a few. More details about these technologies in this blog post. I’m also publishing a book about these topics, Developing Conversational Interfaces for iOS, early next year.
Machine learning will continue to be a hot topic in the following years. Users always expect their apps to be smarter and adjustable to their preferences. One thing that was popular the previous year was content filtering. There are a lot of fake news generated on the social media and providing good automated solutions to remove them will be a challenge in the following years.
Apart from the content filtering, there should be advances in natural language understanding in more languages, that will bring chatbots and conversational interfaces to as many people as possible. Mozilla’s project Common Voice tries to tackle this area, by encouraging developers to launch the website in other languages. Netcetera has already done this with the Project Jargon in Macedonian language.
This year, there were two big announcements for machine learning in the mobile world. First, Apple announced Core ML, which allows you to convert already trained machine learning models to Apple’s proprietary format. This model can then be easily integrated in an iOS application. Core ML only makes predictions on previously trained models, it’s not a machine learning framework itself. The main role of Core ML currently is to bridge the gap between the academia (that does the process of researching, designing algorithms and training datasets) and the developers (that don’t have much machine learning expertise, but know how to bring production-ready apps to the real world). Learn more about Core ML in this blog post.
The other framework that was announced is TensorFlow Lite. TensorFlow is popular, open-source machine learning framework by Google. Apps developed with TensorFlow Lite will have a smaller binary size, fewer dependencies, and better performance, compared to TensorFlow Mobile.
With these two frameworks, we can expect more machine learning to be done on the devices, which will reduce the infrastructure costs and it will enable apps to be smart even without an internet connection.
Machine learning, like in the previous years, will be relevant and improving in many other areas, such as shopping, risk predictions, recommendations, business intelligence and much more.
Cyber security will always be a topic in IT. In the past year, there were innovations in this area – Apple’s Face ID among others. Face ID and Touch ID are shifting the industry more to a biometric authentication of the users. That feels natural and easier, instead of typing long passwords and passcodes, users can just show their face, touch the screen with their fingers or use their voice to authenticate themselves.
There are companies like BehavioSec, that analyse how the users are holding their phones and the way they type on the keyboard. Using machine learning over large sets of data, collected while the users are using the apps, they are able to determine (with some probability), whether it’s really the user that’s trying to access the system. If the data is different than usual, that might indicate that an attacker is trying to access the system, giving you a possibility to block that access.
In the future, probably a combination of the biometric and behavioural characteristics of the users will be used a lot more in their authentication to the systems, depending on the level of security required.
This year, Apple also released password auto-fill for third-party apps. With this feature, apps can pre-fill credentials saved in Safari and the iCloud keychain, so users don’t have to type the credentials every time. And the good thing is that you are not saving them in the keychain of your application.
Finding the right balance between enough security and ease of authentication will be still a challenge in the following years.
A lot is happening in the payments industry. Only few days ago, Apple announced Apple Pay Cash, which makes sending and receiving money a lot easier. With Apple Pay Cash, you can transfer money from your bank account to Apple’s Wallet and vice versa. Sending money is very simple – either via the iMessage app or just by telling that to Siri. This year, Apple extended their SiriKit Payments domain, with options to check the status of your account, send money and pay the bills.
Paying or sending money in messaging apps is really interesting and handy concept, that goes well with the idea of chatbots and conversational interfaces. Subway has really cool Facebook Messenger chatbot, which provides users an option to order and directly pay in Messenger. You just show up at Subway and pick-up your ordered food.
We have also explored this concept with our grocery list app ShoppieTalkie. In a proof-of-concept chatbot, we have enabled users an option to add items to a grocery list and pay the items directly in Facebook Messenger. We have used Masterpass API to accomplish this.
Apple Pay is still not supported in many countries, leaving room for other innovative wallets. It will also be exciting to see more NFC or Bluetooth Low Energy enabled payment solutions.
The following years will be very exciting in terms of innovation, products and technologies. Of course, there are a lot more topics than the listed above, that are and will be relevant in the future. We are lucky to be in an industry that’s changing so fast, leading us to constant learning and improving.
Martin strikes with a great post once again 🙂
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Thanks Martin for this outlook!
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