A lot of the most innovative technology we saw in 2016 was focused on the integration of the real world and the virtual world—whether it was Pokemon Go*, an augmented reality game using GPS and cameras, or new chatbots that can provide real-time info and help make travel arrangements via messaging platforms. As we move into 2017, we’re going to see even more ways of using technology to connect to the world around us, and we’re going to see conversations between the things themselves—and the software that supports them—become even more sophisticated and complex.
Whether your focus is on consumer apps or you're interested in the B2B space, here are the major tech trends to pay attention to in 2017.
Artificial Intelligence (AI) & Machine Learning
The field of Artificial Intelligence once isolated to the dreams of science fiction, continues to grow and develop, expanding our definition of what’s possible. Encompassing everything from self-driving cars to smart product recommendations to chatbots, AI is essentially the idea of machines that can sense, reason, act—and then adapt based on experience. As we continue to increase our level of connectedness—through more IoT devices, and massive amounts of data—there’s a lot more information to work with, and a lot of important advances being made.
The big news in 2016 was that Google and Microsoft added AI services to their clouds, making AI available to a lot of smaller businesses and start-ups. As we move forward into 2017, we expect to see a lot more of these companies integrating AI into their offerings—and as the market increases, we expect to see a lot more collaboration. There’s a lot to be gained by understanding how devices and systems can work together, something smart companies and smart developers will be putting a lot of energy and resources toward. Rather than existing in their own silos, companies and developers will look to collaborate and integrate with each other.
What’s Intel Doing?
Intel has hardware, optimized frameworks, and resources to help with AI development.
Optimized frameworks – Caffe* is one of the most popular community applications for image recognition, and Theano* is designed to help write models for deep learning. Both frameworks have been optimized for use with Intel® architecture. Learn how to install and use these frameworks, and find a number of useful libraries, here.
Hardware - Intel® Xeon Phi™ processor family – These massively multicore processors deliver powerful, highly parallel performance for machine learning and deep learning workloads. Get a brief overview of deep learning using Intel® architectures here, and learn more about the Intel® Xeon Phi™ processor family’s competitive performance for deep learning here.
- Intel® Software Developer Zone for AI
- Intel® Software Student Developer Program
- Intel® Nervana AI Academy
- Intel® Deep Learning SDK
Virtual assistants aren’t new, and consumers have become increasingly comfortable with them over the last few years—with Siri* always available on their phone and Alexa* sitting on their kitchen counter, ready to answer a question when asked. One of the things that changed in 2016 was that there was a big push for virtual assistants to become more integrated with other systems and services, so that users will be able to access third party software, or handle more complicated transactions just by interacting with their virtual assistant. In June, Apple finally opened up Siri* to third-party developers, which has opened up a wide new range of possibilities.
As we move into 2017, look for new collaborations, and also look for digital services to become even more conversational. Instead of typing a search term into Google, the advent of virtual assistants means that you can make your request or ask your question in the same way you normally talk—“Should I wear a jacket today?”—and then your virtual assistant can translate your question into the series of steps or actions that need to be taken. For example, checking the weather forecast, checking your schedule, and checking wardrobe inventory, all while using AI to continually improve quality and accuracy of results. As virtual assistants become more integrated, the tasks they’re capable of will continue to get more complicated, and ultimately—a lot more helpful.
With IoT, or the Internet of Things, everyday objects in our world continue to grow smarter. No longer just a shoelace, or a lightbulb, or an electric socket, objects which have been outfitted with chips and sensors become a way for us to track and understand the world around us, improving our experience with software that can help make sense of it—by identifying upcoming repair needs in manufacturing, turning off our lights when we leave the room, or encouraging us to get more physical exercise.
In 2017, watch for devices to start to communicate and with each other, and help each other make decisions, especially when it comes to smart buildings and cities. Really interesting models can be built as more data is collected and more systems are opened to collaboration. For example, if a rain gauge can measure precipitation, and then that information can be combined with the latest weather forecast, then a smart building can make the right decision about whether or not the front entry lights should be turned on. Another place to look for opportunities is in manufacturing. Sensors and chips can help identify repairs before they need to happen, saving time and money in the process.
What’s Intel Doing?
Intel provides a wide range of hardware options—including Intel® Edison and Intel® Curie modules and the Intel® IoT Gateway for enterprise—in addition to software tools and code samples to get you started, tools for cloud and analytics, and support . Intel also hosts and participates in numerous hackathons and other IoT-related events worldwide.
- Intel® Software Developer Zone for IoT
- IoT Basics
- Intel IoT Developer Kit
Virtual Reality was a big topic of conversation in 2016, but it has yet to really move into the mainstream. Look for that to change in 2017. Microsoft will be releasing a new HoloLens this year, and Facebook has also previewed some new work applications with Oculus Rift*. As we see continued improvements in AI, and more connected devices, we’ll start to see immersive experiences like VR become more connected as well—to the real world, to other devices, and even to other virtual experiences. We’ll also see a move toward more business use-cases.
Much of the promise for VR, or Augmented Reality (AR), has been in consumer applications such as gaming, or events like football games or concerts where you can “be” right in the middle of the action without ever leaving your couch. But there are plenty of opportunities for this technology in the B2B market as well, especially when it comes to training.
Think of any hard-to-access location—whether that’s outer space, the depths of the ocean, or the inside of a person’s body—and then think about how we can train people to work in that environment. Surgeons can use VR to practice surgical techniques, while underwater technicians can learn how to perform complicated repairs. There are also a lot of great industrial applications, such as digital overlays on machinery in factories, which can help provide education and assist in repairs.
In 2017, as VR moves further into the mainstream, look for these more functional and business-related use-cases to really shine.
What’s Intel Doing?
Intel is focused on Merged Reality, which feels less virtual and more real. In this type of experience, you see the real world, but you're also able to see and manipulate digital objects. With no restrictions on where you go next in the experience, you have the power to change the story, or to experience important moments with important people, no matter where they are in the actual world.
- Intel’s Vision For The Future of Virtual Reality
- Comparison of Projection Methods for Rendering Virtual Reality
Essentially, chatbots are entities that you can communicate with on Facebook*, Skype*, and other popular messaging platforms. Instead of going to a retailer’s website to purchase a new belt, you might just send them a message using Facebook Messenger*, letting them know what kind of belt you’re looking for and what size you need. Or you might send a message to Marriott*, letting them know the dates of your upcoming trip and what kind of room you need, or asking them about upcoming special deals. Facebook* launched bots in Messenger* in 2016, allowing companies to deliver automated customer support, e-commerce guidance, content and interactive experiences, and we should start to see a lot more of that in the next year.
In 2017, look for further integration between messaging apps and other companies—some of these chatbot interactions will happen within messaging platforms, and some will happen within existing product apps. Some companies will be interested in custom chatbots, while others will want to make use of off-the-shelf versions. Also look for increased functionality within chatbots, like more complex transactions and e-commerce, as well as enhanced AI which will allow chatbots to actually emulate certain people or personalities, bringing in humor and the “sound” of particular voices.
The following four trends are less developed than the ones we've discussed above—but no less exciting. The focus here is on how we can better support all of this new technology that’s being created, and make it work even better. How will we access and secure the data that’s being collected? How can we find efficiencies, and collaborate to find the promise of all of these connected devices and systems? Read more to get on the ground floor.
Bitcoin* was all the rage a few years ago, but in 2017, what we’re most excited about is the underlying technology behind it. So what is it, and why is it important? Essentially, blockchain is a way of distributing a database across many different computers, maintaining a growing list of records, called blocks. Each block is then chained to the previous block in a specially-encrypted peer-to-peer network—a system that's able to provide a high level of security from tampering and revision. This technology can be used to keep track of digital coins, like Bitcoin*, but it also has many other potential uses, and can change the way we store and transfer data in a number of different industries. Because the data is recorded and shared by a community, there is not only a higher level of security, but also transparency and trust—each member of the community has their own copy of the record, and must validate updates collectively. Think about the impact this might have, not just in the financial industry, but in health care, social networks, loyalty and reward programs, contracts, music distribution, asset management, identity verification, title registry, supply chain, and even in electronic voting.
There are use-cases to consider in nearly any industry where we want to keep and maintain accurate records. Consider ticketing for events, something that the entertainment industry works hard to manage, usually with the help of third-party vendors charging additional fees. Using blockchain, fans can verify the transfer of ownership from one digital wallet to another, without having to worry that the PDF of the ticket they received might have been sold to multiple other people.
2016 was a year for experimenting with blockchain, and in 2017 we’ll start to see real-world applications. Because the technology is new, and there's not yet a critical mass of assets on blockchain, growth will still be limited—and likely, focused on the financial industry—but we should see multiple networks moving forward with their own standards and protocols, determining best practices and paving the way for future collaborations.
A digital twin is a software model of a physical thing. By using sensor data to understand the current state of the real thing—and visualize changes as they occur—digital twins provide us with new opportunities to to improve operations and add value. If we were to look at a report containing data, such as the performance of machines in a factory, we would need to review the data and then reconceptualize how the product is moving through individual stations in order to understand what's happening—and how operations might be improved, or where we might soon anticipate problems. With digital twins, we're able to actually see the progress of the physical product as it moves through stations, actually see information about the characteristics of the physical product. Instead of looking at the numbers, we can look at the products in the virtual factory and see the actual trend lines that indicate a problem is developing.
In some ways, you can think of digital twins as the confluence of AI, IoT and VR/AR. We use data from sensors and devices—whether that's machine readings for a factory, or wearables for a serious athlete in training—then we use VR/AR to visualize the status of the factory or the athlete, and then we use AI to do what AI does best—sense, reason, act and adapt. We have an amazing amount of information being collected by sensors and devices, and digital twins provide a new way to make use of it.
According to Gartner, within the next five years, hundreds of millions of things will have digital twins, used to plan for equipment service, operate factories, predict equipment failure, increase efficiency, and develop new products. These digital twins will act as the critical connection between data about the physical world, and information contained in the digital world.
Mesh App & Service Architecture (MASA)
As we continue to invent and rely upon new online devices, we’ll need IT systems that allow these devices to talk to each other, and that's where the mesh app and service architecture, or MASA, comes in. MASA will continue to develop alongside these other technologies in 2017 and beyond. By exposing APIs at multiple levels and across traditional boundaries, MASA weaves together web, mobile, desktop and IoT apps, allowing for a continuous, optimized experience.
Essentially, the idea is to link up multiple endpoints in a way that offers a seamless experience. It has to be faster than traditional architecture, and needs to be collaborative across multiple devices and applications, working in and outside of the cloud, and accommodating the rapidly changing needs of users.
Adaptive Security Architecture
As devices get smarter and more connected, we need even better ways to protect data and ensure the security of our systems. Adaptive security architecture is the idea that we can bring the power and learning of AI to the field of security—not just putting safeguards in place, but being able to predict, block/prevent, detect and report—managing threats as they occur, and gaining critical insights for improvement. With ASA, smarter computers and devices will be able to learn how to protect themselves better.
It's more important than ever to have intelligent, flexible strategies for dealing with a very high volume of security data. To be truly adaptive and to provide a high level of security, we can’t afford to simply log data, or alert a human operator. The architecture itself needs to be able to make decisions and respond within seconds, and that's what we'll begin to see as we move into 2017 and ASA is further developed.
As we move into 2017, there’s a lot to look forward to—and a lot that hasn't even been discovered yet. If you're interested in one of these fields, continue to experiment and explore, and check out the Intel® Developer Zone for more articles, tools, and to connect with other developers.