Dejan Milojicic, Hewlett Packard Enterprise Distinguished Technologist and IEEE Computer Society past president (2014), said, “In 2019 we expect to see ever-increasing adoption of deep learning accelerators in the areas of transportation, advanced security, and technologies for humanity. Fueled by advanced materials, adoption of virtual reality and the Internet of Bodies will stretch the future to new unknowns. We are excited about our predictions and the bets we have made for 2019’s technology trends.”
Deep learning accelerators such as GPUs, FPGAs, and more recently TPUs. More companies have been announcing plans to design their own accelerators, which are widely used in data centers. There is also an opportunity to deploy them at the edge, initially for inference and for limited training over time. This also includes accelerators for very low power devices. The development of these technologies will allow machine learning (or smart devices) to be used in many IoT devices and appliances.
Assisted transportation While the vision of fully autonomous, self-driving vehicles might still be a few years away, increasingly automated assistance is taking place in both personal and municipal (dedicated) vehicles. Assisted transportation is already very useful in terms of wide recognition and is paving the way for fully autonomous vehicles. This technology is highly dependent on deep learning accelerators (see #1) for video recognition.
The Internet of Bodies (IoB). IoT and self-monitoring technologies are moving closer to and even inside the human body. Consumers are comfortable with self-tracking using external devices (such as fitness trackers and smart glasses) and with playing games using augmented reality devices. Digital pills are entering mainstream medicine, and body-attached, implantable, and embedded IoB devices are also beginning to interact with sensors in the environment. These devices yield richer data that enable more interesting and useful applications, but also raise concerns about security, privacy, physical harm, and abuse.
Social credit algorithms. These algorithms use facial recognition and other advanced biometrics to identify a person and retrieve data about that person from social media and other digital profiles for the purpose of approval or denial of access to consumer products or social services. In our increasingly networked world, the combination of biometrics and blended social data streams can turn a brief observation into a judgment of whether a person is a good or bad risk or worthy of public social sanction. Some countries are reportedly already using social credit algorithms to assess loyalty to the state.
Advanced (smart) materials and devices. We believe novel and advanced materials and devices for sensors, actuators, and wireless communications, such as tunable glass, smart paper, and ingestible transmitters, will create an explosion of exciting applications in healthcare, packaging, appliances, and more. These technologies will also advance pervasive, ubiquitous, and immersive computing, such as the recent announcement of a cellular phone with a foldable screen. The use of such technologies will have a large impact in the way we perceive IoT devices and will lead to new usage models.
Active security protection. The traditional method of protecting computer systems involves the deployment of prevention mechanisms, such as anti-virus software. As attackers become more sophisticated, the effectiveness of protection mechanisms decreases as the cost increases. However, a new generation of security mechanisms is emerging that uses an active approach, such as hooks that can be activated when new types of attacks are exposed and machine-learning mechanisms to identify sophisticated attacks. Attacking the attacker is a technological possibility as well, but is almost always illegal.
Last modified: February 8, 2019