IEEE Computer Society Predicts the Future of Tech: Top 10 Technology Trends for 2019
LOS ALAMITOS, Calif., Dec. 18, 2018 /PRNewswire/ -- IEEE Computer Society (IEEE-CS) tech experts unveil their annual predictions for the future of tech, presenting what they believe will be the most widely adopted technology trends in 2019. This year, the experts also review additional technologies that have not yet reached broad adoption and will be revisited next year--such as digital twins--as well as technologies that have outpaced many others, including Kubernetes and Docker. The forecast by the world's premier organization of computer professionals consistently ranks as one of its most anticipated announcements.
"The Computer Society's predictions, based on an in-depth analysis by a team of leading technology experts, identify top technologies that have substantial potential to disrupt the market in the year 2019," said Hironori Kasahara, IEEE Computer Society President. "The technical community depends on the Computer Society as the source of technology IP, trends, and information. IEEE-CS predictions represent our commitment to keeping our community prepared for the technological landscape of the future."
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."
The top 10 technology trends predicted to reach adoption in 2019 are:
1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. Virtual reality (VR) and augmented reality (AR). These related
technologies have been hitting the mainstream in some respects for a
number of years. For a well-known example, Pokemon Go is a game that uses
the camera of a smartphone to interpose fictional objects in real-world
surroundings. Gaming is clearly a driver of these technologies, with
other consumer devices becoming affordable and commonplace. VR and AR
technologies are also useful for education, engineering, and other
fields. However, there has been a Catch-22 in that there is a lack of
applications resulting from the high cost of entry, yet the cost has
stayed high due to a lack of applications. With advertisements for VR
headsets appearing during prime-time television programs, we may have
finally reached a tipping point.
8. Chatbots. These artificial intelligence (AI) programs simulate
interactive human conversation using key pre-calculated user phrases and
auditory or text-based signals. Chatbots have recently started to use
self-created sentences in lieu of pre-calculated user phrases, providing
better results. Chatbots are frequently used for basic customer service
on social networking hubs and are often included in operating systems as
intelligent virtual assistants. We have recently witnessed the use of
chatbots as personal assistants capable of machine-to-machine
communications as well. In fact, chatbots mimic humans so well that some
countries are considering requiring chatbots to disclose that they are
not human. Industry is looking to expand chatbot applications to
interaction with cognitive-impaired children as a way to provide
therapeutic support.
9. Automated voice spam (robocall) prevention. Spam phone calls are an
ongoing problem of increasing sophistication, such as spoofing the caller
ID number of the victim's family and business associates. This is leading
people to regularly ignore phone calls, creating risks such as true
emergency calls going unanswered. However, emerging technology can now
block spoofed caller ID and intercept questionable calls so the computer
can ask questions of the caller to assess whether he or she is
legitimate.
10. Technology for humanity (specifically machine learning). We are
approaching the point where technology can help resolve societal issues.
We predict that large-scale use of machine learning, robots, and drones
will help improve agriculture, ease drought, ensure supply of food, and
improve health in remote areas. Some of these activities have already
started, but we predict an increase in adoption rate and the reporting
of success stories in the next year. "Sensors everywhere" and advances
in IoT and edge computing are major factors contributing to the adoption
of this technology. Recent events, such as major fires and bridge
collapses, are further accelerating the urgency to adopt monitoring
technologies in fields like forests and smart roads.
Below are some of the technologies we considered very promising but felt that they will reach broad adoption after 2019. We will consider these technologies again next year.
1. Digital twins. These are software representations of assets and processes
to understand, predict, and optimize performance for improved business
outcomes. A digital twin can be a digital representation of any
characteristic of a real entity, including humans. The choice of which
characteristics are digitized is determined by the intended use of the
twin. Digital twins are already being used by many companies: according
to analysts, 48% of companies in the IoT space have already started
adopting them. This includes digital twins for very complex entities,
such as an entire smart city (for example, Digital Singapore). Digital
twins are also expected to play a transformational role in healthcare
over the next three years.
2. Real-time ray tracing. RT(2) has long been considered the Holy Grail for
rendering computer graphics realistically. Although the technique itself
is quite mature, it was too compute-intensive to perform in real time
until recently--so all ray-traced scenes had to be scripted and rendered
in advance. In 2018, we witnessed the debut of a consumer product family
with RT(2 )capabilities. In the next couple of years we expect to see
incremental iterations until true RT(2) is widespread. Initially, we
expect the growth to be driven by consumer applications, such as gaming,
followed by professional applications, such as training and simulation.
Combined with #7 (VR), this technology could open up new frontiers in
high-fidelity visual simulations.
3. Serverless computing. This is used to refer to the family of lambda-like
offerings in the cloud, such as AWS Lambda, Google Cloud Functions, Azure
Functions, or Nuclio. "Serverless" is the next step in the continuum
along the line of virtualization, containers, and microservices. Unlike
IaaS, in serverless computing the service provider manages the resources
at a very fine granularity (all the way down to an individual function).
End users can focus on the functions and don't have to pre-allocate
instances or containers or manage them explicitly. While it's still at an
early stage of adoption, there's appeal on both sides (better resource
utilization for the providers, and pay-for-what-you-use for the users),
so we expect that it will pick up rapidly and we will start seeing
significant adoption in the next couple of years.
Finally, we considered some technologies that we felt already reached broad adoption:
1. Kubernetes and Docker. Acceptance of Docker and Google's decision to make
Kubernetes open source inspired the wider open source community to stand
behind these two technologies. This made Kubernetes one of the most
popular open source projects in the last two years and the de facto
standard for running containerized distributed applications on
on-premises clusters and the public cloud. Kubernetes is already used in
production by early adopters, with planned advances in security and
reliability expected to attract further use by traditional enterprise
companies. In 2019, we expect Kubernetes to be used in lieu of
proprietary orchestration infrastructure for running big data processing
and refactored open source code.
2. Edge computing. This is the conversion of IoT data to usable information
using microprocessors collocated with the sensor, or at the edge of the
network. Edge computing reduces network bandwidth, data storage, and
analysis requirements. The price is increased power at the mobile device,
requiring innovations in energy harvesting and storage. Innovations in
edge computing will accelerate new developments across a wide array of
applications.
The IEEE-CS team of leading technology experts includes Erik DeBenedictis, Sandia National Laboratories; Zoran Dimitrijevic, principal engineer, SAP; Fred Douglis, chief scientist, Perspecta Labs; Paolo Faraboschi, Hewlett Packard Enterprise Fellow; Eitan Frachtenberg, data scientist; Phil Laplante, professor, Penn State; Andrea Matwyshyn, Professor of Law/Professor of Computer Science (courtesy) and Co-Director, Center for Law, Innovation and Creativity, Northeastern University; Avi Mendelson, professor, Technion; Dejan Milojicic, Hewlett Packard Enterprise Distinguished Technologist and IEEE Computer Society president 2014; and Roberto Saracco, Chair of the Symbiotic Autonomous Systems Initiative of IEEE-FDC. The technical contributors for this document are available for interviews.
At the end of 2019, we will review the predictions and determine how closely they match up to technology's reality. Check back in December 2019 as we grade our 2019 predictions.
For past predictions, see the 2018 technology predictions as well as the 2018 predictions scorecard for the final evaluation and grades of our predictions.
To learn more about future technology news, research, and developments, visit ComputingEdge here.
About IEEE Computer Society
The IEEE Computer Society is the world's home for computer science, engineering, and technology. A global leader in providing access to computer science research, analysis, and information, the IEEE Computer Society offers a comprehensive array of unmatched products, services, and opportunities for individuals at all stages of their professional career. Known as the premier organization that empowers the people who drive technology, its unparalleled resources include membership, international conferences, peer-reviewed publications, a unique digital library, standards, and training programs. Visit www.computer.org for more information.
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