The top 10 technology trends in 2019
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. RT2 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 RT2 capabilities. In
the next couple of years we expect to see incremental iterations until true RT2 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.
Thank You!
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