Top 10 Technology Trends for 2018: IEEE Computer Society Predicts the Future of Tech

Top 10 Technology Trends for 2018: IEEE Computer Society Predicts the Future of Tech

Top 10 Technology Trends for 2018: IEEE Computer Society Predicts the Future of Tech

Tech experts at the IEEE Computer Society (IEEE-CS) annually predict the “Future of Tech” and have revealed what they believe will be the biggest trends in technology for 2018.

The forecast by the world’s premier organization of computing professionals is among its most anticipated announcements.

Jean-Luc Gaudiot, IEEE Computer Society President, said:

“The Computer Society’s predictions, based on a deep-dive analysis by a team of leading technology experts, identify top-trending technologies that hold extensive disruptive potential for 2018.”

“The vast computing community depends on the Computer Society as the provider for relevant technology news and information, and our predictions directly align with our commitment to keeping our community well-informed and prepared for the changing technological landscape of the future.”

Dejan Milojicic, Hewlett Packard Enterprise Distinguished Technologist and IEEE Computer Society past president, said:
“The following year we will witness some of the most intriguing dilemmas in the future of technology. Will deep learning and AI indeed expand deployment domains or remain within the realms of neural networks? Will cryptocurrency technologies keep their extraordinary evolution or experience a bubble burst? Will new computing and memory technologies finally disrupt the extended life of Moore’s law? We’ve made our bets on our 2018 predictions.”

The top 10 technology trends predicted to reach adoption in 2018 are:

1

Deep learning (DL)

Machine learning (ML) and more specifically DL are already on the cusp of revolution. They are widely adopted in datacenters (Amazon making graphical processing units [GPUs] available for DL, Google running DL on tensor processing units [TPUs], Microsoft using field programmable gate arrays [FPGAs], etc.), and DL is being explored at the edge of the network to reduce the amount of data propagated back to datacenters. Applications such as image, video, and audio recognition are already being deployed for a variety of verticals. DL heavily depends on accelerators (see #9 below) and is used for a variety of assistive functions (#s 6, 7, and 10).

2

Digital currencies

Bitcoin, Ethereum, and newcomers Litecoin, Dash, and Ripple have become commonly traded currencies. They will continue to become a more widely adopted means of trading. This will trigger improved cybersecurity (see #10) because the stakes will be ever higher as their values rise. In addition, digital currencies will continue to enable and be enabled by other technologies, such as storage (see #3), cloud computing (see B in the list of already adopted technologies), the Internet of Things (IoT), edge computing, and more.

3

Blockchain

The use of Bitcoin and the revitalization of peer-to-peer computing have been essential for the adoption of blockchain technology in a broader sense. We predict increased expansion of companies delivering blockchain products and even IT heavyweights entering the market and consolidating the products.

4

Industrial IoT

Empowered by DL at the edge, industrial IoT continues to be the most widely adopted use case for edge computing. It is driven by real needs and requirements. We anticipate that it will continue to be adopted with a broader set of technical offerings enabled by DL, as well as other uses of IoT (see C and E).

5

Robotics

Even though robotics research has been performed for many decades, robotics adoption has not flourished. However, the past few years have seen increased market availability of consumer robots, as well as more sophisticated military and industrial robots. We predict that this will trigger wider adoption of robotics in the medical space for caregiving and other healthcare uses. Combined with DL (#1) and AI (#10), robotics will further advance in 2018. Robotics will also motivate further evolution of ethics (see #8).

6

Assisted transportation

While the promise of fully autonomous vehicles has slowed down due to numerous obstacles, a limited use of automated assistance has continued to grow, such as parking assistance, video recognition, and alerts for leaving the lane or identifying sudden obstacles. We anticipate that vehicle assistance will develop further as automation and ML/DL are deployed in the automotive industry.

7

Assisted reality and virtual reality (AR/VR)

Gaming and AR/VR gadgets have grown in adoption in the past year. We anticipate that this trend will grow with modern user interfaces such as 3D projections and movement detection. This will allow for associating individuals with metadata that can be viewed subject to privacy configurations, which will continue to drive international policies for cybersecurity and privacy (see #10).

8

Ethics, laws, and policies for privacy, security, and liability

With the increasing advancement of DL (#1), robotics (#5), technological assistance (#s 6 and 7), and applications of AI (#10), technology has moved beyond society’s ability to control it easily. Mandatory guidance has already been deeply analyzed and rolled out in various aspects of design (see the IEEE standards association document), and it is further being applied to autonomous and intelligent systems and in cybersecurity. But adoption of ethical considerations will speed up in many vertical industries and horizontal technologies.

9

Accelerators and 3D

With the end of power scaling and Moore’s law and the shift to 3D, accelerators are emerging as a way to continue improving hardware performance and energy efficiency and to reduce costs. There are a number of existing technologies (FPGAs and ASICs) and new ones (such as memristor-based DPE) that hold a lot of promise for accelerating application domains (such as matrix multiplication for the use of DL algorithms). We predict wider diversity and broader applicability of accelerators, leading to more widespread use in 2018.

10

Cybersecurity and AI

Cybersecurity is becoming essential to everyday life and business, yet it is increasingly hard to manage. Exploits have become extremely sophisticated and it is hard for IT to keep up. Pure automation no longer suffices and AI is required to enhance data analytics and automated scripts. It is expected that humans will still be in the loop of taking actions; hence, the relationship to ethics (#8). But AI itself is not immune to cyberattacks. We will need to make AI/DL techniques more robust in the presence of adversarial traffic in any application area.

Existing Technologies: We did not include the following technologies in our top 10 list as we assume that they have already experienced broad adoption:

A. Data science
B. “Cloudification”
C. Smart cities
D. Sustainability
E. IoT/edge computing

IEEE-CS technical contributors include Erik DeBenedictis, Sandia National Laboratories; Fred Douglis, systems researcher and member of IEEE-CS Board of Governors; David Ebert, professor, Purdue University; Paolo Faraboschi, Hewlett Packard Enterprise Fellow; Eitan Frachtenberg, data scientist; Phil Laplante, professor, Penn State University; and Dejan Milojicic, Hewlett Packard Enterprise Distinguished Technologist and IEEE Computer Society past president. The technical contributors for this document are available for interview.

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IoT Business News

Highways England details its V2I plans for roads ‘of the future’

Highways England has set out its plans on how V2I (Vehicle-to-Infrastructure) technology in connected vehicles will be used to improve the nation’s road network.

Connected cars offer a huge opportunity to optimise Britain’s congested roads to improve safety and reduce journey times. Roadside infrastructure can report data into cars such as traffic density, and the range of sensors in the vehicles can be used to relay information back  such as dangerous road conditions.

Transport Secretary Chris Grayling said:

This government is making people’s journeys better, faster and safer to give people better access to jobs, schools and their community.

We are planning to spend more than ever before to upgrade England’s motorways and major A roads from 2020 through to 2025.

As the government’s authority on the operation, maintenance, and improvement of the nation’s motorways, Highways England has been looking into how this data-sharing can be used. In a report today, it’s set out its vision.

'Safe, efficient, and reliable journeys'

If you’ve used Britain’s roads, you'll be all too familiar with potholes. In a car, they’re uncomfortable but don’t often pose much danger. For bikes, however, they can throw a rider off and cause serious injury — or even death.

Connected motorbikes are not something often brought up in conversations, but data from any vehicle type will help to improve the experience for all road users. Highways England wants connected vehicles to automatically report potholes so the most serious can be prioritised for repair.

The authority also wants to lay around 700 miles of high-speed fibre optic cables along the busiest motorways to beam live travel information to car dashboards. Some of the motorways specifically mentioned include the M1, M4, M6, M25, M40, and M42.

The system will be an update to the existing ‘smart’ motorway network introduced in 2016 which uses electronic gantries to close lanes and change the speed limit to ease congestion.

In the RAC’s annual Report on Motoring, 61 percent of motorists believe congestion and journey times on the motorways have worsened in the last 12 months. In October, a report claimed that jams cost the economy £9 billion per year.

Beyond using connected vehicles and roadside infrastructure, Highways England intends to employ drones. The drones will be used to fly overhead and report back on incidents to improve response times.

Highways England Chief Executive, Jim O’Sullivan, said:

We are delivering a record £15 billion of government investment to give people safe, efficient, and reliable journeys, and provide businesses with the links they need to prosper and grow.

Because people’s journeys are important to us we are setting out our high-level aspirations which will help ensure the network continues to drive economic growth, jobs and prosperity, and keeps traffic moving today, and into the future.

The report will be used to inform the government’s next road investment strategy which begins in 2020.

What are your thoughts on Highways England’s plans? Let us know in the comments.

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These 3 technologies will shape the future of healthcare

Transforming healthcare through technology is no longer the Sisyphean task it once was. Technologies such as blockchain, artificial intelligence (AI), and the Internet of Things (IoT) are all helping to drive change, prevent illnesses, and even reshape healthcare IT.

1. Internet of Things

One executive in the mobility industry recently told me that healthcare is a ‘very careful’ market, which traditionally looks at industries such as defence and avionics and follows suit. Yet progress is being made. For the IoT, there are two benefits: assisting diagnosis and making sure treatment is working. With the latter, for example, sensors are now being piloted in intensive care units. In the former, telephone microphones are now being used to develop algorithms which can assess the early stages of chronic obstructive pulmonary disease.

“AI is a little behind this, but its scope is potentially more wide-ranging. The key here is in terms of the volume of work; and the fact intelligent health assistants get smarter the more work they do. Millions of samples can be analysed in quick time and patterns gleaned from them. Take CATI as a recent example. The system, short for ‘cognitive automation of time lapse images’, can, alongside aneuploidy screening (PGS), improve embryo selection for pregnancy by preventing the misdiagnosis of mosaic embryos…”

2. Artificial Intelligence

AI is a little behind this, but its scope is potentially more wide-ranging. The key here is in terms of the volume of work; and the fact intelligent health assistants get smarter the more work they do. Millions of samples can be analysed in quick time and patterns gleaned from them. Take CATI as a recent example. The system, short for ‘cognitive automation of time lapse images’, can, alongside aneuploidy screening (PGS), improve embryo selection for pregnancy by preventing the misdiagnosis of mosaic embryos.

3. Blockchain

While these are all fascinating and potentially transformative use cases, they may not stop healthcare from being a risk-averse industry simply due to the sensitivity of the data involved. Blockchain, however, could. By using a secure, distributed ledger, the potential is there to secure patient data in an unprecedented way. There are other benefits too; as one industry executive told me, it will help organisations be more efficient with healthcare budgets, allowing a ‘greater focus on illness prevention rather than cure.’

The combination of blockchain, AI and IoT could therefore be an irresistible one. Patient data secured on the blockchain; AI-enabled assistants and automated health checks cutting time and costs; and millions of ‘things’ connecting the dots and finding better, clearer diagnoses. This is the future of healthcare – and it cannot come soon enough.

(c) istockphoto.com / Antiv3D | deepblue4you

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To find out more about the potential of IoT, blockchain and AI, attend the co-located IoT Tech Expo, Blockchain Expo and AI Expo Global 2018 taking place in London’s Olympia on 18-19 April 2018. You can find out more and register for a free pass here. The event will host 12,000 attendees, a free exhibition of 300+ companies, 500+ speakers across 15 conference tracks. The co-located event series will also host events in Amsterdam and Silicon Valley in 2018.

IoT Tech Expo, Blockchain Expo & AI Expo World Series 2018
Global: 18-19 April 2018, Olympia, London
Europe: 1-2 October 2018, RAI, Amsterdam
North America: 28-29 November 2018, Santa Clara, Silicon Valley

The post These 3 technologies will shape the future of healthcare appeared first on IoT Tech Expo.

IoT Tech Expo

Face authentication and the future of security

Apple’s iPhone X has given us a glimpse into the future of personal data security. By 2020 we’ll see billions of smart devices being used as mobile face authentication systems, albeit with varying degrees of security. The stuff of science fiction for years, face recognition will surpass other legacy biometric login solutions,such as fingerprint and iris scans, because of a new generation of AI-driven algorithms, says Kevin Alan Tussy, CEO of FaceTec.

The face recognition space had never received more attention than after the launch of Face ID, but with the internet now home to dozens of spoof videos fooling Face ID with twins, relatives and even olives for eyes, the expensive hardware solution has left many questioning if this is just another missed opportunity to replace passwords.

Face Recognition is a biometric method of identifying an authorised user by comparing the user’s face to the biometric data stored in the original enrolment. Once a positive match is made and the user’s liveness is confirmed the system grants account access.

A step up in security, Face Authentication (Identification + Liveness Detection), offers important and distinct security benefits: no PIN or password memorisation is required, there is no shared secret that can be stolen from a server, and the certainty the correct user is logging in is very high.

Apple’s embrace of Face ID has elevated face recognition into the public consciousness, and when compared to mobile fingerprint recognition, face recognition is far superior in terms of accuracy. According to Apple, their new face scanning technology is 20-times more secure than the fingerprint recognition currently used in the iPhone 8 (Touch ID) and Samsung S8. Using your face to unlock your phone is, of course, a great step forward, but is that all a face biometric can do? Not by a long shot.

While the goal of every new biometric has been to replace passwords, none have succeeded because most rely on special hardware that lacks liveness detection. Liveness detection, the key attribute of Authentication, verifies the correct user is actually present and alive at the time of login.

True 3D face authentication requires: identity verification plus depth sensing plus liveness detection. This means photos or videos cannot spoof the system, nor animated images like those created by CrazyTalk; and even 3D representations of a user like projections on foam heads, custom masks, and wax figures are rebuffed.

With the average price of a smartphone hovering around £150 (€170.58), expensive hardware-based solutions, no matter how good they get, won’t ever see widespread adoption. For a face authentication solution to be universally adopted it must be a 100% software solution that runs on the billions of devices with standard cameras that are already in use, and it must be be more secure than current legacy options (like fingerprint and 2D face).

A software solution like ZoOm from FaceTec can be quickly and easily integrated into nearly any app on just about any existing smart device. ZoOm can be deployed to millions of mobile users literally overnight, and provides […]

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Building An IoT Foundation For The Future

Part 3 of the “Manufacturing Value from IoT” series

In my last blog, I talked about characteristics of manufacturing IoT innovators that help them outperform others in the industry. Here, I will talk about the short-term and long-term investments your company needs to bring your IoT transformation to fruition.

Interest in the Internet of Things (IoT) among manufacturers has reached a fever pitch. Executives in every sector recognize opportunities to improve quality, speed, security, and costs by applying smart devices to operations and plant processes.

Unfortunately, hoping for IoT benefits isn’t enough to achieve IoT success – especially when a company doesn’t have the network infrastructure and information technology (IT) to deploy IoT solutions. Yet many executives simply don’t realize how complicated and far-reaching an IoT transformation will be.

  • Vision, strategy, and leadership: An IoT deployment will link many functions and fiefdoms within an organization; to make sure that connection leads to collaboration, senior executives must offer strategic guidance and commitment. That’s a problem at most companies, because only 11% of manufacturers have implemented an IoT strategy for operations. Even worse, 10% of manufacturing executives “don’t know” who leads their company’s IoT strategy. It’s no wonder that the biggest IoT challenge in operations is “identifying opportunities/benefits of IoT” (44% of manufacturers).
  • Skills and experience: Industries as diverse as consumer goods, chemical processing, and textile milling can leverage the IoT – if they have the smarts to do so. The IoT requires new skillsets within plants and among suppliers. The ability to incorporate high-tech electronics into products – including commodities such as concrete, fabrics, rubber, etc. – will be new to most manufacturers. More than a third of manufacturers report that skills/talent to leverage data/intelligence is an IoT operations challenge.
  • Network capabilities and capacities: Antiquated technology is the biggest IoT headache that manufacturers encounter in capturing, communicating, and leveraging data from operations. Only 10% have network infrastructures capable of machine-to-machine communications, and just 13% have networks capable of machine-to-enterprise communications. A quarter of manufacturers report that network capacity is a problem, too. And even when technology and bandwidth are available, cooperation among operations technology (OT) staff in the plant and IT staff in the business is often limited, hindering transfer and optimization of IoT data.

Manufacturers can achieve game-changing competitive advantage with the IoT – but few are ready. Most still need to develop networks, systems, and applications that transform data into insights. That will require short-term upgrades (e.g., update antiquated equipment, sensors, and controls; apply IoT intelligence to pressing problems, such as safety and data security) and longer-term investments and change (e.g., connect enterprise and supply-chain data streams; combine IoT intelligence with business analytics for improved forecasting, planning, and decisions).

Can your IoT infrastructure deliver on the promise of the IoT?

Stay tuned for more on how your company can increase productivity and profitability with IoT, analytics, machine learning, and artificial intelligence. In the meantime, download the report “The IoT is Delivering the Future – Now” to learn more about the complexity of an IoT transformation.


Internet of Things – Digitalist Magazine