Trends Identified
Gene Drive - A genetic tool that can alter—and potentially eliminate—entire species has taken a dramatic leap forward
Research into a genetic engineering technology that can permanently change the traits of a population or even an entire species is progressing rapidly. The approach uses gene drives—genetic elements that pass from parents to unusually high numbers of their offspring, thereby spreading through populations rather quickly. Gene drives occur naturally but can also be engineered, and doing so could be a boon to humanity in many ways. The technology has the potential to stop insects from transmitting malaria and other terrible infections, enhance crop yields by altering pests that attack plants, render corals resistant to environmental stress, and keep invasive plants and animals from destroying ecosystems. Yet investigators are deeply aware that altering or even eliminating a species could have profound consequences. In response, they are developing rules to govern the transfer of gene drives from the laboratory into future field tests and wider use.
2018
Top 10 Emerging Technologies of 2018
Scientific American
Plasmonic Materials - Light-controlled nanomaterials are revolutionizing sensor technology
Writing in Scientific American in 2007, Harry A. Atwater of the California Institute of Technology predicted that a technology he called “plasmonics” could eventually lead to an array of applications, from highly sensitive biological detectors to invisibility cloaks. A decade later various plasmonic technologies are already a commercial reality, and others are transitioning from the laboratory to the market. These technologies all rely on controlling the interaction between an electromagnetic field and the free electrons in a metal (typically gold or silver) that account for the metal’s conductivity and optical properties. Free electrons on a metal’s surface oscillate collectively when hit by light, forming what is known as surface plasmon. When a piece of metal is large, the free electrons reflect the light that hits them, giving the material its shine. But when a metal measures just a few nanometers, its free electrons are confined in a very small space, limiting the frequency at which they can vibrate. The specific frequency of the oscillation depends on the size of the metal nanoparticle. In a phenomenon called resonance, the plasmon absorbs only the fraction of incoming light that oscillates at the same frequency as the plasmon itself does (reflecting the rest of the light). This surface plasmon resonance can be exploited to create nanoantennas, efficient solar cells and other useful devices.
2018
Top 10 Emerging Technologies of 2018
Scientific American
Algorithms for Quantum Computers - Developers are perfecting programs meant to run on quantum computers
Quantum computers exploit quantum mechanics to perform calculations. Their basic unit of computation, the qubit, is analogous to the standard bit (zero or one), but it is in a quantum superposition between two computational quantum states: it can be a zero and a one at the same time. That property, along with another uniquely quantum feature known as entanglement, can enable quantum computers to resolve certain classes of problems more efficiently than any conventional computer can. This technology, while exciting, is notoriously finicky. A process called decoherence, for example, can disrupt its function. Investigators have determined that stringently controlled quantum computers that have a few thousand qubits could be made to withstand decoherence through a technique known as quantum error correction. But the largest quantum computers that laboratories have demonstrated so far—the most notable examples are from IBM, Google, Rigetti Computing and IonQ—contain just tens of quantum bits. These versions, which John Preskill of the California Institute of Technology named noisy intermediatescale quantum (NISQ) computers, cannot perform error correction yet. Nevertheless, a burst of research on algorithms written specifically for NISQs might enable these devices to perform certain calculations more efficiently than classic computers.
2018
Top 10 Emerging Technologies of 2018
Scientific American
Augmented Reality Everywhere - Coming soon: the world overlaid with data
Virtual reality (VR) immerses you in a fictional, isolated universe. Augmented reality (AR), in contrast, overlays computer-generated information on the real world in real time. As you look at or wear a device equipped with AR software and a camera—be it a smartphone, a tablet, a headset or smart glasses—the program analyzes the incoming video stream, downloads extensive information about the scene and superposes on it relevant data, images or animations, often in 3-D.
2018
Top 10 Emerging Technologies of 2018
Scientific American
Advanced Diagnostics for Personalized Medicine - A new generation of tools could help end one-size-fits-all therapeutics.
For most of the 20th century all women with breast cancer received similar treatment. Therapy has since become more individualized: breast cancers are now divided into subtypes and treated accordingly. Many women whose tumors produce estrogen receptors, for instance, may receive drugs that specifically target those receptors, along with standard postsurgery chemotherapy. This year researchers took a step closer to even more personalized treatment. They identified a significant fraction of patients whose tumors possess characteristics that indicate they can safely forgo chemo—and avoid its often serious side effects.
2018
Top 10 Emerging Technologies of 2018
Scientific American
Immersive Technologies – Enhancing the Digital Experience
Two distinct but merging technologies are behind immersive technologies: Virtual Reality (VR), computer- generated, digital environments that fully immerse users in a virtual world and Augmented Reality (AR), which overlays digital information on the physical world and augmented reality operating environments. Companies are already experimenting in pilot projects and the technology has the potential to become the next computing platform. Mobile devices are currently at the center of the implementation of AR/ VR and they could replace large parts of the PC landscape. We observe a co-evolution with digital twins and gaming. Hardware is important, but the user experience such as ease and comfort of use and real-time performance as well as a structured content are keys for success. High data quality including realtime analytics is also a prerequisite. Immersive Technologies are still in early development and are five to ten years from wide mainstream adoption. They currently show a slower adoption than smartphones but will probably experience a similar cost reduction and speed up significantly cost reduction and development speed. Hardware vendors are accelerating computing and speeding up application performance and vendors are already developing and offering enterprise-level collaboration tools and augmented reality operating environments. For now, the clumsiness of the current devices has reduced mobility and the cost for adopting content are slowing progress and cybersecurity and safety of usage remain issues. Immersive technologies promise unique user experiences including 3D interactions, new ways of data handling, and interaction with the physical world and has the potential to accelerate and simplify business practices or even invent new ones. Use cases are hands-free tasks like in field service and maintenance, digital twins for operations, architecture, real-estate etc., live media streams, and augmented information for any digital supported workplace.
2018
Trend Report 2018 - Emerging Technology Trends
SAP
Conversational Systems and User Interfaces –Talking to Machines
Machine Learning has reached a state in which conversational systems can have increasingly engaging and human like conversations. These chatbots or digital assistants use mainly natural language processing (NLP) to interact with humans but will use sight, sound, tactile, etc. in the near future as well. Conversational systems can be viewed as a step towards true personal virtual assistants and will replace most common interfaces, simplifying human-machine interactions and replacing less interactive interfaces. The technology will probably merge in part with Immersive technologies and will also leverage the digital network of sensors, appliances, and IoT systems to interact with humans. The chatbot ecosystem is already robust, with many different third-party chatbots, native bots, distribution channels, and enabling technology companies. Messaging platforms such as Facebook Messenger, WhatsApp, Tencent’s WeChat, Google Allo, Apple iMessage, Slack and Kik all use chatbots as well as voice-controlled consumer devices like Amazon’s Alexa, Apple’s Siri, Microsoft’s Cortana, Google Assistant and SAP Co-Pilot. While the focus is on consumer applications right now, especially based on mobile technologies, chatbots will go business very fast, allowing businesses to leverage the inexpensive and wide-reaching technology to engage with more consumers and to support, simplify and speed-up many business processes. Examples are commerce applications as taxi cab ordering (such as Uber), concierge facilities (such as Sephora), B2B contract workfl ows (such as Apttus), food ordering (such as Domino’s Pizza) and others. Use cases for chatbots are helping humans to use technology without the need to know technology like in navigating and exploring, automated customer services, and supporting functions for hand-busy tasks in fi eld service, logistics, maintenance, medicine and others.
2018
Trend Report 2018 - Emerging Technology Trends
SAP
Robotic Process Automation – Rule-Based Bots
Robotic Process Automation (RPA) refers to rule-based and easily programmed software, that promises to eliminate repetitive, easily automated tasks. RPAs are fully programmed, cannot learn and have to be updated when IT systems change. RPA is seen as a next step towards automation where machine learning is not needed. It can be tied together with machine learning to automate more complex tasks to create intelligent enterprises. Most RPA today uses text analytics, image processing, text search, or optical character recognition. Analytics will become critical as RPA will soon be developed towards more smarter versions. While we are heading towards an automated world, the process seems to take longer than anticipated and is more complex than anticipated, so RPAs could be a fast forward solution for those processes that yet do not need sophisticated algorithms. For now, a central control of bots is not fully established and changes of IT systems could lead to expensive updates. Besides that, RPA can boost productivity with minimal process change, it offers easy-to-calculate ROIs and it is an in-route to more complex automation with machine learning.
2018
Trend Report 2018 - Emerging Technology Trends
SAP
Machine Learning – The Algorithms to Make Everything More Intelligent
The success of Google AlphaGo in 2016 marks the rise of machine learning. It is based on the idea that machines can learn for themselves if they get access to large volumes of data and comprises deep learning, neural networks and natural-language processing algorithms. The technology has very strong connections to big data and real-time analytics but needs high quality data and a clear process to be trained. Machine learning and IT vendors will in build intelligent capabilities into business intelligence systems, analytics and into many devices, robots and machines. This is rising a number of ethical questions such as the future of our work, how machine learning will affect our behavior, how can we guard us against mistakes and unintended consequences, and how do we stay in control. Extracting the qualitative data needed for machine learning training is a challenge, as well as finding experts. Machine learning can be used to solve a tremendous variety of problems and automate tasks that had to be handled by humans before, such as translations, face, speech and pattern recognition, text analytics, analyzing large data sets and more. The technology promises to save costs and improve the quality of work by automating 80% of the roughly 50% work activities that are suitable for automation. Most of todays’ knowledge work contains the following activities: pattern recognition (99%), generating and understanding natural language (75%), optimizing and planning (30%). Furthermore, machine learning may generate faster insights and speed up decision making based on large data sets and accelerate innovation by faster prototyping. While machine learning will affect all industries, expectations are the highest for media, telecom, technology, consumer and fi nancial services. The fi rst wave of machine learning will focus on automating repetitive tasks and analyzing large data sets such as invoicing, text analysis, image recognition, and fraud detection but will be used for autonomous vehicles, virtual assistants, product intelligence and advanced robotics as well.
2018
Trend Report 2018 - Emerging Technology Trends
SAP
Contextual Workspace – Driving Insights And Decisions
The need for faster and better innovation and improved productivity, and the shift towards high-value work will drive the evolution of improved workspaces. They will allow professionals to quickly create (virtual) project teams according to the project needs and the individual capabilities across any structural and organizational hierarchy. For now, siloed data sources, classical hierarchies and missing innovation and knowledge cultures are barriers for a more value- and project-driven work. Knowledge work will be more consumer like with contextual interfaces and new, social and open collaboration infrastructures and individuals will be able to identify the right people across all silos, networking with them more easily. The user experience will be a seamless one across devices, locations, and context. Machine learning based technologies such as immersive technologies, digital assistants and conversational bots will help with the handling, extraction and representation of data and generate new insights based on the individual roles and projects. Information and recommendations will be presented in a context- sensitive way and increase the user efficiency. The individualization of information will be used to understand knowledge and project management needs, and to support learning on the level of teams and individuals. Security and identity and access of information will be organized people-centric, to make it easier to get to information resources. Vendors are now bundling work stream collaboration with their productivity and unified communications products, which will a push towards even more unified and smart solutions. Contextual workspace will start around challenge-oriented and self-organizing project teams in research, innovation, design and engineering, and education as well as any knowledge and research heavy tasks and real-time data work such as pricing, logistics, supply-chains, maintenance etc.
2018
Trend Report 2018 - Emerging Technology Trends
SAP