Trends Identified
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
Blockchain – Decentralized Trust
Blockchain is based on distributed ledger technology, which records data (transactions, files, or information) across a peer-to-peer network. Participant can see the data and verify (or reject) it using consensus algorithms. Approved data is entered into the ledger as a collection of “blocks”, stored in a chronological “chain”, and secured through cryptography. The disruptive nature of Blockchain is its ability to move control over interactions from centralized systems to distributed users. For now, legal and institutional barriers restrict a shift away from central systems but blockchain has a high disruptive potential for all trust-bound activities. Four types of blockchain are evolving: the consortium (controlled by a pre-selected group), the semi-private (a single company granting access to any user), and private and public blockchains like Bitcoin and Ethereum. Up to now the consortium model is the most accepted model for business although the technology is still unproven in a larger business context. Blockchain might make systems more transparent, potentially more democratic and help inventing new trust models. It could improve cash flow, compliance and accountability, it could lower transaction costs and reduce fraud. Furthermore, it offers a huge potential to unify flows of payments, physical goods and information in the rather chaotic relationships among untrusted parties, like in complex supply chains. Blockchain will impact organizations and businesses firstly in a first non-disruptive, incremental change, by leaving processes unchanged and realizing cost savings and process improvements. A use case will start around verifications, smart contracting, transparency and accountability, sharing and leasing models, rights, and IP and government records. The second wave of blockchain will radically restructure existing industry sectors or business ecosystems into systems of trust.
2018
Trend Report 2018 - Emerging Technology Trends
SAP
Digital Twin – Virtualized Insights
Digital twins are virtual models of physical things, products, buildings and systems and their data and information flows. Digital twins create a holistic view of products, buildings and processes and allow virtual simulations and modifications. The real power of a digital twin is the close to real-time linkage between physical and digital worlds. The building elements are: sensors, data, integration layer, analytics and the digital twin, the virtual model, itself. Digital twins promise to improve situational awareness, enable better responses to changes, particularly for asset optimization and preventive maintenance. They can help extending the lifetime of assets and optimizing the performance. Digital twins are increasingly and successfully used for product prototyping, reducing the development times and costs. The market is immature and we are still observing rather simple digital twins like virtual models of buildings, oil platforms and prototypes. The demand is increasing fast and digital twin templates, platforms and services will proliferate. Digital twins won’t stop at assets or things but will be expanded to operations, systems, people, business processes and metadata structures over time. These digital representations will be connected more tightly to their real-world counterparts and infused with more sophisticated artificial intelligence. Obstacles are the heterogeneous and disconnected sources of data and the complexity of the projects. Digital twins will start around asset monitoring, optimization and rapid prototyping. Midterm, operation of factories and companies will follow, long term, we will see generating insights around product and services use and business modelling.
2018
Trend Report 2018 - Emerging Technology Trends
SAP
Quantum Computing – Using Particle Physics for Computation
Quantum Computing uses the characteristics of quantum mechanics, i.e. the superposition and entanglement of subatomic particles. The so-called quantum bits (qubits) allow for an exponential gain in computing power compared to classical bits and promise to solve certain problems that are intractably complex and go beyond todays computing power. Quantum computing might threaten cryptography and cryptocurrency, as the unlimited computing power could make many encryptions ineffective. Potential application areas of quantum computing are quantum chemistry, encryption and security, optimization problems, large database search and operations, machine/deep learning, cryptography, DNA and other forms of molecular modeling. Quantum computing is at the very early stage of basic research mainly on quantum computational hardware, with no unambiguous quantum speed up observed yet and with few known algorithms,. The probabilistic nature of quantum computers makes utilization challenging for now. The technology is currently driven by research institutes, big corporate players like Google, IBM, Microsoft, Intel, HP, and most recently investors. According to Gartner, quantum computing is more than 10 years away and it is questionable if we will ever realize general purpose quantum computers. We might instead see rather narrow use cases. At this stage of research, we see the biggest potential in hybrid approaches like using classical FPGA’s in a quantum inspired way. Another idea would be to use build hybrid computers where classical and quantum CPUs are co-located on the same computer. Blind quantum computing could be used to delegate the computation to a quantum server without leaking any information, which might solve some of the expected security issues.
2018
Trend Report 2018 - Emerging Technology Trends
SAP
Neuromorphic Hardware – Using Nature’s Designs
Neuromorphic hardware is based on conventional processors that are conceptually inspired by neurobiological architectures. Neuromorphic systems are at the very early prototype stage between basic and applied research but the topic is gaining traction within the industry. Companies such as IBM, Intel, Samsung, HP and Google are using the neuromorphic concept to build energy-effi cient networks inspired by biology. Neuromorphic hardware promises new designs for diff erent ways of computing and extreme performance while using little energy. It is suitable for use cases based on machine learning, in particular for pattern recognition, event-driven vision processing, and robotics. It is in competition with quantum computing and in both cases the complexities are potential threats. For now, classical GPUs are more accessible and easily programmable than neuromorphic silicon and programming neuromorphic hardware requires new methodologies that still have to be developed. Based on our learning from the neuromorphic hardware research project within the “Human Brain Project” at University Heidelberg, but we believe that the neuromorphic approach will lead to new concepts in combination with machine learning and powerful graphical GPUs.
2018
Trend Report 2018 - Emerging Technology Trends
SAP
Autonomous Robots/Drones/ Vehicles – The Rise of the Machine
The robotics market is highly dynamic now, companies outside the classical robotics market invest and with China, a new international player has emerged. Hardware costs will go down and light-weight materials and 3D printing will allow to create new and cheaper models faster than before. Battery and energy efficiency will be a decisive factor. Robots are now able to learn and they have gained flexibility, speed, and manual finesse. The advances in data processing will free robots from former computing restraints. Advanced sensors, voice recognition, and machine learning algorithms will drive the interactivity of robot and human-robot collaboration will make major breakthroughs including voice, face, emotional and behavioral recognition. For now, robots have no broad understanding of the context nor the environment and they do not understand complex human behavior. Empathy seems to be out of scope for now but would have with big implications for employment and the future of work and life. Industrial robots will increasingly use intelligent features such as predictive analytics, self-learning and swarm behavior. Developments are adaptive robots with scanning and sensor technologies, 3D printing, high level semantics, collaboration with operator and new human-machine interfaces. Professional service robots will mostly found in medical, field, and entertainment but increasingly in classical services such as butler, kiosk, service robot. The ratio of connected and autonomous cars will rise fast. Nontraditional tech companies are gaining traction in the very technology that makes cars run, such as driver assistance systems, dashboard functions and autonomous driving and mapping. Drones in all variants are now a stable technology with demand rising mainly around agriculture use cases, delivery, remote maintenance as well as asset and inventory tracking. Personal robots are developed for household/daily care, assisting functions, and multipurpose work. They still have problems with most daily tasks, which will need an-other 10 years of development.
2018
Trend Report 2018 - Emerging Technology Trends
SAP
Intelligent Assistants – Smart
The intelligent assistant would be an advanced version of conversational systems, using machine learning and robotics. Assistants have the potential to transform the way we interact with IT, with each other, how we learn and do our jobs and tasks. They would erase the borders between humans and IT/ machines and create true co-workers. Intelligent assistants would not only be able to take over tasks that previously only humans could do but assisting humans as well, including context sensitivity, human- machine interactions via voice, language and gesture, potentially neuronal interfaces and learning capabilities. Assistants will be able to predict and recommend actions, too, and to build relationships with humans over time. Up to now, conversational systems still need to learn the full human communication spectrum including emotional intelligence and the ambiguity of human behavior is still a problem for algorithms. Individuals may use several different assistants and we will probably see a mix of very specialized and more generic ones. If and to what extend these assistants will be embedded in devices or robots or have human-like features will be a matter of role, context and working environment. If assistants will merge partly or fully with humans via neuronal links or implants will depend on feasibility, costs, risks and acceptance by the society and we will need to answer many ethical considerations.
2018
Trend Report 2018 - Emerging Technology Trends
SAP
Brain-Computer Interface – Merging with the Machine
In the years to come, we will explore new ways to collaborate with machines. One way, still considered to be radical by most today, would be brain-computer interfaces, moving towards a human-machine convergence. Now that wearable technology gets miniaturized and more powerful and hands-free applications are within reach, it is likely that non-invasive versions of this technology will be included in VR headset designs. Brain-computer interface designs have shown major progress and can be seen as the ultimate human-machine communication. Prominent organizations working on it are Elon Musks Neurolink, Facebook, Kernel, Emotiv and DARPA. The market is segmented into neurogaming, neuroprosthetics, and neuroanalysis, with interfaces increasingly used in healthcare for locked-in syndrome, paralysis, artifi cial limbs and others. Neuroanalysis and neuroprosthetics are the largest commercial segments driven by rehabilitation, psychological research centers and military applications. Neurogaming is mostly nascent. Currently there are three approaches used, but in all cases extensive training is necessary: • Invasive, where electrodes directly connect to the brain • Partially invasive, where the skull is penetrated, but not the brain • Noninvasive headbands • The human brain is probably the most complex organ in the universe, so brain surgery and even noninvasive neurolinks might have unknown impacts on psychology and neurology
2018
Trend Report 2018 - Emerging Technology Trends
SAP
Human-Machine Convergence – Leaving Biology Behind
The convergence of humans and machines, also known as transhumanism, human augmentation, cyborging, hacktivism or cybernetics, is a prominent theme in many science fiction stories but we are finally reaching a tipping point, where reality is starting to look more and more like science finction. Drivers of this development are immersive technologies, machine learning/AI, brain-machine Interfaces, artificial/robotic body parts, artificial sensors, skin manipulations etc. We already use technology in our body such as bionic hands and limbs, artificial skin and artificial retinas, but the ideas goes far beyond it, using intellectual and physical improvements as an integral part of the human body. The idea behind it is to either permanently or temporarily merge with technology to enhance performance that exceeds normal human limits, to cure illness and deficiencies and improving mental and body strength. Examples are increased physical power via exoskeletons, improved perception with sensors, inbuild immersive and intelligent technologies, braincomputer interfaces, artificial/ robotic body parts, skin manipulations and others like new drugs and genetic updates. It will start around work and activities that demand extreme physical or mental performance, such as the military, emergency services and sports and all areas where humans need an increased mental focus or altered state, like in arts, creativity, and deep thinking. A convergence will rise ethical questions and in the future, we have to decide which enhancements we would allow, if they have to be visible or not etc. As robotics may involve fewer ethical and legal minefields, future scenario might be to allow limited conversions.
2018
Trend Report 2018 - Emerging Technology Trends
SAP