Trends Identified
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
Advanced Robotics – The Rise of the Super Machine
Advanced robots are featured in many science fiction films and are probably 10-15 years away from mainstream, with new materials, fuel cells, motors, algorithms, sensors and designs. They would feature many aspects of true AI, like working fully autonomously, sensing the environment, recognizing and solving problems and learning from their environment and from humans. Some of the advanced robots may have humanoid appearances but the majority will probably have special functions and look more like machines or will merge into the background. Most of the advanced robots will interact with humans using voice, gesture, face/emotional recognition and neurolinks. We expect many smart robots to work collaboratively with humans and the upcoming of transhumanism, i.e. the merging of man and machine into cyborgs. Applications for advanced robots would be extensive, going beyond the first wave of automation and optimization, into an economy operated in large parts by machines and/or human-machine units. We would either have solved the ethical questions by setting up robot laws or the questions that we see around contemporary machine learning and robotics would be even more pressing, such as the borders between humans and machines: do we allow robots to control humans, how can we guard us against robot mistakes, if we will allow smart robots to design and build themselves and how we would stay in control.
2018
Trend Report 2018 - Emerging Technology Trends
SAP
Bio Computing – Using Nature‘s Computation
One way to solve the limits of current miniaturization is to use biological molecules for computing. Biological computing uses synthesized biological components – mostly DNA – to store and manipulate data, analogous to processes in the human body. It computes by using enzymes that react with DNA strands. Biological computing allows very small and fast and potentially paralell computing process, with great accuracy and unmatched energy efficiency. The first DNA based computer was launched in 2002 but the technology is still in very early prototype stage, with the MIT being one of the most prolific research institutes. Present barriers result in low accuracy, the need for new methodologies, and interoperability issues with other computing systems. Use cases would be ID cards, DNA chips, cryptography, and genetic programming.
2018
Trend Report 2018 - Emerging Technology Trends
SAP
4D Printing – Shape-Shifting and Dynamic Materials
In 4D printing, invented by MIT Self-Assembly Lab, the material used has dynamic capability and can change function, color, confirmation or properties, when certain qualities are changed, such as chemical, electronics, particulates or nanomaterials. The application of 4D printing will allow a completely new and re-design of currently used materials. Shape-shifting materials could disrupt many industries. The technology is in very early prototyping stage with an evolving intellectual property landscape with over 10 years before this technology becomes mainstream. Shape-shifting materials have already been leveraged in the automotive, aerospace, defense and medical industries. Further examples are transformable tissues that can support cell growth or NASA’s space chain mail, which can flexibly create a shield in space. Challenges are to obtain the exact shape-shifting results as designed and well as modeling the geometries, determining interactions for changing states and calculating the energy.
2018
Trend Report 2018 - Emerging Technology Trends
SAP
AI for Molecular Design - Machine-learning algorithms are speeding up the search for novel drugs and materials
Want to design a new material for solar energy, a drug to fight cancer or a compound that stops a virus from attacking a crop? First, you must tackle two challenges: finding the right chemical structure for the substance and determining which chemical reactions will link up the right atoms into the desired molecules or combinations of molecules. Traditionally answers have come from sophisticated guesswork aided by serendipity. The process is extremely time-consuming and involves many failed attempts. A synthesis plan, for instance, can have hundreds of individual steps, many of which will produce undesired side reactions or by-products or simply not work at all. Now, though, artificial intelligence is starting to increase the efficiency of both design and synthesis, making the enterprise faster, easier and cheaper while reducing chemical waste. In AI, machine-learning algorithms analyze all known past experiments that have attempted to discover and synthesize the substances of interest—those that worked and, importantly, those that failed. Based on the patterns they discern, the algorithms predict the structures of potentially useful new molecules and possible ways of manufacturing them. No single machine-learning tool can do all this at the push of a button, but AI technologies are moving rapidly into the real-world design of drug molecules and materials.
2018
Top 10 Emerging Technologies of 2018
Scientific American