Rüschlikon Dialogue Insight Report

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1 Insight Report April 2014 Research and Development 2020 Dr. Matthias Kaiserswerth, Vice President; Europe, IBM Research March 2014 marked the launch of the inaugural Rüschlikon Dialogue, a new kind of interactive event hosted by IBM Research. The idea behind this is simple: convene a group of leading Chief Technology Officers and R&D leaders from some of the world s most innovative organizations and IBM Research s top scientists, to explore and discuss some of the key trends shaping the future of R&D over the rest of this decade. For this first event, we focused on three key themes that we believe will be at the heart of innovation by These include: Big data and advanced analytics. It is already clear that data is set to become the new natural resource for research. But advances in cognitive computing are opening up the possibility of significantly strengthening and accelerating the work of human researchers. Tools such as the Watson Discovery Advisor are allowing for wholly new kinds of questions to be asked, for instance: where should R&D resources be allocated in 2020, or why should we consider making solar panels from perovskite crystals? So what does the rise of thinking machines mean for your R&D aims? High-performance computing and simulation. We know that computing systems will continue to scale exponentially between now and In fact, the data suggests that over this six-year time frame, today s most powerful supercomputers will struggle to hit 2020 s TOP500 list. By 2024, your notebook will be capable of doing the analysis of today s Watson-level supercomputers. So how will this shape your research agenda and simulation possibilities? Millennials, social media and going mobile. Already by 2015, 50% of your R&D team will be comprised of millennials, born sometime between 1980 and By 2025, they will represent three-quarters of the team. We know they work differently: they are adept social networkers, they are always connected, and they expect instant feedback. So what will businesses do to make full use of this resource, both internally and externally? The 2014 Rüschlikon Dialogue helped explore these questions in greater depth, tapping into leadingedge research from our scientists, along with the practical considerations and insights of today s top R&D practitioners. To facilitate this, we relied on four key components: keynote speeches from leading scientists; interactive Think Lab sessions with all participants; live technology demonstrations, and many opportunities for direct networking. A few of the insights from these sessions are briefly summarized in this report. Finally, our deep thanks are due to all those who participated and made this event a success. Your input, engagement and feedback were instrumental in shaping this and will help drive the agenda for future events.

2 From Big Data to Smarter Decisions Dr. Abdel Labbi, Manager, Analytics Research, IBM Research Zurich Cognitive computing represents a new era in the evolution of information technology. The arrival of computers like IBM s Watson is a milestone every bit as significant as the creation of the first calculating machines at the start of 20 th century or indeed the emergence of programmable systems 50 years ago. Why is cognitive computing so important? Because for the first time we are talking about machines that can learn to improve their performance from experience on a given task. In practice, this means computers that can understand language, contextualize information, and solve complex problems. It is this ability to learn and interpret that enabled Watson to beat the reigning champions of the U.S. TV game show Jeopardy! back in But of course cognitive computing has much more serious applications, from smarter traffic management to better healthcare. Alongside cognitive computing, a convergence of wider trends in data and analytics are set to change the way we think about technology and the opportunities it creates: Growing volumes of data will put the emphasis on real-time analysis, rather than storing for later processing. A growing proportion of the data generated today cannot practically be stored in databases. Instead, new tools are emerging to analyze information on the fly, making continuous analytics the focus. A move from databases to integrated data platforms. Relational databases remain important but are simply not enough to deal with the evolving challenges of big data. Organizations can no longer rely on analyzing data in a structured database or warehouse, but need to consider how they can use platforms to intelligently integrate data from a diverse set of sources. Computers can now deal with uncertainty. Stochastic approaches to data modeling are emerging, in which the computer comes back with a range of possible answers to a given problem, each with an attached level of confidence rather than a single right or wrong answer. Data is the new natural resource. The way we think about data itself also needs to change. We believe data is the new natural resource and like oil, or other raw materials, enterprises will increasingly compete for control and access to this resource, as they develop the products and services of the future. Spotlight on innovation: The Crystal+ big data analytics platform One of the live technology demonstrations featured was IBM s Crystal+ analytics platform. Our executives use this to find in-depth commercial insights on any business, in a structured and accessible form that is customized for individual users. Drawing on sources as diverse as Hoovers, LinkedIn, Reuters, and Google Finance, the tool helps crystallize a 360-degree view of a firm. Crystal+ also gives a tangible example of several data and analytics trends in action. First, data is processed and analyzed in real-time. Second, the platform enables live integration of information from a diverse array of data services, rather than storing any the real time systems smarts lie with its ability to bring together inherently incompatible datasets. Third, it is highly contextualized for any individual user or application. Fourth, it s real: Crystal+ is available as an IBM Research technology or service for others to tap into. 2

3 The Crystal+ Big Data Analytics Platform Apps SupplierIQ Analytics Services 360º Client View Industry KPIs Benchmarking Text Analytics Predictive Analytics Client Profiling News Alerts Next Best Action People Network Contextual Integration App. Driven User Driven Data Integration Connectors Streams Graphs DB DB DB DB DB DB API API API API API APIs Marketing data base MDb Finance Info. Warehouse FIW IBM Contracts, Inst. Base BMIS Sales Opptys Siebel Market Size & Share GMV-E Client Information CMR 2014 (Master International Record) Business Machines Corporation Data Services S&P/Capital IQ Reuters Google/Yahoo/Bing IDC (Competitive Info.) LinkedIn Twitter Think Lab Ideas and insights from our Think Lab on data and analytics. We need new business models to underpin data sharing. Some of the biggest opportunities with big data arise where data is shared between industry players but fears over disclosing sensitive or competitive data need to be overcome. The whole concept of the user interface needs to be rethought. Ultimately, people want to be able to ask questions and get answers. Advances in naturallanguage processing will help take advanced analytics outside the realm of the data scientists and deliver the power of big data to decision makers across the organization. Data is a natural resource that businesses will learn how to price and value. Although huge volumes of data are freely available online, leaders understand that they will need a mix of internally-owned, paid-for and free data sources. They will invest strategically in this resource, with control of key data sources becoming an increasingly competitive issue. Tools like Watson will augment but not replace human judgment. Cognitive computing is designed to address problems where the answers are rarely black or white. For this, new supercomputing platforms such as Watson produce probability-weighted solutions, which are dynamically reweighted as new information is added. More fundamentally, cognitive computers are designed to assist humans to look at problems in new ways, supported by data. 3

4 Advanced Computer Simulation: The Third Pillar of Innovation and Discovery Dr. Alessandro Curioni, IBM Fellow and Department Head, Cognitive Computing, IBM Research Zurich Computational science has become the third pillar of the scientific enterprise, a peer alongside theory and physical experimentation. With advanced simulation, you start to be able to simulate very complex problems that would have been extremely difficult, time consuming or costly to address using traditional experimentation. A practical example of how this can help is in the simulation of high-k materials for CMOS technology. As CMOS devices have shrunk in size, the insulating materials used within them have reached the boundaries of their physical limits, compromising performance. The goal was to find a material with at least ten times better insulating capacity, but which otherwise had similar physical properties to the existing material. Advanced simulation facilitated this, helping to uncover a new material while ensuring optimal performance. As advanced computer simulation develops as a tool for innovation, it is changing how we think and approach it: Advances in simulation are not purely driven by Moore s law, but by parallelism. This exponential increase in computing power has been important, and since the 1990s it has increasingly been driven by linking processing units to develop huge parallel machines. Planning for parallelism in computing is thus a vital element of advanced simulation. Simulation allows you to understand the intrinsic properties of your system, something you could never get from experimentation. One example of this is IBM Research s work to test new types of energy-efficient batteries, which has helped identify lithium-air as the most affordable and viable solution for development something that wouldn t be practical via experimentation alone. Sustained increases in computing power are opening up new frontiers for scientific discovery. From materials science and genomics to financial risk and climate modeling, simulation is transforming both the approach and potential of research in manufacturing firms, energy providers, media and life sciences enterprises. Are big data and cognitive computing the next step? Even as simulation finds a role as the third pillar of innovation and discovery, the advent of big data and cognitive computing is potentially adding a fourth dimension to consider. Spotlight on innovation: Simulation in the oil and gas sector Advanced computer simulation is playing a vital role in the global oil and gas industry. One example highlighted at the Rüschlikon Dialogue was an operator conducting high-level modeling of wells it plans to drill. This is done in part to meet regulatory demands, but also to improve the chances of success, given the high cost of drilling any new well. To drive this, the firm now has some 600 people engaged in modeling and simulation as part of its R&D team. A practical example of its work is in High Pressure Air Injection (HPAI) which explores how best to blast air into a reservoir to improve the extraction of reserves. But the limits of today s computer power mean that current simulations are largely at a 100x100m resolution, whereas the future will lie in simulating the interaction of air and chemicals in a reservoir at a molecular level a challenge that is several orders of magnitude more complex than today s computing capabilities. 4

5 Simulation-Based Science & Technology: Materials Modeling 2014 International Business Machines Corporation 1 Think Lab Ideas and insights from our Think Lab on advanced simulation: Advances in simulation capability are opening up new areas to exploit. Enterprises of all types are using advances in processing power and tools to find new areas for simulation in their business. One airline industry firm, for example, is expanding from simulations of the in-flight performance of various materials to considering how to simulate the production and maintenance lifecycles of these structures, opening up new opportunities for predictive maintenance. Integration across process stages and access to data are major challenges to overcome. The difficulty of integrating simulations across different stages of a given process is a major issue for many companies. This problem is exacerbated by limited access to the data and parameters required for key compounds or materials. This is especially problematic for those engaging with a complex supply chain, where key suppliers (or customers) may be reluctant to share their data on materials or parts. A key future goal lies in moving simulation closer to real time. Several companies are seeking to move closer to real-time simulation, while still struggling with the complexities involved. However, as the availability of computing resources continues to scale exponentially, while costs continue to drop, this is moving closer to reality in certain areas. As an SME, we struggle to convince management to invest in simulation. One of the most striking differences observed during the Think Lab session was the gulf between global multinationals and SMEs. For the former, advanced simulation is simply the standard way of working. But for many SMEs, there are considerable barriers to overcome. One firm s management prefers to conduct physical experimentation of new structures in China, as they believe it is cheaper, largely because of an inability to convince a conservative management team of the merits of a simulation-based approach. 5

6 Social Business, Millennials and the Impact on R&D Chris Sciacca, Manager Communications, IBM Research Zurich The future of R&D will be shaped not only by advances in technology, but also by wider social and demographic trends. In particular, organizations need to be thinking about how the millennials the generation that was born between 1980 and 2000 are going change the way they innovate. Already there are big shifts in the workforce. Baby boomers are moving into retirement, taking valuable knowledge with them. Meanwhile, the millennials, who will make up 50% of the workforce in 2015, will have entirely different expectations of the working environment and their career paths. The way they communicate and collaborate is heavily conditioned by their use of technology, including mobile devices and experience of social networks. For example, because of the popularity of liking and retweeting using social networks, they expect immediate gratification on everything they do, and they are conditioned to absorb information in short, bite-size chunks. Enterprises therefore need to be aware of these different expectations, and know how to adapt working practices so that they can create the right environment for this new generation to fulfill its potential. To address this, many organizations are experimenting by using social networks within the enterprise to help create communities and collaborative ways of working. It s a social business concept that IBM started experimenting with back in One simple example is a mobile application that enables employees to find the right expert to help them solve a problem no mean task in a company with 430,000 people. IBM has also experimented with crowdsourcing for example, creating a network for scientists where an individual s research can be tested, validated and built upon as a complementary process of traditional publication peer review. It s these kind of practical applications that can help transform a social network into a tool that drives better innovation. Rüschlikon Dialogue Engaging with Millennials ( ) and Why they are Important 97% agree people my age are the most digitally savvy 38% can t go 10 minutes without checking their mobile device 75% have social networking profiles <! IBM Corporation 6

7 Think Lab Ideas and insights from our Think Lab on millennials and social networks: Use internal social networks to accelerate knowledge sharing. It is an age-old problem for large organizations. A team is working on a problem in one part of the world, unaware that individuals in another departmental silo have expertise that could help. Social networks make it much easier for people with common interests and goals to connect and share information, regardless of geographic boundaries. We need more intelligent tools to help filter signals from the noise created by social networking. One challenge with social networking is the huge volume of information produced, often with variable quality and accuracy. One idea to tackle this could be to apply cognitive computing to help sift through this information to extract more relevant and reliable insights from the mass of content available on the network. Move from crowdsourcing ideas to crowd-managed projects. One participant suggested that there is a sizable potential unused unless we create ways to not only crowdsource ideas but subsequently also crowd-manage the resulting projects. Thereby, it is becoming possible to fully leverage the expertise and creativity of those employees that shaped the idea in the first place. Social networks can open a generational gap in the workplace unless top leadership drives cultural change. Several participants observed that while younger employees rapidly adopt useful networks, older managers can view participation as a duty that adds to their list of chores. A key part of addressing this is for top executives to show, through their own actions and participation, that these social tools are an integral part of the new way of working. The divide between internal and external social networks is becoming more fluid. Some participants felt that it would become increasingly irrelevant to talk about internal and external social networks as separate entities. These divisions in knowledge sharing make less sense as time goes on. But this is a contentious point: others argue that organizations will work hard to protect internal knowledge as a key source of competitive advantage. 7

8 Collaboration 2020 Dr. Erich Rütsche, Technology and Intellectual Property, Europe, IBM Research Since its inception in 1945, IBM Research has evolved to meet the changing needs and challenges of the R&D environment. This has been true for our research focus, which has evolved as our needs have shifted: starting with hardware, then adding software and services, to considering how we can build a smarter planet. But it is also true of our approach, which has shifted from a focus on purely in-house innovation early on, to an openly collaborative, knowledge-based approach today. By 2020, we believe collaboration will be more critical than ever, both internally across our business and externally with an array of partners. This collaboration will be at the heart of what we believe will be the key differentiating elements for R&D: combining intellectual property, brand and data to build insight and technology that create a distinct competitive advantage for both our partners and us. IBM Research in numbers 3,000 scientists US$6bn invested annually into R&D 12 labs globally, 4 added since Nobel Laureates, 6 Turing Awards, 5 National Medals of Science, 10 National Medals of Technology 21 years of U.S. patent leadership, including 6,809 patents in 2013 How can we collaborate? To discuss further how you can collaborate with IBM Research on cutting-edge challenges, please contact: Dr. Erich Rütsche Dr. Oliver Boxler Text: Longitude Editors: Nicole Strachowski, Dr. Karin Vey, IBM Research Image of cavitation simulation courtesy of Petros Koumoutsakos zvg, CSE Laboratory, ETH Zurich Copyright IBM Corporation

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