Innovation in most large companies these days is fairly incremental. There is nothing inherently wrong in this, as much of our progress as a society has resulted from such innovation. Over recent years, however, we are seeing a radical departure from incremental innovation. Instead, we look at organizations who intentionally set extremely ambitious innovation objectives, where incremental innovation cannot get the job done.
The focus of this talk is to discuss the ways in which organizations mobilize resources to go after bold objectives which can move the needle: Moonshots. These are not incremental innovation activities, but instead multi-year missions that mobilize extensive scientific and technological resources to expand the horizons for both organizations and societies, and transform both in the process.
From the original Apollo mission, the original IBM 360 mainframe computer, NASA, DARPA, Google X, or Telefonica´s new spinoff company — Alpha, more and more organizations are trying to inductively develop a coherent approach to creating and executing organizational moonshots.
A major driving force to tackle Moonshots is the incredible advances in Data Science and Artificial Intelligence. It is widely believed that global human progress depends on the collection and analysis of data to fuel our increasingly digital world. There is tangible benefit including economic opportunity to be gained. But arguably most important, is data as a force for global and impactful social good and, here, the possibilities are endless.
We will examine the process against which Moonshot organizations transform new science into new progress. Why companies should do them, the resources the company must bring to the initiative, partnerships required, talent and values. How Data Science, Artificial Intelligence and Machine Learning can be applied to many disciplines to solve long standing problems with extraordinary results, and which will soon enable the vast majority of humanity to experience many things that today are restrict to none or the very few. We will also see both successful and unsuccessful Moonshot cases, and will discuss the internal organization of these initiatives, as well as their external objectives.
Dr. Pablo Rodriguez is the CEO of Alpha. Prior to Alpha, Pablo led Telefonica´s corporate research lab and incubator. He has worked in several Silicon Valley startups and corporations including Inktomi, Microsoft Research and Bell-Labs. His current interests are privacy and personal data, re-thinking the Internet ecosystem and network economics. He has co-founded the Data Transparency Lab, an NGO to drive data privacy and transparency. He is on the advisory board of Akamai, EPFL, and IMDEA Networks. He has worked with chef Ferran Adria (El Bulli) on computational gastronomy and with F.C. Barcelona applying data science to soccer. He received his Ph.D. from the Swiss Federal Institute of Technology. He is an IEEE Fellow and an ACM Fellow. For further information on Pablo, please see www.rodriguezrodriguez.com
Alpha is an innovation facility established by Telefonica to define and solve big societal problems and democratise access to new opportunities. Alpha supports the creation of moonshots – multi-year development projects that address these big societal problems. Alpha aims to conceive and deliver radical solutions and breakthrough technology by collaborating with the right talent and the people impacted by the problems we are trying to solve.
Designing an auction that maximizes expected revenue is an intricate task. Despite major efforts, only the single-item case is fully understood. We explore the use of tools from deep learning on this topic. The design objective that we adopt is revenue optimal, dominant-strategy incentive compatible auctions. For a baseline, we show that multi-layer neural networks can learn almost-optimal auctions for a variety of settings for which there are analytical solutions, and even without leveraging characterization results. We also show that deep learning can be used to derive auctions for poorly understood problems, including settings with multiple items and budget constraints. Our research also demonstrates that the deep learning framework is quite general, being applicable to other problems of optimal economic design.
Joint work with Paul Duetting (LSE), Zhe Feng (Harvard University), and Harikrishna Narasimhan (Harvard University).
Working paper: https://arxiv.org/abs/1706.03459
We constantly generate digital traces in our online and offline lives, for example by using our smartphones, by interacting with everyday devices and the technological infrastructure of our cities or simply by posting content on online social media platforms. This information can be used to model and possibly predict human behaviour in real-time, at a scale and granularity that were unthinkable just a few years ago.
In this talk, Mirco will present his recent work in modelling human behaviour using these “digital traces” with a specific focus on mobile data. He will provide an overview of the methodological, algorithmic, and systems issues related to the development of solutions that rely on the online analysis and modelling of this type of data. As a case study, he will show how mobile phones can be used to collect and analyse mobility patterns of individuals in order to quantitatively understand how mental health problems affect their daily routines and behaviour and how potential changes can be automatically detected. Mirco will demonstrate that it is possible to observe a non trivial correlation between mobility patterns and depressive mood using data collected by means of smartphones. Finally, I will also introduce our efforts in using cellular data for modelling mobility patterns of individuals at scale and their applications in the area of data for development.
Mirco Musolesi is a Reader in Data Science at the Department of Geography at University College London and a Turing Fellow at the Alan Turing Institute. He received a PhD in Computer Science from University College London and a Master in Electronic Engineering from the University of Bologna. He held research and teaching positions at Dartmouth College, Cambridge, St Andrews and Birmingham. He is a computer scientist with a strong interest in sensing, modelling, understanding and predicting human behaviour and dynamics in space and time, at different scales, using the “digital traces” we generate daily in our online and offline lives. He is interested in developing mathematical and computational models as well as implementing real-world systems based on them. This work has applications in a variety of domains, such as intelligent systems design, digital health, security&privacy, and data science for social good. More details about his research profile can be found at: http://www.ucl.ac.uk/~ucfamus/
Determining policy priorities is a challenging task for any government. The interdependency between policies and corruption of government officials creates a rugged landscape that governments need to navigate in order to reach their goals. We develop a framework to model the evolution of development indicators as a public goods game on a network. Our approach accounts for the complex network of interactions among policy issues as well as the principal–agent problem arising from budget assignment. Using development indicator data from more than 100 countries over 11 years, our main results are as follows: (i) well known empirical patterns involving aggregate corruption and income can be explained by the opaque relationship between policy outcomes and contributions of public agencies; (ii) achieving a multidimensional target depends on a learning process during the allocation of resources; (iii) the network of spillover effects provides country-specific context that is critical to order policy priorities; and (iv) a country may reach different development targets but how `easy’ it is and through which policies it can be achieved may vary considerably. Our framework provides an analytic tool to generate bespoke advise on development strategies.