New AI capabilities are emerging, including extensions to conventional predicting and forecasting techniques that are quite often using machine learning technologies to model the real world.
In a nutshell, while a large part of machine learning (mostly classification) aims are predicting what will happen next, decision makers may also be interested in various future scenarios that represent what could happen next, under what conditions and what actions should be taken.
We define Machine Foresight as the technology and theoretical ground for the generation of such actionable scenarios. Knowledge of these scenarios equips decision makers with information that they can use to bend the future towards desirable states and away from unwanted situations by taking the right actions.
IBM Research data scientists have developed leading edge foresight capabilities in a collaborative approach to scale big data discovery.
Notable examples are:
Scenario Planning. IBM’s scenario planning advisor is a solution able to ingest news stories and data from social media to identify important story lines for the end user and then apply AI planning algorithms to generate future scenarios.
Simulation Platforms. Simulations can help organizations plan for different future outcomes and understand what actions they need to take to get to the desired outcome. One IBM example of work we are doing is a business simulation platform designed to help government officials of a given country play out scenarios and a set of actions that can be taken to improve a country’s World Bank “Ease of Doing Business” ranking. These actions are automatically learned and extracted from unstructured reports produced by similar countries. The results can be significant: The Kenyan government, working with IBM Research Africa, was able to boost its “Ease of Doing Business” rank by 44 countries in just two years.
Advanced Discovery: IBM Research can uncover unexpected white space and innovation opportunities and predict where to make the most profitable research bets. The inability to discover the next “new thing” quickly is a huge shortcoming faced by companies today across multiple industries including retail, medicine, materials and consumer goods.
We are using machine-based discovery technology to mine millions of published papers, patents, material properties databases combined with internal company data. Then using advanced analytics, modeling and simulation to aid human discovery. A diverse set of skills and tools are needed to integrate and analyze these many sources of data, from deep domain knowledge of chemistry, biology and medicine, to data modeling and knowledge representation, to systems optimization. The data sets, skills and infrastructure provided by IBM Research not only enabled this work, but also are allowing the re-use of the tools in domains from materials discovery to cancer research.
Want to know more about IBM data science and machine foresight?
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