2018 - Dutch Artificial Intelligence Manifesto - SIGAI

Texto

Dutch Artificial Intelligence Manifesto

Special Interest Group on Artificial Intelligence1, The Netherlands

Executive Summary

Artificial Intelligence (AI), the science and engineering that studies and creates intelligent systems, has become a disruptive force revolutionizing fields as diverse as health care, finance, law, insurance, HR, communication, education, energy, transportation, manufacturing, agriculture, and defense.2 Driven by the increased availability of compute power, access to massive amounts of data3, and advanced sensor technology, AI techniques - such as reasoning, imaging processing and machine learning algorithms - have become powerful enablers of automation, predictive analytics, and human-machine interaction. AI has already changed online interactions in the retail sector (e.g., recommender systems and chatbots) but also enable sophisticated AI-enabled user experiences (e.g., AI assistants) that profoundly affect how people live, work, and play.4 To ensure these developments are beneficial for all, we should invest in making AI highly robust, and include all stakeholders in their development.'5 The Netherlands is well positioned to benefit from these developments as strong enablers are in place including strong digital absorption and economic innovation. AI research and education is also strong, but to avoid a brain drain, investments are needed in human talent.6 In the meantime, a technology race has started with the US taking a leading role, China closely following and heavily investing in AI7 , and Europe still in the process of formulating its AI strategy at EU as well as national levels.8 We urgently need a national agenda for AI that provides a national strategy that is supported by academy, industry, and government. The Netherlands must make substantial investments in high-quality Dutch AI research and innovation if it is to compete at all.

In this manifesto the Special Interest Group on Artificial Intelligence (SIGAI) proposes a research agenda and identifies priorities that require investments to ensure AI research in the Netherlands is able to establish and maintain its leading role in the world. How successful the Netherlands will be will depend on the Dutch government prioritising research in AI. We have defined a research agenda that identifies (1) priorities, (2) the need to invest in AI foundational research, and (3) unique opportunities to invest in multidisciplinary challenges. Our recommendations are based on input from all AI research institutes in the Netherlands.

The Netherlands stands out internationally in the high quality of its AI educational system but we do not yet sufficiently exploit our capability to increase our national AI talent pool. Dutch academia, industry, and government should create a strong national AI alliance to promote R&D in AI; and should invest in a strong AI infrastructure that is needed to benefit from AI by providing access to quality data for research in, e.g., health.

In order to ensure Dutch AI research can stay competitive world-wide, it is essential to invest in the foundations of AI research. We have analysed research on seven foundational AI areas strongly represented in the Netherlands:

- Autonomous Agents & Robotics,

- Computer Vision,

- Decision Making,

- Information Retrieval,

- Knowledge Representation & Reasoning,

- Machine Learning, and

- Natural Language Processing.

Based on the strengths in each of these areas, we have identified research challenges that will ensure that the Netherlands will continue to be able to conduct cutting-edge AI research that stands out in the world. We focus in particular on how the academic AI community in the Netherlands can contribute and where investments are needed to ensure that the world-renowned Dutch research in AI is strengthened.

AI is having a big impact on the world-wide society, and can be a tremendous opportunity to increase the quality of life of humanity. We should aim for sustainable next-generation AI systems that are human-centered. AI will provide new opportunities but will also pose several multidisciplinary challenges that we should address. Because society is relying more and more on decisions that are taken by or together with AI systems, their role and impact on society will increase, and there is a need for AI techniques and models for making these systems socially-aware, explainable, and responsible. These priorities are visualized in a grid (Figure 1) composed of the foundational AI areas and multidisciplinary challenges.

Finally, we believe It is important to educate the broader public about current AI driven changes and developments.9

Introduction

AI systems are capable of sensing their environment, learn from and reason about it, and change it based on advanced decision making. AI already has had a big impact on our society; this impact will further increase due to ongoing algorithmic developments, the availability of data and increasing computational power, advances in sensor technology and robotics. AI applications are becoming ubiquitous in all areas of our society including science, industry, health, high tech, energy, public safety, food, retail, and education.

In order to enable and facilitate the Dutch AI research community to have a significant impact in these areas and increase its ability to compete internationally, we have formulated a clear research agenda. To this end, we have identified priorities that require investments in Dutch AI research and innovation. First of all, this includes the general priorities of investing in AI education, a national AI alliance, and AI infrastructure. Secondly, to ensure that next-generation AI systems are accurate, reliable, and robust, we need to invest in foundational AI research. Thirdly, due to the enormous impact AI has on our society, multidisciplinary challenges are becoming increasingly important. Figure 1 provides an overview of the relation between AI foundations and key multidisciplinary challenges of AI in the form of a grid structure.

Foundational AI challenges include the development of algorithms that are more powerful, more data effective, more computationally efficient, and more robust. We identified seven AI foundational areas10 in which the Dutch AI community has made (and is expected to make) important contributions:

●Agents & Robotics: developing autonomous computer systems acting in (either digital or physical) environments in order to achieve their design objectives. Challenges: (1) improving the perception, manipulation, and navigation capabilities of robots, (2) developing sophisticated interaction and coordination models, and (3) integrating different techniques into a coherent decision making architecture.

●Computer Vision: obtaining a visual understanding of the world. Challenges: (1) developing novel algorithms for visual interpretation based on precise appearance and geometry understanding, (2) design algorithms which require less expert supervision, and (3) integrating these with techniques from machine learning, natural language, and robotics.

●Decision Making: planning and scheduling, heuristic search and optimization. Challenges: (1) developing uncertainty classification techniques, (2) developing algorithms to support sequential decision-making under uncertainty and involving multiple parties, and (3) combining reasoning and machine learning algorithms (as in, e.g., AlphaGo).

●Information Retrieval: technology to connect people to information, e.g., in the form of search engines, recommender systems, or conversational agents. Challenges: (1) algorithmic understanding of information seeking intent, (2) machine interpretation of information interaction behavior, and (3) developing online and offline result generation techniques.

●Knowledge Representation & Reasoning: representing information computationally, and processing information in order to solve complex reasoning tasks. Challenges: (1) integrating symbolic with sub-symbolic techniques, (2) robust representation and reasoning techniques for knowledge that is large, dynamic, heterogeneous and distributed, and (3) integration of knowledge representation and reasoning with other AI challenges, such as vision, natural language understanding, question answering, robotics, and others.

●Machine Learning: learning from data (using e.g. neural networks also known as ‘deep learning’ and/or statistical techniques). Challenges: (1) integrating pattern recognition techniques with higher level knowledge, (2) developing more efficient reinforcement learning algorithms, and (3) developing uncertainty classification techniques.

●Natural Language Processing: Extracting information and knowledge about the world from (large amounts of) spoken, written, and signed natural language, enabling human-machine communication, and supporting multilingual human-human communication. Challenges: (1) dealing with the rich variation and cultural differences in language use at personal and group level, (2) achieving technological language-independence, and (3) achieving naturalness in generated text and speech.

__________________________________________________________

Notes

1 The Special Interest Group of AI, SIGAI, is a special member of IPN, the ICT Platform Netherlands, representing all computing science academic institutes and researchers in the Netherlands that perform AI research. All academic institutes represented have contributed to this document (CWI, LU, RU, RUG, TUD, TUe, UM, UT, UU, UvA, UvT, VU).

2 See e.g., Deloitte in WSJ July 2015. Also see https://aiindex.org/, a US initiative which reports that the number of AI papers produced each year has increased by more than 9x since 1996, introductory AI class enrollment (at Stanford, but this trend is visible more broadly) has increased 11x since 1996, the number of active US startups developing AI systems in the US has increased 14x since 2000, the annual VC investment into US startups developing AI systems has increased 6x since 2000 (at >3 billion dollar, which represents only “a very small sliver of total investment in AI Research & Development” in the US), and the share of jobs requiring AI skills in the US has grown 4.5x since 2013.

3 Those with access to data have an edge over the competition in the AI era, see The Economist, May 6, 2017.

4 See e.g. Forbes July 2018, Forbes August 2018.

5 See also the ASILOMAR AI principles.

6 See McKinsey Global Institute’s report: Modeling the impact of AI on the world economy August 2018.

7 See McKinsey Global Institute’s report: Artificial Intelligence: Implications for China April 2017.

8 See e.g. the report from Denkwerk July 2018. See also the Overview of worldwide AI strategies at the national level, and NRC 25 April 2018.

9 See also Denkwerk July 2018.

10 Based upon among others https://ai100.stanford.edu/2016-report and https://www.ijcai-18.org/cfp/.

Enlaces

URL: Wayback Machine: