Merging AI and Human Intelligence: Martechadvisor.com
The Covid-19 effect
The awareness that a highly contagious virus like Covid-19 can disrupt our daily lives, threatening our health and socioeconomic infrastructure, has led to a global wake-up call about the fragility of our very human existence. Moreover, it reminds us of previous pandemics with death rates significantly higher than we are experiencing so far. Due to the stress caused by the fear of infection with all its threatening consequences, alternatives to centralisation and/or living in large densely populated cities are widely discussed. Enhanced by ever growing internet bandwidth, mobile connectivity and new visual community communication tools, decentralisation with the home-office as a complementary working environment is likely to stay with us. Local communities and their potential for an enjoyable and productive work-life balance with nearby access to nature signal a fundamental socioeconomic paradigm shift. Why not live and work in a pleasant environment without the daily crunch of mass-commuting? Think global act local will be the winning scenario especially for small enterprises and start-ups. Strengthening this local community with the development and application of collective intelligence generates value for the benefit of an entire region. As the technology, research and policy communities continue to seek new ways to improve governance in solving socioeconomic problems, two types of assets are of increasing importance: data and people. Adding AI to the equation sets the stage for merging human and machine intelligence, taking advantage of the assets that both bring to the table. The human capacity for contextual reasoning, for example, augments the machine’s capacity to interpret high-dimensional data and vice versa.
Different views on the relationship between Humans and Intelligent Machines
Originally focused on mathematics and artificial neural networks, AI-research and its quest for reaching human-like intelligence has expanded into neuroscience and behavioural research. Along this path different perspectives have evolved:
- The technology-centric perspective, whereby AI will soon outperform humankind in all areas, with the primary concern of machines reaching superintelligence. If AI performs at a superhuman level, human involvement in decision making can only worsen or slow down performance. At some point, humans will become incapable of being involved as they can no longer understand the computer’s highly intelligent line of reasoning. This perspective has shifted from science-fiction to real war-scenarios along with many ethical issues being raised.
- The human-centric perspective, where humans remain superior to AI in a social and societal context. Human-centrists are convinced that human and artificial intelligence are different by nature and therefore cannot substitute one another. Following this line of reasoning, AI-systems function well in environments in which they are trained yet become brittle in novel situations. For example, real-world environments tend to be ‘messy’, containing factors of influence that are ill-defined, inherently uncertain, or difficult to foresee. Compared to AI, humans are better equipped for solving problems in novel and unstructured domains.
- The collective intelligence-centric perspective, claiming that true intelligence lies in the collaboration of both human and artificial agents. Collective intelligence can be defined as “shared” or group intelligence that emerges from the collaboration and collective efforts of individuals applying AI tools, allowing them to collectively act more intelligently than individual entities. Empowered by advances in communication technology, collective intelligence has yielded novel applications such as crowdsourcing for developing software (e.g. Linux) or encyclopaedias (e.g. Wikipedia).
The regional view
A principal feature of innovative regions is their capacity to create environments favourable to turning knowledge into new products and services, attracting venture capital, building organisational learning, integrating skills and as a result generate innovation. Silicon Valley, beside its cyclical ups and downs, has set the standards for such regions, stipulating a mindset that started many of today’s high-tech giants. Spurred by this success, technological innovation territories are being developed all over the world. Regional intelligence is ‘collective’. It is a territory-based form of intelligence with people or organisations motivated to cooperate in a process leading to the definition and solution of a problem. Following this trend, the term ‘collective intelligence’ today encompasses an extensive library of literature, addressing subjects such as distributed cognition, distributed knowledge systems, connected intelligence, networked intelligence, distributed artificial intelligence, multi-agent systems, etc. With the influx of Covid-19, regional development has gained additional momentum as decentralisation is seen as a means to bring the pandemic under control.
Structuring regional intelligence
The major challenge for building collective intelligence is to combine knowledge from different sources, to integrate information that collects and elaborates data and customises information to the needs of users and organisations. An AI-Collaboration Platform is the prerequisite for forming and supporting regional intelligence with modules covering the entire innovation process as depicted by the following graph:
The functionality of the different modules can be defined as follows:
Marketing and Communication Module
Structuring regional intelligence requires a coordinating body, typically a local government unit or a private organisation. A membership schema is required to establish the identity and activities of all parties engaged in building the collective intelligence platform and to collate and match the specific interests of individuals and organisations. Membership fees and donations cover the cost of operating the AI-collaboration platform as well as its associated informational activities while maintaining partnerships with other local, national or international organisations.
Research and Innovation Fund module
Especially in small enterprise (SME) or start-up settings, innovation funding from other institutions or private donners is essential for the development of new AI-focused products and services. To meet the legal and governance requirements in providing funds it might be beneficial to establish a separate organisational unit. This module represents the single contact point for the financial support of AI-focused innovation ventures in the region, providing guidance on how to obtain funding.
Challenge-based R&D Support module
This module provides information on the current state of research in public and private organisations operating within the boundaries of a region. It gathers and disseminates information about new product /service projects. Contrary to fundamental research (Grundlagenforschung), performed by globally recognized universities, regional R&D projects are challenge based, focused on the development of value generating AI-based products and services. The platform’s ‘matching engine’ provides support in aligning project initiators with potential suppliers.
Interdisciplinary Education Module
Schools of Higher Education and Universities engaged in AI and machine learning facilitate the acquisition of different skills in a dynamically changing ecosystem with a strong focus on interdisciplinarity. These educational units are challenged to meet the requirements of the corporations and organisations active in that region. In that respect they represent a major building-block in the value generation cycle of knowledge generation and competitive product and service delivery.
Market and technology watch module
AI-technology is exponentially advancing. It impacts a growing list of application opportunities across more and more market segments. Keeping-up with these developments, global access to information resources represents a huge task. To channel this information for the benefit of the region, it is vital that organisations and individuals submit their specific interest-profile. Periodic reports matching their specific information needs, coupled with a more general trend analysis of AI-development is enhanced by local information events.
Summary: All modules combined define the functionality of the AI-Collaboration platform. As more and more data is passed through the platform, the scenario is set for generating collective intelligence with its own value propositions, especially in open-source ventures. Realizing this vision requires a high degree of security, both in terms of data stored on the platform as well as the secured, identified access by its members. Hence trust in the quality of the platform and the individuals managing it is a key success-factor for the sustainability in support of the entire region.
Design Principles for AI-Collaboration Platforms (ACPs)
Designing ACPs is an ambitious task. It needs to encompass diversity from different human talents working towards a common goal, adhering to the fact that AI technology represents a moving target. Ongoing advances in neuroscience, providing new insights to the cognitive functionality of the human brain and its relationship to human behaviour stipulate that AI today is not AI in six months from now. To be sustainable over a time-horizon of several years the ACPs’ architecture must be very modular and must leave room for adjustment to the learning experience operating the platform. The following provides a brief list of design considerations:
Defining the purpose of the platform:
An ACP generates new knowledge based on the exchange of information passed through the platform. The purpose is best described by the definition of the value the community driving the platform expects to receive. In farming, for example, one value is to gain knowledge about optimizing the use of fertilizers based on the location and weather conditions at the farm. This ‘give-and-take’ of information and knowledge touches on issues of intellectual property which need to be settled together with the question under what business model the platform is being designed and operated and how the costs are reimbursed.
Making sense of data:
Today’s AI applications largely depend on the availability and quality of huge data sets to train the neural networks with supervised learning algorithms. In the future AI will increasingly apply results from neuroscience research, exploring the biological functionality of the human brain in respect to learning and memorizing. Learning like a child, distinguishing between a cat and a dog, for example, stipulates the development of non-supervised learning algorithms that will greatly facilitate the application of ACPs while the use of smaller data sets will significantly reduce the cost of training neural networks. The quality of the data, void of human bias, remains essential for the successful implementation of ACPs.
Setting the rules for exchanging information and skills:
Realising the benefits of a group’s diversity is only possible when there are effective mechanisms for people to access information and skills. The rules governing how group members interact with one another implies how easily ideas spread and take hold in the group. The structure of a community network can play an important role in determining whether a group is able to exchange information efficiently. ACPs rely on shared open repositories of knowledge, both generated by AI for specific problem-solving as well as publicly available as a source of collective memory.
Conclusion
As AI is maturing and eventually turning cognitive intelligence into a commodity, integrating human assets such as curiosity, creativity and semantic understanding are a key to realizing the value of merging human and machine intelligence. ACPs provide the tools for regional development, assuring prosperity in a very rapidly changing socioeconomic context as local SMEs are challenged to adapt to and benefit from ongoing advancements in AI-technology.