Data is the black gold in the information era and has similar value creation and ecology to that of petroleum. Data in its raw format needs to be refined (as does crude oil) to make sense and to add meaning and usefulness to any domain.
AI and its parts (machine learning, natural language processing, deep-learning etc.) are set to be a societal game changer in all collective human imagination domains.
The ambition should be to design for a sustainable AI future, aiming to incorporate the UNs 17 development goals with ethics at the core. One omnipresent hurdle still is the black box or opaque setting i.e. being able to understand how, why and where different AI operates and influences
The open paradigm
Since all known to man utilities with AI, have a simple model, being:
input – model – output and feedback (learning).
There is a need to shift the control from the computer back towards the human, and thereby enable the addition of meaning and semantics along with conceptual models.
By using open innovation, -standards, -models (knowledge graphs, ontologies, terminologies, code systems and the like), -software, -platforms (technology stacks, i.e. Singularity net) in the design for future AI utilities and cognitive computing, there exists opportunities for leverage learning in a meaningful way – away from the opaque regime and towards cognitive-informed artificial intelligence. Efficient communication through interoperability that can accommodate data from different semantic domains that traditionally have been separate. Open domain knowledge and data-sets (as linked-data) will provide very good platforms for continuously improved datasets within the AI loop, both in terms of refining and addressing the contextual matter, but also enabling improved precision and outcome.
Informative communication – the word’s meaning should allow accurate mental reconstruction of the senders intended meaning, but we are well aware of the human messiness (complexity) within a language as described in Information bottleneck (Tishby), rate distortion theory (Shannon).
To take on the challenges and opportunities within AI, there are strong undercurrents to build interdisciplinary capacities as with Chalmers AI Research and AI innovation of Sweden and the like. Where computer science, cognitive science, data science, information science, social sciences and more disciplines meet and swap ideas to improve value creation within different domains, while at the same time beginning to blend industry, public sector, academia and society together.
The societal challenges that lay ahead, open up for innovation, where AI-assisted utilities will augment and automate for the benefit of mankind and the earth, but to do so require a balancing act where the open paradigm is favoured. AI is designed and is an artefact, hence we need to address ethics in its design with ART (Accountability, Responsibility and Transparency) The EU draft on AI ethics.
Tinkering with AI
The emerging development of AI shows a different pathway than that of traditional software engineering. All emerging machine learning, NLP and/or Deep-Learning machinery relies on a tinkering approach with trial and error -re-model, refine data-set, test-bed with different outcomes and behaviours -before it can reach a maturity level for the industrial stages in digital infrastructure, as with Google Cloud, or similar services. A great example is image recognition and computer vision with its data optimization algorithms. and processing steps. Here each development has emerged from previous learnings and tinkering. Sometimes the development and use of mathematical models simply do not provide up for real AI matter and utilities.
Here in the value creation, or the why in the first place, we should design and use ML, NLP and Deep-Learning in the process with an expected outcome. AI is not, and never will be the silver bullet for all problem domains in computing! Start making sense, in essence, is needed, with contextual use-cases and utilities, long before we reach Artificial General Intelligence
The 25th of April an event will cover Sustainable Knowledge Graphs and AI together with linked-data Sweden network.
