A New Paradigm: Knowledge Bases and Machine Reasoning
In his seminal book The Structure of Scientific Revolution, Thomas Kuhn explained that paradigms are replaced when they are unable to provide scientific explanations for certain phenomena. It is not that some answers can not be found, the questions to get them cannot be formulated within that paradigm. We believe that the Data-driven AI paradigm is reaching that point and we need a new approach that could help us move beyond this impasse.
Enter Machine Reasoning.
What is MR?
Let’s start with Reasoning.
According to Leon Boutou an MR research pioneer “a plausible definition of ‘reasoning’ could be algebraically manipulating previously acquired knowledge in order to answer a new question.”
As for MR systems, Jerry Kaplan notes that they divide “tasks requiring expertise into two components: “knowledge base” – a collection of facts, rules, and relationships about a specific domain of interest represented in symbolic form – and a general-purpose “inference engine” that describes how to manipulate and combine these symbols.”
In short, MR systems combine well-defined accumulated knowledge with symbolic knowledge to “understand” a problem and its context. Also known as the common sense. And use that to answer a different set of questions for which it did not have data training.
The advantage of MR systems is that datasets are not the determining element in their training. As the knowledge base provides the symbolic logic and the definitions and correlations of the dynamic contextual elements, MR systems require much smaller datasets to “understand” a complex set of relations and to reach conclusions. And their results are explainable and justifiable.
This new paradigm was pioneered almost forty years ago by Douglas Lenat who realized that without the contextual understanding of the human mind, AI tools would not be able to go beyond Eliza-type gimmicky implementations. He proceeded to build the first “Common Sense” knowledge base known as the Cyc project. Three decades after he started, specialized versions of his common-sense knowledge base are being used in a variety of fields to solve problems characterized by a dearth of data.
Another attempt to build a “common-sense” AI has recently been proposed by Yann LeCun the chief scientist of Meta Corporation, the parent of Facebook and Instagram. His model is a hybrid construction where the AI has a basic model of the world and symbolic logic-based reasoning and it “learns” by observing the world and comparing and altering its model based on its observations. This is known as the “Joint Embedding Predictive Architecture” of JEPA.
Tellingly, Yann LeCun has been quite dismissive about LLMs and Generative AI, declaring them a dead-end.
While both of these MR approaches share a common-sense emphasis, the Meta perspective is largely graphic whereas the Cyc vision is anchored in the knowledge base and symbolic logic inference capabilities. Moreover, unlike JEPA which is currently just a concept, the Cyc AI has a four-decades-old effort behind it.
What we propose is to bring together various actors to build a “gender knowledge base” and couple it with a specialized inference engine to create a negative societal bias-free and gender-representative AI.
If successful, the same approach can be scaled up to apply to other areas of social bias such as ethnicity, race, and religion.
To achieve this goal we brought together a team of gender and AI experts led by Saniye Gulser Corat the former Director of UNESCO’s Division of Gender Equality who started the gender and AI discussion with two groundbreaking studies on the gender gap in digital skills and gender bias in AI algorithms. She was named by The Digital Future Society as one of the top ten women leaders in technology for 2020. In December 2020, she was selected the Global Leader in Technology by the Women in Tech global movement, and in March 2021, she was included on Apolitical 100 Most Influential People in Gender Policy 2021.
The think tank is in the process of raising funds for this project.
We expect to be at the forefront of the new paradigm to provide a satisfactory answer to the gender equality conundrum.
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