This article discusses some trends and concepts in developing a new generation of future Artificial General Intelligence (AGI) systems which relate to complex facets and different types of human intelligence, especially social, emotional, attentional, and ethical intelligence. We describe various aspects of multiple human intelligences and learning styles, which may affect a variety of AI problem domains. Using the concept of “multiple intelligences” rather than a single type of intelligence, we categorize and provide working definitions of various AGIs depending on their cognitive skills or capacities. Future AI systems will be able not only to communicate with human users and each other but also to efficiently exchange knowledge and wisdom with abilities of cooperation, collaboration, and even cocreating something new and valuable and have metalearning capacities. Multiagent systems such as these can be used to solve problems that would be difficult to solve by any individual intelligent agent.

Many Data Analysis tasks deal with data which are presented in high-dimensional spaces, and the 'curse of dimensionality' phenomena is often an obstacle to the use of many methods, including Neural Network methods, for solving these tasks. To avoid these phenomena, various Representation learning algorithms are used, as a first key step in solutions of these tasks, to transform the original high-dimensional data into their lower-dimensional representations so that as much information as possible is preserved about the original data required for the considered task. The above Representation learning problems are formulated as various Dimensionality Reduction problems (Sample Embedding, Data Manifold embedding, Data Manifold reconstruction and newly proposed Tangent Bundle Manifold Learning) motivated by various Data Analysis tasks. A new geometrically motivated algorithm that solves all the considered Dimensionality Reduction problems is presented.

No abstract