Envisaging Future

Global Challenges

cited from Imperial College, London

• Climate Change and Environment
• Global Health
• Energy Crisis
• Security (data, cyber, war, terrorism)
• Data Science
• Molecular Science and Engineering
• Infection

Complex Systems

Complex Systems are pervasive and the most exciting and intriguing fields and challenges for future intelligence. Certainly they must be decomposed into smaller manageable subproblems to be tractable.

• healthcare
• robot football
• wargaming
• world economy
• stock market
• internet
• atmosphere
• and more…

Future Challenges

• Complex Systems
• short-termism
• exploration of the unknown, such as a new medicine or interstellar travel
• and more…

Unknown Threats

• asteroid impact
• global infection
• alien civilisation invasion
• and more…

Intelligence

The following characteristics are indispensable for both intelligence and future AI:

• Understanding: one of the pivots
• Learning: based on understanding, for generalisation
• Reasoning: the basis
• Intent: of the subject
• Optimal individualised decision: the intervention
• Explainability: based on understanding
• Persuasion: based on understanding, for better decision
• Reflection and remediation: punish/reinforce/modify/change decision based on assessment
• Adaptation and evolution: for better intelligence
• perception, knowledge representation, communication, interactions, similarity

AI is still far from high intelligence

Present stage AI still cannot match human domain expert intelligence in critical areas including healthcare, self-driving, security, etc. where safety and accuracy are crucial, because:

• AI is still probability calculation and unable to generalise well
• insufficient data or small dataset for training
• lack of high intelligence like similarity, divide and conquer, abstraction, analogy, induction, abduction
• missing key extra-knowledge beyond pure data
• lacking cause-and-effect analysis
• lack of automatic translation between different knowledge representations

Illusions of present AI

• AlphaGo could defeat human world champion because of enormous computing power. MIT and FAR AI have designed special strategies that can 90% defeat open source version of AlphaGo, therefore AlphaGo’s robustness is still highly doubtful.
• LLMs can partially understand/reason due to massive training datasets with nearly all the knowledge of human civilisation and chain of thought. Scaling law and hallucinations are still impeding their application in critical areas, which is typical reflection of elementary intelligence.

Proposed Solutions

Future-Complex

Proposed solutions to addressing global challenges and future challenges

Human civilisation is facing global challenges such as global health, future challenges such as Complex Systems, unknown threats such as pandemic. To address these challenges, Future Intelligence (FI) equipped with Super Auxiliary Abilities (SAAs) is required, furthermore, a distributed system of decentralised, heterogeneous and specialised Units consisted of FI/SAAs is necessary. Synergies of Units, such as complementarity, cooperation, collaboration, coordination, management, communication, explanation, negotiation, making group decisions, could exponentially augment the capacities of such a system as a whole. We therefore envisage such a system as Future-Complex whose overall capacities could reach far beyond existing human intelligence and abilities, and is designed to assist human civilisation to addressing those challenges existing intelligence and technologies unable to handle. Unprecedented Future Intelligence will be at the centre of Future-Complex and will be a combination of Biological Intelligence, future Emotional AI and future Rational AI. SAAs will be less-intelligent yet significant and innovative technologies, such as wireless devices, biomarker analyser, CT (Computerised Tomography), physical therapy (like ultrasound, microwave, infrared), clairvoyance, clairaudience, perceiving previously undetectable matters, etc. Units could either have physical existence (like embodied AI) or non-physical existence (like pure software) and their communications and interactions could be resolved by existing technologies such as IoT, blockchain, natural language processing. Further studies of Future-Complex could be directed by Complex Systems theories, such as collective behaviour, fractals, emergence, game theory (like collective intelligence), dynamic systems, Cybernetics etc., and certainly joint efforts of multi-disciplinary research. Highlighted benefits of Future-Complex to addressing Global Health challenge include telemedicine, understanding pathology and mechanism of diseases, intention of patients, automatically measuring biomarkers and early warning of risks, providing emergency assistance, finding optimal individualised treatment and making prognosis, explaining diseases and treatments based on evidence, robot assisted surgery, simulation and prevention of global infection, effective global coordination of pandemic control, etc.

Future Intelligence

envisaged by BenAI

Present stage AI like Large Language Models (LLMs) and AlphaGo are still far from high intelligence and people are bewildered by the enormous computing power and massive training datasets behind the scene, they could neither generalise well (like small dataset) nor match human domain expert intelligence in critical areas like healthcare/self-driving because of several reasons: insufficient data or small datasets; real-world issues are complex systems that are computationally intractable and impossible to accurately emulate; no high intelligence like similarity, analogy, abstraction, explainability; missing key extra knowledge; no cause-and-effect analysis; lacking automatic translations between different knowledge representations. Profound AI (PAI) is our vision of future AI that aims to remedy these deficiencies and prominent features of which include: similarity reasoning; understanding; finding optimal individualised decisions under uncertainties; explainability; controlling peripherals; persuasion; communication; exchange; interactions; assessing outcome; reflection and remediation; simulation of complex systems; evolution and adaptation according to different environments. It also has genius intelligence such as decomposing complex problem into smaller manageable subproblems, then identifying optimal decision for similar subproblems; adaptive behaviours/strategies by observing environment/opponents/teammates and predicting their behaviours/strategies; different knowledge representations at different hierarchical levels and automatic translations between them; explain/persuade in the target(s) personal/subgroup/domain knowledge representations (like language/experience/sense/knowledge-base/knowledge-structure); cross-domain generalisation; automatic cause-and-effect analysis; inductive/abductive learning; analogy, association, inspiration, intuition, insights; genius intelligence like imagination, creativity and innovation; super human intelligence like super memory, knowledge of whole human civilisation, instant understanding/learning/reasoning. Furthermore, appropriate reasoning architectures/algorithms are necessary depending on the specific dataset/task/domain and variable relationships, such as linear, nonlinear, uni-/bi-directional, time-series, sequential, correlations, inconstant-length, long dependency, regional, tree-like, graphical, partially-observable, deterministic/non-deterministic/probabilistic, confounders/covariates, hidden factors, from zero to high dimensional spaces, step change. Future Intelligence (FI) will be high intelligence, genius intelligence, collective intelligence, possibly super natural intelligence and unimaginable intelligence; we envisage that FI will consist of Biological Intelligence, Emotional AI and Rational AI, each of these three sub-component will supplement each other, and synergy of them will take Future Intelligence to a new level far beyond human intelligence and present stage AI. The BenAI-PAI project family are preliminary implementations.