Latest Research

Envisaging future AI and future intelligence

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
• Intention: for the benefits of the subject
• Optimal individualised decision: the intervention
• Explainability: based on understanding, related to abstraction
• Persuasion: based on understanding, for better decision
• Reflection and remediation: punish/reinforce/modify/change based on assessment of previous decision
• Adaptation and evolution: for better intelligence
• perception, knowledge representation, communication, interactions, similarity
• High Intelligence:
  • Abstraction and inverse-abstraction
  • relevant cause-and-effect analysis
  • Decomposition of complex problem into probabilistic optimal substructure and probabilistic overlapping subproblems, then parallelising them
  • Automatic translation between different knowledge representations
  • creativity, innovation, analogy, inspiration, association, intuition, insights, etc.
• Other aspects…

AI is still not high intelligence

present AI still cannot match human domain expert intelligence in critical areas, critical areas include healthcare, self-driving, aerospace, finance, etc. where safety and accuracy are crucial

• AI merely calculates probabilities 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 background partial-knowledge beyond pure data
• lacking cause-and-effect analysis
• lack of automatic translation between different knowledge representations

Illusions of present AI brought to human

• AlphaGo could defeat human world champion because of enormous computing power. After deeply analysed the deficiencies of the maths principles of its algorithm and the reasons the only one game that Lee Sedol won against AlphaGo, geeks have designed special strategies that can 90% defeat AlphaGo, therefore AlphaGo is still elementary intelligence and cannot compete with human high intelligence.
• LLMs could partially understand/reason due to massive training datasets which are nearly all the knowledge of human civilisation. They are unreliable, can’t generalise well and unable to make decisions in critical areas, which are typical behaviours 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. Profound AI (PAI) is our vision of 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, etc. Units could either have physical existence (like embodied AI) or non physical-existence (like pure software). Their communications and interactions could be resolved by existing technologies such as IoT, blockchain, Large Language Models, etc., provided existing issues of which (like generalisation) could be resolved. 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 simulate; no high intelligence like similarity, analogy, abstraction, explainability; missing key background partial-knowledge; no cause-and-effect analysis; lacking automatic translations between different knowledge representations. Profound AI (PAI) is our vision of future Rational 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 high 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 object’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 it’s 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, rules, logic, step change, continuous/discrete values. Future Intelligence (FI) will be high intelligence, genius intelligence, collective intelligence, possibly super natural intelligence and unimaginable intelligence; we envisage that FI will be consisted of Biological Intelligence (BI), future Emotional AI (EAI) and future Rational AI (RAI), each sub-component has both advantages and disadvantages, and complements each other, synergy of these sub-components will take Future Intelligence to a new level far beyond existing human intelligence and present stage AI. We argue that PAI will be future RAI. The BAI-PAI project family are preliminary implementations of PAI, which are consisted of BAI-OptiMa, TAIR-CDSS and several Research Proposals.

BAI-OptiMa project

BAI-OptiMa is a preliminary implementation of the proposed Profound AI

TAIR-CDSS project

Clinical Decision Support System based on innovations of BAI-OptiMa

Research Proposals

• Analogy reasoning
• Adaptive strategies
• Inductive learning

Open thoughts

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