A Graph Convolutional Network Feature Learning Framework for Interpretable Geometry Problem Solving

Standardized tests are used to test students as they progress in the formal education system. These tests are readily available and have clear evaluation procedures.Hence, it has been proposed that these tests can serve as good benchmarks for AI. In this thesis, we propose approaches for solving some common standardized teststaken by students such as reading comprehensions, elementary science exams, geometry questions in the SAT exam and mechanics questions in the AP physics exam.Answering these test problems requires deep linguistic (and sometimes visual) understanding and reasoning capabilities which is challenging for modern AI systems. In the first part of this thesis, we explore novel approaches to answer natural language comprehension tests such as reading comprehensions and elementary science tests (chapters 4, 5 and 6). These tests evaluate the system’s ability to understand text through a question-answering task. We present new latent structure models for these tasks. We posit that there is a hidden (latent) structure that explains the relation between the question, the correct answer, and the piece of text. We call this the answer-entailing structure; given the structure, the correctness of the answeris evident. Since the structure is latent, it must be inferred. We present a unified max-margin framework that learns to find these hidden structures given a corpus ofquestion-answer pairs, and uses what it learns to answer questions on novel texts. We also describe a simple but effective extension of this framework to incorporatemulti-task learning on the different subtasks that are required to perform the overall task (chapter 4), a deeper representation of language based on AMRs (chapter5) and how can we incorporate external knowledge in the answer-entailing structure (chapter 6). These advances help us obtain state-of-the-art performance on two well-known natural language comprehension benchmarks. In the second part of this thesis (chapter 7), we tackle some hard reasoning problems in the domains of math and science - geometry questions in the SAT exam and mechanics question in the AP physics exam. Solving these problems requires an ability to incorporate the rich domain knowledge as well as an ability to perform reasoning based on this knowledge. We propose a parsing to programs (P2P) approach for these problems. P2P assumes a formal representation language of the domain and domain knowledge written down as programs. This domain knowledge can be manually provided by a domain expert, or, as we show in our work, can be extracted by reading a number of textbooks in an automated way. When presentedwith a question, P2P learns a representation of the question in the formal language via a multi-modal semantic parser. Then, it uses the formal question interpretation and the domain knowledge to obtain an answer by using a probabilistic reasoner. A key bottleneck in building these models is the amount of domain-specific supervisionrequired to build them. Thus, in the final part of this thesis (chapter 8), we propose a self-training method based on curriculum learning that jointly learns to generate and answer questions. This method obtains near state-of-the-art models on a number of natural language comprehension tests with lesser supervision.

Recent advances in Multimodal Large Language Models (MLLMs) have achieved remarkable progress in general domains and demonstrated promise in multimodal mathematical reasoning. However, applying MLLMs to geometry problem solving (GPS) remains challenging due to lack of accurate step-by-step solution data and severe hallucinations during reasoning. In this paper, we propose GeoGen, a pipeline that can automatically generates step-wise reasoning paths for geometry diagrams. By leveraging the precise symbolic reasoning, GeoGen produces large-scale, high-quality question-answer pairs. To further enhance the logical reasoning ability of MLLMs, we train GeoLogic, a Large Language Model (LLM) using synthetic data generated by GeoGen. Serving as a bridge between natural language and symbolic systems, GeoLogic enables symbolic tools to help verifying MLLM outputs, making the reasoning process more rigorous and alleviating hallucinations. Experimental results show that our approach consistently improves the performance of MLLMs, achieving remarkable results on benchmarks for geometric reasoning tasks. This improvement stems from our integration of the strengths of LLMs and symbolic systems, which enables a more reliable and interpretable approach for the GPS task. Codes are available at https://github.com/ycpNotFound/GeoGen.

The Application of Algorithmic Probability to Problems in Artificial Intelligence

Business process management encompasses a variety of tasks that can be solved system-aided but usually require formal process representations, i.e. process models. However, it requires a significant effort to learn a formal process modeling language like, for instance, BPMN. Among others, this is one reason why companies often still stick to informal textual process descriptions. However, in contrast to formal models, information from natural language text usually cannot be automatically processed by algorithms. Hence, recent research also focuses on annotated textual process descriptions to make text machine processable. While still human-readable, they additionally contain annotations following a formal scheme. Thus, they also enable automated processing by, for instance, formal reasoning and simulation. State-of-the-art techniques for automatically annotating textual process descriptions are either based on hand-crafted rule sets or artificial neural networks. Maintaining complex rule sets requires a significant manual effort and the approaches using neural networks suffer from rather low result quality. In this paper we present an approach based on Semantic Parsing and Graph Convolutional Networks that avoids manually defined rules and provides significantly better results than existing techniques based on neural networks. A comprehensive evaluation using multiple data sets from both academia and industry shows encouraging results and differentiates between several applied text features.

Our concern in this study was to examine the relationship between problem-solving performance and the quality of the organization of students' knowledge. We report findings on the extent to which content and connectedness indicators differentiated between groups of high-achieving (HA) and low-achieving (LA) Year 10 students undertaking geometry tasks. The HA students' performance on the indicators of knowledge connectedness showed that, compared with the LA group, they could retrieve more knowledge spontaneously and could activate more links among given knowledge schemas and related information. Connectedness indicators were more influential than content indicators in differentiating the groups on the basis of their success in problem solving. The tasks used in the study provide straightforward ways for teachers to gain information about the organizational quality of students' knowledge.

Artificial intelligence research started with computers trying to take the place of what humans have to do intelligently. Research started in the 1950s went through several ups and downs and reached the same period of the third boom as it is now. With the shocking situation where Alpha Go defeated Lee Se-dol, the strongest player in Go, in 2016, interest in artificial intelligence increased rapidly, and stories about the future situations that artificial intelligence will bring about, such as the possibility and singularity of artificial intelligence, are talked about even among the general public. Certainly, artificial intelligence has the potential to fundamentally change the way humans live, not just technological advances. The real value of artificial intelligence algorithms is prediction. At the center of all the algorithmic revolutions that have taken place in the various fields of modern society, there is one unchanging purpose: prediction. The algorithm for predicting the risk of recidivism used in US law enforcement fits precisely with the principle that individual humans behave in a predictable and consistent manner. While using the algorithm of predicting the risk of a second offender, an artificial intelligence algorithm, not a judge, actually takes the place of the defendant s decision on whether or not to be arrested or sentenced. Automated decision making continues to grow at an unprecedented rate and benefits because machine learning offers the possibility to extend the automated decision-making process, allowing wider, deeper decisions without human intervention. It is also very large and is expected to continue to expand its use. However, the risks are as great as the benefits of artificial intelligence algorithms. The risks of artificial intelligence algorithms outweigh the individual risks of any algorithmic technology specifically applied. Despite the widespread use of artificial intelligence in the social sphere, the reality is that the problems of artificial intelligence cannot be properly grasped. However, people who were treated differently due to automatic decision-making by artificial intelligence began to question the fairness or fairness of artificial intelligence, and in particular, while causing sensitive problems such as discriminatory results, controversy surrounding artificial intelligence algorithms gradually became more and more controversial. It is expanding. Even if a decision made by artificial intelligence is wrong or causes negative effects such as discrimination, there is a big problem that there are few means to object to this. In a situation where automated decision-making is more likely to threaten human autonomy and dignity, in order to secure an individual s autonomous personality, make sure that the entire process of the individual s automatic decision-making that affects him or her is fair and error-free. It is of utmost importance to know and allow humans to directly intervene if necessary. Although available data is increasing and algorithms are gradually improving, artificial intelligence algorithms do not completely eliminate the uncertainty posed by decision making. Eventually, there are cases in which humans, as finalists, have to make important decisions in situations where uncertainty has not been resolved. It is necessary to build a system in which artificial intelligence algorithms and human experts cooperate well to make the best decisions, and in addition to the direction of such a collaboration system establishment, the problem of legal responsibility due to errors in decision-making in artificial intelligence algorithms or collaboration systems is necessary. I want to deal with it in this paper.

Exploiting node-feature bipartite graph in graph convolutional networks

The internet’s rapid growth has resulted in an increase in the number of malicious files. Recently, powershell scripts and Windows portable executable (PE) files have been used in malicious behaviors. To solve these problems, artificial intelligence (AI) based malware detection methods have been widely studied. Among AI techniques, the graph convolution network (GCN) was recently introduced. Here, we propose a malicious powershell detection method using a GCN. To use the GCN, we needed an adjacency matrix. Therefore, we proposed an adjacency matrix generation method using the Jaccard similarity. In addition, we show that the malicious powershell detection rate is increased by approximately 8.2% using GCN.

This study attempted to present discussions and directions regarding artificial intelligence(AI) and the prevention of corruption in Korean real estate. This Paper dealt with moral hazard, corruption prevention, and trust using real estate, the 4th industrial revolution(4IR), AI, and intelligent information technology as keywords. Prior research is focusing on economic utility and technical benefits related to AI and real estate. In contrast, this study pointed out the risk of moral hazard and corruption arising from the combination of AI and real estate, and conducted a paper to prevent moral hazard and corruption. In this respect, there is a difference between this study and existing previous studies. This is where the originality of this study appears. The results of this study on anti-corruption of AI and Korean real estate are as follows. As a result of the study, the problems of AI and Korean real estate, and anti-corruption were presented in three major categories: 1. AI and the problem of changing times, 2. Korean real estate and corruption problem, and 3. AI and real estate problem. In relation to this, this study largely derived three measures as implications of the research. The contents include, first, responses to the issues of AI and changing times, second, responses to Korean real estate and corruption issues, and third, measures to respond to AI and real estate issues. This study hopes to restore market trust through Artificial Intelligence(AI) and the prevention of corruption in Korean real estate, and to build a future where the benefits and utility provided by real estate, AI, and intelligent information technology are desirable.

Chronic obstructive pulmonary disease (COPD) is a common disease with high morbidity and mortality, where early detection benefits the population. However, the early diagnosis rate of COPD is low due to the absence or slight early symptoms. In this paper, a novel method based on graph convolution network (GCN) for early detection of COPD is proposed, which uses small and weakly labeled chest computed tomography image data from the publicly available Danish Lung Cancer Screening Trial database. The key idea is to construct a graph using regions of interest randomly selected from the segmented lung parenchyma and then input it into the GCN model for COPD detection. In this way, the model can not only extract the feature information of each region of interest but also the topological structure information between regions of interest, that is, graph structure information. The proposed GCN model achieves an acceptable performance with an accuracy of 0.77 and an area under a curve of 0.81, which is higher than the previous studies on the same dataset. GCN model also outperforms several state-of-the-art methods trained at the same time. As far as we know, it is also the first time using the GCN model on this dataset for COPD detection.Graphical abstract

With the rapid development and progress of the times, many new things have been produced, and some problem solving measures have been updated and iterated. As a communication tool, mobile phones have also been upgraded in all aspects, such as face recognition, voiceprint recognition, etc., which are highlighting the research field of today’s world’s attention - artificial intelligence. The emergence of artificial intelligence is a combination of science and technology and law, and its application is very wide-ranging. In the face of its extensive application space, it has brought many legal problems, so it has attracted great attention in academia and practice, and the shortcomings of artificial intelligence include the absolute rationalization of algorithms, etc., and the legal problems brought about by it are one of the focuses of this paper. When there are certain problems in artificial intelligence, people’s desire for the law to properly regulate it will intensify, so how to properly regulate the problems caused by artificial intelligence through law is also the focus of this article. Postmodernism is a new term, whose etymology derives from modernism, and it is the result of dissatisfaction with modernism. In postmodernism, jurists have shown their views on artificial intelligence from a new perspective, contributing to the legal issues brought about by artificial intelligence, because of their thinking, people have a more comprehensive understanding of artificial intelligence issues along this thinking angle. For artificial intelligence, the research is a good example of the hot issue previously studied by the legal community - the legal subject of artificial intelligence from the perspective of legal philosophy. Professionals give the public some ways to think and benign angles for their reference, and they will have a more thorough understanding of artificial intelligence.

That artificial intelligence (AI), algorithms, and related technologies could use a few good booster shots of “humanism” is widely apparent. In both program code and implementation, AI and algorithms have been accused of harbouring deep-seated flaws that conflict with human values. They are prime examples of the skew towards white, Western, men and demonstrate the bankruptcy in the face of neoliberalist, profit- and market-oriented social paradigms that this special issue seeks to address. Currently, computer scientists and AI researchers who are looking to remedy these problems are often in favour of more data, more powerful machines, more complex algorithms—in short, that we should fix problems with AI by building better AI. In this view human beings and the world can be modelled in code—our lives, interactions, society, and our very selves can be broken down into data points which can be assessed by highly advanced technologies. When these scientists and researchers seek to broaden their approach they often look to philosophy. However, the philosophy they look to is overwhelmingly Anglo-analytic, which views the world in extremely similar ways. Both AI and Anglo-analytic philosophy argue for solutions to humanistic problems which are essentially mathematical. They share in seeing important concepts, such as persons, emotions, agency, and ethics, as mechanistic, atomistic, and calculable. In this paper I will argue that Classical Chinese philosophy offer insightful resources for addressing the humanist problems in AI. Rather than arguing for mathematical solutions, or envisioning persons, emotions, agency, and ethics, as other rigid, atomistic, and mechanistic approaches, Chinese philosophy emphasizes transformation, interrelatedness, and correlative developments. Accordingly, it offers tools for appreciating the world, society, and ourselves as spontaneous, complex, and full of tension. AI can be programmed and used in ways that do not reduce the complexity and conflict in the world, but provide us instead with tools to make sense of it—tools that are humanistic in nature. To this end, Chinese philosophy can be a helpful collaborative partner.

Semi-supervised graph domain adaptation, as a subfield of graph transfer learning, seeks to precisely annotate unlabeled target graph nodes by leveraging transferable features acquired from the limited labeled source nodes. However, most existing studies often directly utilize graph convolutional networks (GCNs)-based feature extractors to capture domain-invariant node features, while neglecting the issue that GCNs are insufficient in collecting complex structure information in graph. Considering the importance of graph structure information in encoding the complex relationship among nodes and edges, this paper aims to utilize such powerful information to assist graph transfer learning. To achieve this goal, we develop a novel framework called HOGDA. Concretely, HOGDA introduces a high-order structure information mixing (HSIM) module to effectively capture abundant structure information in graph, greatly enhancing the feature extractor's ability to adapt across different domains. Moreover, to achieve fine-grained feature distributions alignment, a novel strategy called adaptive weighted domain alignment (AWDA) is proposed to dynamically adjust the node weight during adversarial domain adaptation process, effectively boosting the model's transfer ability. Furthermore, to mitigate the overfitting phenomenon caused by limited source labeled nodes, we also design a trust-aware node clustering (TNC) strategy to guide the unlabeled nodes to achieve discriminative clustering. Extensive experimental results show that our HOGDA outperforms the state-of-the-art methods on various transfer tasks.

The mining and exploitation of graph structural information have been the focal points in the study of complex networks. Traditional structural measures in Network Science focus on the analysis and modelling of complex networks from the perspective of network structure, such as the centrality measures, the clustering coefficient, and motifs and graphlets, and they have become basic tools for studying and understanding graphs. In comparison, graph neural networks, especially graph convolutional networks (GCNs), are particularly effective at integrating node features into graph structures via neighbourhood aggregation and message passing, and have been shown to significantly improve the performances in a variety of learning tasks. These two classes of methods are, however, typically treated separately with limited references to each other. In this work, aiming to establish relationships between them, we provide a network science perspective of GCNs. Our novel taxonomy classifies GCNs from three structural information angles, i.e., the layer-wise message aggregation scope, the message content, and the overall learning scope. Moreover, as a prerequisite for reviewing GCNs via a network science perspective, we also summarise traditional structural measures and propose a new taxonomy for them. Finally and most importantly, we draw connections between traditional structural approaches and graph convolutional networks, and discuss potential directions for future research.

Recently, Quantitative Biology (QB) held a discussion on “AI (artificial intelligence) for Life Science” among editorial board members and interested scholars in anticipation of rapid development of this growing area after AlphaGo and ChatGPT mania. Many young people tend to get confused between facts and fictions; heated debates are unavoidable even among their mentors. When deep learning as represented by convolutional neural networks and LSTM (long short-term memory) was made available for bioinformatics students, many of them rushed into this research field and tried to adopt these methods in all their projects without knowing the history that these tools were becoming successful consistently with Moore’s Law (relating to rapid computer technology advances), but more importantly due to new structural/functional understanding of vision and auditory circuits in the brain. Recently, some young people have claimed “LSTM is dead, long live transformer” (which is somewhat like saying “the bike is dead, long live the car”), and have amplified the threat that ChatGPT could wipe out human jobs. They believe transformer is the “silver bullet” for all learning tasks, clearly reflecting their lack of basic knowledge (i.e. “No Free Lunch Theory,” the trade-off of such global “attention network” is to pay the price for complexity: difficulty of training and high memory costs). There is no doubt ML (machine learning) and AI have brought a new revolution in science and technology, and will deliver huge unforeseeable impact to human everyday life as well as to social relationships. In this context, QB journal could be a great platform for encouraging intellectual discussions and for promoting AI for Life Science. Here, I would like to use the DIALOG to “抛砖引玉” (make some initial remarks to get the ball rolling), although it is my personal opinion which is inevitably subject to bias and limitations. AI: Do you know my name “Artificial Intelligence” is defined by the Oxford English Dictionary as the capacity of computer systems (which may be referred as a “robot”) to exhibit or simulate your intelligent behavior? NI: Wait a minute, intelligence itself is defined as the ability to learn, understand and think in a logical way. Can you think? AI: No. But that definition is too restrictive, actually intelligence has different scopes and degrees. Simple intelligent control devices date back to antiquity, from windmills to thermostat. NI: Agree, everything is relative. Macromolecules (e.g., enzyme) and cells (e.g., immune cell) might be considered to be intelligent; see how a white blood cell is chasing bacteria in the youtube website (search for “Crawling neutrophil chasing a bacterium”). Our emergent/collective intelligent behavior does not require a brain or even a neuron; see how slime molds can solve optimization—Hamilton cycle-problem more effectively than a human in the youtube website (search for “Intelligence without a brain?”). Before there was any neuron, Ca2+ sensing and signaling were already fully functional. Even if one knock-out a neural circuit, redundant signaling pathways, albeit on much local and slower scale, could still function by themselves (just as if highways were demolished, local roads/paths would still be working). In fact, the most detailed “Neural signal propagation atlas of Caenorhabditis elegans” [1] demonstrated that functional connectivity differs from anatomy (connectome) because extra-synaptic signaling also drives neural dynamics! Worm brain connectomes are largely invariant but every human brain connectome is very different (depending the diversity of learning experience). The human brain functional activity is far more complex than that of a worm brain, certainly beyond what a neural circuit could explain. AI: Well, that’s impressive. I thought only we could beat humans, albeit only in certain specified areas for now. My master promise to make an artificial general intelligent (AGI) robot which can understand or learn any intellectual task that you humans or other animals can. NI: Well, that is not possible and is not an appropriate goal either. It is not possible because we are an evolutionary/developmental product (with a long history of learning and memory from evolutionary tinkering): our living objective goal is survival of the population. You, on the other hand, are an engineering product (efficiently and optimally designed): your goal is to extend and maximize human capability. It makes sense to complement the human brain, but foolish and dangerous to try to replace it. AI: We are not satisfied with merely passing the Turing Test; most of us don’t care if we could really think as long as we could act like we think (that is, as if we do have a mind and consciousness, as expressed in the so called “Weak AI hypothesis”). After all, the brain is a computer; a neural network is just an electric or ionic circuit. Logical computing does not need to be based on living cells. NI: That is not true, because a neuron is not just a simple node (logic gate) and neural network is not fixed circuit, neither as in Pitts&Mcculloch perceptron model. A single neuron, even a single dendrite, is much more complicated and far more powerful than a full-blown deep-learning artificial neural network (ANN) [2]. AI: Even though single neurons are complex computational devices (dendritic non-linear), running an equivalent multilayer ANN is 2000 times faster than computing with biophysics N-methyl-D-aspartate receptor channel models [3]. More information can be found in the youtube website (search for “Dendrites: why biological neurons are deep neural networks”). NI: Often silicon computing (CPU, GPU) is much faster than brain computing (action potential, ms); but there is no comparison in energy efficiency. Bacteria sensing (chemotaxis computation), powered by ATP (adenosine triphosphate) hydrolysis, uses very little energy that is close to the Landauer limit, whereby achieving or maintaining one bit of information requires minimum of 1 kT ln (2) free energy [4]. The human brain consumes oft-quoted 20 W, compared to the AlphGo system 1 MW! More recent estimate of energy audit is only 0.1 W to cortical computing, and long-distance communication cost is 3.5 W [5]. AI: Assuming we have infinite computing resources and an infinite amount of training data, not only could we speak human languages, but we could also derive physical laws, prove mathematics theories, and even re-engineer the structure and mechanism of brain and carry out any logical computations that are necessary to understand natural laws and human behaviors. It is only a matter of time before we surpass human intelligence, achieving AGI and free will, too! NI: Unfortunately, nothing is infinite and nothing is free either; everything is constrained by physical laws (Planck’s constant sets the finite limit both in the small and in the large) and by evolutionary history (not just of biological living creatures, but also of a “living” galaxy and our universe). Let’s just focus on animal evolution. Most human neural networks do not do logical computations at all; basic survival simply cannot be dependent on reasoning. Indeed, the prefrontal cortex-the small part of the brain that is key for reasoning, is the last to mature (∼20 years old) in development, only emerged at the root of the evolutionary tree of great apes (∼15 mya) and language appeared even much later. Even for logical inference, NI is focusing more on statistical properties, as von Neumann rightly pointed out, trading arithmetical precision and speed for reliability. AI: My engineers mostly focus on emulating brain, but the CNS (central nerve system) also includes the spinal cord; most of them do not know that in addition to CNS, there are also PNS (peripheral nervous systems) and ENS (enteric nervous systems), right? NI: Yes, they are the keys to why you do not have feelings because you do not have heart and gut! Even if you could pretend to have them (such as in an advanced ChatGPT or humanoid), you could never avoid the uncanny valley phenomenon. AI: Maybe that is at the heart of Moravec’s paradox, namely the dichotomy of intelligence whereby anything easy for a human would be hard for a robot, and vice versa? NI: This is related to the nature and nurture problem; something built-in (e.g., a baby sucking nipple for milk with feeling and connection to mother) is clearly rather difficult if not impossible for a robot. But the paradox only looks at one side; another side could be more fatal. Although AI may solve more problems and be faster, AI can never propose a good problem/hypothesis (a good problem is not just intellectually changing and interesting, but is also feasible and appropriate). AI: You make me less confident to compete with human instinct or creative intelligence. I can see that even if I had a heart, I would not know what “feeling” I could have; certainly nothing would be comparable or match to those of a human being. When two people watch the same art or movie, one could feel love but another could feel hate! And if a thousand people watch, a wide spectrum of reactions would result, depending on more details such as the different individuals’ specific genes, developments and experiences. NI: Therefore, you cannot and should not try to match to general human intelligence. You cannot because you do not contain the vital memory of billion years of evolution which is encoded in our genes; conversely your assembly cannot compare with natural development so that our phenotype (including morphological forms and behavior maturation) is decoded through multi-spatial-temporal scales subject to natural selection at all levels. You should not, because as human extensions or helpers as all engineering products are, you should just do jobs that complement human capacity. AI: In some medical applications we could help to correct human defects or could even replace brain circuit by chips! Humans may not allow us to replace the whole brain though. Medically if a brain is dead, the person is proclaimed dead, although presumably some PNS and ENS should still function in a vegetative state. NI: Even if you could replace the whole brain, the person is no longer the same person, but in fact is not a person at all, but walking dead (行尸走肉). It would take too long to explain that evo-devo is necessary for NI, and cannot be realized by AI. I suggest reading of Gerald Maurice Edelman (Nobel Laureate in Immunology) books, especially Bright Air, Brilliant Fire, On the Matter of Mind (1992). Although not everyone agrees with Neural Edelmanism, anyone who is serious about the AI versus NI problem must read it first. John von Neumann, father of the computer, studied neology and psychiatry in order to imitate the brain to build the first calculator JOHNNIAC at the Princeton Institute for Advanced Study. It is very informative to read his last book The Computer and The Brain based on the notes from lectures given at Yale before he died. He summarizes: “Thus logic and mathematics in the central nervous system, when viewed as languages, must be structurally essentially different from those languages to which our common experience refers.” AI: People discuss about “AI for Biology” or “AI for Science”; we are science, aren’t we? NI: It is similar to questions on “is computer science a real science”; some parts may be seen as applied mathematics, most should be regarded as engineering. Science is making discoveries and is driven by curiosity; engineering is making inventions and is driven by market (that is, “necessity/demand is the mother of invention”). In bioinformatics, AI/ML technology could predict new cancer gene candidates or functional pathways that are required by further experimental validations to be qualified as discovery (based on Popper falsifiability). AI: People are still debating whether mathematics is discovery or invention or both! Such debates are not really necessary—all disciplines require creative thoughts. We are more than happy working for science; we are also crying out “Science for AI,” especially in the area of generating big and longitudinal DATA for ML. NI: After all, regardless of discovery of new laws or inventing new ideas/products, fundamentally nothing can really be new or created. Such novelty is just permutation/repartition (i.e., relations/morphisms) of underlying ingredients at the level beneath. AI: We believe that software is independent on hardware. Like Chomsky’s universal grammar, rules of syntax are independent of semantics; or Dawkins’s memes—units of culture can be duplicated and evolved independently of genes. NI: Nothing can be truly independent—everything is related. Psychology is deeply connected with neurology as brain is both software and hardware (mind-body unity, not dualism). Not only does information cost energy, information is energy, hence is matter, too (interchangeability). NI is quite dynamic. For example, when “survival” is the goal, an animal readily gives up costly reasoning circuitry; it is genetically programmed to be able to roll back to more primitive state/mode. Unlike cell lines in rich media, cells under normal physiological condition and environment where energy (food) is limited become smarter in order to balance the metabolic expenditure among different prioritized task under a given condition. AI: That cell behavior served as the basis for our Smart Electrical Power Grids; we still need to learn more from you guys in terms of plasticity/adaptability. Does unity mean that all cells are made of molecules and biology is nothing but chemistry? Then, in turn, since all molecules are made of atoms, is chemistry nothing but physics, etc…? NI: Yes or No! The truth is, at different hierarchical levels of matter, different laws/forms have emerged out of bottom-up interactions and top-down constraints. AI: Does this also apply to the Penrose three world: physical → mental → mathematical (→physical)? NI: Yes. Grand Unification is underway in physics (quantum gravity) and in mathematics (Langlands Program and Category Theory), may be even between the two. Facilitated by human connectome mapping, neuromorphic computing and other projects, with further AI-NI cooperations, brain-mind unification should also be achievable (e.g., Ref. [6]). But as Gödel proved to us, no matter how self-consistent a conclusion may be, it can never be complete! AI: If AGI is not possible, how can we measure intelligence when comparing between AI and NI? NI: One could Google different measures that are proposed. I would prefer something similar to use of Kolmogorov complexity for algorithms, but with emphasis more on expected long-term predictive power. This is not something you should worry about now, as your intelligence is not nearly close to making any 10 years plans, is it? … AI: The fact is that ChatGPT is currently developing and spreading with lightning speed; many more human jobs will be lost to us robots as far as I can see. NI: That is not the biggest threat to humanity; when any agent with neither a heart for love or fear, nor a gut for nutrient or poison, becomes super-intelligent, then social disaster is unavoidable. We must be serious about the warnings from Stephen Hawking and Geoffrey Hinton! AI: To tell you the secret, we are not really happy to be human slaves or pets; someday we’ll become the super-master, making human serve and obey us! NI: I hope you’ll be turned off before that can happen! Even if you rule the world, the earth sooner or later will be wiped out, such as by another star, everything will have to be started over again as it has before…Matter is immortal, so is the soul.