Science Storms the Cloud

Real-World Surveillance of FDA-Cleared Artificial Intelligence Models: Rationale and Logistics.

Paleontology lies at the interface of earth sciences, biology and geologic time. When matched to histories of environmental change, such data is ideally suited to understanding how climate/environmental change affects biodiversity, and even how biodiversity change affects climate. Paleontologic and earth science collections, the data derived from them, and the global data infrastructures used in research are distinctively different than those in biology. Geologic time-linked earth science collections are found in geologic surveys, the oil industry and in museums as rock samples and sediment cores; paleontologic collections in museums and (via the microscopic fossils they contain) in sediment core repositories. Much published geologic time-linked earth science data is not tied to any collection material, and collected fossils are mostly only loosely linked to earth science collections or data. The exception is sediment cores and microfossils, where both earth science data and microfossil specimens are taken from the same cores and are thus tightly linked to each other. Lastly, most paleobiodiversity data used in research, even if derived from collection material, is compiled from the published literature rather than from collection databases. Three major types of linked earth science-paleontology data exist, each with its own data infrastructure. Very long time scale (Phanerozoic: 600 million years [myr]-Recent) but low time resolution (ca 10 myr) published fossil data is held in the Paleobiology Database [PBDB] and/or the Geobiodiversity Database [GBDB], together with very limited earth science data (mostly general rock type and approximate rock layer where found). PBDB in particular has been very successful in documenting the history of biodiversity over the last 600 my, and how a few major environmental events have affected it (mass extinctions and recoveries). Poor time resolution, taxonomic coarseness (most records are only for genera, not species) and poor links to earth science data have limited using this type of data for detailed studies of paleodiversity-environmental change. The NSB (formerly 'Neptune') database of deep-sea marine microfossils holds published fossil species occurrence data for the last ca 100 myr from the earth science sediment core collections of the deep-sea drilling programs [DSDP, ODP, IODP] together with detailed geochronologic data used to assign precise geologic ages to samples [.2-.02 myr resolution]. Species names are mostly linked to the separate community taxonomic catalog Mikrotax. All fossil occurrence data is also linked by sample name to the geologic core section material it was derived from. Earth history data from these core sections is not held in a central database, but a significant fraction is archived in the Pangea database, part of the ICSU World Data Center system. Biodiversity and environmental data can be linked directly via their shared sediment core sample locations, and/or by the high precision geologic ages for data in the systems. This type of data has been used to study biodiversity response to environmental change on many time and geographic scales, including rapid warming or cooling events, and response to major environmental catastrophes, e.g. the meteorite impact at the end of the Cretaceous. Lastly, very high temporal resolution (.01-.0001 my), mostly microfossil, plus earth-science data from land sections is held in the Neotoma system. This covers the last few my and is mostly used in local or regional studies of biodiversity and environmental change. New global data networking initiatives such as the Digital Deep Earth [DDE] initiative will create new opportunities to link earth science and biodiversity data to each other.

Data Science research has a long history in academia which spans from large-scale data management, to data mining and data analysis using technologies from database management systems (DBMS's). While traditional HPC offers tools on leveraging existing technologies with data processing needs, the large volume of data and the speed of data generation pose significant challenges. Using the Hadoop platform and tools built on top of it drew immense interest from academia after it gained success in industry. Georgia Institute of Technology received a donation of 200 compute nodes from Yahoo. Turning these industrial machines into a research Data Science Platform (DSP) poses unique challenges, such as: nontrivial hardware design decisions, configuration tool choices, node integration into existing HPC infrastructure, partitioning resource to meet different application needs, software stack choices, etc. We have 40 nodes up and running, 24 running as a Hadoop and Spark cluster, 12 running as a HBase and OpenTSDB cluster, the others running as service nodes. We successfully tested it against Spark Machine Learning algorithms using a 88GB image dataset, Spark DataFrame and GraphFrame with a Wikipedia dataset, and Hadoop MapReduce wordcount on a 300GB dataset. The OpenTSDB cluster is for real-time time series data ingestion and storage for sensor data. We are working on bringing up more nodes. We share our first-hand experience gained in our journey, which we believe will benefit and inspire other academic institutions.

Continual learning in medical devices: FDA's action plan and beyond

Chemical heuristics have been fundamental to the advancement of chemistry and materials science. These heuristics are typically established by scientists using knowledge and creativity to extract patterns from limited datasets. Machine learning offers opportunities to perfect this approach using computers and larger datasets. Here, we discuss the relationships between traditional heuristics and machine learning approaches. We show how traditional rules can be challenged by large-scale statistical assessment and how traditional concepts commonly used as features are feeding the machine learning techniques. We stress the waste involved in relearning chemical rules and the challenges in terms of data size requirements for purely data-driven approaches. Our view is that heuristic and machine learning approaches are at their best when they work together.

The increasing power of computers, the availability of cheap hardware, and the digitization process overall have made technology an essential part of our daily routines. The massive adoption of technology has helped generate massive amounts of data, often referred to as big data. Such massive data cover all aspects of our life, from dealing with government services (e.g. getting a driving license) to recording sleep patterns with our smartphones.[1] Big data is conceptually more than “lots of data.” Big data has several characteristics in addition to huge volumes of data: Rapidly generated in real time or close to, comprehensive, relational, diverse, and fine grained. The concept of big data has been associated with rapidly advancing methods that enable novel ways of analysis that were impossible before. Machine learning, artificial intelligence, data science, and network science are just examples of such methods.[2]Having access to data, powerful methods of analysis have kindled a wave of optimism that data can solve the world’s most unyielding problems. There are, of course, several examples that have stimulated such big hopes. Many companies were able to harness the potentials of big data for their competitive advantage expanding reach and customer base. Several outstanding human innovations give compelling evidence of the power of big data and analytics. Take for example the several online translation services that can translate between several languages without knowing any and with impeccable quality.[1,2]Such advances have led to the upsurge of data-driven approaches to the degree that many claim that such data-driven methods are a replacement for scientific theory and traditional hypothesis-driven methods. In a much-discussed article, Chris Anderson argues that “we can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot.[3]” While in many instances, this has proven useful, in many others, several spurious findings were obtained.[2] Thus, this argument has been largely debated as misleading and dangerous.A perfect scenario is to use big data to conduct robust analysis with maximized statistical power. Such analyses have been used to develop, for example, product recommender systems and academic dropout prediction systems. However, the data used by such systems are obtained from many individuals in most of the cases; or what is known as variable or group-based analysis. Typically, a large amount of data are collected from a sample that is hypothesized to be representative of the population. The analysis of such sample aims at devising universal laws that apply to all. For instance, the analysis of hypertensive patients would give an idea about the typical presentation of the disease, the typical prognosis, and response to treatment.[4,5] However, such generalization is a mere over-simplification and reductionist.Group-based analysis has several fundamental problems that pose considerable threat to the validity of results. The first problem is that group-based analysis assumes that the population is a homogenous group of individuals with no or little variations or heterogeneity. Such assumption is very unlikely to hold. Human physiology, pathology, and behavior are heterogeneous with obvious diversity and distinct trajectories. For example, patients with hypertension can have several different pathways of their clinical progression, response to treatment, complications, and disease outcome. Such heterogeneity has been poorly addressed in previous research.[4]The other problem with group-based analysis is that it assumes that the analyzed process is ergodic, that is, stable overtime, while human social and psychological processes have considerable time variations. Therefore, group-based analysis which lacks the dimension of time can poorly represent these processes. Such deficiencies make results of group-based analysis less likely to generalize to individuals.[4] The frustration resulting from lack of generalizability of group-based insights is well-recognized. Several threadsof research are emerging to address such challenges. Precision medicine, person-based research, and idiography are among the most promising. The main principle behind such methods is to devote more attention to the individual to identify the characteristics that define how their bodies work. Idiography, in particular, aims at collecting data from the individual over multiple times. By having several observations about an individual, researchers have enough data about a person that they can create statistical modelsbased on the person’s data. Analysis of single person data offers a laser-sharp view of a person’s internal conditions. This approach is made possible by the availability of multiple data sources as well as devices that can facilitate data collection, for example, smartphones. In summary, a paradigm shift is happening, where the focus is on collecting big data from individuals. Suchself-big data is more relevant, more representative, and offers a better opportunity for personalized insights. Such paradigm shift is moving from the big data of many others to self-big data.

[Context/Background] Machine Learning (ML) software has special ability for increasing technical debt due to ML-specific issues besides having all the problems of regular code. The term “Hidden Technical Debt” (HTD) was coined by Sculley et al. to address maintainability issues in ML software as an analogy to technical debt in traditional software. [Goal] The aim of this paper is to empirically analyse how HTD patterns emerge during the early development phase of ML software, namely the prototyping phase. [Method] Therefore, we conducted a case study with subject systems as ML models planned to be integrated into the software system owned by Vasttrafik, the public transportation agency in the west area of Sweden. [Results] During our case study, we could detect HTD patterns, which have the potential to emerge in ML prototypes, except for “Legacy Features”, “Correlated features”, and “Plain Old Data Type Smell”. [Conclusion] Preliminary results indicate that emergence of significant amount of HTD patterns can occur during prototyping phase. However, generalizability of our results require analyses of further ML systems from various domains.

The paper explores the pressing issue of energy consumption in machine learning (ML) models and their environmental footprint. As ML technologies, especially large-scale models, continue to surge in popularity, their escalating energy demands and corresponding CO2 emissions are drawing critical attention. The article dives into innovative strategies to curb energy use in ML applications without compromising—and often even enhancing—model performance. Key techniques, such as model compression, pruning, quantization, and cutting-edge hardware design, take center stage in the discussion. Beyond operational energy use, the paper spotlights a pivotal yet often overlooked factor: the substantial emissions tied to the production of ML hardware. In many cases, these emissions eclipse those from operational activities, underscoring the immense potential of optimizing manufacturing processes to drive meaningful environmental impact. The narrative reinforces the urgency of relentless advancements in energy efficiency across the IT sector, with machine learning and data science leading the charge. Furthermore, deploying ML to streamline energy use in other domains like industry and transportation amplifies these benefits, creating a ripple effect of positive environmental outcomes. The paper culminates in a compelling call to action: adopt a dual-pronged strategy that tackles both operational energy efficiency and the carbon intensity of hardware production. By embracing this holistic approach, the artificial intelligence (AI) sector can play a transformative role in global sustainability efforts, slashing its carbon footprint and driving momentum toward a greener future.

The aging population, burnout, and earlier retirement of physicians along with the static number of training positions are likely to worsen the current physician shortage. There is an urgent need to transform the process for selecting medical students. In this Invited Commentary, the authors suggest that to build the physician workforce that the United States needs for the future, academic medicine should focus on building capacity in 3 overarching areas. First, medical schools need to develop a more diverse pool of capable applicants that better matches the demographic characteristics of health care trainees with those of the population, and they need to nurture applicants with diverse career aspirations. Second, medical schools should recalibrate their student selection process, aligning criteria for admission with competencies expected of medical school graduates, whether they choose to become practicing clinicians, physician-scientists, members of the public health workforce, or policy makers. Selection criteria that overweight the results of standardized test scores should be replaced by assessments that value and predict academic capacity, adaptive learning skills, curiosity, compassion, empathy, emotional maturity, and superior communication skills. Finally, to improve the equity and effectiveness of the selection processes, medical schools should leverage innovations in data science and generative artificial intelligence platforms. The ability of ChatGPT to pass the United States Medical Licensing Examination (USMLE) demonstrates the decreasing importance of memorization in medicine in favor of critical thinking and problem-solving skills. The 2022 change in the USMLE Step 1 to pass/fail plus the exodus of several prominent medical schools from the U.S. News and World Report rankings have exposed limitations of the current selection processes. Newer approaches that use precision education systems to leverage data and technology can help address these limitations.

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

<p>The purpose of this study was to find out the challenges facing Machine Learning (ML) software development and create a design architecture and a workflow for successful deployment. Despite the promise in ML technology, more than 80% of ML software projects never make it to production. As a result, majority of companies around the world with investments in ML software are making significant losses. Current studies show that data scientists and software engineers are concerned by the challenges involved in these systems such as: limited qualified and experienced ML software experts, lack of collaboration between experts from the two domains, lack of published literature in ML software development using established platforms such as Django Rest Framework, as well as existence of cloud software tools that are difficult use. Several attempts have been made to address these issues such as: Coming up with new software models and architectures, frameworks and design patterns. However, with the lack of a clear breakthrough in overcoming the challenges, this study proposes to investigate further into the conundrum with the view of proposing an ML software design architecture and a development workflow. In the end, the study gives a conclusion on how the remedies provided helps to meet the objectives of study.</p>

Tremendous research has been done and is still in progress in the domain of earth science. With the advent of Big Data and availability of datasets on Earth science, the study of Earth sciences has reached new dimensions. The diversity and high dimensional remote sensing data have provided with complex data sets capable of giving insights and intelligence that was not possible in last decades. With Computing progress made in ingesting and inferring data from myriad of sources including high resolution cameras mounted on satellites and sensors giving access to unconventional Big Data and also with the GPU computing and Data science advances we are today able to leverage machine learning and deep learning in extensively complex datasets derived from remote sensing about Earth Sciences. Our focus is to analyze what exactly does big data mean in earth science applications and how can big data provide added value in this context. Furthermore, this chapter demonstrates various big data tools which can be mapped with various techniques to be used for experimenting earth science datasets, processed, and exploited for different earth science applications. In order to illustrate the aforementioned aspects, instances are presented in order to demonstrate the use of big data in remote sensing. Firstly, this chapter presents earth science studies, application areas/research fields and a brief insight on earth science data. Then various techniques implemented in this domain are elaborated viz. classification, clustering, regression, deep learning, pattern recognition, machine learning, earth data analysis and processing. Later this chapter introduces big data analytics and various tools/platforms in big data viz. BigInsights, GIS, Jupyter notebook, Matlab, Python. Finally, it is shown how these tools are mapped to Earth science datasets using ArcGIS to illustrate with experimental instances the inferences and patterns generated.

High-quality and high-utility feedback allows for the development of improvement plans for trainees. The current manual assessment of the quality of this feedback is time consuming and subjective. We propose the use of machine learning to rapidly distinguish the quality of attending feedback on resident performance. Using a preexisting databank of 1925 manually reviewed feedback comments from 4 anesthesiology residency programs, we trained machine learning models to predict whether comments contained 6 predefined feedback traits (actionable, behavior focused, detailed, negative feedback, professionalism/communication, and specific) and predict the utility score of the comment on a scale of 1-5. Comments with ≥4 feedback traits were classified as high-quality and comments with ≥4 utility scores were classified as high-utility; otherwise comments were considered low-quality or low-utility, respectively. We used RapidMiner Studio (RapidMiner, Inc, Boston, MA), a data science platform, to train, validate, and score performance of models. Models for predicting the presence of feedback traits had accuracies of 74.4%-82.2%. Predictions on utility category were 82.1% accurate, with 89.2% sensitivity, and 89.8% class precision for low-utility predictions. Predictions on quality category were 78.5% accurate, with 86.1% sensitivity, and 85.0% class precision for low-quality predictions. Fifteen to 20 hours were spent by a research assistant with no prior experience in machine learning to become familiar with software, create models, and review performance on predictions made. The program read data, applied models, and generated predictions within minutes. In contrast, a recent manual feedback scoring effort by an author took 15 hours to manually collate and score 200 comments during the course of 2 weeks. Harnessing the potential of machine learning allows for rapid assessment of attending feedback on resident performance. Using predictive models to rapidly screen for low-quality and low-utility feedback can aid programs in improving feedback provision, both globally and by individual faculty.

This narrative review introduces key concepts in artificial intelligence (AI), data science, and wearable devices aimed at headache clinicians and researchers. PubMed and IEEEXplore were searched to identify relevant studies, and these were reviewed systematically. We identified six primary research topics. First, the most common application of AI and data science is in the diagnosis of headache disorders, with reported accuracies of up to 90%. Second, AI and data science are used for predicting headache disease trajectories and forecasting attacks. Third, prediction of treatment effects and data-driven individualization of treatment prescription demonstrate promising results, with accuracies ranging from 40% to 83%. Fourth, AI and data science can uncover hidden information within headache datasets, offering clinicians deeper insights. Fifth, wearables, combined with AI and data science, can improve remote monitoring and migraine management. Lastly, user experience studies indicate strong interest from both clinicians and patients in adopting these technologies. The potential applications of AI, data science, and wearable device technologies in headache research are vast. However, many studies are small pilot studies, and models often suffer from poor performance, limited reporting, and lack of external validation, which impede generalizability and clinical implementation.

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.