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Artificial Intelligence Explained Basic definitions and categorization I. Overview Artificial Intelligence (AI) represents nowadays a paradigm shift that is driving at the same time the scientific progress as well as the industry evolution. Given the intense level of domain knowledge required to really appreciate the technicalities of the artificial engines, what AI is and can do is often misunderstood: the general audience is fascinated by its development and frightened by terminator-like scenarios; investors are mobilizing huge amounts of capital but they have not a clear picture of the competitive drivers that characterize companies and products; and managers are rushing to get their hands on the last software that may improve their productivities and revenues, and eventually their bonuses. Even though the general optimism around creating advancements in artificial intelligence is evident (Muller and Bostrom, 2016), in order to foster the pace of growth facilitated by AI I believe it would be necessary to clarify some concepts. II. Basic Definitions Categorization First, let’s describe what artificial intelligence means. According to Bostrom (2014), AI today is perceived in three different ways: it is something thatmight answer all your questions, with an increasing degree of accuracy (“the Oracle”); it could do anything it is commanded to do (“the Genie”), or it might act autonomously to pursue a certain long-term goal (“the Sovereign”). However, AI should not be defined by what it can do or not, and thus a broader definition is appropriate. An artificial intelligence is a system that can learn how to learn, or in other words a series of instructions (an algorithm) that allows computers to write their own algorithms without being explicitly programmed for. Although we usually think about intelligence as the computational part of our ability to achieve certain goals, it is rather the capacity to learn and solve new problems in a changing environment. In a primordial world then, it is simply the attitude to foster survival and reproduction (Lo, 2012; 2013; Brennan and Lo, 2011; 2012). A living being is then defined as intelligent if she is driving the world into states she is optimizing for. No matter how accurately we defined this concept, we can intuitively understand that the level of intelligence machines are provided with today is years far from the average level of any human being. While human being actions proceed from observing the physical world and deriving underlying relationships that link cause and effect in natural phenomena, an artificial intelligence is moved entirely by data and has no prior knowledge of the nature of the relationship among those data. It is then “artificial” in this sense because it does not stem from the physical law but rather from pure data. We then have just defined what artificial intelligence is and what mean to us. In addition to that, though, there are two other concepts that should be treated as part of this introduction to AI: first of all, how AI is different and/or related to other buzzwords (big data, machine learning, etc.); second, what features a system has to own to be defined intelligent. We think of AI as an interdisciplinary field, which covers (and requires) the study of manifold sub-disciplines, such as natural language processes, computer vision, as well as Internet of things and robotics. Hence, in this respect, AI is an umbrella term that gathers a bucket of different aspects. We can somehow look at AI to be similar to a fully-functional living being, and we can establish comparisons to figure out the degree of relationship between AI and other (sub)fields. If AI and the human body are alike, it has to possess a brain, which carries out a variety of tasks and is in charge of specific functions such the language (NLP), the sight (computer vision), and so on so forth. The body is made of bones and muscles, as much as a robot is made by circuits and metals. Machine learning can be seen as specific movements, action or thoughts we develop and that we fine-tune by doing. The Internet of things (IoT) corresponds to the human senses, which is the way in which we perceive the world around us. Finally, big data is the equivalent of the food we eat and the air we breathe, i.e., the fuel that makes us tick, as well as every input we receive from the external world that is captured by our senses. It is a quite rough comparison, but it conveys a simple way on how all the terms are related to each other. Although many other comparisons may be done, and many of them can be correct simultaneously, the choice of what kind of features a system should have to be a proper AI is still quite controversial. In my opinion, the system should be endowed with a learning structure,an interactive communication interface, and a sensorial-like input digestion. Unfortunately, this idea is not rigorous from a scientific point of view, because it would involve a series of ethical, psychological, and philosophical considerations that should be taken into account. III. 3 Types of AI Instead of focusing much longer on this non-provable concept, I rather prefer to illustrate how those characteristics would reflect the different types of AI we are (and we will) dealing with. An AI can indeed be classified in three ways: a narrow AI, which is nothing more than a specific domain application or task that gets better by ingesting further data and “learns” how to reduce the output error. An example here is DeepBlue for the chess game, but more generally this group includes all the functional technologies that serve a specific purpose. These systems are usually quite controllable because limited to specific tasks. When a program is instead not programmed for completing a specific task, but it could eventually learn from an application and apply the same bucket of knowledge to different environments, we face an Artificial General Intelligence (AGI). This is not technology-as-a-service as in the narrow case, but rather technology-as-a-product. The best example for this subgroup is Google DeepMind, although it is not a real AGI in all respects. We are indeed not there yet because even DeepMind cannot perform an intellectual task as a human would. In order to get there, much more progress on the brain structure functioning, brain processes optimization, and portable computing power development have to be made. Someone might think that an AGI can be easily achieved by piling up many narrow AIs, but in fact, this is not true: it is not a matter of number of specific skills a program can carry on, but rather the integration between all those abilities. This type of intelligence does not require an expert to work or to be tuned, as it would be the case for narrow AI, but it has a huge limitation: at the current state, it can be reached only through continuously streaming an infinite flow of data into the engine. The final stage is instead called Superintelligent AI (ASI): this intelligence exceeds largely the human one, and it is able of scientific and creative thinking; it is characterized by general common wisdom; it has social skills and maybe an emotional intelligence. Although we often assume this intelligence to be a single super computer, it is more likely that it is going to be made by a network or a swarm of several intelligences. The way in which we will reach the different stages is though still controversial, and many schools of thoughts exist. The symbolic approach claims that all the knowledge is symbolic and the representation space is limited, so everything should be stated in formal mathematical language. This approach has historically analyzed the complexity of the real world, and it had suffered at the same time from computational problems as well as understanding the origination of the knowledge itself. The statistical AI instead focuses on managing the uncertainty in the real world (Domingos et al., 2006), which lives in the inference realm contrarily to the more deductive logical AI. On a side then, it is not clear yet to what degree the human brain should be taken as an example: biological neural network seems to provide a great infrastructure for developing an AI, especially regarding the use of sparse distributed representations (SDRs) to process information. How Does AI Compare to Humans? So the natural question everyone is asking is “where machines stand with respect to humans?” Well, the reality is that we are still far from the point in which a superintelligence will exceed human intelligence—the so-called Singularity (Vinge, 1993). The famous futurist Raymond Kurzweil proposed in 1999 the idea of the law of accelerating returns, which envisages an exponential technological rate of change due to falling costs of chips and their increasing computational capacity. In his view, the human progress is S-shaped with inflection points corresponding to the most relevant technological advancements, and thus proceeds by jumps instead of being a smooth and uniform progress. Kurzweil also borrowed Moore’s law to estimate accurately the precise year of the singularity: our brain is able of 10¹⁶ calculations per second (cps) and 10¹³ bits of memory, and assuming Moore’s law to hold, Kurzweil computed we will reach an AGI with those capabilities in 2030, and the singularity in 2045. I believe though this is a quite optimistic view because the intelligence the machines are provided with nowadays is still only partial. They do not possess any common sense, they do not have any sense of what an object is, they do not have any earlier memory of failed attempts, they are not conscious - the so-called the “Chinese room” argument, i.e., even if a machine can perfectly translate Chinese to English and vice versa, it does not really understand the content of the conversation. On the other side, they solve problems through structured thinking, they have more storage and reliable memory, and raw computational power. Humans instead tried to be more efficient and select ex-ante data that could be relevant (at the risk of losing some important information), they are creative and innovative, and extrapolate essential information better and faster from only a few instances, and they can transfer and apply that knowledge to unknown cases. References Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies, OUP Oxford. Brennan, T. J., Lo, A. W. (2011). “The Origin of Behavior”. Quarterly Journal of Finance, 7: 1043–1050. Brennan, T. J., Lo, A. W. (2012). “An Evolutionary Model of Bounded Rationality and Intelligence”. PLoS ONE 7(11), e50310. Domingos, P., Kok, S., Poon, H., Richardson, M., Singla, P. (2006). “Unifying logical and statistical AI”. Proceeding of the 21st National Conference on Artificial Intelligence, 1: 2–7. Lo, A. W. (2012). “Adaptive Markets and the New World Order”. Financial Analysts Journal, 68(2): 18–29. Lo, A. W. (2013). “The Origin of Bounded Rationality and Intelligence”. Proceedings of the American Philosophical Society, 157(3): 269–280. Müller, V. C., Bostrom, N. (2016). “Future progress in artificial intelligence: A Survey of Expert Opinion”, in Vincent C. Müller (ed.): Fundamental Issues of Artificial Intelligence, Springer: 553–571. Vinge, V. (1993). “The Coming Technological Singularity: How to Survive in the Post-Human Era”. In NASA. Lewis Research Center, Vision 21: Interdisciplinary Science and Engineering in the Era of Cyberspace: 11–22. This article is an excerpt of my book “Artificial intelligence and exponential technologies: business models evolution and new investment opportunities”, edited by Springer.

"Elementary particles are the building blocks of al matter everywhere in the universe. Their properties are connected with the fundamental forces of nature" Murray Gell Mann Getting a new periodic table of elements using AI Abstract Objective: To obtain an atomic classification based on clustering techniques using non-supervised learning algorithms. Design: The sample of atoms used in the experiments is defined using a set of atomic elements with known properties that are not null for all the individuals of the sample. Different clustering algorithms are used to establish relationships between the elements, getting as result a cluster of atoms related with each other by the numerical values of some of their structural properties. Results: Sets of elements related with the atom that represents each cluster. Keywords: Clustering, atoms, periodic table of elements, unsupervised algorithms, Random Forest, K-Means, K-Nearest Neighbour, Weka, Bayesian Classifier. Introduction The periodic table of elements is an atomic organisation based on two axis. The horizontal axis establishes an increasing order based on the atomic number (number of protons) of each element. The vertical arrangement is managed by the electronic configuration and presents a taxonomic structure designed by the electrons of their latest layer . Furthermore, four main blocks arrange the atoms by similar properties (gases, metals, nonmetals, metalloids). Additionally to the number of protons and the electronic configuration, the atoms are characterised by other attributes that are not ascendant nor cyclic in the periodic table of elements. The values of these properties constitute a sample of numbers that represent different atomic magnitudes that distinguish in some how the chemical elements. In this experiment some of these chemical and physical dimensions have been involved in the training of a set of machine learning algorithms to obtain representative clusters of each element. Research problem The hypothesis of this experiment considers the use of some variants of unsupervised learning models to discover relationships between atomic elements based on a few of chemical and physical matter attributes. Moreover, these techniques calculate clusters of categories based on their numerical attributes. The research problem drives also to an element clustering that could offer a new atomic distribution based on the inferred functions processed by the machine learning processes. The goal is to present an organisation of elements based on the clustering calculation applied on a specific set of atomic properties. Units of analysis The following atomic properties have been used to train and evaluate the unsupervised algorithms: melting point [K], boiling point [K], atomic radius [pm], covalent radius [pm], molar volume [cm3], specific heat [J/(Kg K)], thermal conductivity [W/(m k)], Pauling electronegativity [Pauling scale],first ionisation energy [kJ/mol] and lattice constant [pm]. Only atoms with non null values for each magnitude have been selected in the sample. Notice that some of these properties have not been already discovered or calculated for some atoms that do not appear in the sample. The raw data can be downloaded from this link. The following graphical representation shows how some of these properties are distributed across the spectrum of elements sorted by the ascending number of protons: Graphic 1. Distribution of the melting point, boiling point, lattice constant and the atomic radius versus the atomic number. In this graphic there is not any seeming correlation among the displayed magnitude values and the atomic number. At the first glance there are not correlations nor any pattern between the displayed attributes and the elements upward sorted. Methods The unsupervised machine learning algorithms allow to infer models that identify hidden structure from "untagged" data. Thus no categories are included in the observation and data used to learn can not be used in the accuracy evaluation of the results. Using the machine learning library Java- ML and the non null values for the above specified magnitudes, two exercises were performed: 1 - Clustering of elements The scope of this exercise is to create clusters of atomic elements using three different machine learning techniques provided by the Java-ML library. The result was three atomic configurations based on the following algorithms: K-Means clustering with 10 clusters. This algorithm divides the selected atomic elements into k clusters where each individual is associated to each cluster through the nearest mean calculation. Iterative Multi K-Means implements an extension of K-Means. This algorithm works performing iterations with a different k value, starting from kMin and increasing to kMax, and several iterations for each k. Each clustering result is evaluated with an evaluation score. The result is the cluster with the best score. The applied evaluation in the exercise was the sum of squared errors. K-Means cluster wrapped into Weka algorithms. Classification algorithms from Weka are accessible from within the Java-ML library. An experiment with 3 clusters were calculated just to compare with the first exercise (K-Means with 10 clusters). The results were presented using the TreeMap provided by the d3 - TreeMap graphic library. Graphic 2. Applying K-Means clustering to the sample. 2 - Atomic elements classifications and relationships between themselves. The following exercise was intended to evaluate the degree of relationship among the atoms contained in the sample. Three algorithms were applied: Random Forest with 30 trees to grow and 10 variables randomly sampled as candidates at each split (one for each atomic magnitude). This technique works by constructing a multitude of decision trees at training time and providing the class that is the mode of the classes. Bayesian Classifier. The Naive Bayes classification algorithm has been used to classify the set of elements in different categories. K nearest neighbour (KNN) classification algorithm with KDtree support. The number of neighbours was fixed to 8, considering that this number of potential elements could establish the boundaries for each element positioned in the center of a square (laterals and corners are not managed in the current hypothesis). Graphic 3. Schema of 8 neighbours surrounding the target element Each algorithm worked such as a classifier and they produced a membership distribution with the associated degree evaluation. The classes with a membership evaluation equals to zero were not considered. In this experiment, the physical and chemical attribute values have been clusterized and afterwards, each atom belonging to the same sample, has been classified in the set of the calculated clusters. Therefore, each element is identified with a specific group where the only requirement is that the atom that is being classified must be the representative for the selected category. The calculated clusters have been distributed in pairs of atoms with their corresponding degree evaluation following this structure: [Xi, Yj, Ej] Where Xi is each atom in the sample, Yj is each element in the category Y and Ej the related degree evaluation to the pair. The relationships between the individuals and their categories are shownthrough the chord graphic representation based on the Chord Viz component provided by d3. Graphic 4. Nitrogen relationships considering the evaluation of different classifiers Results The three tree maps (one per clustering algorithm) where the chemical elements have been organised, are showing interesting groups of components. For instance all of them include in the same group the S and the Se. Other atoms (all of them gases) such as the Ne, Ar, Kr and Xe are also enclosed in the same group by all the algorithms (remember that neither the atomic number nor the electronic configuration were included in the models). It is interesting to mention that the configuration generated by the two K-Means algorithms are presenting the H and the Li in a separated and mono-element clusters. Regarding the weighted relationships between the elements, a chord graphic has been created for each machine learning algorithm. This data representation shows how the atomic elements can be related with each other through unsupervised machine learning techniques taking some of their chemical and physical properties and assigning a relational degree to them. There are some interesting behaviours such as the set of relationships found for the Nitrogen. The Random Forest algorithm determined that the O, Ne and Ar are highly related, the Bayesian Classifier calculated that only the Oxygen was related and the results of the K-NN method evaluated that the O, Ne, Cl, Ar, Br, Kr and the I are related when the number of neighbours was fixed to 8. Some familiar associations can be found in the calculated relationships when comparing the components in the clusters and their distribution in the periodic table of elements. Nevertheless, other non evident atomic relations have been set up by these methods. Additionally, the non commutative property is a remarkable characteristic. For instance the Nitrogen is not related in the reverse way with the Hydrogen when they are selected in the results calculated using the Random Forest algorithm. Conclusions Although the calculated atomic organisation through the machine learning algorithms are not following any physic or chemical rule, some associations arise creating groups of components that follow similar configurations like the provided by the periodic table of elements. Beyond the calculated results, the applied library (Java-ML) and the used algorithms, the exercise is interesting by itself. The proof that chemical or physical relationships can be stablished among the elementary components based on the similarity of their properties using machine learning can drive to new lines of research. Acknowledgments I want to thank to Montse Torra her task gathering the physical and chemical properties for each used atom in the sample. References Bostjan Kaluza. "Machine Learning in Java". Packt Publishing Ltd, Apr 29, 2016 Eibe Frank, Mark A. Hall, and Ian H. Witten. "The WEKA Workbench". Online Appendix for “Data Mining: Practical Machine Learning Tools and Techniques”. Morgan Kaufmann, Fourth Edition, 2016 Physical and chemical atomic properties extracted from WebElements and PeriodicTable.

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