TASK DEFINITION: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
PROBLEM: The shortest path between two concepts in a taxonomic ontology is commonly used to represent the semantic distance between concepts in the edge-based semantic similarity measures. In the past, the edge counting is considered to be the default method for the path computation, which is simple, intuitive and has low computational complexity. However, a large lexical taxonomy of such as WordNet has the irregular densities of links between concepts due to its broad domain but. The edge counting-based path computation is powerless for this non-uniformity problem. In this paper, we advocate that the path computation is able to be separated from the edge-based similarity measures and form various general computing models. Therefore, in order to solve the problem of non-uniformity of concept density in a large taxonomic ontology, we propose a new path computing model based on the compensation of local area density of concepts, which is equal to the number of direct hyponyms of the subsumers of concepts in their shortest path. This path model considers the local area density of concepts as an extension of the edge-based path and converts the local area density divided by their depth into the compensation for edge-based path with an adjustable parameter, which idea has been proven to be consistent with the information theory. This model is a general path computing model and can be applied in various edge-based similarity algorithms. The experiment results show that the proposed path model improves the average correlation between edge-based measures with human judgments on Miller and Charles benchmark from less than 0.8 to more than 0.85, and has a big advantage in efficiency than information content (IC) computation in a dynamic ontology, thereby successfully solving the non-uniformity problem of taxonomic ontology.

SOLUTION: A density compensation-based path computing model for measuring semantic similarity

PROBLEM: We show that collaborative filtering can be viewed as a sequence prediction problem, and that given this interpretation, recurrent neural networks offer very competitive approach. In particular we study how the long short-term memory (LSTM) can be applied to collaborative filtering, and how it compares to standard nearest neighbors and matrix factorization methods on movie recommendation. We show that the LSTM is competitive in all aspects, and largely outperforms other methods in terms of item coverage and short term predictions.

SOLUTION: Collaborative Filtering with Recurrent Neural Networks

PROBLEM: ions of these networks, eg, cube on cube (The Society of Mind by Marvin Minsky, MIT, 1985) and “learning” mechanisms (The Organization of Behavior by Donald O Hebb, 1949) may not constitute intelligence. These processes populate fields with values from the user environment which can be selectively used (per contra hard coded defined sets). For example, NEST Learning Thermostat uses input values to tune your preferred temperatures. Page 2 ● I am an AI optimist. My article on AI (Agents: Where Artificial Intelligence Meets Natural Stupidity) is here http://dspace.mit.edu/handle/1721.1/41914 (2002) ▪ Dr Shoumen Palit Austin Datta, Research Affiliate, MIT Auto-ID Labs, Department of Mechanical Engineering, MIT ▪ Please explore website http://autoid.mit.edu Is Intelligence an Illusion in Artificial Intelligence? It’s better to keep your mouth closed and be thought a fool than to open your mouth and remove all doubt ● Twain Elements of the equation/rule -based (brittle and static) structures caused the bust of the expert systems and ended The AI Business (Winston and Prendergast, MIT, 1984) lure before the rise of artificial neural networks (ANN popularity circa 1990). Topology and synaptic weights, if combined, offered a flexible infrastructure to acquire more relevant values and profit from data. This is an important advancement. But, is it really intelligence? Rather than partial differential equations exploding due to increase in state functions (due to the large number of parameters), Agents allowed each variable to be represented as a single-function entity. The collective output from an Agency of Agents improved predictive or prescriptive precision compared to operations research applications (see illustration below). The behavior of Agents and Agencies using “AI” concepts originated from the foundation laid by the principles of stigmergy (Pierre-Paul Grasse, 1959) which continues to evolve as artificial life and are able to address complex business problems. The recent surge in the hype associated with “big data” has navigated profitability from analytics to the front and center. Intelligence is marketed as a commodity in this scenario. In order to market intelligence as a service, the AI paradigm is being refurbished as a commodity (brain in a box) and touted to business and industry as an essential tool to reach the luminous summit [$]. Winning at games using ANN is advocated as intelligence. Smart and intelligence are emerging as speculative tabloid fodder. Witnessing the rapid transmutation of tabloid fodder about speculation to (business) truth is deeply troubling. Claims about “original” thinking in containerization of data and process are good ideas but appeared as concepts proposed almost half a century ago by Marvin Minsky (page 315 in the original book or search page 311 in this PDF version of the book). Connecting entities (containers) using IPv6 resonates with ideas suggested about a decade ago. However, it is reassuring that the concepts are not lost but are being developed to advance the march of digital transformation (see Digital Twins https://dspace.mit.edu/handle/1721.1/104429). Page 3 ● I am an AI optimist. My article on AI (Agents: Where Artificial Intelligence Meets Natural Stupidity) is here http://dspace.mit.edu/handle/1721.1/41914 (2002) ▪ Dr Shoumen Palit Austin Datta, Research Affiliate, MIT Auto-ID Labs, Department of Mechanical Engineering, MIT ▪ Please explore website http://autoid.mit.edu Is Intelligence an Illusion in Artificial Intelligence? It’s better to keep your mouth closed and be thought a fool than to open your mouth and remove all doubt ● Twain Is Learning a Myth in “Human-level” AI Systems? I cannot improve the content from Rodney Brooks, hence, I shall quote verbatim as follows: “We have found a way to build fixed topology networks of our finite state machines which can perform learning, as an isolated subsystem, at levels comparable to these examples. At the moment of course we are in the very position we lambasted most AI workers for earlier in this paper. We have an isolated module of a system working and the inputs and outputs have been left dangling.” (Intelligence without Representation, 1991) Learning triggers profound, sustained, often long term changes in our neural networks at many levels that we cannot even begin to understand or grasp its cognitive repercussions. Thus, almost all assumptions made by McCulloch & Pitts (1943) are violated (Appendix 1). The “all or none” phenomena assumed by McCulloch & Pitts (1943) is only relevant from a mechanical perspective if one assumes (incorrectly, of course) that input data is supposed to transduce a signal and the resultant action potential (neuronal activation) may be one form of a proof of learning. Neurologists will strenuously and vociferously take exception. AI experts may wish adopt this view to claim learning in the AI context. The neurological state of learning, cognition and behavior is usually a continuous function modulated by evolutionary weights, which are not subject, in the least, to the limitations of discrete-state machines. Application of machine learning models are often inconsistent and incorrect. Discrete systems have a finite (countable) number of states which may be described in precise mathematical models. The computer is a finite state machine which may be viewed as a discrete system. The brain is not a computer. The neural infrastructure and networks are not finite state machines. Imposing any such model (real-world continuous systems) or ill-advised abstraction or gross extrapolation (by those not so well informed) may only perpetrate great lengths of fantasy about intelligence and learning related to AI systems. “Of the vast stream of sense data that pour into our nervous systems we are aware of few and we name still fewer. For it is the fact that even percepta are wordless. Only by necessity do we put a vocabulary to what we touch, see, taste, and smell, and to such sounds as we hear that are not themselves words. We look at a landscape, at the rich carving and majestic architecture of a cathedral, listen to the development of harmonies in a symphony, or admire special skill in games and find ourselves woefully lacking in ability to describe our percepts. Words, as we very rightly say, fail us either to describe the plain facts of these experiences or to impart to others, our feelings.” (G Jefferson CBE, FRS, MS, FRCS, Professor of Neurosurgery in The Mind of Mechanical Man in British Medical Journal, 25th June 1949). The author was aware of “Dr Wiener of Boston, his entertaining book Cybernetics (1948).” Page 4 ● I am an AI optimist. My article on AI (Agents: Where Artificial Intelligence Meets Natural Stupidity) is here http://dspace.mit.edu/handle/1721.1/41914 (2002) ▪ Dr Shoumen Palit Austin Datta, Research Affiliate, MIT Auto-ID Labs, Department of Mechanical Engineering, MIT ▪ Please explore website http://autoid.mit.edu Is Intelligence an Illusion in Artificial Intelligence? It’s better to keep your mouth closed and be thought a fool than to open your mouth and remove all doubt ● Twain Alan Turing was cognizant of the over-reach in claiming “intelligence” in AI and outlined potential objections including Godel's theorem (mathematical objection) and “Argument from Consciousness” which he reproduced from Professor Geoffrey Jefferson as a quote (from his Lister Oration, 1949) "Not until a machine can write a sonnet or compose a concerto because of thoughts and emotions felt, and not by the chance fall of symbols, could we agree that machine equals brain that is not only write it but know that it had written it. No mechanism could feel (and not merely artificially signal, an easy contrivance) pleasure at its successes, grief when its valves fuse, be warmed by flattery, be made miserable by its mistakes, be charmed by sex, be angry or depressed when it cannot get what it wants." A. M. Turing (1950) Computing Machinery and Intelligence. Mind 49 433-460 (PDF) Page 452 (see Appendix 2) removes any doubt that Turing had grave doubts regarding claims of intelligence in the context of computers. Turing’s suggested starting point is “the child machine” (Appendix 2). Then he proposes to add the roles or processes of “evolution” “hereditary material” “mutation” “education” and “natural selection” in order to mature “the child machine” to “imitate an adult human mind” as a path forward to intelligence. To understand even vaguely what happens after “the initial state of mind, say at birth” the reader is urged to review Patterns in the Mind by Ray Jackendoff (1966) and then take into consideration the field of linguistics and natural language development (1970, PhD thesis of Terry Winograd, MIT http://hci.stanford.edu/winograd/shrdlu/AITR-235.pdf). For all this to happen, we must process information encoded via developmental and environmental signals. Hence, the suggestion, research and convergence on the concept of molecular logic gates. The complexity of the process may help deter one from concluding that we are dealing with intelligence with respect to computers, machinery or AI systems. However, the human spirit and the fabric of scientific research cannot step away from problems even if all available reason suggests that something is impossible, at the time. It is with this fervor the 1956 Dartmouth Summer Research Project on Artificial Intelligence (June 17 August 16) was proposed in 1955 by a visionary group of eminent and erudite academic scholars (www.aaai.org/ojs/index.php/aimagazine/article/view/1904/1802). The proposal (see Appendix 3) admits it is a “conjecture” but continued “that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.” Great strides (Appendix 4) have been made, yet the 1956 Summer Research “conjecture” looms overhead. But, our “faith” in progress of AI is evident from the 1145 page book by Russell and Norvig (AI A Modern Approach, 3rd ed). We are learning how decisions can be made without a brain in cognitive organisms (unicellular mould Physarum polycephalum). Page 5 ● I am an AI optimist. My article on AI (Agents: Where Artificial Intelligence Meets Natural Stupidity) is here http://dspace.mit.edu/handle/1721.1/41914 (2002) ▪ Dr Shoumen Palit Austin Datta, Research Affiliate, MIT Auto-ID Labs, Department of Mechanical Engineering, MIT ▪ Please explore website http://autoid.mit.edu Is Intelligence an Illusion in Artificial Intelligence? It’s better to keep your mouth closed and be thought a fool than to open your mouth and remove all doubt ● Twain Neurobiology 101 neurons their numbers and networks Topology and weights are the foundational underpinnings of artificial neural nets (ANN) which are the mainstay of AI systems. How reliable are these extrapolations? Are we still talking about AI? Let me reiterate what Rodney Brooks has stated but in a different vein. Structural design of network topology aims to mimic the commonly observed organization of neurons. Topology based on neural organization (small world networks) may be fraught with errors as evidenced by studies on wiring configuration and neuroanatomical analysis which reveals differences in circuit architecture and connectivity if viewed at mesoscopic vs microscopic scale. On a mesoscopic scale, seemingly random networks exhibit consistent properties. It may be difficult, if not impossible, to extract useful/meaningful abstractions from these counter-intuitive non-linear yet dynamic structure-function complementarities. In ANN, weights are assigned to signify connectivity strengths (the links between the perceptrons). These are arbitrary, at best, because synaptic weights between neurons and clusters are subjected to conditions that we think we know only by name. Even if one acquired neurophysiological data related to frequency variations of action potentials (~200 Hertz) in an attempt to understand synaptic weights between neurons, the results may not be revealing. The complexity may be compounded by the fact nerve transmissions are modified by ions, electrical threshold potential and chemical neuro-transmitters. In synaptic design, one assumes the all-or-none process (Appendix 1) and the weights are modeled based on extrapolation from “inferential changes” which are in the order of milliseconds to seconds (hence, subject to observation, data collection and extrapolation). But, the nature of the connectivity and resultant weight is also influenced by epigenetic factors (time scale – seconds to days), ontogenic factors (days to years) and phylogenetic factors which are the result of generations or are derived from the evolutionary time scale, as noted in Appendix 2. Hence, the nature of the weight deduced from “inferential” changes (primarily sense and response mechanisms) are only the tip of the iceberg. We are almost completely in the dark about the nature of the influence from these other three factors. Taken together, perhaps we are starting out on the wrong foot about the design of ANN by holding on to assumptions which are generally incorrect because we remain significantly uninformed. Having said that, one must hasten to add, that, no matter how approximately correct the synthetic weights, may be, it may not be impossible to conceive building ANNs with partially unqualified numbers and unsure topologies. Using tools eg back propagation algorithms, the AI system may be tuned and re-tuned in a dynamic data-driven manner (some may still refer to it as “learning”) to yield actionable information from higher order systems. Over-fitting the model may cause harm, for example, collision avoidance systems. Page 6 ● I am an AI optimist. My article on AI (Agents: Where Artificial Intelligence Meets Natural Stupidity) is here http://dspace.mit.edu/handle/1721.1/41914 (2002) ▪ Dr Shoumen Palit Austin Datta, Research Affiliate, MIT Auto-ID Labs, Department of Mechanical Engineering, MIT ▪ Please explore website http://autoid.mit.edu Is Intelligence an Illusion in Artificial Intelligence? It’s better to keep your mouth closed and be thought a fool than to open your mouth and remove all doubt ● Twain Scholars continue to discuss new ways of using robots to make robots, create self-healing intelligent machines and adaptive machines to optimize up-time. Thinkers are conjuring up ways to harness the developmental foundations of neurons – neurogenesis. Emulation of neural development using computational AI systems can incorporate characteristics of natural neural systems into engineering design. Scientists are claiming that rather than designing neural networks, emulation of neurogenesis shall enable us to generate neural networks to serve dynamic and even more complex systems of the future. This emerging field of programmable artificial neurogenesis appears to call for a metadesign paradigm which may begin with components (object oriented?). It aims to build higher order intelligent systems which will adapt (to demands, environment, resource) without re-programming component level entities. When components are updated, the changes will be propagated, via appropriate “learning” functions, up/down hierarchies. The great desire to emulate the grand vision latent in intelligence, cognition and the brain, works almost as an aphrodisiac. The immense powers of biology and the ability to distil and capture even an iota of that potential in bio-inspired systems through convergence with computation will continue to be a Holy Grail. Here is one example of bio-power: We have about 3 million base pairs (A-T, G-C) in the human genome (3x106) which codes for about 10,000 – 20,000 genes resulting in a human body with 100 trillion cells (1x1014). At least a third of the approximately 20,000 different genes that make up the human genome are active (expressed) in the brain. We have about 8.5x1010 neural cells (there are an equivalent number of glial cells). Each neural cell connects on an average with 1,000 other neural cells to create about 1x1014 neural connections. This is the neural network which makes us human, creates intelligence and cognition. If we may think in terms of a compression ratio, the ratio approaches 1011 (7,000 genes creating 1x1014 connections). The most effective compression algorithm CMIX doesn’t even come close. The illegal 42.zip bomb which unfolds to 4.5 petabytes (pb) from a 42 kilobytes (kb) single symbol zip approaches a compression ratio of 1011 but in an artificial circumstance devoid of any intelligence. The human compression of 1011 offers sustainable, real, life-long intelligence. Conclusion – These ‘intelligent’ machines may never be intelligent in a human sense (p339) A quantum leap, still cryptic within the unknown unknowns, may unleash intelligence in AI, in the future. We must continue to explore far and wide, emulate insects and think about the Octopus. “We can only see a short distance ahead, but we can see plenty there that needs to be done.” Convergence of tools (statistics, math) with data curation (noise vs signal) is replete with promise and profitability even if it lacks (human-level) intelligence. Page 7 ● I am an AI optimist. My article on AI (Agents: Where Artificial Intelligence Meets Natural Stupidity) is here http://dspace.mit.edu/handle/1721.1/41914 (2002) ▪ Dr Shoumen Palit Austin Datta, Research Affiliate, MIT Auto-ID Labs, Department of Mechanical Engineering, MIT ▪ Please explore website http://autoid.mit.edu Is Intelligence an Illusion in Artificial Intelligence? It’s better to keep your mouth closed and be thought a fool than to open your mouth and remove all doubt ● Twain

SOLUTION:
The Elusive Quest for Intelligence in Artificial Intelligence