The most basic level (where computational machines still do not appear, strictly, apart from as tools) is the level of the neurotransmitters, membrane phenomena and action potentials. Tools present in this level are Biochemistry and Biophysics. Then comes Biophysics of Neural codes and multiple codes, where this is a word used in neurophysiology to indicate multiplex. Then we move onto Biophysics and Signal Processing. We continue through sensorial codes, decodification in effec- tors - motor and glandular action - and the code of advanced peripheral neurons such as the ganglion cells in the retina. We are now in the realm of Signal Theory almost at the level of logic. Then, we have the neural net level, the interaction of input and output of the neurons themselves, and the coordination of the output -effectors. We are now at the level of the Language of Logic bordering on Symbolic Languages and, finally, we come to the central cortex neural code, the cooperative processes between masses of brain tissue, the extraction of Universals and the social processes of interaction between neuron masses. We are at the level of Symbolic language. The structure in levels is summarized in table 3. Upper square bounds the more classical formal tools of computational neuroscience. Lower square bounds techniques close to Artificial Intelligence tools.
Carotenoids are important in photosynthesis, and with the mimicking of this natural pro‐ cess, they have raised their importance due to the fundamental need for renewable energy sources such as artificial photosynthesis . There are other fields in which carotenoids are important as well, such as food or health. Fruits and vegetables are the principal sources of carotenoids and play an important role in diet due to vitamin A activity [5–6]. In addition to this, carotenoids are also important for antioxidant activity, intercellular communication, and immune system activity [6–8]. Epidemiological studies reported that the consumption of diets rich in carotenoids is associated with a lower incidence of cancer, cardiovascular diseases, age‐related macular degeneration, and cataract formation [9–10]. Deficiency of carotenoids results in clinical signs of conjunctiva and corneal aberrations, including xerophthalmia, night blindness, corneal ulceration, scarring, and resultant irreversible blindness .
Furthermore, the ratio of glial cells to neurons varies in different brain regions. In the cerebellum, for instance, there are almost five times more neurons than astrocytes. However, in the cortex, there are four times more glial cells than neurons [121,137]. All these data suggest that the more complex the task, performed, by either an animal or a brain region, the greater the number of glial cells involved. Currently, there are two projects aimed at implementing astrocytes in neuromorphic chips, one is BioRC developed by the University of Southern California [138–141] and the other project is carried out by the University of Tehran and University of Kermanshah, Iran [142–144]. Moreover, the RNASA-IMEDIR group from the University of A Coruña developed an Artificial Neuron-Glia Network (ANGN) incorporating two different types of processing elements: artificial neurons andartificial astrocytes. This extends classical ANN by incorporating recent findings and suppositions regarding the way information is processed via neural and astrocytic networks in the most evolved living organisms [145–149]. In our opinion, neurons are specialized in transmission and information processing, whereas glial cells in processing and modulation. Besides, glial cells play a key role in the establishment of synapses and neural architecture. That is why it would be interesting to combine these two types of elements in order to create a Deep Artificial Neuron–Astrocyte Network (DANAN). 5. Conclusions
A bi-factorial experimental design was considered to assess moisture variation of sweet potato-quinoa-kiwicha flakes (SP-Q-K) caused by the changes in the rotational speed and steam pressure of a rotary drum dryer (RDD). As it is a design with discrete variables, there is a limitation in the modeling and optimization thus techniques of Artificial Intelligence (AI): Artificial Neural Networks (ANN), Fuzzy Logic (FL) and Genetic Algorithms (GA), were applied, and their prediction ability evaluated. Due to the limitation of data for proper training, the ANN did not allow a correct prediction of the experimental data. Response Surface Methodology (RSM) was employed to obtain the relational equation among the experimental variables, which was used as the objective function with GA, and this allowed moisture optimization. Because of this, it is recommended to integrate RSM and GA into optimization studies. In this research the use of FL among variables, enabled us to get the best prediction adjustment of experimental values (R 2 = 0.99), with a mean absolute error of
Afortunadamente, hay quienes han asumido frontalmente la misión de explorar las complejidades del razonamiento probatorio en el derecho. De entre quienes cultivan este tópico en nuestra tradición jurídica podemos mencionar a Daniel González Lagier, a Marina Gascón, a Jordi Ferrer y, por supuesto, a quien me parece que ha llevado la batuta, es decir, a Michele Taruffo. Así mismo, desde una perspectiva más tecnológica orientada al diseño de sistemas computacionales capaces de proporcionar asistencia de diversa índole a la profesión jurídica, podemos mencionar, en términos generales, al novedoso y transdisciplinario campo de la Artificial Intelligence and Law (o AI and Law), y particularmente, a Floris Bex.
Las redes neuronales artificiales son un proyecto de emulación del sistema nervioso central, constituyen el paradigma conexionista de la inteligencia artificial (Cazorla, et al., 1999; y Caicedo y López, 2009). Una técnica de procesamiento de información, una aproximación a los procesos mentales, para solucionar una amplia gama de problemas complejos con datos iniciales de baja precisión, reconocimiento de patrones, predicción, codificación, gestión, clasificación, control y optimización… de modo colectivo para construir estructuras específicas (Briceño, 2004; Olmeda y Barba, 1993; Caicedo y López, 2009; y García, 2017). Es un modo de computación auto-programable, no lineal, distribuida y organizada por capas (Olmeda y Barba, 1993). Elementos simples de proceso interconectados en estado dinámico operando de forma paralela (Hecth, 1988; Briceño, 2004; y Gutiérrez, 2005). Cada red neuronal artificial posee un algoritmo de aprendizaje y la capacidad de aprender aparece de la actualización de los pesos numéricos de cada conexión mediante un proceso de entrenamiento, la interacción de ciclos de aprendizaje hasta alcanzar valores deseados a partir de ejemplos para generar sus propias reglas (Olmeda y Barba, 1993; Russell y Norvig, 1996; Mukesh, 1997; Briceño, 2004; Gutiérrez, 2005; y García, 2017).
The teaching method is focused on easing the learning of knowledge and increasing the student critical thinking on artificial intelligent methods. Teaching objectives require the active participation of the student. In addition, the in-class activity should be complemented by the individual student work performed out of class. Both aspects are taken into account in the evaluation method.
It hardly needs stating how multithreaded the fabric of human experience is. Almost every aspect of our collective or individual, cultural or biological lives (and upon scrutiny the precise boundaries of each of those demarcations cannot fail but to dissolve into a field of rich and fuzzy fractal blurriness) can be unspun into gripping narratives and be visited as a treasure house of insights for and into the mind. But just like the edifice of what being human is all about offers countless doors in but no Grand Unified Theory, the same restriction is in effect when dealing with one of the latest offshoots of our collective ingenuity: Artificial Intelligence. Speaking about what our thinking machines are, unavoidably requires talking in turn about what we are. (And when this is not done explicitly, tacit assumptions seriously risk muddling our conversations). In this piece, I have chosen games and play as an entry point for a discussion about the relationship between humankind and its machines which today show promise for eventual autonomous thought. I hope to show that games are not only a lavishly detailed telescope both into the development of our species and into the progressive evolution of our creations, but, more importantly for our present purposes, that they also afford us the chance to groundedly anticipate how our mutual interactions may continue to unfold. Games were a crucial stepping stone in the development of mechanical thinking systems and were it not for them, the state of advance that we see now and take for granted would in all likelihood have taken considerably longer to achieve. And if our machines learn to bypass us and play among themselves, what mind-boggling outcomes could we reasonably expect down the road? We are already seeing notable signs of the power of adversarial self-play for artificial virtual agents, with researchers at OpenAI having recently shown how emergent strategies of high complexity arise without direct human instruction in a simulated game of hide-and-seek; strategies some of which even the researchers themselves had not known were possible within their system (Baker et al., 2019). In what follows I shall chiefly focus on two possible outcomes for our relationships with thinking machines, mutual collaboration and merging or utter dependency (a third and darker possibility, annihilation and replacement is not mentioned at length here but is explored in [MYTHS]).
Este Postgrado en Inteligencia Artificial para Programadores le prepara para conocer las principales técnicas de Inteligencia Artificial y, para cada una de ellas, su inspiración, biológica, física o incluso matemática, así como los distintos conceptos y principios (sin entrar en detalles matemáticos), con ejemplos y gráficos para cada uno de ellos, aprender sobre los dominios de aplicación se ilustran mediante aplicaciones reales y actuales y diferenciar y observar un ejemplo de implementación genérico, que se completa con una aplicación práctica, desarrollada en C#; además le prepara para tener una visión amplia y precisa del entorno de la programación y el desarrollo de aplicaciones gracias a la adquisición de conocimientos sobre el lenguaje C#5 y el manejo de la herramienta Visual Studio 2013.
para lo que es usado. Una consecuencia de esta concepción es que el estatus metafísico de los artefactos técnicos, en la forma de una respuesta precisa a la cuestión de qué clase de artefacto sea, o si es o no un artefacto de una clase particular, es vaga o indeterminada cuando su uso no encaja con su diseño” (Frassen, M. (2008), “Design, Use, and the Physical and Intentional Aspects of Technical Artifacts” en Vermaas, P. E.; P. Kroes; A. Light; S. A. Moore (eds) (2008), Philosophy and Design. From Engineering to Architecture3$>]"=U también Frassen, M. (2006), “Normativity of the Artefacts”, Stud. Hist. Phil. Sci. 37, pp. 42-57).
• In 2006 Kuo et al  made an study dedicated to a novel two-stage method, which first uses a SOM neural network to determine the number of clusters as the starting point, and then uses genetic K-means algorithm to find the final solution. Papageorgiou et al  identified the FCM model problems and proposed to restructure the system through adjustment the weights of FCM interconnections using specific learning algorithms for FCMs. Two unsupervised learning algorithms are presented and compared for training FCMs and how they define, select or fine-tuning weights of the causal interconnections among concepts. The simulations results of training the process system verified the effectiveness, validity and advantageous characteristics of those learning techniques. Zhang & Zulkernine  worked in an anomaly detection for Network Intrusion Detection Systems (NIDS). Most anomaly based NIDSs employ supervised algorithms, whose performances highly depend on attack-free training data. However, this kind of training data is difficult to obtain in real world network environment that leads to high false positive rate of supervised NIDSs. The authors applied one data mining algorithms called random forests algorithm in anomaly based NIDSs. Without attack- free training data, random forests algorithm can detect outliers in data sets of network traffic.
The Artificial Intelligence Section felt that the use of a conventional language, such as C, would eliminate most of these problems, and initially looked to the expert system tool vendors to provide an expert system tool written using a conventional language. Although a number of tool vendors started converting their tools to run in C, the cost of each tool was still very high, most were restricted to a small variety of computers, and the projected availability times were discouraging. To meet all of its needs in a timely and cost effective manner, it became evident that the Artificial Intelligence Section would have to develop its own C based expert system tool. The prototype version of CLIPS was developed in the spring of 1985 in a little over two months. Particular attention was given to making the tool compatible with expert systems under development at that time by the Artificial Intelligence Section. Thus, the syntax of CLIPS was made to very closely resemble the syntax of a subset of the ART expert system tool developed by Inference Corporation. Although originally modelled from ART, CLIPS was developed entirely without assistance from Inference or access to the ART source code.
Abstract. This article intends to make an analysis about the Artificial Intelligence (AI) and the Knowledge Management (KM). Faced with the dualism mind and body how we be able to see it AI? It doesn’t intent to create identical copy of human being, but try to find the better form to represent all the knowledge contained in our minds. The society of the information lives a great paradox, at the same time that we have access to an innumerable amount of information, the capacity and the forms of its processing are very limited. In this context, institutions and centers of research devote themselves to the finding of ways to use to advantage the available data consistently. The interaction of the Knowledge Management with Artificial Intelligence makes possible the development of filtering tools and pre-analysis of the information that appear as a reply to the expectations to extract resulted optimized of databases and open and not-structuralized source, as the Internet.
Figure 6 shows the SUT measurements of snowfall events on bare targets at CARE (plastic), Sodankylӓ (artificial turf) and Col de Porte (natural ground). It appears that both the artificial plastic target (Figure 6a) and the artificial turf targets (Figure 6b) make a good reflective surface for sonic measurements as shown by the relatively low level of signal noise and the discernible increase in snow depth during the precipitation events. The natural target (Figure 6c) exhibits more noise than the other two targets, likely due to the presence of grass, but does not inhibit the sensor from registering a change in snow depth during the precipitation event. The target types, as evaluated for
Over the last years, most works on ACO and PSO have focused on the improvement of the basic algorithms. Among the most successful variants of ACO are the ‘Elitist Ant System’ , the ‘Rank-Based Ant System’ , the ‘Max–Min Ant System’ , and the ‘Hypercube Framework’ . Whilst in the beginning most ACO researchers were concerned with its application to engineering problems, such as the ones listed above, currently researchers in biomedics and bioinformatics have also gained interest in ACO [154,254]. Theoretical investigations into ACO include convergence analysis ; the comparison with other approaches, like optimal control  and gradient- based search ; and the hybridization with more classical artificial intelligence and operations research methods . Dorigo and Blum  list a number of theoretical open problems concerning the ACO algorithm, such as their relation with other probabilistic algorithms, their convergence speed, and the development of new algorithmic components based on solid theoretical foundations. Recent research into the particle swarm optimization algorithm has emphasized its application to multi-objective, dynamic, constrained and combinatorial optimization problems [33,52,214,287]; theoretical investigations, such as scalability issues, novel operators and convergence analysis [41, 136,288]; hybridization with other approaches [230,253]. The current trends and open problems of particle swarm optimization are similar to the ones for the other techniques, like the development of more robust constraint handling techniques, automatic tuning of parameters (or guidelines for a better choice), application to dynamic, multi-objective and combinatorial problems, convergence and scalability analysis, etc. [90,159,161].
Within this model, the systems failure comes not from the technology, but from the faulty interventions of its human operators. In the Tele-Garden, the first growing season was terminated by the over-watering of a single user who flooded the garden; in other instances, the garden would become overgrown without members banding together to cooperatively manage pruning, weeding and replanting across a larger area. In the contemporary incarnation of the Tele-Garden, the FarmBot automates many of these processes with “Sequences”, “Regimens”, and “Farmware”, including the use of image-recognition processes to detect weeds, which simplifies the production of coded cultivation sequences and management. Increasingly, the planetary scale of remote sensing and modelling projects and these small scale remote and robotic cultivation management processes are converging. At a planetary scale, Joppa, the Microsoft chief environment officer, presents a case that those judgments are also better removed from humans: “We need artificial intelligence to save us from ourselves”, he says, “My worry is AI won’t come soon enough 32 . More specifically, AI’s work will
In the Artificial Cognition field there are three main paradigms: Cognitivist ap- proach, based on symbolic systems and explicit learning mechanisms; Emergent ap- proach, ground-ed in sub-symbolic systems and implicit cognitive processes; and Hybrid approach, which integrates the best of both approaches. The latter, in spite of being a conciliatory outlook that intends both to compensate the weaknesses and to enhance the strengths of each approach, is a very recent research field whose results are still incipient. In this thesis, it is proposed a hybrid and original cognitive model, called M ET AFORA ´ , whose main contributions are: (1) the integration of multi-disciplinary theories about cognition – cognitive psychology, neurology, biology, artificial intelligence, etc.; (2) an architectonic design based on cybernetic building blocks, which adapts the best of the most representative cognitive models – a layered, modular, multi-component, decentral- ized and distributed design; (3) the fusion of the main features proposed by both cogni- tivist and emergent approaches, and the definition of the integration and synchronization mechanisms necessary to communicate these two levels; and (4) an innovative design which articulates three levels of self-organization, inspired on principles about evolu- tionary and developmental bio-logy from which diverse cognitive structures can emerge: epigenesis, ontogenesis, and phylogenesis.