Artificial Intelligence

A long paper of mine on the relation between cognition and communication in both phylogenic and psychogenetic perspectives has just been published in the journal Psychology Research in New York. This study is the deep investment of the most advanced researches in archaeology and the psychology of communication into psychomechanics within the fast evolving environment of language processing machines and Artificial Intelligence. Linguistics is becoming the key science to any serious development in these fields. The paper is in open-access on the publisher's site.

AI and the Environment are partners in caring for the planet.  Tierney Thys, a world renowned oceanographer, educator, speaks about technology's role in caring for the ocean and remarks on the spirit of early pioneering efforts of Dr. Cindy Mason and colleagues as they held the first gathering of scientists using AI to help solve environmental problems like sewage treatment, water levels and quality, storm and weather prediction, data archiving and analysis, nuclear test ban treat verification, forest management, and more.

The malware threat landscape is constantly evolving, with upwards of one million new variants being released every day. Bad actors have embraced automation, and current malware analysis systems cannot keep up with the ever-increasing load of malware being created daily. Additionally, some static analysis of malware can be computationally expensive, and not all static analysis should be considered for every sample that is part of a large malware dataset. As a result, highly expressive and inexpensive characterizations of malicious code, coupled with low resource machine learning classification platforms are required. In this paper, we use deep learning to build a meta-model that finds the simplest classifiers to characterize and assign malware into their corresponding families. Using static analysis of malware, we generate descriptive features to be used in conjunction with deep learning, in order to predict malware families. Our meta-model can determine when simple and less expensive malware characterization will suffice to accurately classify malicious executables or when more computationally expensive descriptions are required. Finally, our meta-model is able to predict the simplest features and models to classify malware with an accuracy of up to 90%.

IBM researchers are applying deep learning to discover ways to overcome some of the technical challenges that AI can face when analyzing X-rays and other medical images. [27] Now, a team of A*STAR researchers and colleagues has developed a detector that can successfully pick out where human actions will occur in videos, in almost real-time. [26] A team of researchers affiliated with several institutions in Germany and the U.S. has developed a deep learning algorithm that can be used for motion capture of animals of any kind. [25] In 2016, when we inaugurated our new IBM Research lab in Johannesburg, we took on this challenge and are reporting our first promising results at Health Day at the KDD Data Science Conference in London this month. [24] The research group took advantage of a system at SLAC's Stanford Synchrotron Radiation Lightsource (SSRL) that combines machine learning—a form of artificial intelligence where computer algorithms glean knowledge from enormous amounts of data—with experiments that quickly make and screen hundreds of sample materials at a time. [23] Researchers at the UCLA Samueli School of Engineering have demonstrated that deep learning, a powerful form of artificial intelligence, can discern and enhance microscopic details in photos taken by smartphones. [22] Such are the big questions behind one of the new projects underway at the MIT-IBM Watson AI Laboratory, a collaboration for research on the frontiers of artificial intelligence. [21]

8th International Conference on Computer Science, Engineering and Applications (ICCSEA 2018) Will Provide An Excellent International Forum For Sharing Knowledge And Results In Theory, Methodology And Applications Of Computer Science, Engineering And Applications. The Conference Looks For Significant Contributions To All Major Fields Of The Computer Science And Information Technology In Theoretical And Practical Aspects. The Aim Of The Conference Is To Provide A Platform To The Researchers And Practitioners From Both Academia As Well As Industry To Meet And Share Cutting-Edge Development In The Field.

The essay critically investigates the media-political dimension of modern AI technology. Rather than examining the political aspects of certain AI-driven applications, the main focus of the paper is centred around the political implications of AI’s technological infrastructure, especially with regard to the machine learning approach that since around 2006 has been called Deep Learning (also known as the simulation of Artificial Neural Networks). First, the paper discusses in how far Deep Learning is a fundamentally opaque black box technology, only partially accessible to human understanding. Second, and in relation to the first question, it takes a critical look at the agenda and activities of the research company OpenAI that supposedly aims to promote the democratization of AI and tries to make technologies like Deep Learning more accessible and transparent.

ABSTRACT The characterization of the influence of lexicon and syntax on the pitch contour in the Italian language is presented. The results of the work will be used in a Speech Understanding System for defining some constraints in the emission of hypotheses and for evaluating the likelihood that a synctatic structure can generate a given pattern of fundamental frequency. The pitch evolution is only determined in voiced tracts; then it is subdivided in a sequence of primitives and translated in symbols. Such strings of symbols are considered as sentences of a set regular languages, whose productions are learned using a stochastic inference procedure. Some results obtained from controlled experiments in which various factors are varied separately are presented.

Skin colour detection is frequently been used for searching people, face detection, pornographic filtering and hand tracking. The presence of skin or non-skin in digital image can be determined by manipulating pixels' colour and/or pixels' texture. The main problem in skin colour detection is to represent the skin colour distribution model that is invariant or least sensitive to changes in illumination condition. Another problem comes from the fact that many objects in the real world may possess almost similar skin-tone colour such as wood, leather, skin-coloured clothing, hair and sand. Moreover, skin colour is different between races and can be different from a person to another, even with people of the same ethnicity. Finally, skin colour will appear a little different when different types of camera are used to capture the object or scene. The objective in this study is to develop a skin colour classifier based on pixel-based using RGB ratio model. The RGB ratio model is a newly proposed method that belongs under the category of an explicitly defined skin region model. This skin classifier was tested with SIdb dataset and two benchmark datasets; UChile and TDSD datasets to measure classifier performance. The performance of skin classifier was measured based on true positive (TF) and false positive (FP) indicator. This newly proposed model was compared with Kovac, Saleh and Swift models. The experimental results showed that the RGB ratio model outperformed all the other models in term of detection rate. The RGB ratio model is able to reduce FP detection that caused by reddish objects colour as well as be able to detect darkened skin and skin covered by shadow.

Researchers find AI-generated reviews and comments pose a significant threat to consumers, but machine learning can help detect the fakes. [28] Following the old saying that "knowledge is power", companies are seeking to infer increasingly intimate properties about their customers as a way to gain an edge over their competitors. [27] Researchers from Human Longevity, Inc. (HLI) have published a study in which individual faces and other physical traits were predicted using whole genome sequencing data and machine learning. [26] Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology. [25] Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning—and it has already beaten humans at a complex image comprehension test. [24] A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19] Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. [18]

Scientists at the Allen Institute have used machine learning to train computers to see parts of the cell the human eye cannot easily distinguish. [21] Small angle X-ray scattering (SAXS) is one of a number of biophysical techniques used for determining the structural characteristics of biomolecules. [20] A deep neural network running on an ordinary desktop computer is interpreting highly technical data related to national security as well as—and sometimes better than— today's best automated methods or even human experts. [19] Scientists at the National Center for Supercomputing Applications (NCSA), located at the University of Illinois at Urbana-Champaign, have pioneered the use of GPU-accelerated deep learning for rapid detection and characterization of gravitational waves. [18] Researchers from Queen Mary University of London have developed a mathematical model for the emergence of innovations. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12]

The application of algorithms to decision making processes both within the public governance and administrative sectors, specifically targeted toward serving policy goals is increasing. Computer algorithms and analytics are playing an increasingly influential role in government, business and society. They underpin popular information services and autonomous intelligence systems such as machine learning and artificial intelligence applications and all its subsets: Machine Learning, Deep Learning and Reinforcement Learning Powered by algorithms, these smart technologies are having a direct and significant impact on human lives across a wide socioeconomic and cultural spectrum. Algorithms allow the exploitation of rich and varied data sources from different spheres including cultures, in order to support human decision-making and/or take direct actions that could serve the diverse interests of the societies in which they operate. As a result, this has invariably led to a standardized, speedy, efficient and comprehensive system of public decision making and implementation. Nevertheless, there are growing concerns regarding the social, ethical, political and legal implications of this and as to whether these implications are in fact being produced or reinforced by these systems. As gatekeepers of consequences in delegated administrative decisions including policing, there are rising concerns as to who is liable when decisions are made that violate cultural and societal norms in a given setting. Such cases are prompting the formation of multidisciplinary teams, for example, the IEEE P7003 Working Group on Algorithm Bias to rethink old ethical and moral questions, such as what constitutes cultural fairness and social equality and whether such definitions change when they intersect with algorithmic culture. Asking such questions is necessary since varying degrees of bias and exclusion, depending on the context, may emerge in the way that algorithms determine knowledge and present them in ways that are both digestible and consumable by users. For instance, it is evident that the employment of algorithms used to categorize and present knowledge, depending on the context, results in not only engendering hegemonic assumptions in a given culture but causes these assumptions to become coded as the default. The identification of this phenomenon has led international multidisciplinary teams to work on research and to translate their findings into evidence based tutorials, guidelines and standards. The aim is to eliminate or to reduce the probability of such algorithms and autonomous intelligence systems making decisions that are biased or that are not sensitive to the diverse cultures that they are meant to serve. This paper proposes that achieving cultural parity in algorithm design and application could benefit from three major routes: Firstly, through the technological route, designers can direct algorithm and associated AI's outputs by way of hard coded instructions and constraints for fairness and nondiscrimination, this will open up opportunities for affirmative action equivalents and paying attention to the 'translation' problems that arise when policy goals need to be converted into computer code. Secondly, governments can institute legal

We prove the eventological H-theorem that complements the Boltzmann H-theorem from statistical mechanics [1] and serves as a mathematical excuse (mathematically no less convincing than the Boltzmann H-theorem for the second law of thermodynamics) for what can be called the second law of eventology, which justies the application of Gibbs and anti-Gibbs distributions [2] of sets of events minimizing relative entropy, as statistical models of the behavior of a rational subject, striving for an equilibrium eventological choice between perception and activity in various spheres of her/his co-being.

Workflow management systems allow users to prepare, produce and analyze scientific processes to help simplify complex simulations. [27] Now, a team of A*STAR researchers and colleagues has developed a detector that can successfully pick out where human actions will occur in videos, in almost real-time. [26] A team of researchers affiliated with several institutions in Germany and the U.S. has developed a deep learning algorithm that can be used for motion capture of animals of any kind. [25] In 2016, when we inaugurated our new IBM Research lab in Johannesburg, we took on this challenge and are reporting our first promising results at Health Day at the KDD Data Science Conference in London this month. [24] The research group took advantage of a system at SLAC's Stanford Synchrotron Radiation Lightsource (SSRL) that combines machine learning—a form of artificial intelligence where computer algorithms glean knowledge from enormous amounts of data—with experiments that quickly make and screen hundreds of sample materials at a time. [23] Researchers at the UCLA Samueli School of Engineering have demonstrated that deep learning, a powerful form of artificial intelligence, can discern and enhance microscopic details in photos taken by smartphones. [22] Such are the big questions behind one of the new projects underway at the MIT-IBM Watson AI Laboratory, a collaboration for research on the frontiers of artificial intelligence. [21] The possibility of cognitive nuclear-spin processing came to Fisher in part through studies performed in the 1980s that reported a remarkable lithium isotope dependence on the behavior of mother rats. [20] And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience Society (CNS), cognitive neuroscientists increasingly are using those emerging artificial networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18]