The tension between deduction and induction is perhaps the most fundamental issue in areas such as philosophy, cognition and artificial intelligence (AI). The deduction camp concerns itself with questions about the expressiveness of formal languages for capturing knowledge about the world, together with proof systems for reasoning from such knowledge bases. The learning camp attempts to generalize from examples about partial descriptions about the world. In AI, historically, these camps have loosely divided the development of the field, but advances in cross-over areas such as statistical relational learning, neuro-symbolic systems, and high-level control have illustrated that the dichotomy is not very constructive, and perhaps even ill-formed. In this article, we survey work that provides further evidence for the connections between logic and learning. Our narrative is structured in terms of three strands: logic versus learning, machine learning for logic, and logic for machine learning, but naturally, there is considerable overlap. We place an emphasis on the following "sore" point: there is a common misconception that logic is for discrete properties, whereas probability theory and machine learning, more generally, is for continuous properties. We report on results that challenge this view on the limitations of logic, and expose the role that logic can play for learning in infinite domains.

With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems. A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today's increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems constantly arise. In this work, we systematically review the literature on explanations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advice-giving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objective, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated.

Malware classification is a difficult problem, to which machine learning methods have been applied for decades. Yet progress has often been slow, in part due to a number of unique difficulties with the task that occur through all stages of the developing a machine learning system: data collection, labeling, feature creation and selection, model selection, and evaluation. In this survey we will review a number of the current methods and challenges related to malware classification, including data collection, feature extraction, and model construction, and evaluation. Our discussion will include thoughts on the constraints that must be considered for machine learning based solutions in this domain, and yet to be tackled problems for which machine learning could also provide a solution. This survey aims to be useful both to cybersecurity practitioners who wish to learn more about how machine learning can be applied to the malware problem, and to give data scientists the necessary background into the challenges in this uniquely complicated space.

We propose a filtering feature selection framework that considers subsets of features as paths in a graph, where a node is a feature and an edge indicates pairwise (customizable) relations among features, dealing with relevance and redundancy principles. By two different interpretations (exploiting properties of power series of matrices and relying on Markov chains fundamentals) we can evaluate the values of paths (i.e., feature subsets) of arbitrary lengths, eventually go to infinite, from which we dub our framework Infinite Feature Selection (Inf-FS). Going to infinite allows to constrain the computational complexity of the selection process, and to rank the features in an elegant way, that is, considering the value of any path (subset) containing a particular feature. We also propose a simple unsupervised strategy to cut the ranking, so providing the subset of features to keep. In the experiments, we analyze diverse settings with heterogeneous features, for a total of 11 benchmarks, comparing against 18 widely-known comparative approaches. The results show that Inf-FS behaves better in almost any situation, that is, when the number of features to keep are fixed a priori, or when the decision of the subset cardinality is part of the process.

You'll even discover how to use artificial neural networks and deep learning structures for classification! With such a rigorous grounding in so many topics, you will be an unbeatable data scientist by the end of the course.

Choosing what they will work on in the future is a difficult decision that adolescents often make considering their skills and the needs of a job market that in a matter of 5 years can be radically transformed. Beyond the aptitude tests, a Colombian venture states that using artificial intelligence it is not only possible to find out what someone will be good at, but also to bring them closer to their life purpose. That ambitious mission is the dream of Life Design, a professional guidance software that is postulated as a tool of self-knowledge through technology. The company, which started in January 2018, closed the first round of investment of $ 150,000 and has worked with 2,000 active users since October last year. According to Felipe Rojas, one of the co-founders, unlike traditional methods, Life Design's philosophy is not to create only the professionals that the industry needs, but to identify what the student is interested in, their tastes, preferences and skills and then connect them.

Data science, Artificial Intelligence (AI) and Machine Learning (ML), since last five to six years these phrases have made their places in Gartner's hype cycle curve. Gradually they have crossed the peak and moving toward the plateau. The curve also has few related terms such as Deep Neural Network, Cognitive AutoML etc. This shows that, there is an emerging technology trend around AI/ML which is going to prevail over the software industry during the coming years. Few of their predecessors such as Business Intelligence, Data Mining and Data Warehousing were there even before these years.

UC Berkeley AI researchers are using an iPhone X and Apple's ARKit to train a robotic arm how to grasp an object. ARKit creates point clouds from data generated by moving an RGB camera around an object for two minutes. Robotic grasping is a particular robotics subfield focused on the challenge of teaching a robot to pick up, move, manipulate, or grasp an object. The Dexterity Network, or Dex-Net, research project at UC Berkeley's Autolab dates back to 2017 and includes open source training data sets and pretrained models for robotic grasping in an ecommerce bin-picking scenario. The ability for robots to quickly learn how to grasp objects has a big impact on how automated warehouses like Amazon fulfillment centers can become.

Made your mind towards machine learning but are confused so much that where to get started? I faced the same confusion that what should be a good start? Should I learn Python or go for R? Mathematics was always a scary part for me, and I was always worried that from where should I learn math? I was also worried about how I should get a strong basis for Machine Learning. Anyway, you should be congratulated that at least you have made your mind.

At the Faculty of Social Sciences, Department of Business and Management, a PhD scholarship in Economic Complexity and Emerging Ecosystems of AI Technologies is open for appointment on 1 September 2020 or as soon as possible. The capability to fast and efficiently select and acquire relevant knowledge, and transfer insights and applications into new fields of economic activity is critical in times of rapid technological change, where new technological opportunities spring up frequently. In the advent of potential general-purpose-technologies (GPT), these capabilities provide opportunities for firms to disrupt established industries, and for countries to trigger changes in industrial and technological leadership. Machine learning and artificial intelligence (ML&AI) are generally perceived as a likely candidate for a GTP that will revolutionize the economy. Consequently, many developed and emerging economies have entered the race for leadership in the field of AI, leading to numerous national and supranational strategic initiatives to boost the development and application of AI technologies.