"The addition of these faculty is expanding our interdisciplinary cohort of AI researchers across the entire campus. We expect these four outstanding faculty members are the first wave of hires who will increase our capabilities for AI and machine learning research across all five of Rensselaer's schools," said James Hendler, director of the AIRC, and a Rensselaer Tetherless World Professor of Computer, Web, and Cognitive Science. The Rensselaer-IBM AIRC is dedicated to advancing the science of artificial intelligence and enabling the use of AI and machine learning in research investigations, innovations, and applications of joint interest to both Rensselaer and IBM. The collaboration fosters the growth of AI and machine learning capabilities through faculty hires, by funding specific research initiatives, and through funding top graduate students as IBM AI Horizons fellows. For more information about the AIRC, watch this video.

January 25, 2020 - The AI revolution is upon us. How much is Croatia lagging behind, and are we going to do something about it? But even if we start those processes, where would we be in comparison to the rest of the world? What are other countries already doing and what should we be aware of? Fortunately, a fear of missing out is spreading around the globe or at least among some countries.

Python is a very popular multi-paradigm programming language. Object-oriented programming and structured programming are fully supported in this, and many of its features support functional programming and aspect-oriented programming – for that matter. This easy-to-understand course aims to teach everyone the basics of Python Language, learning outcomes, benefits of learning Python, advantages of Python etc. You will learn where to use Python Language and know about who would actually use it in their daily office lives. You will also learn the comparative parameters of python with other programming languages, in the world – with a highlight on popularity and frameworks of Python.

Artificial intelligence (AI) has already had a profound impact on business and society. Applied AI and machine learning (ML) are creating safer workplaces, more accurate health diagnoses and better access to information for global citizens. The Fourth Industrial Revolution will represent a new era of partnership between humans and AI, with potentially positive global impact. AI advancements can help society solve problems of income inequality and food insecurity to create a more "inclusive, human-centred future" according to the World Economic Forum (WEF). There is nearly limitless potential to AI innovation, which is both positive and frightening.

Artificial Intelligence seems to be everywhere. Experts claim AI is the Next Industrial Revolution bringing in changes as Electricity did decades back. With every massive change it's important that children are provided the right education to become ready. At WiselyWise we have been researching and working on the right Curriculum for School students. We wanted to ensure the content and intensity is appropriate for different age groups.

Many companies have turned to artificial intelligence to lead job candidacy searches and cherry pick job applicants… Welcome to the'Wild West of Hiring.' Pixabay The new job search question to ask yourself: What if AI doesn't like me? This is the obstacle now faced by college graduates hoping to land their first dream job. Yes, the human resources departments at companies are steering further away from humans and embracing artificial intelligence in their job candidacy searches. SEE ALSO: Can Artificial Intelligence Determine If You Have a Toxic Workplace? According to CNN, career counselors at bigwig schools, such as Duke University, Purdue University and the University of North Carolina at Charlotte, are priming students on what companies use AI--and how to outfox the algorithms. Gone are such job interview preparations as mentioned in this old-timey video from the archaic days of 2010.

How can societies learn to enforce and comply with social norms? Here we investigate the learning dynamics and emergence of compliance and enforcement of social norms in a foraging game, implemented in a multi-agent reinforcement learning setting. In this spatiotemporally extended game, individuals are incentivized to implement complex berry-foraging policies and punish transgressions against social taboos covering specific berry types. We show that agents benefit when eating poisonous berries is taboo, meaning the behavior is punished by other agents, as this helps overcome a credit-assignment problem in discovering delayed health effects. Critically, however, we also show that introducing an additional taboo, which results in punishment for eating a harmless berry, improves the rate and stability with which agents learn to punish taboo violations and comply with taboos. Counterintuitively, our results show that an arbitrary taboo (a "silly rule") can enhance social learning dynamics and achieve better outcomes in the middle stages of learning. We discuss the results in the context of studying normativity as a group-level emergent phenomenon.

Feature Structures (FSs) are a widespread tool used for decompositional frameworks of Attribute-Value associations. Even though they thrive in simple systems, they lack a way of representing higher-order entities and relations. This is however needed in Frame Semantics, where semantic dependencies should be able to connect groups of individuals and their properties, especially to model quantification. To answer this issue, this master report introduces wrappings as a way to envelop a sub-FS and treat it as a node. Following the work of [Kallmeyer, Osswald 2013], we extend its syntax, semantics and some properties (translation to FOL, subsumption, unification). We can then expand the proposed pipeline. Lexical minimal model sets are generated from formulas. They unify by FS value equations obtained by LTAG parsing to an underspecified sentence representation. The syntactic approach of quantifiers allows us to use existing methods to produce any possible reading. Finally, we give a transcription to type-logical formulas to interact with the context in the view of dynamic semantics. Supported by ideas of Frame Types, this system provides a workable and tractable tool for higher-order relations with FS.

Over the past year, the emergence of transfer learning with large-scale language models (LM) has led to dramatic performance improvements across a broad range of natural language understanding tasks. However, the size and memory footprint of these large LMs makes them difficult to deploy in many scenarios (e.g. on mobile phones). Recent research points to knowledge distillation as a potential solution, showing that when training data for a given task is abundant, it is possible to distill a large (teacher) LM into a small task-specific (student) network with minimal loss of performance. However, when such data is scarce, there remains a significant performance gap between large pretrained LMs and smaller task-specific models, even when training via distillation. In this paper, we bridge this gap with a novel training approach, called generation-distillation, that leverages large finetuned LMs in two ways: (1) to generate new (unlabeled) training examples, and (2) to distill their knowledge into a small network using these examples. Across three low-resource text classification datsets, we achieve comparable performance to BERT while using 300 fewer parameters, and we outperform prior approaches to distillation for text classification while using 3 fewer parameters. 1 Introduction Over the past year, rapid progress in unsupervised language representation learning has led to the development of increasingly powerful and gener-alizable language models (Radford et al., 2019; Devlin et al., 2018).

We consider the problem of inferring the input and hidden variables of a stochastic multi-layer neural network from an observation of the output. The hidden variables in each layer are represented as matrices. This problem applies to signal recovery via deep generative prior models, multi-task and mixed regression and learning certain classes of two-layer neural networks. A unified approximation algorithm for both MAP and MMSE inference is proposed by extending a recently-developed Multi-Layer Vector Approximate Message Passing (ML-VAMP) algorithm to handle matrix-valued unknowns. It is shown that the performance of the proposed Multi-Layer Matrix VAMP (ML-Mat-VAMP) algorithm can be exactly predicted in a certain random large-system limit, where the dimensions $N\times d$ of the unknown quantities grow as $N\rightarrow\infty$ with $d$ fixed. In the two-layer neural-network learning problem, this scaling corresponds to the case where the number of input features and training samples grow to infinity but the number of hidden nodes stays fixed. The analysis enables a precise prediction of the parameter and test error of the learning.