The fast pace of artificial intelligence (AI) and automation is propelling strategists to reshape their business models. This is fostering the integration of AI in the business processes but the consequences of this adoption are underexplored and need attention. This paper focuses on the overall impact of AI on businesses - from research, innovation, market deployment to future shifts in business models. To access this overall impact, we design a three-dimensional research model, based upon the Neo-Schumpeterian economics and its three forces viz. innovation, knowledge, and entrepreneurship. The first dimension deals with research and innovation in AI. In the second dimension, we explore the influence of AI on the global market and the strategic objectives of the businesses and finally, the third dimension examines how AI is shaping business contexts. Additionally, the paper explores AI implications on actors and its dark sides.

Meta-learning is a tool that allows us to build sample-efficient learning systems. Here we show that, once meta-trained, LSTM Meta-Learners aren't just faster learners than their sample-inefficient deep learning (DL) and reinforcement learning (RL) brethren, but that they actually pursue fundamentally different learning trajectories. We study their learning dynamics on three sets of structured tasks for which the corresponding learning dynamics of DL and RL systems have been previously described: linear regression (Saxe et al., 2013), nonlinear regression (Rahaman et al., 2018; Xu et al., 2018), and contextual bandits (Schaul et al., 2019). In each case, while sample-inefficient DL and RL Learners uncover the task structure in a staggered manner, meta-trained LSTM Meta-Learners uncover almost all task structure concurrently, congruent with the patterns expected from Bayes-optimal inference algorithms. This has implications for research areas wherever the learning behaviour itself is of interest, such as safety, curriculum design, and human-in-the-loop machine learning.

This survey samples from the ever-growing family of adaptive resonance theory (ART) neural network models used to perform the three primary machine learning modalities, namely, unsupervised, supervised and reinforcement learning. It comprises a representative list from classic to modern ART models, thereby painting a general picture of the architectures developed by researchers over the past 30 years. The learning dynamics of these ART models are briefly described, and their distinctive characteristics such as code representation, long-term memory and corresponding geometric interpretation are discussed. Useful engineering properties of ART (speed, configurability, explainability, parallelization and hardware implementation) are examined along with current challenges. Finally, a compilation of online software libraries is provided. It is expected that this overview will be helpful to new and seasoned ART researchers.

SpaceX is the undisputed rock star of the Space Coast's commercial launch boom. Their social media plays a role. Video posted Feb. 19, 2017 Rachael L Thomas, FLORIDA TODAY Space exploration is serious business. Missions cost tens of millions of dollars and frequently put human lives on the line. That's probably why rockets and spacecraft tend to have serious-sounding names.

Conversational agents, also known as chatbots, are versatile tools that have the potential of being used in dialogical argumentation. They could possibly be deployed in tasks such as persuasion for behaviour change (e.g. persuading people to eat more fruit, to take regular exercise, etc.) However, to achieve this, there is a need to develop methods for acquiring appropriate arguments and counterargument that reflect both sides of the discussion. For instance, to persuade someone to do regular exercise, the chatbot needs to know counterarguments that the user might have for not doing exercise. To address this need, we present methods for acquiring arguments and counterarguments, and importantly, meta-level information that can be useful for deciding when arguments can be used during an argumentation dialogue. We evaluate these methods in studies with participants and show how harnessing these methods in a chatbot can make it more persuasive.

The only systematic mapping of the HLAI technical landscape was conducted at a workshop in 2009 [Adams et al., 2012]. However, the results from it were not what organizers had hoped for [Goertzel 2014, 2016], merely just a series of milestones, up to 50% of which could be argued to have been completed already. We consider two more recent articles outlining paths to human-like intelligence [Mikolov et al., 2016; Lake et al., 2017]. These offer technical and more refined assessments of the requirements for HLAI rather than just milestones. While useful, they also have limitations. To address these limitations we propose the use of alternative techniques for an updated systematic mapping of the paths to HLAI. The newly proposed alternative techniques can model complex paths of future technologies using intricate directed graphs. Specifically, there are two classes of alternative techniques that we consider: scenario mapping methods and techniques for eliciting expert opinion through digital platforms and crowdsourcing. We assess the viability and utility of both the previous and alternative techniques, finding that the proposed alternative techniques could be very beneficial in advancing the existing body of knowledge on the plausible frameworks for creating HLAI. In conclusion, we encourage discussion and debate to initiate efforts to use these proposed techniques for mapping paths to HLAI.

Domain classification is the task of mapping spoken language utterances to one of the natural language understanding domains in intelligent personal digital assistants (IPDAs). This is a major component in mainstream IPDAs in industry. Apart from official domains, thousands of third-party domains are also created by external developers to enhance the capability of IPDAs. As more domains are developed rapidly, the question of how to continuously accommodate the new domains still remains challenging. Moreover, existing continual learning approaches do not address the problem of incorporating personalized information dynamically for better domain classification. In this paper, we propose CoNDA, a neural network based approach for domain classification that supports incremental learning of new classes. Empirical evaluation shows that CoNDA achieves high accuracy and outperforms baselines by a large margin on both incrementally added new domains and existing domains.

The problem at the heart of this tutorial consists in modeling the path choice behavior of network users. This problem has extensively been studied in transportation science and econometrics, where it is known as the route choice problem. In this literature, individuals' choice of paths are typically predicted from discrete choice models. The aim of this tutorial is to present this problem from the novel and more general perspective of inverse optimization, in order to describe the modeling approaches proposed in related research areas and thereby motivate the use of so-called recursive models. The latter have the advantage of predicting path choices without generating choice sets. In this paper, we contextualize discrete choice models as a probabilistic approach to an inverse shortest path problem with noisy data, highlighting that recursive discrete choice models in particular originate from viewing the inner shortest path problem as a parametric Markov Decision Process. We also illustrate through simple numerical examples that recursive models overcome issues associated with the path-based discrete choice models commonly found in the transportation literature.

Reinforcement learning (RL) is about sequential decision making and is traditionally opposed to supervised learning (SL) and unsupervised learning (USL). In RL, given the current state, the agent makes a decision that may influence the next state as opposed to SL (and USL) where, the next state remains the same, regardless of the decisions taken, either in batch or online learning. Although this difference is fundamental between SL and RL, there are connections that have been overlooked. In particular, we prove in this paper that gradient policy method can be cast as a supervised learning problem where true label are replaced with discounted rewards. We provide a new proof of policy gradient methods (PGM) that emphasizes the tight link with the cross entropy and supervised learning. We provide a simple experiment where we interchange label and pseudo rewards. We conclude that other relationships with SL could be made if we modify the reward functions wisely.

While in recent years machine learning (ML) based approaches have been the popular approach in developing end-to-end question answering systems, such systems often struggle when additional knowledge is needed to correctly answer the questions. Proposed alternatives involve translating the question and the natural language text to a logical representation and then use logical reasoning. However, this alternative falters when the size of the text gets bigger. To address this we propose an approach that does logical reasoning over premises written in natural language text. The proposed method uses recent features of Answer Set Programming (ASP) to call external NLP modules (which may be based on ML) which perform simple textual entailment. To test our approach we develop a corpus based on the life cycle questions and showed that Our system achieves up to $18\%$ performance gain when compared to standard MCQ solvers.