Communication in today's times has taken a whole new meaning with technological innovations becoming a more central part of our social routine. "Chatting" or texting via messenger apps is the preferred way of staying in touch -- professionally and socially. Chat apps are evolving at a frenzied rate and becoming more human. Incorporation of AI elements in a messaging app to help ease conversations is the obvious next step, in a time when AI is transforming many industries and businesses around the world. OLPORTAL has created an ecosystem where AI neurobots can be developed that can help in messaging.

In the early 1980s, the psychologist Harry Heft put a 16 mm camera in the back of a sports car and made a movie. It consisted of a continuous shot of a residential neighborhood in Granville, Ohio, where Heft was a professor at Denison University. It didn't have a plot or actors, but it did have a simple narrative: The car started moving at 5 miles per hour and made nine turns from one street to another and then came to a stop after traveling just under a mile. One showed just the vistas along the route, the expansive layout of environmental features, such as a group of houses or trees seen from a distance. The second film showed the transitions of the route, the parts between each vista where the view is occluded by, say, a turn in the road or the crest of a hill.

Machine learning can tackle these challenges in addition to handling the sheer amount of data.

Music that is generated by recurrent neural networks often lacks a sense of direction and coherence. We therefore propose a two-stage LSTM-based model for lead sheet generation, in which the harmonic and rhythmic templates of the song are produced first, after which, in a second stage, a sequence of melody notes is generated conditioned on these templates. A subjective listening test shows that our approach outperforms the baselines and increases perceived musical coherence.

Question and answer generation is a data augmentation method that aims to improve question answering (QA) models given the limited amount of human labeled data. However, a considerable gap remains between synthetic and human-generated question-answer pairs. This work aims to narrow this gap by taking advantage of large language models and explores several factors such as model size, quality of pretrained models, scale of data synthesized, and algorithmic choices. On the SQuAD1.1 question answering task, we achieve higher accuracy using solely synthetic questions and answers than when using the SQuAD1.1 training set questions alone. Removing access to real Wikipedia data, we synthesize questions and answers from a synthetic corpus generated by an 8.3 billion parameter GPT-2 model. With no access to human supervision and only access to other models, we are able to train state of the art question answering networks on entirely model-generated data that achieve 88.4 Exact Match (EM) and 93.9 F1 score on the SQuAD1.1 dev set. We further apply our methodology to SQuAD2.0 and show a 2.8 absolute gain on EM score compared to prior work using synthetic data.

In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will lack a well-defined goal. Aside from providing a clearer objective for XAI, focusing on understanding also allows us to relax the factivity condition on explanation, which is impossible to fulfill in many machine learning models, and to focus instead on the pragmatic conditions that determine the best fit between a model and the methods and devices deployed to understand it. After an examination of the different types of understanding discussed in the philosophical and psychological literature, I conclude that interpretative or approximation models not only provide the best way to achieve the objectual understanding of a machine learning model, but are also a necessary condition to achieve post-hoc interpretability. This conclusion is partly based on the shortcomings of the purely functionalist approach to post-hoc interpretability that seems to be predominant in most recent literature.

In the past, KDnuggets has covered collections of key terms, including those for machine learning, deep learning, big data, natural language processing, and more. As we get into a new year, and as we have not published any collections of key terms in the recent past, we thought it would be a good idea to highlight some AI, data science, and machine learning terms that we should all now be familiar with in the constantly evolving landscape. As such, these terms are a combination of some more recently-emerging concepts, as well as existing concepts which may be of perceived increased importance of late. The definitions for these are a combined effort from the KDnuggets team, including Gregory Piatetsky, Asel Mendis, Matthew Dearing, and myself, Matthew Mayo. And so without any further ado, here are the first 10 terms you need to know, with the second 10 coming next week, giving us a total of 20 terms to know for 2020. Automated machine learning (AutoML) spans the fairly wide chasm of tasks which could reasonably be thought of as being included within a machine learning pipeline.

Gabriel is a professional transcriber, and for years he earned a middle-class living. He'd sit at his desk, "knock it out" for hours using custom keystrokes, and watch the money roll in. "I sent my son to private schools and university on transcribing," he tells me. "It was a nice life." But in the past decade, the bottom fell out. As audio recordings went digital and broadband spread, clients could ship work to India and the Philippines.

Over the past decade, we have witnessed notable breakthroughs in Artificial Intelligence (AI), thanks in large part to the development of deep learning approaches. Healthcare, finance, human resources, retail, there is no field in which AI has not proven to be a game-changer. Who would have said just a few years ago that there would be autonomous vehicles on public roads, that large-scale facial recognition would no longer be science fiction, or that fake news could have such an impact socially, economically, and politically? Some statistics related to AI are dizzying. According to Forbes, 75 countries are currently using AI technology for surveillance purposes via smart city platforms, facial recognition systems, and smart policing.