Examples of artificial intelligence (AI) in pop culture usually involve a pack of intelligent robots hell-bent on overthrowing the human race, or at least a fancy theme park. Sentient machines with general artificial intelligence don't yet exist, and they likely won't exist anytime soon, so we're safe... for now. That's not to make light of AI's potential impact on our future. In a recent survey, more than 72% of Americans expressed worry about a future in which machines perform many human jobs. Additionally, tech billionaire Elon Musk, long an advocate for the regulation of artificial intelligence, recently called AI more dangerous than nukes. Whether we realize it or not, artificial intelligence is all around us and playing an active role in our daily lives. Every time we open our Facebook newsfeed, do a Google search, get a product recommendation from Amazon or book a trip online, AI is lurking in the background.

Despite recent advances in the field of explainable artificial intelligence systems, a concrete quantitative measure for evaluating the usability of such systems is nonexistent. Ensuring the success of an explanatory interface in interacting with users requires a cyclic, symbiotic relationship between human and artificial intelligence. We, therefore, propose explanatory efficacy, a novel metric for evaluating the strength of the cyclic relationship the interface exhibits. Furthermore, in a user study, we evaluated the perceived affect and workload and recorded the EEG signals of our participants as they interacted with our custom-built, iterative explanatory interface to build personalized recommendation systems. We found that systems for perceptually driven iterative tasks with greater explanatory efficacy are characterized by statistically significant hemispheric differences in neural signals, indicating the feasibility of neural correlates as a measure of explanatory efficacy. These findings are beneficial for researchers who aim to study the circular ecosystem of the human-artificial intelligence partnership. Keywords: Affect; Brain Lateralization; EEG; Explanatory Efficacy; Human-centric Explainable Artificial Intelligence; Interactive Explanation; Workload 1. Introduction Recent advances in artificial intelligence (AI) and machine learning algorithms have resulted in models that not only achieve high predictive performance but also provide explanatory features to support their decisions, increasing model interpretability and transparency in real-world environments [1]. However, merely providing explanations is insufficient. Ultimately, AI should address the problems hindering human-agent interaction. Much of the current work for human-interpretable machine learning systems suffers from a lack of usability and efficacy [2]. Developing such a feedback-based interface for AI systems requires an evaluation on the strength of the cyclic relationship the interface exhibits, which we define as explanatory efficacy . Failing to integrate user knowledge with machine systems can decrease interaction quality to the point of causing interaction breakdowns. Consequently, the systems will lose their ability to justify their recommendations, decisions, or actions, resulting in a loss of trust from their users.

With the increasing growth of social media, people have started relying heavily on the information shared therein to form opinions and make decisions. While such a reliance is motivation for a variety of parties to promote information, it also makes people vulnerable to exploitation by slander, misinformation, terroristic and predatorial advances. In this work, we aim to understand and detect such attempts at persuasion. Existing works on detecting persuasion in text make use of lexical features for detecting persuasive tactics, without taking advantage of the possible structures inherent in the tactics used. We formulate the task as a multi-class classification problem and propose an unsupervised, domain-independent machine learning framework for detecting the type of persuasion used in text, which exploits the inherent sentence structure present in the different persuasion tactics. Our work shows promising results as compared to existing work.

Where do I even begin this rant? I am a machine learning intern. We have a labelling problem in which we want to classify strings into category "Something" and category "Not Something". These are not sentences so we can't use any standard NLP library. My boss is convinced we should turn these strings into ASCII codes, in order to make them "non categorical", with each feature being the ASCII code for the character in question.

It's ironic, but artificial intelligence is on the cusp of harnessing the collective wisdom locked inside the human hive mind.

We've always pictured the rise of artificial intelligence as being the end of civilization, at least from watching movies like'The Terminator – Judgement Day'. We could not have imagined that something as insignificant as misinformation, would lead to the collapse of organisations; beginning wars and even mass suicides. The definition of what we regard as "Fake" news has a broad spectrum. Consider an article published in 2001, which was true at the time. That same article being published now, excluding the date… giving it an appearance of recently occurring events.

Wipro Limited (NYSE:WIT) has been recognized as a leader by global research and advisory firm Forrester Research Inc. in the latter's recently-published report, The Forrester Wave: AI Consultancies, Q3 2019. "AI and Intelligent Automation is changing the very fabric of society, business, and governance. It is disrupting processes, creating new business models and delivering exceptional customer experiences. One of the major hurdles in harnessing the power of AI by businesses is the challenge posed by change adoption and scaling AI culture to enterprise level. Using AI led Automation Advisory and Wipro HOLMES platform, we help enterprises overcome these challenges by creating outcome-based roadmaps, agile delivery, innovative service models and seamless customer experience. Our customers have spoken on our behalf in this report and we believe that being recognized as a Leader in the Forrester Wave is a testament to our AI and Automation transformation and consulting services capabilities."

We all consider ourselves experts on separating fact from fiction. Sharing disinformation on social media is something that happens to other people. Let's be honest, when we're cruising through our feeds, sharing or liking baby pictures and cat videos, we don't always have our critical thinking caps on, especially when confronted by emotional appeals. But how do we stop falling for and spreading bogus information? Disinformation is the deliberate spread of verifiably false or misleading content to gain financially or to deceive the public.