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.

This article is an excerpt from the Pearson Addison-Wesley book "Pragmatic AI" by Noah Gift. Reprinted here with permission from Pearson and 2019. What do Russian trolls, Facebook, and US elections have to do with machine learning? Recommendation engines are at the heart of the central feedback loop of social networks and the user-generated content (UGC) they create. Users join the network and are recommended users and content with which to engage. Recommendation engines can be gamed because they amplify the effects of thought bubbles.

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.

AI presents incredible opportunities for organizations to change the way they do business. With 1,000 researchers--including winners of the Turing Award and Fields Medal--in 11 labs, Microsoft has established itself as a leader in AI through its dogged focus on innovation, empowerment, and ethics. Now, the groundbreaking capabilities of AI can move beyond the lab to make a positive impact on every enterprise, every industry. Manufacturers have accrued vast stores of data through digital technologies and IoT--offering new windows into operational efficiency and business performance. Culling that data for actionable insights on predictive maintenance and performance improvements has become increasingly essential for an industry that operates on thin margins.

VANCOUVER, BC / ACCESSWIRE / December 12, 2019 / Dimension Five Technologies Inc. (CSE:DFT) (the "Company"), is pleased to announce that it has entered into a share exchange agreement dated December 11, 2019 (the "SEA") with Digital Cavalier Technology Services Inc. doing business as Youneeq ("Youneeq") to acquire all of the issued and outstanding securities of Youneeq (the "Transaction"). The Company and Youneeq have signed the SEA and signatures are being gathered from Youneeq shareholders in order to obtain a fully executed version. The proposed transaction is dependent on all Youneeq shareholders signing the SEA. Youneeq is an award-winning personalization and recommendation engine powered by artificial intelligence (AI). The company is poised to become a leading multi-channel AI personalization engine focused on the anonymous audience, the single biggest segment for marketers.

Rx.Health is adding a suite of tools to prevent physician burnout. How do you keep physicians from being overwhelmed by a mountain of paperwork? Give them a voice assistant, similar to Amazon's Alexa or Apple's Siri. That's the thinking behind Suki, a Redwood City-based startup that recently struck a partnership with Mount Sinai Health System spinoff Rx.Health. Rx.Health curates digital tools for doctors, allowing them to prescribe digital therapeutics and care plans from electronic health record systems.

Bank of America hits a milestone with Erica. It's not only fintechs that can lure millions of customers their way. Bank of America's voice-activated virtual assistant Erica now has more than ten million users. It's also on track to complete 100 million client requests in the coming weeks and that's all since its launch in June of 2018. "Erica is ushering in a new era of personalized banking and providing our clients never-before-possible convenience," said David Tyrie, head of advanced solutions and digital banking at Bank of America in prepared remarks.

Me: "Alexa, tell me what will happen in 2020." Amazon AI: "Here's what I found on Wikipedia: The 2020 UEFA European Football Championship…[continues to read from Wikipedia]" Me: "Alexa, give me a prediction for 2020." Amazon AI: "The universe has not revealed the answer to me." Well, some slight improvement over last year's responses, when Alexa's answer to the first question was "Do you want to open'this day in history'?" As for the universe, it is an open book for the 120 senior executives featured here, all involved with AI, delivering 2020 predictions for a wide range of topics: Autonomous vehicles, deepfakes, small data, voice and natural language processing, human and augmented intelligence, bias and explainability, edge and IoT processing, and many promising applications of artificial intelligence and machine learning technologies and tools. And there will be even more 2020 AI predictions, in a second installment to be posted here later this month. "Vehicle AI is going to be ...

The trend toward healthcare consumerism--a movement to make the delivery of health services more efficient, convenient, and cost-effective--means the industry needs to find better ways to connect members and patients. The good news is that, with masses of plan information and mounds of data sprung from digital health records, the healthcare industry has the input needed to provide enriched consumer interactions from top to bottom. AI-enabled virtual assistants for payers, providers, and members can help make user experiences simpler and highly personalized.

A chatbot is a computer program that allows humans to interact with technology using a variety of input methods such as voice, text, gesture and touch, 24/7 365. For several years chatbots were typically used in customer service environments but are now being used in a variety of other roles within enterprises to improve customer experience and business efficiencies. Known by a variety of different names such as a conversational AI bot, AI assistant, intelligent virtual assistant, virtual customer assistant, digital assistant, conversational agent, virtual agent, conversational interface and more, chatbots are growing in popularity. But just as chatbots have a variety of different names, they also have varying degrees of intelligence. A basic chatbot might be little more than a front-end solution for answering standard FAQs.