This paper presents a focused analysis of human studies in explainable artificial intelligence (XAI) entailing qualitative investigation. We draw on the social science corpora of qualitative research to illustrate opportunities for making the human studies where XAI researchers used observations, interviews, focus groups, and/or questionnaires to capture qualitative data more rigorous. We contextualize the presentation of the XAI contributions included in our analysis according to the components of rigor described in the qualitative research literature: 1) underlying theories or frameworks, 2) methodological approaches, 3) data collection methods, and 4) data analysis processes. The results of our analysis support calls from others in the XAI community advocating for collaboration with experts from social disciplines to bolster rigor and effectiveness in human studies.

The term AI Winter, first appeared in 1984 having been discussed at the American Association of Artificial Intelligence. This discussion then saw a rise in pessimism and a reduction of funding. Many minds that had survived the first'winter', prior to it being cited as such, suggested that an increase in enthusiasm for AI, perhaps without the technical capabilities to match, has seen a sharp rise and later collapse. Having hit extreme lows in the early 1990s, the enthusiasm for AI began to rise again, and as they say, the rest is now history with it now so ubiquitous in society. That said, we have seen this year that anything is certainly possible, therefore, we thought we would ask our community of AI expert friends what they thought on the topic, asking - 'Do you think we will see another AI Winter?

Managing Marketing is a weekly podcast hosted by TrinityP3. Each one is a conversation with a marketing thought-leader, professional, practitioner and experts on the issues and topics of interest to marketers and business leaders everywhere. In this special series, TrinityP3's Anton Buchner discusses the rise of Artificial Intelligence and the impact it is having on marketing. Alastair Herbert is the founder of the research consultancy Linguabrand. He shares his wisdom having developed a deep-listening robot (Bob), that analyses visual and verbal language. Alastair introduces you to how Bob listens and analyses the psychology of language that humans potentially miss in data analysis and research groups. Bob can uncover insights to help brands shift the conversation away from sounding generic, to position themselves more persuasively. Follow Managing Marketing on Soundcloud, TuneIn, Stitcher, Spotify and Apple Podcast. Welcome to Managing Marketing, a weekly podcast where we sit down and talk with thought leaders and experts on the issues and opportunities in the marketing and business world. And it's quite warm here, so windows are open, so if you hear barking dogs, police cars, or squawking birds, you all know the reason why. It's nothing to do with COVID, it's actually just to do with enjoying summer. Now I'm really excited to have a chat with you today. As in most communications, I think most people realise that the vast majority of it is actually subconscious. And hopefully, by the end of this session, your listeners will have a much better understanding of how communications work. I'm sure they'll be excited. Before we jump in, I met you relatively recently through a colleague, Jeremy Taylor-Riley. He's now a business colleague of yours, I believe. Well, we actually go back to school days together. And what was great is that we – I think this was back when dinosaurs ruled the earth.

Federated learning enables training a global model from data located at the client nodes, without data sharing and moving client data to a centralized server. Performance of federated learning in a multi-access edge computing (MEC) network suffers from slow convergence due to heterogeneity and stochastic fluctuations in compute power and communication link qualities across clients. We propose a novel coded computing framework, CodedFedL, that injects structured coding redundancy into federated learning for mitigating stragglers and speeding up the training procedure. CodedFedL enables coded computing for non-linear federated learning by efficiently exploiting distributed kernel embedding via random Fourier features that transforms the training task into computationally favourable distributed linear regression. Furthermore, clients generate local parity datasets by coding over their local datasets, while the server combines them to obtain the global parity dataset. Gradient from the global parity dataset compensates for straggling gradients during training, and thereby speeds up convergence. For minimizing the epoch deadline time at the MEC server, we provide a tractable approach for finding the amount of coding redundancy and the number of local data points that a client processes during training, by exploiting the statistical properties of compute as well as communication delays. We also characterize the leakage in data privacy when clients share their local parity datasets with the server. We analyze the convergence rate and iteration complexity of CodedFedL under simplifying assumptions, by treating CodedFedL as a stochastic gradient descent algorithm. Furthermore, we conduct numerical experiments using practical network parameters and benchmark datasets, where CodedFedL speeds up the overall training time by up to $15\times$ in comparison to the benchmark schemes.

Artificial intelligence has become such a big part of our lives, you'd be forgiven for losing count of the algorithms you interact with. But the AI powering your weather forecast, Instagram filter, or favorite Spotify playlist is a far cry from the hyper-intelligent thinking machines industry pioneers have been musing about for decades. Deep learning, the technology driving the current AI boom, can train machines to become masters at all sorts of tasks. But it can only learn only one at a time. And because most AI models train their skillset on thousands or millions of existing examples, they end up replicating patterns within historical data--including the many bad decisions people have made, like marginalizing people of color and women. Still, systems like the board-game champion AlphaZero and the increasingly convincing fake-text generator GPT-3 have stoked the flames of debate regarding when humans will create an artificial general intelligence--machines that can multitask, think, and reason for themselves. Beyond the answer to how we might develop technologies capable of common sense or self-improvement lies yet another question: who really benefits from the replication of human intelligence in an artificial mind? "Most of the value that's being generated by AI today is returning back to the billion dollar companies that already have a fantastical amount of resources at their disposal," says Karen Hao, MIT Technology Review's senior AI reporter and the writer of The Algorithm. "And we haven't really figured out how to convert that value or distribute that value to other people."

For this week's ML practitioner's series, Analytics India Magazine got in touch with Hiroki Yamamoto (tereka), a Kaggle Master. Hiroki is currently working as a data scientist and is ranked in the top 100 of the world's largest platforms for data science competitions– Kaggle. In this interview, Hiroki shares his experience of competing on Kaggle and how it has helped in growing as a data scientist. Hiroki: I got a master's degree in information technology back in 2015. During my graduation, I have worked on image processing research using deep learning -- for example, autoencoders.

How to Do It is Slate's sex advice column. Send it to Stoya and Rich here. I am an older woman who has recently gotten back into the dating game. My problem is I hope going to be fun for you. So I am a semi-conservative woman and am hoping to meet someone similar. But I also am very sexual.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. U.K.'s chief of the Defense Staff said in a televised interview aired Sunday that economic uncertainty caused by the coronavirus pandemic increases the risk of a third world war, adding that robot soldiers could make up at least a quarter of the British army by the 2030s In an interview with Sky News ahead of Remembrance Day, Gen. Sir Nick Carter, the professional head of the British armed forces, said tributes to those who perished during wartime still hold relevance today even though there is no one alive who served in World War I and the number of veterans from World War II is dwindling. "We have to remember that history might not repeat itself but it has a rhythm and if you look back at the last century, before both world wars, I think it was unarguable that there was escalation, which led to the miscalculation, which ultimately led to war at a scale we would hopefully never see again," he said. Veteran Charlie MacVicar, who served for 23 years with Royal Scots (Edinburgh Unit) pays his respects at the Royal British Legion Remembrance Garden, on Remembrance Sunday, in Grangemouth, Scotland, Sunday, Nov. 8, 2020.

Joe Biden has defeated President Donald Trump in the 2020 presidential election and will become the 46th president of the United States. Although Trump has not conceded, Biden has been named the president-elect by multiple outlets, including AP News. The decision to name the 77-year-old the president-elect sent Twitter into a frenzy, which resulted in Biden's supporters using Alexa, Amazon's virtual assistant, to taunt Trump and celebrate the democrat's victory. On Saturday, "Alexa" began trending on Twitter as people began sharing the songs they wanted to play to celebrate the president-elect and say goodbye to Trump. In the song, Meek Mill raps, "See my dreams unfold, nightmares come true It was time to marry the game and I said, 'Yeah, I do' If you want it you gotta see it with a clear-eyed view."

Best listening experience is on Chrome, Firefox or Safari. The Defense Department's data strategy released just a few weeks ago says improving data management will help it fight and win wars. It says artificial intelligence will become an important component of data-fueled digital modernization. For an assessment, the CEO of data analysis company Govini, Tara Murphy Dougherty joined Federal Drive with Tom Temin. Insight by BOX: Federal News Network showcases several examples of agencies and industry partnering to create and evolve the future of work in this exclusive ebook.