When dealing with complex data, the effectiveness of a classifier/predictor is limited by its ability to extract useful information. As such, representations that clearly expose the semantics of the data should then be most amenable to downstream learning [1, 2]. This is often referred to as a challenge of acquiring a disentangled representation over the factors of the data [3]. A popular recent trend that has had significant success in this regard uses semi-supervised Variational AutoEncoders (VAE) [4, 5, 6, 7, 8, 9]. Whilst fully unsupervised VAE methods have been shown to require strong inductive bias [10], semi-supervised methods achieve disentanglement by training additional auxiliary tasks that are defined on the factors, alongside the standard VAE objective (see Appendix Eqn. 3).

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.

Deep reinforcement learning has a variety of different algorithms that solves many types of complex problems in various situations, one class of these algorithms is policy gradient (PG), which applies to a wide range of problems in both discrete and continuous action spaces, but applying it naively is inefficient, because of its poor sample complexity and high variance, which result in slower learning, to mitigate this we can use a baseline. The cause of the high variance problem is the reward scale, we think of policy gradient as it increases the probability of taking good actions and decreases it for bad actions, but mostly this is not the case, imagine a situation where the "good" episode return was 10 and the "bad" one was 5, then both probabilities of the actions in those episodes will be increased, which is not what we want, this problem is what baselines can solve. Mathematically, a baseline is a function when added to an expectation, does not change the expected value (or does not introduce bias), but at the same time, it can significantly affect the variance. Following this definition, we want a baseline for the policy gradient that can reduce its high variance and does not change its direction, a natural thing to do is to take the actions that are better than average, increase their probability, and decrease the probability of the actions that are worse than average, this is implemented by calculating the average reward over the trajectory and subtract it from the reward at the current timestep, this kind of baselines is called the average reward baseline. Now, we will show how baselines do not change the expected value, and we can choose any baselines we want.

However, the most significant challenges to implementing machine learning are the perceived threat of redundancy or unemployment. In reality, machine learning should be developed to adapt to different purposes in the labour market rather than make it redundant. Machine learning might support the efficiency in the workforce, as labour intensive and monotonous tasks can be done by machines, while humans focus on higher-level qualitative tasks. The World Economic Forum (WEF) reports that despite machine learning and algorithms substituting 75 million jobs by 2022, they will create another 133 million new roles by the same period. Machine learning technologies and algorithms are influencing our everyday lives in numerous ways.

HRH The Prince of Wales (Front L) Federico Marchetti Front (R) and Back row, members of The Modern ... [ ] Artisan Project team. My recent claim that fashion needs more imagination when it comes to using artificial intelligence has been unexpectedly answered by a project combining eCommerce data and artisanship. Not an obvious pairing, but the brainchild of passionate'dataphile' YOOX NET-A-PORTER GROUP Chairman and CEO, Federico Marchetti, and HRH The Prince of Wales, whose appreciation and support of artisanal craftsmanship (and dedication to safeguarding its future) is decades-long. Marchetti and the YOOX NET-A-PORTER team worked with The Prince's Foundation to create a unique year-long apprenticeship to cultivate the next generation of luxury fashion artisans, informed and guided by customer shopping data and AI analysis of millions of images of historically successful products. To breathe life into artisanship as a viable and attractive career option, underpinned by data that empowers it to deliver the right product, for the right customer on the right sales platform, crucially sustaining the artisans' craft methods and their livelihood.

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.

Africa Data School is a 12 week Program for Data Science, Deep Learning, Machine Learning, Big Data, Natural Language Programming, and Computer Vision.

In the present era of Artificial Intelligence, Deep Learning, advanced quantum computing we humans are surrounded by machines from everywhere. Many critics point to Artificial Intelligence as the main threat to humankind while on the other hand, the supporters of AI claim that humans can never be replaced by machines and would only compliment their abilities. Over the past decade, Artificial Intelligence has undoubtedly emerged as one of the technological successes and with the amount of research and investment going into this domain, it is nowhere near an end. The past decade has shown us that it is no longer a story or science fiction to make machines intelligent -- in terms of learning as we humans do. Artificial Intelligence has impacted our lives greatly, with so many services and products relying on it that it is irrevocably connected with our everyday world.

We consider a compartmental model from which we incorporate a time-dependent health care capacity having a logistic growth. This allows us to take into account the Senegalese authorities response in anticipating the growing number of infected cases. We highlight the importance of anticipation and timing to avoid overwhelming that could impact considerably the treatment of patients and the well-being of health care workers. A condition, depending on the health care capacity and the flux of new hospitalized individuals, to avoid possible overwhelming is provided. We also use machine learning approach to project forward the cumulative number of cases from March 02, 2020, until 1st December, 2020.

We study the algorithmic task of finding a large independent set in a sparse Erd\H{o}s-R\'{e}nyi random graph with $n$ vertices and average degree $d$. The maximum independent set is known to have size $(2 \log d / d)n$ in the double limit $n \to \infty$ followed by $d \to \infty$, but the best known polynomial-time algorithms can only find an independent set of half-optimal size $(\log d / d)n$. We show that the class of low-degree polynomial algorithms can find independent sets of half-optimal size but no larger, improving upon a result of Gamarnik, Jagannath, and the author. This generalizes earlier work by Rahman and Vir\'ag, which proved the analogous result for the weaker class of local algorithms.