Alessandro Betti Ph.D. is a Postdoctoral Researcher in the Department of Information Engineering and Mathematics (DIISM) of the University of Siena (Siena, Italy). Dr. Betti's interests include analysis of algorithms, discrete mathematics, tree structures, and formulation of "learning laws through least action like principles. Stefano Melacci Ph.D. is a Senior Researcher (Tenure-Track Assistant Professor) in the area of Computer Science at the Department of Information Engineering and Mathematics, University of Siena (Siena, Italy). He has been the Research Manager of the Italian company QuestIT S.r.l. Since 2017 he has served as Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems, and he is an active reviewer for several journals and international conferences.

The extensive adoption of business analytics (BA) has brought financial gains and increased efficiencies. However, these advances have simultaneously drawn attention to rising legal and ethical challenges when BA inform decisions with fairness implications. As a response to these concerns, the emerging study of algorithmic fairness deals with algorithmic outputs that may result in disparate outcomes or other forms of injustices for subgroups of the population, especially those who have been historically marginalized. Fairness is relevant on the basis of legal compliance, social responsibility, and utility; if not adequately and systematically addressed, unfair BA systems may lead to societal harms and may also threaten an organization's own survival, its competitiveness, and overall performance. This paper offers a forward-looking, BA-focused review of algorithmic fairness. We first review the state-of-the-art research on sources and measures of bias, as well as bias mitigation algorithms. We then provide a detailed discussion of the utility-fairness relationship, emphasizing that the frequent assumption of a trade-off between these two constructs is often mistaken or short-sighted. Finally, we chart a path forward by identifying opportunities for business scholars to address impactful, open challenges that are key to the effective and responsible deployment of BA.

CommentSold, the leading commercial live selling platform that has generated $1.65B in lifetime GMV and shipped 94M products by enabling thousands of retail businesses to unlock sales growth through live selling, announced the launch of Videeo: a best-in-class white-label, live video commerce technology for enterprises. Videeo gives enterprises the ability to deliver engaging, branded live video commerce experiences by easily integrating live selling into an online retailers' existing e-commerce stack. This bolt-on live commerce solution will play a critical role in increasing brand loyalty and customer engagement, while driving increased repeat purchase rate and revenue growth. "As consumer expectations and shopping behaviors continue to evolve, it's proven that retailers that leverage live video commerce continue to experience major success" Research shows at least 47% of consumers want more live video content from the brands they support, and 67% of live stream event viewers become repeat purchasers. It's clear that live selling is gaining momentum: live commerce initiated sales could reach between 10 to 20 percent of e-commerce sales by 2026.

For about 20 years I've written and produced documentaries, one of the most rewarding ways of telling stories ever invented. It's a privilege to plunge into different cultures and eras and put together deeply human narratives that can be enjoyed by everyone. My clients include National Geographic, Discovery, PBS, and other broadcasters in the US and Europe. My long fascination with Artificial Intelligence came to a head in 2000, when I interviewed inventor Ray Kurzweil, roboticist Rodney Brooks, and sci-fi legend Arthur C. Clarke. Kurzweil and Brooks were casually optimistic about a future they considered inevitable - a time when we will share the planet with intelligent machines.

"If you want to know about AI, read this book…It shows how a supposedly futuristic reverence for Artificial Intelligence retards progress when it denigrates our most irreplaceable resource for any future progress: our own human intelligence."―Peter Thiel A cutting-edge AI researcher and tech entrepreneur debunks the fantasy that superintelligence is just a few clicks away―and argues that this myth is not just wrong, it's actively blocking innovation and distorting our ability to make the crucial next leap. Futurists insist that AI will soon eclipse the capacities of the most gifted human mind. What hope do we have against superintelligent machines? In fact, we don't even know where that path might be. A tech entrepreneur and pioneering research scientist working at the forefront of natural language processing, Erik Larson takes us on a tour of the landscape of AI to show how far we are from superintelligence, and what it would take to get there.

I find this book very useful, as predictive modelling with text is an important field in data science and statistics, and yet the one that has been consistently under-represented in technical literature. Given the growing volume, complexity and accessibility of unstructured data sources, as well as the rapid development of NLP algorithms, knowledge and skills in this domain is in increasing demand. In particular, there's a demand for pragmatic guidelines that offer not just the theoretical background to the NLP issues but also explain the end-to-end modelling process and good practices supported with code examples, just like

After years of growth bulletins, Amazon will likely be rolling out supply by drone for Prime prospects in areas of California and Texas. Whereas that is a small slice of consumers, for now, this information could not be extra necessary for the business drone sector, which has been gathering tons of momentum over the previous couple of months. Amazon will be a part of Walmart in providing restricted drone supply to a subset of consumers, signaling a vote of confidence within the expertise and a shifting regulatory surroundings from the 2 behemoth retailers. Amazon's Prime Air service will ship packages as much as 5 kilos in lower than an hour utilizing drones. The service is about to roll out to prospects in Lockeford, California, and School Station, Texas.

Fraud is evolving, and many Australian businesses may struggle to keep up with fraudsters who are continuing to find new ways to evade detection and exploit vulnerabilities. In the twelve months to June 2021 alone, the Australian Payments Network found fraud on payment card transactions totalled $490.1 million, an increase of 9.2 per cent from the year before. Further, research from Statista shows that as of 2021, around 1.25 million dollars had been lost in online shopping scams in Australia. For retailers of all sizes, it has never been more important to get ahead and proactively find a solution that helps to stop fraudulent transactions without turning away legitimate customers and limiting opportunities for growth. What your business needs, however, depends on the size of your organisation or the trajectory of growth that you are on.

Over the last few years, online education platforms have seen an increase in adoption of and an uptick in demand for video-based learnings because it offers an effective medium to engage learners. To expand to international markets and address a culturally and linguistically diverse population, businesses are also looking at diversifying their learning offerings by localizing content into multiple languages. These businesses are looking for reliable and cost-effective ways to solve their localization use cases. Localizing content mainly includes translating original voices into new languages and adding visual aids such as subtitles. Traditionally, this process is cost-prohibitive, manual, and takes a lot of time, including working with localization specialists.

By combining a platform for creating and operating interactive, real-time 3D content with machine learning tools, you can use the 3D world you create to train a machine learning model, kind of like it's the real world. It's not actually like the real world, but it's fun to imagine, and there are some legitimately useful connections to the real world (such as being able to generate both data for use in real-world machine learning applications, as well as models that can be transposed to physical, real-world objects, like robots). Combining Unity with machine learning is a great way to create both simulations and synthetic data, which are the two different topics we cover in this book. We wrote this book for programmers and software engineers who are interested in machine learning, but are not necessarily machine learning engineers. If you have a passing interest in machine learning, or are starting to work more in the machine learning space, then this book is for you.