In e-commerce recommender and search systems, tree-based models, such as LambdaMART, have set a strong baseline for Learning-to-Rank (LTR) tasks. Despite their effectiveness and widespread adoption in industry, the debate continues whether deep neural networks (DNNs) can outperform traditional tree-based models in this domain. To contribute to this discussion, we systematically benchmark DNNs against our production-grade LambdaMART model. We evaluate multiple DNN architectures and loss functions on a proprietary dataset from OTTO and validate our findings through an 8-week online A/B test. The results show that a simple DNN architecture outperforms a strong tree-based baseline in terms of total clicks and revenue, while achieving parity in total units sold.

With the increasing importance of data and artificial intelligence, organizations strive to become more data-driven. However, current data architectures are not necessarily designed to keep up with the scale and scope of data and analytics use cases. In fact, existing architectures often fail to deliver the promised value associated with them. Data mesh is a socio-technical, decentralized, distributed concept for enterprise data management. As the concept of data mesh is still novel, it lacks empirical insights from the field. Specifically, an understanding of the motivational factors for introducing data mesh, the associated challenges, implementation strategies, its business impact, and potential archetypes is missing. To address this gap, we conduct 15 semi-structured interviews with industry experts. Our results show, among other insights, that organizations have difficulties with the transition toward federated governance associated with the data mesh concept, the shift of responsibility for the development, provision, and maintenance of data products, and the comprehension of the overall concept. In our work, we derive multiple implementation strategies and suggest organizations introduce a cross-domain steering unit, observe the data product usage, create quick wins in the early phases, and favor small dedicated teams that prioritize data products. While we acknowledge that organizations need to apply implementation strategies according to their individual needs, we also deduct two archetypes that provide suggestions in more detail. Our findings synthesize insights from industry experts and provide researchers and professionals with preliminary guidelines for the successful adoption of data mesh.

By Michael Sartori and Matthew Avery (March 2, 2022, 6:27 PM EST) -- Artificial intelligence has seeped into so many areas, and the chemical industry is no exception. The increasing number of patents being sought for AI-based chemical inventions reflects the innovations in the field. This article discusses the increase in the patenting of AI-based inventions related to the chemical industry and provides insights for the industry into how to navigate this new course. Patenting of AI-Based Chemical Inventions According to Andrew Moore at NC State University's College of Natural Resources, the chemical industry has used AI "to increase operational efficiency, reduce costs and improve customer satisfaction."[1]

Global Automotive Artificial Intelligence Market report provides a detailed market overview of the industry including definitions, classifications, …

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The research report on artificial intelligence market, in substance, presents an exclusive understanding of the vast expanse of the business space in question. The report comprises a gist of the industry by means of providing an executive summary, industry insights, industry ecosystem analysis, market segmentation, and global trends. Furthermore, the study also provides deliverables pertaining to the regulatory and competitive landscapes and the strategic perspectives of various industry contenders with respect to the artificial intelligence indutry . However, the major challenges faced by industry players are the low return on investment and the complexity involved in the creation of AI mechanisms and models. Lack of energy-efficient and cost-effective hardware restricts the adoption of such technology in small and medium enterprises, thereby restricting the artificial intelligence market growth during the forecast timeline.

These days consumers and businesses no longer think of artificial intelligence (AI) as something from a sci-fi movie or book – that's because AI is now part of everyday life. Just think of Netflix's suggestion algorithm, Uber's location algorithm and even Tinder's matching algorithm – all business models powered by AI to help drive consumer decisions. And the same AI can be used in the sales process to help sales teams make decisions. At World Tour Sydney Reimagined we saw Salesforce Einstein Voice provide deal coaching to sales reps using predictive technology. And in the Sales keynote, Modern Star explained how their sales reps were using data insights to further understand their customer and close deals.

There is a debate roaring and it seems to be present every day. But whatever people's views are, it's clear that the implications of AI will have impacts for each and every one of us – both positively and negatively. Over my life time there has been great advances in information and communication technology and this has changed how most of us live, work and play. I just need to think back to University. Facebook was just launching, the iPhone a dream of Steve Jobs and a taxi was something you called a central booking number for.

Rest of Europe 8.4 Asia Pacific 8.4.1