The first barrier is data availability. ML and deep learning models require large datasets to accurately classify or predict different tasks.27 Sectors where ML has seen immense progression are those with large datasets available to enable more complex, precise algorithms.28 In healthcare, however, the availability of data is a complex issue. On the organizational level, health data is not only expensive,27 but there is ingrained reluctance towards data sharing between hospitals as they are considered the property of each hospital to manage their individual patients.29

I recently started a new newsletter focus on AI education and already has over 50,000 subscribers. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. It seems that every other month we have a new milestone in the race of building massively large transformer models. GPT-2 set up new records by building a 1.5 billion parameters model just to be surpassed by Microsoft's Turing NLG with 17 billion parameters.

Speech recognition technology has had its place in the enterprise tech stack for years, but the onset of COVID-19 has proven its worth even further. Our recent annual Trends and Predictions for Voice Technology in 2021 report found that 2020 saw a marked increase in voice technology adoption among enterprises, with 68% of respondents reporting their company has a voice technology strategy, an increase of 18% since last year. This is for a number of reasons – it can increase efficiencies across organizations, give them better access to data from conversations, even abade our contact-free wishes during the pandemic. Given that the number of organizations adopting speech technology is set to increase as its capabilities grow, providers need to focus their attention on the barriers to adoption and ensure that user concerns are addressed. Only then will the technology's true value be recognized.

Nowadays, the increase in data acquisition and availability and complexity around optimization make it imperative to jointly use artificial intelligence (AI) and optimization for devising data-driven and intelligent decision support systems (DSS). A DSS can be successful if large amounts of interactive data proceed fast and robustly and extract useful information and knowledge to help decision-making. In this context, the data-driven approach has gained prominence due to its provision of insights for decision-making and easy implementation. The data-driven approach can discover various database patterns without relying on prior knowledge while also handling flexible objectives and multiple scenarios. This chapter reviews recent advances in data-driven optimization, highlighting the promise of data-driven optimization that integrates mathematical programming and machine learning (ML) for decision-making under uncertainty and identifies potential research opportunities. This chapter provides guidelines and implications for researchers, managers, and practitioners in operations research who want to advance their decision-making capabilities under uncertainty concerning data-driven optimization. Then, a comprehensive review and classification of the relevant publications on the data-driven stochastic program, data-driven robust optimization, and data-driven chance-constrained are presented. This chapter also identifies fertile avenues for future research that focus on deep-data-driven optimization, deep data-driven models, as well as online learning-based data-driven optimization. Perspectives on reinforcement learning (RL)-based data-driven optimization and deep RL for solving NP-hard problems are discussed. We investigate the application of data-driven optimization in different case studies to demonstrate improvements in operational performance over conventional optimization methodology. Finally, some managerial implications and some future directions are provided.

Sooner or later, the concept of digitization will completely take over all repetitive tasks. Today, with the help of big data, advanced technologies like automation, artificial intelligence, IoT, and machine learning are leveraging unimaginable amounts and types of information to work from. It is streamlining tedious, repetitive, and difficult tasks, which tend to slow down production and also increases the cost of operation. Owing to the evolution of technology, artificial intelligence startups are mushrooming like never before. The companies are driving the world into a new phase of digitization with a mixture of disruptive statistical methods, computational intelligence, soft computing, and traditional symbolic AI. Artificial intelligence is the combination of two amazing concepts namely science and engineering. With the infusion of disruptive trends and human intelligence, intelligent machines and intelligent computing programs are emerging. Slowly, the flare of innovations moved away from IT and entered into diverse industries including healthcare, education, finance, marketing, business, telecommunication, etc. Organizations realized that by digitizing repetitive tasks, an enterprise can cut the cost of paperwork and labor which further eliminates human error, thus boosting efficiency. Automating processes involve employing artificial intelligence solutions that can support digitization and deliver data-driven insights. Artificial intelligence startups emerge as a ready-made solution provider that supports every company's individual needs. AI startups in 2021 use big data to sophisticated AI models and leverage new solutions that could better serve customers. Analytics Insight has listed the top 100 artificial intelligence startups that are driving the next-generation development in technology. It democratizes the way investments are done by bringing sophisticated elite trading technology to laymen. Accrad is a health tech company that assists radiologists to reduce their workload with the precision of artificial intelligence. Radiologists work under different circumstances and deadlines and might find diagnosis through x-rays a bit difficult. Therefore, Accrad has come up with a futuristic solution to help with accurate and fast image diagnosis. The company has made x-ray processing more convincing and simpler. Its signature product CheXRad, a deep learning algorithm that identifies locations in the chest radiograph has the capability to predict 15 different diseases including Covid-19. Affable.ai is a data-driven influencer marketing platform where customers can find relevant and authentic influencers and manage marketing operations. By using cutting-edge computer vision algorithms on social media posts, the company delivers actionable insights about micro-influencers and their audience. Similar to how Google has sophisticated its search and promote relative ads to users, Affable.ai has also built one-click marketing at a shorter scale.

Machine learning studies algorithms and statistical models that computers use to perform tasks without explicit instructions [1]. The origin behind these technological advances can be largely traced back to a series of breakthroughs in artificial intelligence, in particular those based on deep learning, where data are processed through the sequential combination of multiple nonlinear layers [3]. Deep learning has accelerated the adoption of artificial intelligence with notable advances in areas ranging from computer vision [4] and natural language processing [5], to scientific applications such as drug discovery [6] and protein folding [7]. Recently, the condensed matter physics, quantum information, statistical physics, and atomic, molecular, and optical physics communities have turned their attention to the algorithms underlying modern machine learning with the objective of making progress in quantum matter research. This recent resurgence of research interest at the intersection between strongly correlated systems and machine learning is shaped in part by the commonalities in the structure of the problems that these seemingly unrelated fields attack.

Reproduced under a CC BY 4.0 license. Here are the most tweeted papers that were uploaded onto arXiv during August 2021. Results are powered by Arxiv Sanity Preserver. How to avoid machine learning pitfalls: a guide for academic researchers Michael A. Lones Submitted to arXiv on: 5 August 2021 Abstract: This document gives a concise outline of some of the common mistakes that occur when using machine learning techniques, and what can be done to avoid them. It is intended primarily as a guide for research students, and focuses on issues that are of particular concern within academic research, such as the need to do rigorous comparisons and reach valid conclusions.

Deep neural networks are becoming omnipresent in natural language applications (NLP). However, they require large amounts of labeled training data, which is often only available for English. This is a big challenge for many languages and domains where labeled data is limited. In recent years, a variety of methods have been proposed to tackle this situation. This article gives an overview of these approaches that help you train NLP models in resource-lean scenarios.

Turkey is drawing a road map for its strategy in the field of artificial intelligence (AI), which can be defined as the realization of actions such as making decisions, discovering meaning and learning in dynamic environments specific to intelligent creatures, by a computer or a computer-controlled machine. Accordingly, the Presidential Circular on the National Artificial Intelligence Strategy for 2021-2025 was published Friday in the Official Gazette. The document was prepared by the Presidency's Digital Transformation Office and the Industry and Technology Ministry in line with the 11th Development Plan. The country's priorities in the field and the steps to be taken were determined within the framework of the "Digital Turkey" and "National Technology Move" visions. Digital Turkey aims for a globally competitive Turkey with the increase in productivity it provides by using digital technology, products and services in its social, economic and public activities and the value it generates from data.

Priorly tech-friendly governments around the globe are now cracking down on big tech companies, which is putting the economies through a wind whirl. Since tech companies are forced to comply with the government, it gives the latter more power to manage their image. China has made good progress in the AI arms race in the last decade and is running almost in lockstep with the US. Recent research based on metrics such as patents and research publications ranked China as the top country for AI development, followed by the US and Japan. China's big-tech friendly policies were instrumental in putting the country on the AI map.