Become Process Miner with Process Explorer 360 1.3!

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The solution has many advanced features for process mining and is able to deal with a huge volume of data. Its innovative Process Mining algorithm allows to detect all the steps that are part of a process and the different variants. The platform will detect flaws and time-consuming tasks. With his new AI engine, it makes it possible to predict how will really runs current processes, what are the next steps and how much they cost. The prediction of next steps in a process by AI offers the ability to identify a possible scenario and therefore take appropriate decisions.


Operationalizing Machine Learning at Enterprise Scale

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According to a McKinsey Global Survey, approximately 30% of executives reported active pilot projects, while 71% were expecting a significant increase in AI investment. However, the survey found that progress remained slow, most companies didn't have a clear strategy or infrastructure for sourcing data, and organizations were lacking the foundational building blocks to create value from AI at scale. Deploying AI in industrial operations is difficult for a variety of reasons – complex data management, challenging integration, enterprise security requirements, real-time analytics and capability to handle thousands of models in the production environment. However, a fundamental problem is finding skilled people to implement AI. To circumvent this issue, companies are relying on citizen data scientists – subject matter experts with domain expertise in operations – and providing them with advanced analytical tools.


Operationalizing Machine Learning at Enterprise Scale

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According to a McKinsey Global Survey, approximately 30% of executives reported active pilot projects, while 71% were expecting a significant increase in AI investment. However, the survey found that progress remained slow, most companies didn't have a clear strategy or infrastructure for sourcing data, and organizations were lacking the foundational building blocks to create value from AI at scale. Deploying AI in industrial operations is difficult for a variety of reasons – complex data management, challenging integration, enterprise security requirements, real-time analytics and capability to handle thousands of models in the production environment. However, a fundamental problem is finding skilled people to implement AI. To circumvent this issue, companies are relying on citizen data scientists – subject matter experts with domain expertise in operations – and providing them with advanced analytical tools.


How Businesses & Governments can prosper with Blockchain AI . Slideshare

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How Businesses Governments can prosper with Blockchain and AI? The conjunction between digital transformation, industry 4.0 Blockchain and Artificial Intelligence technology & how governments and enterprises can benefit from embracing these technologies.


Human biases cause problems for machines trying to learn chemistry

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They found that models trained on a small randomised sample of reactions outperformed those trained on larger human-selected datasets. The results show the importance of including experimental results that people might think are unimportant when it comes to developing computer programs for chemists. Machine learning models are a valuable tool in chemical synthesis, but they're trained on data from the literature where positive results are favoured, whereas the dark reactions – the experiments that were tried but didn't work – are usually left out. 'Including these failures is essential for generating predictive machine learning models,' says Joshua Schrier of Fordham University, US, who was part of a team that studied hydrothermal syntheses of amine-templated metal oxides and found that biases were introduced into the literature by people's choices of the reaction parameters. 'We considered extra dark reactions – a class of reactions that humans don't even attempt, not because of scientific or practical reasons, but simply because it's humans who make the decisions,' Schrier says.


How to make your agent gesture in a natural way? Perceiving Systems - Max Planck Institute for Intelligent Systems

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Conversational agents in the form of virtual agents or social robots are rapidly becoming wide-spread. Humans use non-verbal behaviors to signal their intent, emotions and attitudes in human-human interactions. Conversational agents therefore need this ability as well in order to make an interaction pleasant and efficient. An important part of non-verbal communication is gesticulation: gestures communicate a large share of non-verbal content. Previous systems for gesture production were typically rule-based and could not represent the range of human gestures.


A superpixel-driven deep learning approach for the analysis of dermatological wounds

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The image-based identification of distinct tissues within dermatological wounds enhances patients' care since it requires no intrusive evaluations. This manuscript presents an approach, we named QTDU, that combines deep learning models with superpixel-driven segmentation methods for assessing the quality of tissues from dermatological ulcers.


AI for Chemistry - ChemIntelligence

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Artificial Intelligence (AI) is being used more and more by chemists to perform various tasks. Originally, research in AI applied to chemistry has largely been fueled by the need to accelerate drug discovery and reduce its huge costs and the time to market for new drugs. So far, AI has made significant progess towards the acceleration of drug discovery R&D. However, the applications of AI in chemistry are not limited to drug discovery, as discussed in a recent review. In this article, we will provide a general picture of how AI can help chemists be faster and more creative in their research.


A Brief History of Machine Learning in Marketing - Esanosys Marketing Technology

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The usage of artificial intelligence (AI) in marketing has taken the world by storm in the last decade. The answer to why this happened lies in the history of machine learning. Machine learning has existed since the 1950s. It is exciting to know that something like machine learning has existed for such a long time. Alan Turing who is considered to be the father of modern day computer science was one of the first creators of the Turing Learning Machine that relied on AI.