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AI Innovations In Mining

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These super sized rock trucks are on their way to drop a load of Platinum rich ... [ ] rock into the crusher. These trucks can carry more than 200 tons of rock at a time, and are too large for public roads. With the total operating expenses of the top mining companies worldwide reaching USD $15 billion, efficient operational methods using AI now dubbed smart mining is rapidly advancing. McKinsey estimates that by 2035, the age of smart mining achieved through autonomous mining using data analysis and digital technologies like artificial intelligence (AI) will save between $290 billion and $390 billion annually for mineral raw materials producers. The mining industry is increasingly using artificial intelligence in innovative ways to optimize processes, enhance decision-making, derive value from data, and improve safety.


Will Artificial Intelligence Help Us Grieve?

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When a loved one passes, will we continue to communicate with the deceased through artificial intelligence? While that sounds like an episode of Black Mirror, the beginnings of a digital afterlife with some potentially positive ramifications recently took place with one man, as Jason Fagone reports in the San Francisco Chronicle. His story centers around writer Joshua Barbeau, a 33-year old who had lost his fiancee eight years earlier from a rare liver disease. At home one night, he accessed a site called Project December. As Fagone notes, the site is "powered by one of the world's most capable artificial intelligence systems, a piece of software known as GPT-3. It knows how to manipulate human language, generating fluent English text in response to a prompt."


Natural Language Processing of Radiology Text Reports: Interactive Text Classification

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This report presents a hands-on introduction to natural language processing (NLP) of radiology reports with deep neural networks in Google Colaboratory (Colab) to introduce readers to the rapidly evolving field of NLP. The implementation of the Google Colab notebook was designed with code hidden to facilitate learning for noncoders (ie, individuals with little or no computer programming experience). The data used for this module are the corpus of radiology reports from the Indiana University chest x-ray collection available from the National Library of Medicine's Open-I service. The module guides learners through the process of exploring the data, splitting the data for model training and testing, preparing the data for NLP analysis, and training a deep NLP model to classify the reports as normal or abnormal. Concepts in NLP, such as tokenization, numericalization, language modeling, and word embeddings, are demonstrated in the module.


Humans, I quit

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The same pattern keeps repeating itself throughout history. A small group of outliers invent a new future. The smart people, the ones with influence, reject it. Social media has given them a free perch. How could they not "get it"?


Less Talk, More Action: How Emotion Intelligence Reads What You Don't Say

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One area of tech innovation pulling ahead is Emotion Intelligence. Artificial Intelligence (AI), EI uses facial mapping, eye tracking and other experience measurement data points. Know more about EI in this article.



Artificial intelligence projects in healthcare: 10 practical tips for success in a clinical environment

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There is much discussion concerning ‘digital transformation’ in healthcare and the potential of artificial intelligence (AI) in healthcare systems. Yet it remains rare to find AI solutions deployed in routine healthcare settings. This is in part due to the numerous challenges inherent in delivering an AI project in a clinical environment. In this article, several UK healthcare professionals and academics reflect on the challenges they have faced in building AI solutions using routinely collected healthcare data. These personal reflections are summarised as 10 practical tips. In our experience, these are essential considerations for an AI healthcare project to succeed. They are organised into four phases: conceptualisation, data management, AI application and clinical deployment. There is a focus on conceptualisation, reflecting our view that initial set-up is vital to success. We hope that our personal experiences will provide useful insights to others looking to improve patient care through optimal data use. No data are available to share.


iiot ai_2021-07-30_03-17-11.xlsx

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The graph represents a network of 1,283 Twitter users whose tweets in the requested range contained "iiot ai", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 30 July 2021 at 10:25 UTC. The requested start date was Friday, 30 July 2021 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 10-hour, 29-minute period from Tuesday, 27 July 2021 at 13:30 UTC to Friday, 30 July 2021 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


Pinaki Laskar on LinkedIn: #DeepLearning #machinelearning #artificialintelligence

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AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner In deep learning, the'deep' talks more about the architecture and not about the level of understanding that the algorithms are capable of producing. Take the case of a video game. A deep learning algorithm can be trained to play Mortal Kombat really well and will even be able to defeat humans once the algorithm becomes very proficient. Change the game to Tekken and the neural network will need to be trained all over again. This is because it does not understand the context.


ARTIFICIAL INTELLIGENCE, A TRANSFORMATIONAL FORCE FOR THE HEALTHCARE INDUSTRY

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Artificial Intelligence is transmuting the system and methods of the healthcare industries. Artificial Intelligence and healthcare were found together over half a century. The healthcare industries use Natural Language Processes to categorize certain data patterns. Artificial Intelligence can be used in clinical trials, to hasten the searches and validation of medical coding. This can help reduce the time to start, improve and accomplish clinical training.