This work provide a model based on machine learning techniques in welds recognition, based on signals obtained through in-line inspection tool called "smart pig" in Oil and Gas pipelines. The model uses a signal noise reduction phase by means of pre-processing algorithms and attribute-selection techniques. The noise reduction techniques were selected after a literature review and testing with survey data. Subsequently, the model was trained using recognition and classification algorithms, specifically artificial neural networks and support vector machines. Finally, the trained model was validated with different data sets and the performance was measured with cross validation and ROC analysis. The results show that is possible to identify welding automatically with an efficiency between 90 and 98 percent.

Recently, the application of computer vision for anomaly detection has been under attention in several industrial fields. An important example is oil pipeline defect detection. Failure of one oil pipeline can interrupt the operation of the entire transportation system or cause a far-reaching failure. The automated defect detection could significantly decrease the inspection time and the related costs. However, there is a gap in the related literature when it comes to dealing with this task. The existing studies do not sufficiently cover the research of the Magnetic Flux Leakage data and the preprocessing techniques that allow overcoming the limitations set by the available data. This work focuses on alleviating these issues. Moreover, in doing so, we exploited the recent convolutional neural network structures and proposed robust approaches, aiming to acquire high performance considering the related metrics. The proposed approaches and their applicability were verified using real-world data.

You know that text autocomplete function that makes your smartphone so convenient -- and occasionally frustrating -- to use? Well, now tools based on the same idea have progressed to the point that they are helping researchers to analyse and write scientific papers, generate code and brainstorm ideas. The tools come from natural language processing (NLP), an area of artificial intelligence aimed at helping computers to'understand' and even produce human-readable text. Called large language models (LLMs), these tools have evolved to become not only objects of study but also assistants in research. LLMs are neural networks that have been trained on massive bodies of text to process and, in particular, generate language.

Today, Tractable is worth $1 billion. Our AI is used by millions of people across the world to recover faster from road accidents, and it also helps recycle as many cars as Tesla puts on the road. And yet six years ago, Tractable was just me and Raz (Razvan Ranca, CTO), two college grads coding in a basement. Here's how we did it, and what we learned along the way. In 2013, I was fortunate to get into artificial intelligence (more specifically, deep learning) six months before it blew up internationally.

Scholars have a nifty way of alerting colleagues to lengthy treatises that they find simply not worth their time to read. It's kind of a 21st century spin on the 420-year-old notion Shakespeare's Polonius relayed to the king and queen in "Hamlet": "Brevity," he suggested, "is the soul of wit." The Allen Institute for Artificial Intelligence in Seattle has taken both sentiments to heart and this week unveiled a system that offers extreme condensation of lengthy computer-science reports to slash the time it take to review such literature. Semantic Scholar is a research tool powered by AI and used for scientific research. With its new summarization feature, it surveys massive numbers of scientific research papers and reduces them to one-sentence summaries.

TLDR generates one-sentence summaries of computer-science papers on the scientific search engine Semantic Scholar.Credit: Agnese Abrusci/Nature The creators of a scientific search engine have unveiled software that automatically generates one-sentence summaries of research papers, which they say could help scientists to skim-read papers faster. The free tool, which creates what the team calls TLDRs (the common Internet acronym for'Too long, didn't read'), was activated this week for search results at Semantic Scholar, a search engine created by the non-profit Allen Institute for Artificial Intelligence (AI2) in Seattle, Washington. For the moment, the software generates sentences only for the ten million computer-science papers covered by Semantic Scholar, but papers from other disciplines should be getting summaries in the next month or so, once the software has been fine-tuned, says Dan Weld, who manages the Semantic Scholar group at AI2. Preliminary testing suggests that the tool helps readers to sort through search results faster than viewing titles and abstracts, especially on mobile phones, he says. "People seem to really like it."

TLDR generates one-sentence summaries of computer-science papers on the scientific search engine Semantic Scholar.Credit: Agnese Abrusci/Nature The creators of a scientific search engine have unveiled software that automatically generates one-sentence summaries of research papers, which they say could help scientists to skim-read papers faster. The free tool, which creates what the team call TLDRs (the common Internet acronym for'Too long, didn't read'), was activated this week for search results at Semantic Scholar, a search engine created by the non-profit Allen Institute for Artificial Intelligence (AI2) in Seattle, Washington. For the moment, the software generates sentences only for the ten million computer-science papers covered by Semantic Scholar, but papers from other disciplines should be getting summaries in the next month or so, once the software has been fine-tuned, says Dan Weld, who manages the Semantic Scholar group at AI2 and led the work. Preliminary testing suggests that the tool helps readers to sort through search results faster than viewing titles and abstracts, especially on mobile phones, he says. "People seem to really like it."

Across a range of industries, and specifically in the industrial automation vertical, there is broad agreement that the deployment of modern computing resources with cloud native models of software lifecycle management will become ever more pervasive. Placing virtualized computing resources nearer to where multiple streams of data are created is well established. It is the path to address system latency, privacy, cost and resiliency challenges that a pure cloud computing approach cannot address. This paradigm shift was initiated at Cisco Systems around 2010, under the label "fog computing" and progressively morphed into what is now known as "edge computing". The requirements of mission critical industrial systems That said, the full potential of this transformation in both computing and data analytics is far from being realized.

The news: A new AI model for summarizing scientific literature can now assist researchers in wading through and identifying the latest cutting-edge papers they want to read. On November 16, the Allen Institute for Artificial Intelligence (AI2) rolled out the model onto its flagship product, Semantic Scholar, an AI-powered scientific paper search engine. It provides a one-sentence tl;dr (too long; didn't read) summary under every computer science paper (for now) when users use the search function or go to an author's page. The work was also accepted to the Empirical Methods for Natural Language Processing conference this week. The context: In an era of information overload, using AI to summarize text has been a popular natural-language processing (NLP) problem.

What are the foundational technologies today on which we are building tomorrow? That was the question Michael Hainsworth, executive producer of Futurithmic, co-host of the Geeks & Beats podcast and former BNN senior anchor and CTV news reporter, asked the audience at a talk billed Future Forward: Three Technologies That Will Change Our World Forever. The presentation was part of the CanaData construction forecasts conference held recently in Toronto. "You look at 5G wireless, artificial intelligence (AI), augmented reality (AR), these are going to be three key technologies for your industry not for the next 10 years, not for the next 20 years, this is the future forever," explained Hainsworth. "These fundamental technologies are going to give us things that today we can't even predict."