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Interview with Intelenz Co-Founder & Head of Product Development: Renzo Zagni

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Renzo Zagni is the Co-Founder and Head of Product Development at Intelenz, a Silicon Valley Founder Institute portfolio company. Intelenz leverages the power of AI and machine learning to automate workflows and day to day processes for large enterprise organizations. Process automation enables enterprises to design workflows that reduce manual work, minimize risk, and accelerate process execution times while increasing overall business productivity. In short, process automation allows business to do more, with less, while also eliminating the risk of employee burnout, human error and extended product delivery outcomes. Intelenz's platform includes a patented No-Code'Virtual Process Manager' software, which uses AI and machine learning models through an intuitive user interface.


How to avoid being replaced by a robot at work

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Recently, I was at a party in San Francisco when a man approached me and introduced himself as the founder of a small artificial intelligence (AI) start-up. As soon as the founder figured out that I was a technology writer for The New York Times, he launched into a pitch for his company, which he said was trying to revolutionise the manufacturing sector using a new AI technique called "deep reinforcement learning". The founder explained that his company's AI could run millions of virtual simulations for any given factory, eventually arriving at the exact sequence of processes that would allow it to produce goods most efficiently. This AI, he said, would allow factories to replace entire teams of human production planners, along with most of the outdated software those people relied on. "We call it the Boomer Remover," he said.


A.I. Here, There, Everywhere

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I wake up in the middle of the night. "Hey, Google, what's the temperature in Zone 2," I say into the darkness. A disembodied voice responds: "The temperature in Zone 2 is 52 degrees." "Set the heat to 68," I say, and then I ask the gods of artificial intelligence to turn on the light. Many of us already live with A.I., an array of unseen algorithms that control our Internet-connected devices, from smartphones to security cameras and cars that heat the seats before you've even stepped out of the house on a frigid morning.


Facebook develops A.I. to predict likelihood of worsening Covid symptoms

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Artificial intelligence researchers at Facebook claim they have developed software that can predict the likelihood of a Covid patient deteriorating or needing oxygen based on their chest X-rays. Facebook, which worked with academics at NYU Langone Health's predictive analytics unit and department of radiology on the research, says that the software could help doctors avoid sending at-risk patients home too early, while also helping hospitals plan for oxygen demand. The 10 researchers involved in the study -- five from Facebook AI Research and five from the NYU School of Medicine -- said they have developed three machine-learning "models" in total, that are all slightly different. One tries to predict patient deterioration based on a single chest X-ray, another does the same with a sequence of X-rays, and a third uses a single X-ray to predict how much supplemental oxygen (if any) a patient might need. "Our model using sequential chest X-rays can predict up to four days (96 hours) in advance if a patient may need more intensive care solutions, generally outperforming predictions by human experts," the authors said in a blog post published Friday.


The race to teach sign language to computers

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USING A computer used to mean bashing away at a keyboard. Then it meant tapping on a touchscreen. Increasingly, it means simply speaking. Over 100m devices powered by Alexa, Amazon's voice assistant, rest on the world's shelves. Apple's offering, Siri, processes 25bn requests a month. By 2025 the market for such technology could be worth more than $27bn.


Testing for bias in your AI software: Why it's needed, how to do it

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When it comes to artificial intelligence (AI) and machine learning (ML) in testing, much of the interest and innovation today revolves around the concept of using these technologies to improve and accelerate the practice of testing. The more interesting problem lies in how you should go about testing the AI/ML applications themselves. In particular, how can you tell whether or not a response is correct? Part of the answer involves new ways to look at functional testing, but testers face an even bigger problem: cognitive bias, the possibility that an application returns an incorrect or non-optimal result because of systematic inflection in processing that produces results that are inconsistent with reality. This is very different from a bug, which you can define as an identifiable and measurable error in a process or result.


The Future of Plunger Lift Control Using Artificial Intelligence

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Dozens of plunger lift control algorithms have been developed to account for different well conditions and optimization protocols. However, challenges exist that prevent optimization at scale. To address these challenges, a plunger lift optimization software was developed. One aspect of this software is enabling set-point optimization at scale.


How Doctors Are Using Artificial Intelligence to Battle Covid-19

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When the Covid-19 pandemic emerged last year, physician Lara Jehi and her colleagues at the Cleveland Clinic were running blind. Who were the patients likely to get sicker? What kinds of care will they need? "The questions were endless," says Jehi, the clinic's chief research information officer. "We didn't have the luxury of time to wait and see what's going to evolve over time."


Deep Learning in AI Chips

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Deep learning is that form of AI which excels in incorporating the human brain that ultimately aids in better decision-making capabilities. There are numerous applications that rely on deep learning. One such application that garnered attention from everyone across is its incorporation in AI chips. Jeff Dean, an American computer scientist and also Google's brain director had mentioned how Google would be using artificial intelligence to advance its internal development of custom chips about a year ago. This would ultimately pave the way for accelerating its software.


Global Big Data Conference

#artificialintelligence

Deep learning is that form of AI which excels in incorporating the human brain that ultimately aids in better decision-making capabilities. There are numerous applications that rely on deep learning. One such application that garnered attention from everyone across is its incorporation in AI chips. Jeff Dean, an American computer scientist and also Google's brain director had mentioned how Google would be using artificial intelligence to advance its internal development of custom chips about a year ago. This would ultimately pave the way for accelerating its software.