Lifeboat Foundation

Analytics has played a significant role in the fight against COVID-19. Would we be as far along in the battle without it?

The COVID-19 pandemic has changed many things in business, producing a new normal that all of us now operate in—and analytics is no exception.

“As companies adapt to the new normal created by COVID, one of the primary questions we’re asked in analytics is how to retrain artificial intelligence (AI) models with a more diverse data set,” said David Tareen, director of AI and analytics at SAS.

There are 40.3 million victims of human trafficking globally, according to the International Labor Organization. Marinus Analytics, a startup based in Pittsburgh, Pennsylvania, hopes to make a dent in that number. The company’s mission is to “serve those working on the frontlines of public safety by developing technology for them to disrupt human trafficking, child abuse, and cyber fraud.” For its achievements, Marinus won $500,000 as part of its third-place ranking in the 2021 IBM Watson AI XPRIZE competition. The startup is the brainchild of three co-founders: Cara Jones, Emily Kennedy, and Artur Dubrawski, who launched it out of the Robotics Institute at Carnegie Mellon University in 2014.

Marinus implements its mission primarily through its set of AI-based tools called Traffic Jam, whose goal is “to find missing persons, stop human trafficking and fight organized crime.”

Traditionally, finding a missing person would involve taping a picture of the person on the computer and then manually combing through thousands, if not millions, of online ads on adult services websites to see if any of the posted pictures match. Such a process is time-consuming and tiring. A human detective’s attention can start flagging after long hours at the computer doing the same task endlessly.

Okay…very odd indeed.

As already mentioned, the Saudi Arabian Government officially granted Sophia citizenship in 2017. She is the first and only robot to be an official citizen of a country. Her citizenship sparked some controversy, and not just from people who don’t think robots deserve rights. Rather, many people pointed out the contrast to women’s rights in the country.

Continue reading “World’s First Robot Citizen Sophia Said She Wants To Have A Baby” »

What if you could gain 100x more data insights? Agile mobile robots bring your sensors to your industrial assets, collecting critical data at the source. Join t… See More.

Unlocking the Future of Asset Management with Agile Mobile Robots.

Wednesday, november 3rd at 11 AM ET

Continue reading “Webinar: Unlocking the Future of Asset Management with Agile Mobile Robots” »

Ever wanted a jetpack that flies easily? A start-up from England may have just created a jetpack that requires minimal training and runs on autopilot as well.

A versatile robot inspired by birds can walk, balance on a slackline, skateboard and even fly – a mix of skills that could be useful for monitoring power lines or space exploration.

DNA contains the genetic information that influences everything from eye color to illness and disorder susceptibility. Genes, which are around 20,000 pieces of DNA in the human body, perform various vital tasks in our cells. Despite this, these genes comprise up less than 2% of the genome. The remaining base pairs in the genome are referred to as “non-coding.” They include less well-understood instructions on when and where genes should be created or expressed in the human body.

DeepMind, in collaboration with their Alphabet colleagues at Calico, introduces Enformer, a neural network architecture that accurately predicts gene expression from DNA sequences.

Earlier studies on gene expression used convolutional neural networks as key building blocks. However, their accuracy and usefulness have been hampered by problems in modeling the influence of distal enhancers on gene expression. The proposed new method is based on Basenji2, a program that can predict regulatory activity from DNA sequences of up to 40,000 base pairs.

Continue reading “DeepMind Introduces ‘Enformer’, A Deep Learning Architecture For Predicting Gene Expression From DNA Sequence” »

Early in 2,021 the Stanford Virtual Human Interaction Lab looked at the psychological consequences of spending long days videoconferencing and in virtual meetings. The popularized term “Zoom fatigue,” is the result of maxing out cognitive load and even reducing effectiveness. For all of that investment in remote work technology, senior managers feel there is very little payoff.

The University of North Carolina surveyed 182 senior managers and 65% of them felt meetings kept them from completing their own work, 71% felt meetings were inefficient and unproductive, and 64% felt meetings undercut deep thinking.

As technology-dependent remote workers proliferate, new solutions are coming to the fore that may make both in-person and virtual meetings more productive.

There’s a lot of excitement at the intersection of artificial intelligence and health care. AI has already been used to improve disease treatment and detection, discover promising new drugs, identify links between genes and diseases, and more.

By analyzing large datasets and finding patterns, virtually any new algorithm has the potential to help patients — AI researchers just need access to the right data to train and test those algorithms. Hospitals, understandably, are hesitant to share sensitive patient information with research teams. When they do share data, it’s difficult to verify that researchers are only using the data they need and deleting it after they’re done.

Secure AI Labs (SAIL) is addressing those problems with a technology that lets AI algorithms run on encrypted datasets that never leave the data owner’s system. Health care organizations can control how their datasets are used, while researchers can protect the confidentiality of their models and search queries. Neither party needs to see the data or the model to collaborate.