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This AI Neural Network Gives Cats Cute, Yet Horrifying Names

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Bones of the Master, Sparky Buttons, and M. Tinklesby Linklater Soap could be your feline’s next name, thanks to an artificial intelligence neural network.

The Morris Animal Refuge, which is located in Philadelphia, collaborated with Janelle Shane, a research scientist, to name some adorable adoptable cats, including Tom Glitter, Starmaker, and Whiskeridoo, CNET reported. Shane’s neural network, which generated a lot of creative, hilarious, and downright terrifying cat names, could be the future of pet identification.

Establishing the neural network wasn’t an easy task though. According to Shane’s website, she trained a neural network to come up with new names for kittens by providing it with a list of over 8,000 existing cat names to learn from. Even though she tried to predict which letters might be found in the feline names, this neural network generated completely random results, like Hurler and Trickles. The neural network didn’t know what letter combinations to pass on, which was why the names were completely bizarre.

This time, Shane tried to tackle the cat-naming dilemma with a neural network that was a step up when it came to words and letter combinations. Dubbed GPT-2, this neural network has better knowledge of which words and letter combinations are used in conjunction with each other on the English-language internet. Shane then trained GPT-2 on the list of feline names and it resulted in some real words that varied from fancy (Taffeta) to super out-there (Scat Cat Butthole). Some favorites were also in the “badass” category, such as Miss Vulgar, Lillith The Vamp, and Deadbolt.

These neural network names for kitties seem to be a positive project for the Morris Animal Refuge: Mr. Sinister, a feline who was given the badass neural network name, was recently adopted on June 3.

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Polaroid Lab Turns Smartphone Snaps Into Instant Photos

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Like live theater and paper books, printed pictures provide a sense of presence that movies, e-books, and digital images lack.

It’s that feeling—plus a hint of nostalgia—that keeps Polaroid in business.

Now, the company is launching a portable printer* that turns smartphone snaps into instant photos.

“Unlimited cloud storage can actually be your memories’ worst enemy, causing your digital photos to skyrocket in numbers and end up falling into a void of oblivion,” Polaroid CEO Oskar Smolokowski said in a statement.

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With a camera in every pocket, people are taking more pictures than ever. But it’s easy to lose track of special moments among a sea of online pixels.

Enter Polaroid Lab.

The idea, according to Smolokowski, is to turn precious pictures into tangible photographs, “bringing them into the world as something you can hold in your hand and store on the fridge door rather than in the cloud.”

Simply select an image from the Polaroid Originals app, place your handset on top of the machine, press a button, and voila! An instant photo (that you don’t need to shake).

Using a combination of mirrors, light, and “the unique Polaroid chemistry,” snaps are recast as instant pictures (via Polaroid)

The appliance uses a combination of mirrors, light, and “the unique Polaroid chemistry” to recast snapshots as a leave-to-develop, white-rimmed keepsake.

Starting in October, you can make pictures instead of just taking them with the $130 Polaroid Lab, compatible with iPhone 6S/iOS 11 and above, Android 7 or higher, and current Samsung, Huawei, Google Pixel, and One Plus models.

Sign up for the company newsletter “to get your hands on one first.”

*According to Polaroid, its new device is “not a printer, not a scanner, just an instant formula for timeless images.

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McDonald’s Plans to Serve AI Voice Technology at Drive Thru

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McDonald’s is slowly but surely eliminating humans workers from its fast-food equation.

Following the rollout of self-order kiosks and “personalized” menus, the burger empire plans to automate drive-thrus with its acquisition of Apprente.

The Silicon Valley startup, purchased for an undisclosed sum, was founded in 2017 to create voice-based platforms for multilingual, multi-accent, and multi-item conversational ordering.

Now, McDonald’s wants to use the system for faster, simpler, and more accurate order taking.

“Building our technology infrastructure and digital capabilities are fundamental … and enable us to meet rising expectations from our customers, while making it simpler and even more enjoyable for crew members to serve guests,” McD’s CEO Steve Easterbrook said in a statement.

It’s not entirely clear exactly how the Apprente tech will work; McDonald’s has already tested the automation solutions at select restaurants, but provided no further details.

“Apprente was born out of an opportunity to use technology to solve challenging real-world problems,” according to co-founder Itamar Arel. “And we’re thrilled to now apply this to creating personalized experiences for customers and crew.”

Arel & Co. will be founding members of a new in-house team called McD Tech Labs, expected to grow with the addition of engineers, data scientists, and other experts in the future.

While the initial focus will be to enhance McDonald’s drive thrus, the company aims to incorporate this technology into mobile ordering and kiosks.

“As we all know, technology can change by the day. And with that, so do our customers’ expectations,” a Golden Arches press release said. “We are committed to delivering the best possible experience for restaurant crew and our customers. And this is only the beginning.”

Earlier this year, McDonald’s purchased AI platform Dynamic Yield, teasing upcoming “personalized” menu options related to time of day, weather, current restaurant traffic, and trending items.

The electronic bill of fares even understands current service times, suggesting only those things that are easy to make during peak hours, in an effort to keep the drive thru running smoothly.

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Machine Learning Estimates Risk of Cardiovascular Death

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MIT researchers have developed a machine learning model that can estimate, based on the electrical activity of their heart, a patient’s risk of cardiovascular death.

The system, dubbed “RiskCardio,” was created by the Computer Science and Artificial Intelligence Laboratory (CSAIL) with the intention of better predicting health outcomes.

It focuses on folks who have survived acute coronary syndrome (ACS)—a range of conditions involving decreased flow to the heart. Just 15 minutes of a patient’s raw electrocardiogram (ECG) signal can produce a score that places people into different risk categories.

“We’re looking at the data problem of how we can incorporate very long time series into risk scores, and the clinical problem of how we can help doctors identify patients at high risk after an acute coronary event,” lead study author Divya Shanmugam said in a statement.

“The intersection of machine learning and healthcare is replete with combinations like this—a compelling computer science problem with potential real-world impact,” she added.

This isn’t the first machine-learning attempt at risk metrics; previous experiments have used external patient information like age or weight, or knowledge and expertise specific to the system.

RiskCardio, however, relies solely on patients’ raw ECG signal—with no added information.

Say someone checks into the hospital after an ACS: A physician would normally estimate risk of cardiovascular death or heart attack using medical data and lengthy tests before choosing a course of treatment.

MIT’s invention aims to improve that first step.

The team tested their model using data from a study of past patients, separating each person’s signal into a collection of adjacent heartbeats. They then assigned a label—”risky” for those who died of cardiovascular complications and “normal” for those who survived—to each set of beats.

When demonstrated on a new patient, CSAIL analysts were able to estimate whether someone would suffer from cardiovascular death within 30, 60, 90, or 365 days.

Moving forward, the team hopes to make the dataset more inclusive to account for different ages, ethnicities, and genders, and evaluate how their system accounts for ambiguous cases.

“Machine learning is particularly good at identifying patterns, which is deeply relevant to assessing patient risk,” Shanmugam said. “Risk scores are useful for communicating patient state, which is valuable for making efficient care decisions.”

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