LabGenius is currently focused on discovering new treatments for cancer and inflammatory diseases. The firm combines AI, lab automation, and synthetic biology for its potentially life-saving work.

Until now, the company has been using traditional GPUs for its workloads. LabGenius reports that switching to Graphcore’s IPUs in cloud instances from Cirrascale Cloud Services enabled its training of models to be reduced from one month to around two weeks.

“Previously we used GPUs and it took us about a month to have a functioning model of all the proteins that are out there,” said Dr Katya Putintseva, a Machine Learning Advisor to LabGenius.

“With Graphcore, we reduced the turnaround time to about two weeks, so we can experiment much more rapidly and we can see the results quicker.”

Specifically, LabGenius is using IPUs from Bristol, UK-based Graphcore to train a BERT Transformer model on a large data set of known proteins to predict masked amino acids. This, the company says, enables the model to effectively learn the basic biophysics of proteins.

“[The system] is looking across different features we could change about the molecule — from point mutations of simpler constructs to the overall composition and topology of multi-module proteins,” explained Tom Ashworth, Head of Technology at LabGenius.

“It’s making suggestions about what to design next… to learn about a change in the input and how that maps to a change in the output.”

One in two people now develop cancer in their lifetime. Current treatments often cause much suffering themselves and, while survival rates for most forms are increasing, only around 50 percent survive for ten years or more.

AI will help to find new cancer treatments that cause less suffering and greatly increase the odds of long-term survivability. However, while discovering new cancer treatments is the current focus of LabGenius, the company notes how the principles can be applied more widely to find new treatments for other horrible diseases that plague mankind.

“Graphcore has changed what we’re able to do, accelerating our model training time from weeks to days,” adds Ashworth.

“For our data scientists, that’s really transformative. They can move much more at the speed they think.”

(Photo by National Cancer Institute on Unsplash)

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South China Morning Post reports that Shanghai is testing facial recognition terminals in pharmacies that will verify a person’s identity prior to dispensing controlled substances.

Some legal medications can be turned into banned drugs. Cold and allergy medications, for example, often contain ephedrine which is a key component of crystal meth.

Any prescription medication containing ephedrine, psychotropic substances, and/or tranquilisers will be subject to the facial recognition check. Both patients and pharmacists will be checked to prevent any collusion.

China, along with many other countries, is facing an increasing problem with people buying legal medicines in large quantities that can be converted into recreational drugs for selling on the black market. The use of facial recognition is a novel, albeit controversial, method of combating the issue.

The facial recognition terminals have been in testing since November and adopted by 31 healthcare organisations across seven districts so far. Full coverage of Shanghai’s medical premises is expected by the first half of 2021.

Facial recognition is quickly becoming the norm in every part of life in China. The technology is being used to catch and shame jaywalkers, identify criminals, subway rides, and more.

iiMedia Research found that around 118 million people in China signed up for facial recognition-powered payments in 2019, compared to just 61 million in 2018. The report shows how facial recognition is becoming more widely accepted in China.

Many other nations, particularly in the West, are more wary of mass facial recognition deployments. For example, a recent EU document suggested a time-limited ban on the use of facial recognition technology in public spaces of between three to five years to assess the “impacts of this technology and possible risk management measures.”

It’s hard to imagine such initiatives being deployed in such countries without significant outcry but Shanghai’s latest implementation is at least showing how the technology can be used to tackle serious problems like illegal drug sales.

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Nuritas, headquartered in Dublin, has already proven it’s up to the task. The startup used AI to discover an ingredient to help treat inflammation which is expected to reach sports nutrition products by the end of 2019.

The debut ingredient was discovered as part of a collaboration with BASF, a German chemical giant. Whereas the standard to discover a new ingredient is between five and seven years using traditional methods, the pair used AI to discover it in just two.

“To have something in market that quickly is just exceedingly disruptive by comparison to what’s normally the case with that particular arena,” Nuritas CEO Emmet Browne told Yahoo Finance UK.

While it’s unclear what the next discoveries will be, Nuritas is expecting five more ingredients within the next 12-18 months. The company’s success rate of 60 percent is far higher than the industry standard.

“In effect, what we do is use artificial intelligence to unlock nature’s secrets. That’s the depth of it.”

Nuritas has raised £40 million from a series of high-profile investors which includes Salesforce founder Marc Benioff, and U2’s Bono and the Edge.

AI is being increasingly used in healthcare in life-changing (and often life-saving!) ways. You can find all our AI in healthcare coverage here.

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