Developing prescription drugs is a high-cost, high-risk endeavor. Average research and development for an approved prescription drug requires an investment of $2.9 billion and takes more than 11 years. Clinical trials alone can cost an average of $1.1 billion over 6.6 years. In fact, clinical trials account for a staggering 40 percent of the pharmaceutical industry's research budget. To make matters worse, only 14 percent of drugs that enter clinical trials are eventually approved.
It's that time when we start to look ahead to what next year holds for the life science sector...Lu Rahman outlines 2020s big medtech players A decade ago the healthcare advances create by AI would have seemed the stuff of dreams. But back in 2018 Theresa May announced plans to use artificial intelligence and data to transform the way certain diseases like cancer. The technology is moving at a pace – this year we heard that a team led by the University of Surrey had filed the first ever patent for inventions autonomously created by AI without a human inventor. Professor Ryan Abbott explained the implications this had for the life science sector: "These filings are important to any area of research and development as well as any area that relies on patents. Patents are more important in the life sciences than in many other areas, particularly for drug discovery. These tasks can be the foundation for patent filings. "As AI is becoming increasingly sophisticated, it is likely to play an increasing role in R&D including in the life sciences.
Marc Andreessen famously said that "Software is eating the world" and everyone gushed into the room. This was as much a writing on the wall for many traditional enterprises as it was wonderful news for the software industry. Still no one actually understood what he meant. "Today, the world's largest bookseller, Amazon, is a software company -- its core capability is its amazing software engine for selling virtually everything online, no retail stores necessary. On top of that, while Borders was thrashing in the throes of impending bankruptcy, Amazon rearranged its web site to promote its Kindle digital books over physical books for the first time. Now even the books themselves are software."
I called for "call for contributions" recently, but it didn't end well. People were too obsessed with keeping their secrets and know little outside of ML. So I searched myself for challenging problems in science, with high meaningful impact, potential for ML to make breakthrough, ready dataset and benchmark, and I found this ProteinNet for protein folding. These scientists seem to think for the sake of science as a whole, and want to see how ML can help advance their field. You are welcome to use it for your side project if you are already tired of old time CV or NLP tutorials.
Sanofi, which has moved purposefully into high technologies to get more from its manufacturing, will lean heavily on that strategy to shrink costs and fatten margins. Using robotics, artificial intelligence and new generation manufacturing should save it half a billion euros in annual costs by 2022. So says Sanofi CFO Jean-Baptiste Chasseloup de Chatillon who was filling in some details of new CEO Paul Hudson's €2 billion cost-savings plan laid out Tuesday during Sanofi's investor conference. "It is a leapfrogging of productivity. It reduces cycle time," Chasseloup de Chatillon said on a webcast of the conference.
Examples of artificial intelligence (AI) in pop culture usually involve a pack of intelligent robots hell-bent on overthrowing the human race, or at least a fancy theme park. Sentient machines with general artificial intelligence don't yet exist, and they likely won't exist anytime soon, so we're safe... for now. That's not to make light of AI's potential impact on our future. In a recent survey, more than 72% of Americans expressed worry about a future in which machines perform many human jobs. Additionally, tech billionaire Elon Musk, long an advocate for the regulation of artificial intelligence, recently called AI more dangerous than nukes. Whether we realize it or not, artificial intelligence is all around us and playing an active role in our daily lives. Every time we open our Facebook newsfeed, do a Google search, get a product recommendation from Amazon or book a trip online, AI is lurking in the background.
King's College London (KCL) is partnering up with two companies to deliver an artificial intelligence model in the healthcare and life sciences sector. KCL is joining forces with Owkin, a company that develops AI algorithms for cancer centres and pharmaceutical companies, and American technology company, NVIDIA, to provide Federated Learning, a framework for AI. Federated learning is a machine learning technique that trains an algorithm across multiple decentralised servers holding local data samples, without exchanging their data samples. Owkin aims to demonstrate that the Federating Learning model is safer for patients, and statistically equivalent to the traditional pooled model for analysis. KCL will use Owkin's Federated Learning software and NVIDIA's EGX Intelligent Edge Computing platform to develop research, clinical and operational improvements across a large number of clinical pathways, with cancer, heart failure, dementia and stroke likely areas of early focus.
After a successful early career in R&D in Silicon Valley, I spent 12 years working as a carpenter. This may sound like a big U-turn. But, while I loved the intellectual piece of science, I really loved the people aspect of construction. I got to build something and turn raw materials into gratifying, highly visible results: houses that enabled life and buildings that enabled commerce. I get the same kind of rush daily as lead data-ops engineer for Life Sciences at Tamr.*
An expert on artificial intelligence has called for all algorithms that make life-changing decisions – in areas from job applications to immigration into the UK – to be halted immediately. Prof Noel Sharkey, who is also a leading figure in a global campaign against "killer robots", said algorithms were so "infected with biases" that their decision-making processes could not be fair or trusted. A moratorium must be imposed on all "life-changing decision-making algorithms" in Britain, he said. Sharkey has suggested testing AI decision-making machines in the same way as new pharmaceutical drugs are vigorously checked before they are allowed on to the market. In an interview with the Guardian, the Sheffield University robotics/AI pioneer said he was deeply concerned over a series of examples of machine-learning systems being loaded with bias.