Roughly six years ago, former Israel chief technology officer Yonatan Adiri cofounded Healthy.io, a digital health care startup leveraging AI to bring professional-grade medical imaging to the homes of those with chronic kidney disease. Its 10-parameter digital testing kit -- Dip.io -- enables patients to collect and analyze urine samples with nothing more than a smartphone app, a dip stick, and a color-coded slide. It's taken off like wildfire. Following on the heels of an $18 million series B raise in February, Healthy.io CEO Adiri said the fresh capital, which brings Healthy.io's total raised to about $90 million, will accelerate Healthy.io's

Though Deep Neural Networks (DNN) show excellent performance across various computer vision tasks, several works show their vulnerability to adversarial samples, i.e., image samples with imperceptible noise engineered to manipulate the network's prediction. Adversarial sample generation methods range from simple to complex optimization techniques. Majority of these methods generate adversaries through optimization objectives that are tied to the pre-softmax or softmax output of the network. In this work we, (i) show the drawbacks of such attacks, (ii) propose two new evaluation metrics: Old Label New Rank (OLNR) and New Label Old Rank (NLOR) in order to quantify the extent of damage made by an attack, and (iii) propose a new adversarial attack FDA: Feature Disruptive Attack, to address the drawbacks of existing attacks. FDA works by generating image perturbation that disrupt features at each layer of the network and causes deep-features to be highly corrupt. This allows FDA adversaries to severely reduce the performance of deep networks. W e experimentally validate that FDA generates stronger adversaries than other state-of-the-art methods for image classification, even in the presence of various defense measures. More importantly, we show that FDA disrupts feature-representation based tasks even without access to the task-specific network or methodology.

In recent years, the increase in accessible, large-scale computing power and storage has ushered in a new dawn for artificial intelligence (AI), with the technology appearing to finally catch up with the existing algorithms to bring machine learning (ML) to realisation. In this article, we discuss some of the key applications of ML that have shown success in clinical research. Moreover, we consider how machine learning is impacting on clinical trials, utilising decades of structured clinical trial data alongside real-world data (RWD) and other valuable data sources to support clinical trial design, execution and analysis. Combining computational skills and drug development experience, data science teams can support the pharma and biotech industry to generate business value through the application of machine learning. For ML algorithms to be successful they require large, quality data sets for their application.

Erik Learned-Miller is one reason we talk about facial recognition at all. In 2007, years before the current A.I. boom made "deep learning" and "neural networks" common phrases in Silicon Valley, Learned-Miller and three colleagues at the University of Massachusetts Amherst released a dataset of faces titled Labelled Faces in the Wild. To you or me, Labelled Faces in the Wild just looks like folders of unremarkable images. You can download them and look for yourself. There's boxer Joe Gatti, gloves raised mid-fight.

Biotechnology, pharmaceutical, and life sciences industries are where applied artificial intelligence (AI) can greatly accelerate innovation and shorten the product development life-cycle. Developing a drug typically takes 10 to 15 years on average, with only approximately 12 percent of drugs in clinical trials ultimately gaining U.S. Food and Drug Administration (FDA) approval. In an AI milestone in life sciences, Insilico Medicine announced a new machine learning tool for drug discovery that can generate a novel molecule in days instead of years and published their findings in Nature Biotechnology on September 2, 2019. Insilico Medicine is a venture-backed start-up with multiple investors that include WuXi AppTec, Juvenescence, Peter Diamandis' BOLD Capital Partners, and Pavilion Capital. Led by CEO and Founder Alex Zhavoronkov, the company's mission is to extend longevity by applied AI solutions for drug discovery and aging research.

Biotechnology, pharmaceutical and life sciences industries are where applied artificial intelligence (AI) can greatly accelerate innovation and shorten the product development life-cycle. Developing a drug typically takes 10 to 15 years on average, with only approximately 12 percent of drugs in clinical trials ultimately gaining U.S. Food and Drug Administration (FDA) approval. In an AI milestone in life sciences, Insilico Medicine announced a new machine learning tool for drug discovery that can generate a novel molecule in days instead of years, and published their findings in Nature Biotechnology on September 2, 2019. Insilico Medicine is a venture-backed start-up with multiple investors that include WuXi AppTec, Juvenescence, Peter Diamandis' BOLD Capital Partners, and Pavilion Capital. Led by CEO and Founder Alex Zhavoronkov, the company's mission is to extend longevity by applied AI solutions for drug discovery and aging research.

The College of Optometrists called AI "the new buzzword in ophthalmology" and over the last 12 months it has been hitting headlines on a regular basis. The definition of AI can mean different things. AI-equipped machines range from purely reactive ones like IBM's Deep Blue, which famously beat international chess grandmaster Gary Kasparov in the 90's, to the most advanced AI technology today which enables machines to teach themselves new skills by looking at, and processing, the world around them. The latter is what the ophthalmic space has been getting excited about, where computers have been using artificial neural networks that replicate the human brain in order to take in, process and learn from information presented. One of the biggest stories of 2018 came from that, when a study from Moorfield's Eye Hospital was conducted in the UK, working with Google's DeepMind project.

Elon Musk says merging biological intelligence and artificial intelligence is important to help human beings deal with the AI apocalypse. Almost exactly a month ago, Elon Musk introduced a room of engineers and curious consumers to a sci-fi-sounding invention made by his neurotechnology startup Neuralink: an implantable "brain chip" that will "merge biological intelligence with machine intelligence." Per Musk's description, this chip will be installed in a person's brain by drilling a two-millimeter hole in the skull. "The interface to the chip is wireless, so you have no wires poking out of your head," he assured. Musk argued that such devices will help humans deal with the so-called AI apocalypse, a scenario in which artificial intelligence outpaces human intelligence and takes control of the planet away from the human species.

I have a problem when I see the word healthcare next to industry. It seems like we're talking about a basic human right next to a word that means a sellable product. This industry relies heavily on human intervention and subjective opinions. It uses advanced technologies like genome decoding, MRIs, PET scans, and radiotherapy., But it also strongly depends on human interpretations, and humans make mistakes.