New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
Three institutions working together have applied DeepMind's neural network learning system to the task of discovering and diagnosing eye diseases. Moorfields Eye Hospital has been working with Google's DeepMind Health subsidiary and University College London in the effort, and have documented their progress in a paper published in Nature Medicine. As the researchers note, eye doctors currently use a machine that carries out optical coherence tomography (OCT) on patients to find out if they have an eye disease. While the technique is quite useful and accurate, it requires highly trained doctors to spend time looking at results. The researchers suggest this creates a backlog that sometimes prevents patients from getting the care they need in time to save their vision.
Only 7 percent of patients live five years after diagnosis of pancreatic cancer, the lowest rate for any cancer, according to the American Cancer Society. Elliot K. Fishman, MD, a researcher and radiologist at Johns Hopkins, is on the forefront of trying to change this statistic, and he's using artificial intelligence to do it. Fishman aims to spot pancreatic cancers far sooner than humans alone can by applying GPU-accelerated deep learning artificial intelligence to the task. Johns Hopkins is suited to developing a deep learning system because it has the massive amounts of data on pancreatic cancer needed to teach a computer to detect the disease in a CT scan. Hospital researchers also have NVIDIA's DGX-1 AI Supercomputer.
Intel has an ambition to bring more artificial intelligence technology into all aspects of its business, and today is stepping up its game a little in the area with an acquisition. The computer processing giant has acquired Vertex.AI, a startup that had a mission of making it possible to develop "deep learning for every platform", and had built a deep learning engine called PlaidML to do this. Terms of the deal have not been disclosed but Intel has provided us with the following statement, confirming the deal and that the whole team -- including founders Choong Ng and Brian Retford -- will be joining Intel. "Intel has acquired Vertex.AI, a Seattle-based startup focused on deep learning compilation tools and associated technology. The seven-person Vertex.AI team joined the Movidius team in Intel's Artificial Intelligence Products Group.
The team achieved a peak rate between 11.73 and 15.07 petaflops (single-precision) when running its data set on the Cori supercomputer. Machine learning, a form of artificial intelligence, enjoys unprecedented success in commercial applications. However, the use of machine learning in high performance computing for science has been limited. Why? Advanced machine learning tools weren't designed for big data sets, like those used to study stars and planets. A team from Intel, National Energy Research Scientific Computing Center (NERSC), and Stanford changed that.
Deep learning is much more like the human brain than is machine learning. Consider the way your brain interprets faces, for example. Your conscious self recognizes the whole face as a distinct person by interpreting the relationships between the parts at an astounding pace. You can't label each relationship it has identified, or even quantify and write out the variables your brain is interpreting. These things happen without your knowledge, so to speak.
Supercomputer manufacturer Cray has introduced a new set of four artificial intelligence (AI) products to accelerate the adoption of deep learning in science and enterprise. The new products include Cray Accel AI Lab, which aims to advance the development of deep learning technologies and workflows, and Cray Accel AI Offerings, featured with NVIDIA Tesla V100 GPU accelerators. The new Cray Urika-XC software suite, which brings graph analytics, deep learning, and big data analytics tools the Cray XC supercomputers, will now include the TensorFlow computational framework and enhancements to the Cray software environment that are particularly designed to accelerate machine learning frameworks. Also included is a collaboration agreement with Intel. The Cray-Intel team up will deliver a productised software stack for deep learning at scale on Cray systems and leverage Intel's AI technologies to advance the state-of-the-art in distributed deep learning and machine learning.
Machine learning platforms are not the wave of the future. Developers need to know how and when to harness their power. Working within the ML landscape while using the right tools like Filestack can make it easier for developers to create a productive algorithm that taps into its power. The following machine learning platforms and tools -- listed in no certain order -- are available now as resources to seamlessly integrate the power of ML into daily tasks. H2O was designed for the Python, R and Java programming languages by H2O.ai.
An AI system created by Google's DeepMind Health, Moorfields Eye Hospital NHS Foundation Trust, and University College London (UCL) Institute of Ophthalmology can correctly determine how to refer optometry patients in 94 percent of cases, putting it on par with top human experts. The advances in AI-driven eye disease treatment were detailed in a study being published online in the journal Nature Medicine. Results of the work that began in 2016 were first made public in a Financial Times report in February, which found AI used to analyze retinal scans for signs of the biggest eye diseases -- like glaucoma, age-related macular degeneration, and diabetic retinopathy -- could be more accurate than trained human experts. Neural networks trained to discover patterns in images have also been used to do things like detect cardiovascular disease, breast cancer, and kidney disease. "The AI technology we're developing is designed to prioritize patients who need to be seen and treated urgently by a doctor or eye care professional," said UCL scientist Dr. Pearse Keane in a statement shared with VentureBeat.
Machine learning, a form of artificial intelligence, enjoys unprecedented success in commercial applications. However, the use of machine learning in high performance computing for science has been limited. Why? Advanced machine learning tools weren't designed for big data sets, like those used to study stars and planets. A team from Intel, National Energy Research Scientific Computing Center (NERSC), and Stanford changed that situation. They developed the first 15-petaflop deep-learning software.