"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
Using machine learning to analyze blockchain datasets is a fascinating challenge. Beyond the incredible potential of uncovering unknown insights that help us understand the behavior of crypto-assets, blockchain datasets presents very unique challenges to a machine learning practitioner. Many of these challenges translate into major roadblocks for most traditional machine learning techniques. However, the rapid evolution of machine intelligence technologies has enabled the creation of novel machine learning methods that result very applicable to the analysis of blockchain datasets. At IntoTheBlock, we regularly experiment with these new methods to improve the efficiency of our market intelligence signals.
AI technology has become increasingly sophisticated in recent years. So many products and services now rely on the technology to provide automation and intelligence that it is deeply and irrevocably intertwined with our everyday world. Whether through devices we use to enable convenience at home or in the way products we use all the time are manufactured, its impact is everywhere, driving innovation in just about every aspect of our lives. But there are missing pieces to this puzzle that still cause frustration for end-users and present significant challenges for researchers trying to improve how AI technology performs. A common sense approach Before his passing in 2018, Microsoft co-founder Paul Allen dedicated an admirable amount of time and resources to solving an essential challenge that seems to come up again and again: The fundamental lack of common sense in AI technologies.
Researchers from Alphabet-owned company DeepMind say a new AI can ingest a patient's medical history and predict, with 90 percent accuracy, whether they're going to need dialysis for acute kidney injury 48 hours before it occurs. "Currently we pick these things up too late and harm is caused to patients, and we think there's a real opportunity for these AI systems to be able to predict and prevent rather than just what currently happens, which is clinicians almost firefighting and running around problems that have already developed," DeepMind clinical lead Dominic King told Wired. The team fed health data from more than 700,000 Veterans Affairs hospital patients across the U.S. to their neural network. Their results were promising, according to a paper about the research published Wednesday in the journal Nature: the system can even tell doctors what piece of medical data tipped it off that a kidney crisis was imminent. But while the system is speedy, it's way too trigger-happy: it reported two false positives for every correctly identified kidney injury.
That would improve health alerts for people at heightened risk of developing problems because of high ozone levels. Yunsoo Choi, associate professor in the Department of Earth and Atmospheric Sciences and corresponding author for a paper explaining the work, said they built an artificially intelligent model using a convolutional neural network, which is able to take information from current conditions and accurately predict ozone levels for the next day. The work was published in the journal Neural Networks. "If we know the conditions of today, we can predict the conditions of tomorrow," Choi said. Ozone is an unstable gas, formed by a chemical reaction when sunlight combines with nitrogen oxides (NOx) and volatile organic compounds, both of which are found in automobile and industrial emissions.
It's not who has the best algorithm that wins; It's who has the most data -- Andrew Ng. Image classification is the task of assigning an input image one label from a fixed set of categories. This is one of the core problems in Computer Vision that, despite its simplicity, has a large variety of practical applications. In this blog I will be demonstrating how deep learning can be applied even if we don't have enough data. I have created my own custom car vs bus classifier with 100 images of each category.
Artificial intelligence can be used to predict molecular wave functions and the electronic properties of molecules. This innovative AI method developed by a team of researchers at the University of Warwick, the Technical University of Berlin and the University of Luxembourg, could be used to speed-up the design of drug molecules or new materials. Artificial intelligence and machine learning algorithms are routinely used to predict our purchasing behavior and to recognize our faces or handwriting. In scientific research, Artificial Intelligence is establishing itself as a crucial tool for scientific discovery. In chemistry, AI has become instrumental in predicting the outcomes of experiments or simulations of quantum systems.
For developers, advances in hardware and software for machine learning (ML) promise to bring these sophisticated methods to Internet of Things (IoT) edge devices. As this field of research evolves, however, developers can easily find themselves immersed in the deep theory behind these techniques instead of focusing on currently available solutions to help them get an ML-based design to market. To help designers get moving more quickly, this article briefly reviews the objectives and capabilities of ML, the ML development cycle, and the architecture of a basic fully connected neural network and a convolutional neural network (CNN). It then discusses the frameworks, libraries, and drivers that are enabling mainstream ML applications. It concludes by showing how general purpose processors and FPGAs can serve as the hardware platform for implementing machine learning algorithms.
Next-generation vehicles such as drones have a hard time landing. Drone controllers usually bring the drone near the ground and then drop it. How low the drone can be brought down depends on the aerodynamics of the drone and other reactions from the ground. Since drones of the future will be carrying medicines and other fragile instruments into mysterious landscapes or hilly areas, dropping the drone isn't always desirable. To address this problem of smooth landing, researchers at CalTech's Center for Autonomous Systems and Technologies (CAST), have imbibed neural networks into their approaches.
The Apache Software Foundation (ASF) recently announced that SINGA, a framework for distributed deep-learning, has graduated to top-level project (TLP) status, signifying the project's maturity and stability. SINGA has already been adopted by companies in several sectors, including banking and healthcare. Originally developed at the National University of Singapore, SINGA joined ASF's incubator in March 2015. SINGA provides a framework for distributing the work of training deep-learning models across a cluster of machines, in order to reduce the time needed to train the model. In addition to its use as a platform for academic research, SINGA has been used in commercial applications by Citigroup and CBRE, as well as in several health-care applications, including an app to aid patients with pre-diabetes.
Artificial Intelligence (AI) and machine learning enter the research mainstream of biopharmaceutical companies, such as GlaxoSmithKline (GSK). GlaxoSmithKline (GSK) is creating a data-focused culture and a global machine-learning team. GlaxoSmithKline's (GSK's) data-first approach to drug discovery and development comes directly from chief executive officer (CEO) Emma Walmsley and chief scientific officer (CSO) Hal Barron. Their goal is doubling the chance of successful medicines being produced by using genetically validated targets. And that demands a strong team in artificial intelligence and machine learning (AI/ML).