"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
Road Watch 2.0 Vision Zero Pedestrian Deaths Project: Learn how an award-winning Richmond Hill and York Regional Police road safety Road Watch program is the base for a space age approach to make Toronto roads safer, as kicked off on the Global News 640 AM John Oakley Show. Hear a plan to make roads safer while mitigating climate through earth and Space LiDAR technology. Learn how road safety and climate change mitigation is combined in the Ethical AI Energy Cloud City master plan, a UN 17 Sustainable Development Goals Emerging Technology Framework to Unite Society. Dave D'Silva founded Intelligent Market Solutions Group (IMSG) to make good on a University of Waterloo pact with Bill Gates. IMSG is a socio-economic emerging technology project management firm creating Star Trek inspired Ethical AI systems.
With his 3-D printed doberman-like head, robot dog Astro may look like something out of a Black Mirror episode -- but this clever canine may be our new best friend. Powered by artificial intelligence technology, the metallic mutt can presently respond to simple commands like'sit', 'stand' and'lie down'. However, by training him in thousands of different scenarios, this robot dog is capable of learning new tricks. His developers expect that he will eventually be able to recognise different languages, hand signals, people and other dogs -- and even team up with drones. Astro is intended to help security forces sniff out prohibited items and first responders scour disaster sites -- but he might even find work as a guide dog.
In a field like Computer Vision, which has been explored and studied for long, Generative Adversarial Network (GAN) was a recent addition which instantly became a new standard for training machines. GAN is an architecture developed by Ian Goodfellow and his colleagues in 2014 which makes use of multiple neural networks that compete against each other to make better predictions. Generator, the network that is responsible for generating new data from training data, and Discriminator, the one that identifies and distinguishes a generated image/fake image from an original image of the training set together form a GAN. Both these networks learn based on their previous predictions, competing with each other for a better outcome. In this article we will break down a simple GAN made with Keras into 8 simple steps.
State-of-the-art machine learning frameworks support a wide variety of design features to enable a flexible machine learning programming interface and to ease the programmability burden on machine learning developers. Identifying and using a performance-optimal setting in feature-rich frameworks, however, involves a non-trivial amount of performance characterization and domain-specific knowledge. This paper takes a deep dive into analyzing the performance impact of key design features and the role of parallelism. The observations and insights distill into a simple set of guidelines that one can use to achieve much higher training and inference speedup. The evaluation results show that our proposed performance tuning guidelines outperform both the Intel and TensorFlow recommended settings by 1.29x and 1.34x, respectively, across a diverse set of real-world deep learning models.
One of the most common applications of machine learning in the finance sector is fraud detection. Fraud detection algorithms can be used to parse multiple data points from thousands of transaction records in seconds, such as cardholder identification data, where the card was issued, time the transaction took place, transaction location, and transaction amount. To implement a fraud detection model, multiple accurately tagged instances of fraud should already exist in a data set to properly train the models. Once the model detects an anomaly among transaction data, a notification system can be programmed to alert fraud detection services in the moment the model identifies a suspicious transaction.
We all see the headlines nearly every day. Whether primitive (gunpowder) or cutting-edge (unmanned aerial vehicles) in the wrong hands, technology can empower bad actors and put our society at risk, creating a sense of helplessness and frustration. Current approaches to protecting our public venues are not up to the task, and, frankly appear to meet Einstein's definition of insanity: "doing the same thing over and over and expecting a different result." It is time to look past traditional defense technologies and see if newer approaches can tilt the pendulum back in the defender's favor. Artificial Intelligence (AI) can play a critical role here, helping to identify, classify and promulgate counteractions on potential threats faster than any security personnel.
If you have SAP HANA data base which stores all the enterprise transactional data and want to apply predictive/machine learning algorithms on the HANA data base tables or views using Rapid Miner. This blog gives you the steps to connect SAP HANA data base from Rapid Miner and retrieve tables/views/procedures data and apply Rapid Miner statistical algorithms or machine learning techniques to get the insights of data. Back Ground and use case:Rapid Miner is a data science platform for teams that unites data prep, machine learning, text mining and predictive model deployment. It is used for business and commercial applications as well as for research, education, training, rapid prototyping, and application development and supports all steps of the machine learning process including data preparation, results visualization, model validation and optimization. SAP HANA is an in-memory, column-oriented, relational database management system developed and marketed by SAP SE.
Machine Learning Yearning is about structuring the development of machine learning projects. The book contains practical insights that are difficult to find somewhere else, in a format that is easy to share with teammates and collaborators. Most technical AI courses will explain to you how the different ML algorithms work under the hood, but this book teaches you how to actually use them. If you aspire to be a technical leader in AI, this book will help you on your way. Historically, the only way to learn how to make strategic decisions about AI projects was to participate in a graduate program or to gain experience working at a company.
Thanks to a Machine Learning Research Award from Amazon Web Services (AWS) to a research alliance supported by UPMC Enterprises, a seed has been planted to accelerate the consortium's medical research initiatives, help participating entrepreneurs more rapidly scale their innovations, and, in some small fashion, contribute to positioning the Pittsburgh area as a healthcare technology innovation hub. The award provides researchers access to Amazon's cloud-based platform and machine learning tools, enabling them to incorporate sophisticated technology into innovations at an early stage of the development process. These innovations "will be able to be deployed more easily in the real world," says Rob Hartman, PhD, director of translational science, UPMC Enterprises. The Amazon award was made to the Pittsburgh Health Data Alliance (PHDA), which was formed four years ago by UPMC, the University of Pittsburgh, and Carnegie Mellon University. PDHA uses "big data" generated in health care--including patient information in the electronic health record, diagnostic imaging, prescriptions, genomic profiles, and insurance records--to transform the way that diseases are treated and prevented, and to better engage patients in their own care, according to a news release.
In 2018 alone, AI-related startups and ventures have secured $9.3 billion in VC funding, according to a report from PwC and CB Insights. The Indian unicorns PayTm, Swiggy, and Oyo have been investing resources to gain AI capabilities and have acquired at least one AI company. In 2018, VCs have funded Indian AI startups with USD 478.38 million in 111 funding rounds. One of the reasons why AI & machine learning-based technologies on the rise are that the competitive advantages one can develop using them, especially with the customer experience and cost optimization. In e-commerce, for example, understanding consumer behaviour and product demand and making the right offer at the right time can be the difference between winning or losing over the competition.