The advent of automated machine learning platforms has expanded the access and availability of algorithmic interpretation over the past several years. But how do the different machine learning platforms stack up from a performance perspective? That's the question that researchers from Arizona State University sought to answer. As the market for machine learning platforms expands, users are naturally inclined to seek sources of information to rank and rate the various options that are available to them. Which systems are the easiest to use?
We have been working hard to understand the core stack of data services that make our cities work, or not work, depending on where you live. This is the current data sets available via existing services, which may or may not exist in a machine readable format, via an API, depending on the city you live in. There is a huge amount of data already available at the municipal level, but here is where we have started as of January. Real Time Streaming 311 Incidents In Chicago 511 - Traffic, Travel & Transit Adding 511 Data To Our Existing Transit Data Research Getting Your 511 Traffic Incidents in the San Francisco Bay Area as a Real Time Streaming API 911 - Emergency Events Making 911 Data Real Time Streaming 911 Emergency Data For Baltimore, MD We've targeted these three areas because they make a difference in our lives at the local level, and have huge potential when it comes to making available via web APIs, and in real time using Server-Sent Events (SSE). Now that we have these three critical aspects of municipal operations profiled, we are going to work to profile as many cities as we can.
News of a specialized computer program beating human champions at games like chess and Go might not surprise people as much as it might have before, as it did when Deep Blue beat world chess champ Garry Kasparov back in 1997, or even more recently when Google DeepMind's AI AlphaGo beat Lee Sedol in a stunning upset back in 2016.
Ayasdi offers an enterprise-grade artificial intelligence platform that leverages big data to make intelligent business applications; for instance, Ayasdi has an application that powers parts of HSBC's anti-money laundering technology stack. Headquartered in California, Ayasdi has further offices in London with global expansion demanding a third office potentially coming to Singapore for 2018. Lots of stuff is going on with AI. Broadly speaking there are two major ways of thinking about problems addressable by AI today. One side is around perception based problems – self driving cars and virtual assistants – these rely on data such as imaging and sensing the environment.
Java Development Lead, Team Lead, NLP, Machine Learning, AWS, ElasticSearch, REST APIs My industry leading global client is looking for a Java Technical Lead / Java Team Lead for a permanent position based in their Oxford offices. This an extremely exciting opportunity to work with some of the world's best technologists using cutting edge technologies, Machine Learning and Natural Language Processing to make radical advancements. As a lead you'll have diverse responsibilities, including hands on development, design, code reviews, mentoring of more junior team members and process improvement. Key Responsibilities of the Java Technical Lead / Software Engineering Lead: • Implement new features in our system from initial design through delivery • Work with users and product management to define what they want, what they need, and what we can deliver • Find opportunities for continuous improvements to our system • Fix issues and rework code, monitors, and alerts for high stability • Learn and apply best practices across the entire stack • Be part of the team • Interfacing with on and offshore teams • Providing technical direction and peer leadership • Monitoring, steering and advising both on and offshore development work The Java Technical Lead / Software Engineering Lead will bring skills/experience in: • Strong Java skills. You are a Java programmer and have stayed current with the evolution of the Java language and its ecosystem of frameworks and build tools.
I wanted to talk to Moore about some of the AI basics -- like how the School of Computer Science defines artificial intelligence. That may seem simplistic, but the term is used so broadly that I think it's worth taking the time to make sure we all know what we're talking about when we talk about AI. So, our conversation started with a definition, it moved to CMU's AI stack, which I'll explain in a minute and which could help CIOs wrap their heads around this sprawling term.
The Fourth Industrial revolution brings the implementation of such technologies like Big Data, Internet of Things, Virtual Reality, Augmented Reality, Machine Learning and Artificial Intelligence. Current market requirements are changing rapidly and use of machine learning algorithms based solutions can significantly increase business competitive advantages in the context of globalization. Back in 1995 an idea of creating a platform that could provide a reliability of data sources andtrained neural networks has been declared. Platform implementation would lead to the rapid development of the whole stack of machine learning based technologies and help to reduce the cost of its development, mass adoption and significant growth of derived systems efficiency. The concept of rationality of several machine learning models merging with their further transfer learning has been proposed and proved later. The common limiting factor of development and implementation of similar systems was the lack of reliable technology that could provide a decentralised digital trustworthiness for final machine learning models and data sources.
Artificial intelligence is bringing new demands to processors. The algorithmic data crunching is different from earlier models of processing data highlighted by benchmarks like LINPACK. It is also changing computing architectures by de-emphasizing the CPU and harnessing the faster computing power of coprocessors. The CPU is just a facilitator, and a lot of deep-learning is done on accelerator chips like GPUs, FPGAs and Google's Tensor processing unit.
Shivam Sharma works as a Subject Matter Expert at CloudThat Technologies and has been involved in various large and complex projects with global clients. He has experience in Machine Learning and Microsoft Infrastructure technology stack including Azure Stack, Office 365, EMS, Lync, Exchange, System Center, Windows Servers, designing Active Directory and managing various domain services, including Hyper-V virtualization. Having core training and consulting experience, he is passionate about technology and is involved in delivering training to corporate and individuals on cutting edge technologies. Arzan has 7 years of experience in Microsoft Infrastructure technology stack including setting up Windows servers, designing Active Directory and managing various domain services, including Hyper-V virtualization. As a Cloud Solutions Architect at CloudThat, he is responsible for deploying, supporting and managing client infrastructures on Azure.
Co-designing efficient machine learning based systems across the whole hardware/software stack to trade off speed, accuracy, energy and costs is becoming extremely complex and time consuming. Researchers often struggle to evaluate and compare different published works across rapidly evolving software frameworks, heterogeneous hardware platforms, compilers, libraries, algorithms, data sets, models, and environments. We present our community effort to develop an open co-design tournament platform with an online public scoreboard. It will gradually incorporate best research practices while providing a common way for multidisciplinary researchers to optimize and compare the quality vs. efficiency Pareto optimality of various workloads on diverse and complete hardware/software systems. We want to leverage the open-source Collective Knowledge framework and the ACM artifact evaluation methodology to validate and share the complete machine learning system implementations in a standardized, portable, and reproducible fashion. We plan to hold regular multi-objective optimization and co-design tournaments for emerging workloads such as deep learning, starting with ASPLOS'18 (ACM conference on Architectural Support for Programming Languages and Operating Systems - the premier forum for multidisciplinary systems research spanning computer architecture and hardware, programming languages and compilers, operating systems and networking) to build a public repository of the most efficient machine learning algorithms and systems which can be easily customized, reused and built upon.