If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Machine learning (ML) is hard; making it work within the resource-constrained environment of an embedded device can easily become a quagmire. In light of this harsh reality, anyone attempting to implement ML in an embedded system must consider, and frequently revisit, the design aspects crucially affected by its requirements. A bit of upfront planning makes the difference between project success and failure. For this article, our focus is on building commercial-grade applications with significant, or even dominant, ML components. We'll use a theoretical scenario in which you have a device, or better yet an idea for one, which will perform complex analytics, usually in something close to real time, and deliver results in the form of network traffic, user data displays, machine control, or all three.
In a short span of time, Artificial Intelligence and Machine Learning (AI ML) have become the central marketable assets for SaaS and Cloud businesses. "Technology Maturity" of the organizations which are already offering and leveraging AI ML and data infrastructure in the Cloud range from start-ups, to innovators and pioneers, to leaders and trend-setters. The worldwide public cloud services market is projected to grow 17.5 percent in 2019 to total $214.3 billion, up from $182.4 billion in 2018, according to Gartner, Inc. Forrester predicts the Public Cloud market to reach $411 billion by 2022. "Fewer, but larger, Public Cloud platform providers and a maturing SaaS ecosystem will dominate Enterprise Cloud spending. CIOs should use this forecast to benchmark the pace and shape of their public Cloud strategies."
Today's modern enterprises are collecting data at exponential rates, and it's no mystery that effectively making use of that data has become a top priority for many. According to a recent survey of 2000 global enterprises by McKinsey & Company, 47% of organizations have embedded at least one AI capability in their standard business processes. This is up from 20% in 2017 and it's clear that this growth has created a global race to enabling the next important evolution of business as we know it: The AI-first enterprise. But what does this actually mean? With investment in AI technologies poised to reach $9.5 billion over the next three years, the imminent opportunity involves embedding data and machine learning intelligence across the business at scale -- predicting the next best move for growth, making every product a data product, or creating entirely new data-driven revenue streams.
ORACLE OPENWORLD -- Oracle Exadata Database Machine X8M, available today, sets a new bar and changes the dynamics of the database infrastructure market. Exadata X8M combines Intel Optane DC persistent memory and 100 gigabit remote direct memory access (RDMA) over Converged Ethernet (RoCE) to remove storage bottlenecks and dramatically increase performance for the most demanding workloads such as Online Transaction Processing (OLTP), analytics, IoT, fraud detection, and high frequency trading. "With Exadata X8M, we deliver in-memory performance with all the benefits of shared storage for both OLTP and analytics," said Juan Loaiza, executive vice president, mission-critical database technologies, Oracle. "Reducing response times by an order of magnitude using direct database access to shared persistent memory accelerates every OLTP application, and is a game changer for applications that need real-time access to large amounts of data such as fraud detection and personalized shopping." Exadata X8M helps customers perform existing tasks faster and accelerates time-to-insight, while also enabling deeper and more frequent analyses.
Pure Storage (NYSE: PSTG) helps innovators build a better world with data. Pure's data solutions enable SaaS companies, cloud service providers, and enterprise and public sector customers to deliver real-time, secure data to power their mission-critical production, DevOps, and modern analytics environments in a multi-cloud environment. One of the fastest-growing enterprise IT companies in history, Pure Storage enables customers to quickly adopt next-generation technologies, including artificial intelligence and machine learning, to help maximize the value of their data for competitive advantage. And with a certified NPS customer satisfaction score in the top one percent of B2B companies, Pure's ever-expanding list of customers are among the happiest in the world.
All industries face similar challenges as they seek to extract information from forms, documents, and visual artifacts - and most agree that is costly, time consuming and prone to errors with manual data entry. In this session, you will learn how to use machine learning on a scalable cloud-based platform to efficiently analyze documents - and use the knowledge hiding within - to improve decision-making at your company. Iron Mountain will show how they have been able to ingest nearly every type of imaged data from a wide variety of origins, both on-premise and in the cloud, to capture, process, analyze and then store data integrated into a complete visual search interface to enable their customers to unlock insights from their documents.
These Docker images use popular frameworks and are performance optimized, compatibility tested, and ready to deploy. Deep Learning Containers provide a consistent environment across Google Cloud services, making it easy to scale in the cloud or shift from on-premises. You have the flexibility to deploy on Google Kubernetes Engine (GKE), AI Platform, Cloud Run, Compute Engine, Kubernetes, and Docker Swarm.
A large majority of enterprises are using cloud computing, but IT directors have committed relatively few resources to the cloud--perhaps because the connection between budget and value can be unclear. The five vectors of progress described here can help drive broader and deeper adoption of cloud computing and enable enterprises to get more value from their shift to the cloud. While three-quarters of enterprises have adopted the cloud to some degree,7 leaders have moved only 20 percent of business processes to the cloud, according to Ovum, a technology market researcher.8 This suggests that companies have a ways to go in utilizing the cloud as a platform for enterprise digital transformation, not just as a way to lower IT capital expenditure and accelerate service delivery. The vectors of progress discussed below could drive faster adoption of cloud and help companies derive additional value from it.
At the ongoing the In ter na tional Motor Show (IAA) in Frankfurt, technology company Continental, together with the French company EasyMile, of which Continental has been a shareholder since 2017, are demonstrating the mobility of the future -- quite literally. Trade fair visitors can commute autonomously and powered only by electricity between two stops. The two companies have set up a demonstration track for a driverless Robo-Taxi between Hall 9 and the IAA Test Drive on the West Outdoor Area.German Chancellor Angela Merkel was one of the visitors to the fair who took a ride in the Cube robo taxi. The Robo-Taxi Cube is a Continental development platform for driverless vehicles technologies based on the EZ10 shuttle and driverless software from EasyMile. The shuttle service runs on all days of the fair during opening hours.
IABM report: "Change is everywhere" The increasingly crowded and competitive media and broadcasting industry is consolidating and searching for scale to compete with firms adopting new technology to support its transformation, an IABM report finds. Leading the industry transformation is the demand for new technology to support efficient workflows, manage content distribution, enhance the user experiences and support revenue growth. "Change is everywhere," according to the latest strategic industry analysis, the IABM Special Report which examined the major trends in the broadcast and media industry ahead of its presentation during IBC2019. With the escalation of OTT streaming services and the continued influx of money invested into video content, traditional media companies are consolidating and forming alliances to remain competitive. The report outlines: "This increasingly competitive and complex environment is forcing media companies to search for digital speed to attract eyeballs to their services. "This is leading to a rapid transformation of demand for media technology, which is sending shockwaves throughout the supply-side of the industry." Based on hard data obtained and analysed by the IABM's Business Intelligence Unit, the report was backed by quantitative and qualitative information and commentary from key players across broadcast and media industry. The report found the technology adoption of cloud, artificial intelligence (AI) and IP have continued to increase. "By deploying cloud-based services, media companies can dramatically reduce time-to-market for their services – thus increasing revenues - and flexibly adjust resources by moving to consumption-based pricing." Global media firms including Discovery have moved portions of their operations to the cloud, however smaller media organisations remain less likely to do so. According to the data: "AI applications in the broadcast and media industry are growing and adoption has significantly increased in recent years.