By 2020, Gartner predicts, there will be 250 million cars connected to each other and to the infrastructure around them via Wi-Fi systems that will allow vehicles to communicate with each other and the roadways. As the amount of information being fed into IVI units or telematics systems grows, vehicles will be able to capture and share not only internal systems status and location data, but also changes in surroundings in real time, according to Gartner analyst Thilo Koslowski. ... Speaking at the New England Motor Press Association Technology Conference at MIT, Pratt said auto makers are more focused on assisting drivers for years to come instead of producing fully autonomous vehicles that take the steering wheel from drivers. A lot of the discussion among automakers and within their R&D organizations involve how much control the car should have.
ART and Nephila have worked with a number of firms to develop the proof of concept and see extensions of this technology having relevance across the insurance industry: for example, in optimizing the payment processes involved in international fronting for captive insurers, where multiple process steps are involved in transferring premium from a corporate to its own subsidiary. Laura Taylor, Managing Principal at Nephila, adds: “We believe technology will drive the future of insurance. We have invested a great deal accordingly and are pleased to extend our long-standing strategic partnership with ART to use of the Blockchain.” “In our journey to become more digital, Blockchain promises to help us create more transparent, more convenient and faster services for our customers,” says Solmaz Altin
Let’s start with a look at why NFV environments elicit performance anxiety, and where it comes from. The core technology for NFV – virtual machines (VMs) running on x86-based servers – emerged from the enterprise world. VMs are designed to “spin up” instances of an operating system that can run applications for an enterprise customer, and then “scale out” by adding more VMs and, if necessary, servers, to keep up with new subscribers. Certain applications in a service provider environment – for example, mobile services – require the capability to handle millions of subscribers. In addition, real-time communications applications have more stringent requirements than, say, a Web server. If a VM fails, there is a process for replacing it or moving it to another server, a move that might take seconds or even minutes in standard cloud environments.
Christian Szegedy from Google begun a quest aimed at reducing the computational burden of deep neural networks, and devised the GoogLeNet the first Inception architecture. By now, Fall 2014, deep learning models were becoming extermely useful in categorizing the content of images and video frames. Most skeptics had given in that Deep Learning and neural nets came back to stay this time. Given the usefulness of these techniques, the internet giants like Google were very interested in efficient and large deployments of architectures on their server farms. Christian thought a lot about ways to reduce the computational burden of deep neural nets while obtaining state-of-art performance (on ImageNet, for example). Or be able to keep the computational cost the same, while offering improved performance. He and his team came up with the Inception module:
With Cortana Intelligence Suite, students had access to a rich set of tools such as Azure ML, Jupyter notebooks with R and Python, and rich visualization capabilities with Power BI. In each city, students were given access to a collection of local data sets and challenged to develop a useful predictive analytical application. Students picked from a wide range of areas including healthcare, environment, smart city design and more. With the support and mentorship of university faculty and Microsoft technical staff, students wrestled through the creative problem solving and technical implementation aspects of data science. Many high-performing teams even published their models in app stores. One example is Live London, the winners from UC London, who developed a safe neighbourhood tracker app available on the Android app store.
The IMPACT platform is modular in its approach, Ploumen said, allowing entities to "mix and match" services like device management or analytics, depending on what third-party components they may already use. It also includes a new edition of Nokia's Motive Connected Device Platform (CDP), which supports more than 80,000 device/sensor models and already has connected and managed more than 1.5 billion devices. Nokia has been in a rocky transitional period since its acquisition of Alcatel-Lucent in April last year, slashing jobs and reporting a loss of €613 million for the first quarter of 2016. However, the deal has allowed the company to focus on more forward-looking revenue streams like IoT. In April, Nokia announced its plans to acquire wearable and health-monitoring company Withings, adding to Nokia's portfolio in one of the fastest-growing IoT segments.
“Conceptually, this is an attractive proposition that will allow financial institutions to introduce a completely new safeguard that reinforces existing authentication processes, while leveraging the growing availability and sophistication of consumer mobile hardware and merchant point-of-sale devices.” The hurdle for biometric authentication today is its strong hardware and software dependency, according to Ho. “Not all consumers have compatible devices to support fingerprint scanning, the quality of sound capture varies from phone to phone and is not regulated by a common industry standard, and merchant biometric requires investment in specialised tools which most stakeholders may be hesitant to bear,” said Ho.
Deep learning is a subfield of machine learning, which is a vibrant research area in artificial intelligence, or AI. Abstractly, machine learning is an approach to approximating functions based on a collection of data points. For example, given the sequence “2, 4, 6,…” a machine might predict that the 4th element of the sequence is 8, and that the 5th is 10, by hypothesizing that the sequence is capturing the behavior of the function 2 times X, where X is the position of the element in the sequence. This paradigm is quite general. It has been highly successful in applications ranging from self-driving cars and speech recognition to anticipating airfare fluctuations and much more. In a sense, deep learning is not unique.
Big data can bring huge benefits to businesses of all sizes. However, as with any business project, proper preparation and planning is essential, especially when it comes to infrastructure. Until recently it was hard for companies to get into big data without making heavy infrastructure investments (expensive data warehouses, software, analytics staff, etc.). But times have changed. Cloud computing in particular has opened up a lot of options for using big data, as it means businesses can tap into big data without having to invest in massive on-site storage and data processing facilities. In order to get going with big data and turn it into insights and business value, it’s likely you’ll need to make investments in the following key infrastructure elements: data collection, data storage, data analysis, and data visualization/output. Let’s look at each area in turn.
It matters a lot to connected cars. We have been working with major telecos (telecommunications companies) and also with some equipment manufacturers to shape 5G and make it useful for connected cars. In the past a lot of the connectivity was related to classical entertainment services. In the future a lot of the functionality will be more "serious." For example, automated driving will require the car to be entirely safe even without a mobile connection. On the other hand, a lot of the services that 5G can enable will help make that a really good product. Automated cars will move based on maps and sensors, relating what they see to what’s in the map. Updating that map is going to be something done through mobile connections.
Quote for the day:
"It is not that I'm so smart. But I stay with the questions much longer." -- Albert Einstein