GrAI Matter Labs (aka GML), a neuromorphic computing pioneer today revealed NeuronFlow – a new programmable processor technology – and announced an early access program to its GrAIFlow software development kit. GML's NeuronFlow technology draws from neuromorphic and dataflow paradigms to uniquely solve core problems for real world AI applications. One of the breakthroughs of the technology is dynamic dataflow processing of real-time data, which drastically reduces application latency for autonomous navigation, cognitive voice & video assistants and smart healthcare applications. NeuronFlow utilizes in-memory compute with a mesh of cores and local neuron/synapse memories, avoiding the memory bottleneck of traditional Von Neumann architectures. It is based on a fully digital design with packet-switched connectivity and sparsely connected event-based neural networks to allow scalable implementations across market segments.
In the analysis phase we carry out two tasks, a real time analysis applying the models to obtain the data shown on the web (without storing them) and which identify separately whether or not there is a parked aircraft, in addition to obtaining the airline, the aircraft model and whether or not there are vehicles for fuel loading and baggage management. We carry out these analyses using two different recognition systems, on the one hand we apply automatic learning models programmed and trained by us (our own models and scripts) and in parallel we carry out the same task using Cloud Auto ML (a package of products that makes it possible to create customised models easily). With this double processing we increase the reliability of the results by comparison, and we improve the training processes of our own models and scripts. In addition to real time processing, we store all collected images for a complete overnight reprocessing. This is useful because depending on the project to which these technologies are applied, it will not always be necessary to obtain data in real time, and in this case, the data processed in batches are stored in Big Query for later analysis obtaining different indicators.
SAN FRANCISCO–(BUSINESS WIRE)–INDUS.AI, a construction software company using computer vision to track and analyze construction project performance in real-time, announced today that it has received an $8 million Series A investment led by Millennium New Horizons with additional participation from strategic investors Foundamental and Groundbreak Ventures. Previous investors Spero Ventures, UP2398, and Bootstrap Labs also joined the round. The new funding will be used to accelerate product development and expand sales, marketing and customer success services. "The construction industry has been a neglected space for too long. This country expends extraordinary energy and capital to build amazing things – tunnels, train stations, skyscrapers and stadiums. But the sad truth is that the vast majority of those projects are painfully over-budget and delayed," said Matt Man, CEO and co-founder.
SAN FRANCISCO, September 17, 2019 -- 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.
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.
The recent rise of insurtechs has spurred modernization in the fundamentals of insurance, including policy creation, underwriting, and claims management. Insurtechs, such as Hippo and Lemonade, have implemented AI and data analytics to provide quick and accurate quotes to their customers while also decreasing their own operating costs. Such innovation has disrupted the traditional insurance industry and has helped alter underlying business models of legacy companies, forcing them to invest heavily in insurtech technology to remain competitive. Hippo, which promotes savings of up to 25% on home insurance, offers customer quotes in less than 60 seconds and also provides complimentary smart home sensor kits to all policyholders. These kits include a two-sensor smart home monitoring system and premium discounts for helping to prevent common risks from fire, water damage, and break-ins.
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.
With over 15 years of experience in Fraud and AML, and over 25 years of experience in financial services, Dan has helped countless customers solve their financial crime challenges in banking, insurance and government. Dan started out his career in IT, then moved to the business side to help business units implement technology. He then moved into Financial Crimes when consulting at TD Bank on their AML implementation in 2003. From there, Dan joined SAS and was the Fraud lead for Canada. Dan departed SAS for 5 years and gained further experience with TransUnion as the Director of Fraud and ID management.
WIRE)--Baker Hughes, a GE company (NYSE:BHGE) and C3.ai today announced the launch of BHC3 Reliability, the first artificial intelligence (AI) software application developed by the BakerHughesC3.ai Unveiled at BHGE's annual digital conference, UNIFY2019, the now generally available application uses deep learning predictive models, natural language processing, and machine vision to continuously aggregate data from plant-wide sensor networks, enterprise systems, maintenance notes, and piping and instrumentation schematics. Using historical and real-time data from entire systems, the BHC3 Reliability machine learning models identify anomalous conditions that lead to equipment failure and process upsets. Application alerts enable proactive action by operators to reduce downtime and lost revenue. Applicable to operations across all sectors of the energy value chain, BHC3 Reliability's system-of-systems approach scales to any number of assets and processes across offshore and onshore platforms, compressor stations, refineries, and petrochemical plants, reducing downtime and increasing productivity.
Artificial Intelligence (AI) and programmatic advertising can prove to be great tools for FMCG brands in garnering better customer engagement. With the use of programmatic data, brands can generate personalised content for consumers. Unlike before, with programmatic data one can also generate real-time data with respect to a consumer's walks into the store. AI will also instantly allow the brand to understand market trends which lead to better customer personalization. We at exchange4media got in touch with marketing leaders and brands to get their perspective on how the FMCG sector can leverage from AI and programmatic advertising.