Built on Feedzai's distributed microservices architecture, the service allows financial institutions and their data science teams to bring their preferred machine learning modelling and runtime frameworks, including open source, research, or commercial, to the Feedzai platform to fight fraudulent activities. Thus, the open machine learning engine enables the use of any language or environment for data science activities (data scientists no longer have to work in a singular environment dictated by a third-party vendor); leverages emerging algorithms as they become available (data science teams can use pre-written machine learning libraries from any open source); and it enables automated end to end connectivity across the data science ecosystem. Feedzai's OpenML is powering Feedzai's fraud prevention products for customers around the world, including 10 of the top 25 banks in the world, processing more than USD 5 billion in transactions every day. For more information about Feedzai, please check out a detailed profile of this company in our dedicated, industry-specific online company database.

WIRE)--Feedzai has launched the Feedzai OpenML Engine, in response to recognizing the need for data science teams to utilize their own tools and expertise. Built on Feedzai's distributed microservices architecture, this powerful service allows data scientists to bring their preferred machine learning modeling and runtime frameworks, including open source, research, or commercial, to the Feedzai platform. Initial product support includes an SDK for Python, R, and Java, and it provides close integration with any open source library, sourcing framework, and modeling environment. The three main innovations in Feedzai's OpenML framework include: "Data scientists shouldn't be forced to comply with closed systems by their legacy vendors, creating unnecessary burdens, and bottlenecks," says Paulo Marques, Feedzai co-founder and CTO. "Feedzai has always worked to make that a thing of the past. OpenML will make those teams more effective, not just by a slight improvement, but by magnitudes that will set them well ahead of their competitors."

IndigoVision has announced that the next release of their Artificial Intelligence powered by BriefCam will include the latest security features in the industry, and will be launched within the coming weeks, additional to it being previewed at ISC West 2018 in Las Vegas. IndigoVision's Artificial Intelligence powered by BriefCam allows IndigoVision customers to quickly and easily review hours of footage in minutes, rapidly identifying people and objects of interest by object type, attribute, direction, color or size. The recently announced BriefCam v5 introduces new capabilities across all three of the platform's seamlessly integrated modules, delivering a powerful approach to making video searchable, actionable and quantifiable. The new version enables customers to rapidly realize both the security and business value their surveillance system can provide by innovatively harnessing the process-once-use-many paradigm throughout the platform. These new features and capabilities are directly aligned with IndigoVision's offering, providing customers with statistical analysis reports, business intelligence insights, as well as multi-camera search and facial recognition.

While Uber's self-driving car endeavors may have been put on hold following a pedestrian fatality in Arizona last month, the company is continuing to expand from its ride-hailing roots at a rapid pace. After launching its dockless bike-sharing initiative Uber Bike in January, the company announced it would be acquiring its electric bike-providing partner Jump earlier this week. Now, the company is embarking on a new venture: a peer-to-peer car rental service called Uber Rent. Like its bike-sharing program, Uber Rent will debut in San Francisco before rolling out to other metropolitan areas. Also like Uber Bike, the service is made possible thanks to a partnership with another startup--Getaround, which lets you rent cars from other people in your area for as little as $5 per hour.

BOSTON--(BUSINESS WIRE)--O'Reilly, the premier source for insight-driven learning on technology and business, today announced the results of its 2018 Artificial Intelligence (AI) survey, "How Companies Are Putting AI to Work Through Deep Learning." Focused on deep learning, a technique used primarily for supervised machine learning, the survey explores the adoption of tools and techniques to build AI applications and the barriers that hinder business adoption. Findings suggest that the democratization of AI and deep learning applications will continue, as development tools and libraries improve. However, the shortage of AI-trained engineers and developers will persist. For example, while 54% of respondents indicated AI will play a big role (35%) or essential role (19%) in their organization's future projects, lack of skilled people was the number one bottleneck reported.

Bitcoin Press Release: Blockchain-based startup Cryptics has announced the launch of the world's first public cryptocurrency analytics based on AI technology. April 10th, 2018, Tallinn, Estonia – The crypto market is still relatively new and lacks many of the traditional institutions of a civilized market. There is a lack of regulation and the volatility factor is detracting a vast majority of classical investors from investing in the new market. Traders are also approaching the market cautiously as the there is a lack of classical application of trading instruments. Despite these issues there are blockchain projects on the market that seek to indemnify or mitigate the associated risks that investors take when deciding to invest in projects.

BOSTON--(BUSINESS WIRE)--Two-thirds (66%) of senior IT decision-makers believe failure to adopt artificial intelligence (AI) will lead to a loss of competitiveness, research by global reviews and customer insights company Feefo has found. The findings were revealed in a survey of 100 senior IT decision-makers, covering their attitudes towards AI and its adoption in their respective organizations. Almost half (46%) said their organization is using or plans to use AI to provide personalized summaries of online reviews, with 100% in the accommodation and food sectors saying they will use AI in this way. "If you are serious about personalizing customer-engagement you've got to use AI," said Matt West, CMO, Feefo. "It's encouraging that this message is getting through, but we need to see a lot more businesses actually using AI to transform the way they personalize their interactions with customers."

Edgewater Wireless aera access point products will offer A.I.R. as a standard feature to deliver the world's first intelligent, real-time device tracking within any network coverage zone. A.I.R. uses an in-real-time artificial intelligence engine to learn from and adapt to any network environment or coverage zone with no setup required by the network operator. A.I.R. relies on patent-pending advanced algorithms to detect, range and track any WiFi-enabled device without the expense of additional monitoring hardware and no additional demands on network throughput or performance. A.I.R.'s intelligence engine automatically learns and adapts to the physical location or floor plan of any WiFi environment – with or without line of sight, and is accurate to within 6ft. Combined with the advanced features of Edgewater Wireless multi-channel WiFi3 including real-time, integrated Spectral Surveillance Architecture, Edgewater Wireless aera access point products with A.I.R. will give network operators accurate locationing of devices and their users physically moving through the network.

The major factors driving the growth of the AI in aviation market include the use of big data in the aerospace industry, significant increase in capital investments by aviation companies, and rising adoption of cloud-based applications and services in the aviation industry. However, limited number of experts in AI is restraining the growth of the AI in aviation market. AI-based virtual assistants help airline companies improve the productivity and efficiency of their pilots by reducing their recurring works, such as changing radio channels, reading wind forecasts, and providing position information on request, among others. These recurring jobs are taken care by AI-enabled virtual assistants. Companies such as Garmin (US) offer AI-enabled audio panels, which are useful for pilots.

Amagi, which deals in cloud-based technology for media processing, has announced the launch of Tornado, a machine learning-based content preparation service that enables TV networks and content owners to scale their operations, accelerate broadcast workflows, generate new revenues and reduce operational costs. Tornado is a first-of-its-kind, cloud-based machine learning-augmented content preparation suite that tackles content preparation challenges head-on. Amagi Tornado is conceptualised as a suite of machine learning-based content preparation services that dynamically evolves as machines learn more about each segment of a video asset as they process higher volumes of content. Tornado can cater to the unique preparation needs of TV networks, content owners, vMVPD platforms, and digital-first networks with the company planning to continually expand the suite functionality and capabilities to optimise the entire broadcast workflow. Factory-scale VOD segment creation: Linear broadcast model is highly reliant on sophisticated processing of video for ad break points identification, credits, color bars and blacks.