The creation of artificial intelligence is the next step in the progression of technology. The prospect of self-driving vehicles and humanoid robots is no longer the stuff of science fiction, and the credit for this achievement goes entirely to the venture capitalists who are investing in AI companies in order to speed up the growth and development of these businesses. We have compiled a list of the most prominent businesses currently investing in AI startups, all of which you should be aware of. Canaan Partners Canaan is a venture capital firm that invests in entrepreneurs in the early stages of their businesses who have visionary concepts. Canaan has invested in some of the most successful technology and healthcare companies in the world over the course of the past 33 plus years, thanks to its diversified fund and more than 200 successful exits to date.

DataVisor, a company that delivers the most sophisticated Artificial Intelligence (AI) powered fraud management solutions, announced a partnership with Equifax, making the global data, analytics and technology company its primary consumer identity data partner. DataVisor will enhance and extend its market-leading behavior analytics and fraud detection solution by combining rich consumer identity data from Equifax with powerful AI signals, for zero-lag detection and a frictionless customer experience. "Fraud losses are expected to grow to more than $600 billion by 2023. For businesses to protect both themselves and their customers from the well-orchestrated tactics of fraudsters, a layered approach to fraud detection is critical" DataVisor will integrate Digital Identity Trust, Secure Multi Factor Authentication (MFA) and Account Verification solutions from Equifax into its next-generation fraud detection platform, enhancing its fraud models for improved detection accuracy and enabling data enrichment features for end-clients globally. When layered together, these Equifax solutions can provide a 90% reduction in fraud risk and a 30% reduction in operational overhead, according to analysis conducted by Equifax.

When it comes to defeating fraud, is your AI reality falling short of AI hype? Are you concerned your organization isn't realizing the AI-powered results you were led to expect? There's a lot of hype around AI, but it takes more than just great algorithms to realize true value. The good news is, your organization can achieve the kind of results you deserve, with dCube, the comprehensive AI-powered fraud management solution from DataVisor. Please expect a confirmation email shortly.

This ebook, based on the latest ZDNet/TechRepublic special feature, offers a detailed look at how to build risk management policies to protect your critical digital assets. Security measures have increased significantly in the last several years, and malicious actors have similarly advanced their techniques to keep pace, particularly with advances in attack methods such as fileless malware. Likewise, the security model of'serverless' computing platforms like AWS Lambda are completely different from traditional computers. These itinerant computing concepts are not effectively secured by the traditional model of checking file hashes against known malware samples. For a robust, modern defense, an adaptive monitoring solution that leverages machine learning to identify anomalous patterns indicative of an attack in its infancy is necessary to defend enterprise systems from cyberattacks.

I've had the good fortune of working with several banks over the last few years guiding them in the development of machine learning models to serve their customers better and enhance risk management. Armed with information on what to look for while conducting a reliability assessment of a given model, banks can practice due diligence and be regulatory compliant. So what does an effective and comprehensive validation framework look like? It entails review of documentation and empirical evidence supporting the methods used and variables selected for the model. A lack of documentation is a red flag. It also includes data sources and as well as the business case.

Describing how his company's fraud prediction system is like an unaccompanied child visiting a zoo, is when Wally Wang becomes the most animated… and the unsupervised machine-learning approach starts to make sense: "There's no mom to teach a child what a tiger is. The child by intelligence will automatically get the point of how to recognize a tiger or a goose. The connection will be built by the child. That's how the AI part plays here, the algorithm does the detection… We build up a model, making the child more intelligent, to be able to tell automatically when an animal is evolving into another species." DataVisor uses unsupervised machine learning to predict fraud attacks on companies.

Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science: Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the O'Reilly Data Show, I spoke with Fang Yu, co-founder and CTO of DataVisor. We discussed her days as a researcher at Microsoft, the application of data science and distributed computing to security, and hiring and training data scientists and engineers for the security domain. DataVisor is a startup that uses data science and big data to detect fraud and malicious users across many different application domains in the U.S. and China. Founded by security researchers from Microsoft, the startup has developed large-scale unsupervised algorithms on top of Apache Spark, to (as Yu notes in our chat) "predict attack vectors early among billions of users and trillions of events."