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Artificial Intelligence

Enhancing Trust with AI-Driven Biometrics

White Paper: Jumio

Biometrics and AI Build the Strongest ID Verification Tech Biometrics are commonplace. They protect our phones, log us into our virtual workspaces, secure our health records and verify our identity when we sign up for new services. Face, fingerprint, iris, voice and other modalities proliferate across our physical and digital lives, facilitating access, managing identity and keeping us safe from fraud. But it is crucial to understand that not all biometric technology is created equal. The fact is, there are smart biometrics and basic ones. Functionally, allbtrue biometrics, regardless of the modality, do what the name implies: measure unique physical or behavioral traits and compare them. Some consumer-grade biometric solutions keep it simple, measuring and comparing and matching the same way every time. On the other hand, smart biometrics, which are enhanced by artificial intelligence and machine learning, adapt with every use, getting stronger, faster and more scalable. The latter type is a foundational aspect of a broader trend that FindBiometrics calls “Intelligent ID” – a key technology for the future of our increasingly digital and mobile lives. Artificial intelligence is a heavy term in our culture, and it brings with it a great deal of baggage in the form of common misconceptions. When it comes to intelligent biometric identity, these misconceptions pool around face biometrics, identity proofing and continuous authentication, which taken together are the basic components of a trust chain. Fears about user privacy, distrust stemming from racial bias reports in surveillance systems, and the expectation that identity proofing must rely on a human element in the onboarding process are all common false precepts clouding the understanding of smart biometrics in ID verification and user authentication.

AI for industrial cameras

White Paper: IDS

Deep learning opens up new fields of application for industrial image processing, which previously could only be solved with great effort or not at all. The new, fundamentally different approach to classical image processing causes new challenges for users – it’s necessary to think differently. Therefore, IDS presents an all-in-one embedded vision solution with which every user can implement AI-based image processing in just a few steps without programming knowledge, and execute a neural net directly on an industrial camera. This makes deep learning particularly user-friendly. Computer vision and image processing have become indispensable tools in various application fields. Image processing systems are increasingly confronted with a constantly growing variety of products and variants and organic objects such as fruit, vegetables or plants. Conventional approaches with rule-based image processing quickly reach their limits if the image data to be analyzed varies too frequently and the differences are difficult or impossible to describe using algorithms. In such cases, a robust automation is not feasible due to an inflexible set of rules, even if the task is supposed to be easy for humans to solve. As an example, a child is able to recognize a car even if it has never seen the special model before. It is sufficient if the child has seen enough other car models previously.

Get optical products to market faster using modern virtual prototyping

White Paper: Zemax

How Modern Virtual Prototyping Can Change The Product Development Process? Outdated workflows limit team collaboration and compromise design integrity causing failures in physical prototypes or production impacting the schedules of an organization. As a result, leading companies have moved away from a linear process to a parallel design environment, in which design faithfulness is maintained throughout the workflow. By collaborating over a shared virtual prototype, optical and mechanical engineers can easily identify and correct design errors early in the design phase. Having the product designed right in the early stage leads to higher top-line revenue, lower cost, and a faster time to market. By harnessing the power of OpticStudio and LensMechanix, optical and mechanical engineers can share complete design data and analyze optical performance in a virtual model, reducing design iterations and physical prototypes—saving time and money. Move ahead and read the following whitepaper that will address all your questions, including these: How outdated workflows limit team collaboration and cause design delays?​ What is the importance of modern virtual prototyping?​ How OpticStudio and LensMechanix together can create a modern virtual prototyping solution?

THE CHIEF ROBOTICS OFFICER 2017 UPDATE

White Paper: RoboBusiness

To advance industry, the Chief Robotics Officer (CRO) must combine management functions, standardize corporate systems, and integrate robotics innovations. The fast evolving availability of specific business/financial models for Robotics & Intelligent Operational Systems (RIOS) and lower financial barriers to adoption, in addition to increasing standardization levels, artificial intelligence, and automation are driving operational effectiveness (e.g. cobots (collaborative robots) in manufacturing processes, semi-homogeneous use of cloud computing capabilities, application specific solutions. This white paper takes a deep dive into this emerging role responsible for evaluating and implementing robotics, automation, or intelligent technologies into their organizations. Read the following white paper that will address all your questions, such as: What is the use of a CRO in any business? What is the impact of CRO in an organization’s growth and adoption of robotics? What is the future of CRO and Robotics-as-a-service? What are the operational trends for CRO adoption? How to define an action plan for existing or aspiring Chief Robotics Officers?  What are the roles and responsibilities of a CRO? What are the current approaches related to Robotics-as-a-Solution?

Artificial Intelligence White Paper: How AI benefits marketers?

White Paper: Emarsys

Today, artificial intelligence (AI) has become one of the most powerful marketing tools by actually delivering on the promise of 1:1 marketing. Instead of merely pushing the key marketer challenges further downstream, AI actually overcomes them. Using advanced machine learning algorithms, AI technology solutions are being built to take over most of the tedious and time-consuming tasks that marketers struggle with on a regular basis. This whitepaper provides insights on how artificial intelligence helps to fulfill the vision of true 1:1 marketing by bridging the gap between data and personalized customer experiences. Key takeaways from this Artificial Intelligence Whitepaper: Revolutionizing the marketer’s role Bridging the marketing gap with Artificial Intelligence Product recommendations: How Artificial Intelligence benefits marketers AI at work: Incentive Recommendations

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