This development was announced in a document published to the Federal Registry on November 2, alongside an official notice inviting applications from individuals with the relevant credentials.

The document states, “This notice is the initial step for NIST in collaborating with non-profit organisations, universities, other government agencies, and technology companies to address challenges associated with the development and deployment of AI.”

The primary objective of this collaboration is to create and implement specific policies and measurements that ensure a human-centred approach to AI safety and governance within the United States.

Collaborators within the consortium will be tasked with a range of functions, including the development of measurement and benchmarking tools, policy recommendations, red-teaming efforts, psychoanalysis, and environmental analysis.

NIST’s initiative comes in response to a recent executive order issued by US President Joseph Biden, which outlined six new standards for AI safety and security.

While European and Asian countries have been proactive in instituting policies governing AI systems concerning user and citizen privacy, security, and potential unintended consequences, the US has lagged.

President Biden’s executive order and the establishment of the Safety Institute Consortium mark significant strides in the right direction, yet there remains a lack of clarity regarding the timeline for the implementation of laws governing AI development and deployment in the US.

Many experts have expressed concerns about the adequacy of current laws, designed for conventional businesses and technology, when applied to the rapidly-evolving AI sector.

The formation of the AI consortium signifies a crucial step towards shaping the future of AI policies in the US. It reflects a collaborative effort between government bodies, non-profit organisations, universities, and technology companies to ensure responsible and ethical AI practices within the nation.

(Photo by Muhammad Rizki on Unsplash)

See also: UK paper highlights AI risks ahead of global Safety Summit

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“From cloud to edge, Paravision’s goal is to help our partners develop and deploy transformative solutions around face recognition and computer vision,” said Joey Pritikin, Chief Product Officer at Paravision.

“With these sweeping updates to our product family, and with what has become possible in terms of accuracy, speed, usability and portability, we see a remarkable opportunity to unite disparate applications with a coherent sense of identity that bridges physical spaces and cyberspace.”

A new Scaled Vector Search (SVS) capability acts as a search engine to provide accurate, rapid, and stable face matching on large databases that may contain tens of millions of identities. Paravision claims the SVS engine supports hundreds of transactions per second with extremely low latencies.

Another scaling solution called Streaming Container 5 enables the processing of video at over 250 frames per second from any number of streams. The solution features advanced face tracking to ensure that identities remain accurate even in busy environments.

With more enterprises than ever looking to the latency-busting and privacy-enhancing benefits of edge computing, Paravision has partnered with Teknique to co-create a series of hardware and software reference designs that enable the rapid development of face recognition and computer vision capabilities at the edge.

Teknique is a leader in the development of hardware based on designs from California-based fabless semiconductor company Ambarella.

Paravision’s Face SDK has been enhanced for smart cameras powered by Ambarella CVflow chipsets. The update enables facial recognition on CVflow-powered cameras to achieve up to 40 frames per second full pipeline performance.

A new Liveness and Anti-spoofing SDK also adds new safeguards for Ambarella-powered facial recognition solutions. The toolkit uses Ambarella’s visible light, near-infrared, and depth-sensing capabilities to determine whether the camera is seeing a live subject or whether it’s being tricked by recorded footage or a dummy image.

On the mobile side, Paravision has released its Face SDK for Android. The SDK includes face detection, landmarks, quality assessment, template creation, and 1-to-1 or 1-to-many matching. Reference applications are included which include UI/UX recommendations and tools.

Last but certainly not least, Paravision has announced the availability of its first person-level computer vision SDK. The new SDK is designed to go “beyond face recognition” to detect the presence and position of individuals and unlock new use cases.

Provided examples of real-world applications for the computer vision SDK include occupancy analysis, the ability to spot tailgating, as well as custom intention or subject attributes.

“With Person Detection, users could determine whether employees are allowed access to a specific area, are wearing a mask or hard hat, or appear to be in distress,” the company explains. “It can also enable useful business insights such as metrics about queue times, customer throughput or to detect traveller bottlenecks.”

With these exhaustive updates, Paravision is securing its place as one of the most exciting companies in the AI space.

Paravision is ranked the US leader across several of NIST’s Face Recognition Vendor Test evaluations including 1:1 verification, 1:N identification, performance for paperless travel, and performance with face masks.

(Photo by Daniil Kuželev on Unsplash)

Find out more about Digital Transformation Week North America, taking place on 9-10 November 2021, a virtual event and conference exploring advanced DTX strategies for a ‘digital everything’ world.

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The latest edition of the report currently has VisionLabs, IDEMIA, and CloudWalk in the lead:

Higher numbers are better as they indicate a lower prevalence of false positives.

The “N” values represent the number of individuals enrolled in each simulation of aircraft boarding. The N = 42,000 simulation, for example, is designed to represent an airport security line where many people are expected. The “k” values give the number of images of each en- rollee in each gallery.

In the test simulating an airport security line, it’s CloudWalk that currently leads the pack in accuracy.

CloudWalk is a controversial facial recognition software developer based in Guangzhou, China that has been sanctioned by the United States government for allegedly participating in major human rights abuses.

According to US officials, CloudWalk was “complicit in human rights violations and abuses committed in China’s campaign of repression, mass arbitrary detention, forced labour and high-technology surveillance against Uighurs, ethnic Kazakhs, and other members of Muslim minority groups in the Xinjiang Uighur Autonomous Region”.

France-based IDEMIA ranks a respectable second in the test overall while beating CloudWalk in the N = 420 simulations and avoiding the same controversies.

Jean-Christophe Fondeur, Chief Technology Officer at IDEMIA, said:

“IDEMIA’s technologies are based on over 30 years of expertise in deep learning and artificial intelligence and we see it as our responsibility to bring this expertise to the everyday traveler, keeping passengers safe across the world.

NIST’s results confirm the robustness of our technologies with regard to managing different demographics. IDEMIA’s facial recognition technology achieves the most accurate results and delivers a key competitive advantage when handling complex scenarios.”

Amsterdam-based VisionLabs is the leader in both of the N = 420 simulations. The company boasts of a face descriptor that’s five times smaller than competitors and with a matching speed that’s at least 50 times faster.

(Image Credit: IDEMIA)

Find out more about Digital Transformation Week North America, taking place on 9-10 November 2021, a virtual event and conference exploring advanced DTX strategies for a ‘digital everything’ world.

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