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No One Gets Quantum Computing, Least Of All America’s National Institute of Standards and Technology – PC Perspective

August 5, 2022 | Author: admin

The only good news about Americas National Institute of Standards and Technology new Supersingular Isogeny Key Encapsulation, designed to be unbreakable by a quantum computer, is that it was subjected to extra testing before it became one of their four new quantum encryption algorithms. As it turns out, two Belgians named Wouter Castryck and Thomas Decru were able to break the Microsoft SIKE in under five minutes using a Intel Xeon CPU E5-2630v2 at 2.60GHz.

Indeed, they did it with a single core, which makes sense for security researchers well aware of the risks of running multithreaded; though why they stuck with a 22nm Ivy Bridge processor almost 10 years old is certainly a question. What makes even less sense is that encryption designed to resist quantum computing could be cracked by a traditional piece of silicon before the heat death of the universe.

This particular piece of quantum encryption has four parameter sets, called SIKEp434, SIKEp503, SIKEp610 and SIKEp751. The $50,000 bounty winners were able to crack SIKEp434 parameters in about 62 minutes. Two related instances, $IKEp182 and $IKEp217 they were able to crack in about 4 minutes and 6 minutes respectively. There are three other quantum encryption standards proposed along with this one, so there is some hope that they will be useful for now at least.

If you would like to read more about quantum computing, encryption as well as Richelot isogenies and abelian surfaces then read on at The Register.

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IonQ to Participate in Third Annual North American Conference on Trapped Ions (NACTI) – Business Wire

August 5, 2022 | Author: admin

COLLEGE PARK, Md.--(BUSINESS WIRE)--IonQ (NYSE: IONQ), an industry leader in quantum computing, today announced its participation in the third annual North American Conference on Trapped Ions (NACTI). The event will take place at Duke University on August 1-4, 2022, and brings together dozens of the worlds leading quantum scientists and researchers to discuss the latest advancements in the field of quantum.

Participating for the third time at this event, IonQ co-founder and CTO Jungsang Kim will speak on the latest IonQ Aria performance updates, IonQ Forte gate results, and the importance of an industry-wide benchmarks based on a collection of real-world algorithms such as algorithmic qubits (#AQ) that can better represent any quantum computers performance and utility.

Other topics on the agenda for NACTI include: quantum scaling and architectures, including networking; fabrication and development of new traps; increasing accessibility; control hardware and software for trapped ions; new qub(d)its and gates; quantum computing and simulation employing ion trapping techniques; looking beyond atomic ions; precision measurements and clocks; among others.

To learn more about IonQ Aria with details on performance and its technical prowess, click the link here for more information.

About IonQ

IonQ, Inc. is a leader in quantum computing, with a proven track record of innovation and deployment. IonQ's current generation quantum computer, IonQ Forte, is the latest in a line of cutting-edge systems, including IonQ Aria, a system that boasts industry-leading 20 algorithmic qubits. Along with record performance, IonQ has defined what it believes is the best path forward to scale. IonQ is the only company with its quantum systems available through the cloud on Amazon Braket, Microsoft Azure, and Google Cloud, as well as through direct API access. IonQ was founded in 2015 by Christopher Monroe and Jungsang Kim based on 25 years of pioneering research. To learn more, visit http://www.ionq.com.

IonQ Forward-Looking Statements

This press release contains certain forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. Some of the forward-looking statements can be identified by the use of forward-looking words. Statements that are not historical in nature, including the words anticipate, expect, suggests, plan, believe, intend, estimates, targets, projects, should, could, would, may, will, forecast and other similar expressions are intended to identify forward-looking statements. These statements include those related to IonQs ability to further develop and advance its quantum computers and achieve scale; IonQs ability to optimize quantum computing results even as systems scale; the expected launch of IonQ Forte for access by select developers, partners, and researchers in 2022 with broader customer access expected in 2023; IonQs market opportunity and anticipated growth; and the commercial benefits to customers of using quantum computing solutions. Forward-looking statements are predictions, projections and other statements about future events that are based on current expectations and assumptions and, as a result, are subject to risks and uncertainties. Many factors could cause actual future events to differ materially from the forward-looking statements in this press release, including but not limited to: market adoption of quantum computing solutions and IonQs products, services and solutions; the ability of IonQ to protect its intellectual property; changes in the competitive industries in which IonQ operates; changes in laws and regulations affecting IonQs business; IonQs ability to implement its business plans, forecasts and other expectations, and identify and realize additional partnerships and opportunities; and the risk of downturns in the market and the technology industry including, but not limited to, as a result of the COVID-19 pandemic. The foregoing list of factors is not exhaustive. You should carefully consider the foregoing factors and the other risks and uncertainties described in the Risk Factors section of IonQs Quarterly Report on Form 10-Q for the quarter ended March 31, 2022 and other documents filed by IonQ from time to time with the Securities and Exchange Commission. These filings identify and address other important risks and uncertainties that could cause actual events and results to differ materially from those contained in the forward-looking statements. Forward-looking statements speak only as of the date they are made. Readers are cautioned not to put undue reliance on forward-looking statements, and IonQ assumes no obligation and does not intend to update or revise these forward-looking statements, whether as a result of new information, future events, or otherwise. IonQ does not give any assurance that it will achieve its expectations.

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wolfSSL Featuring new Updates on FIPS and Post Quantum Cryptography at Black Hat 2022 – PR Web

August 5, 2022 | Author: admin

LAS VEGAS (PRWEB) August 04, 2022

wolfSSL INC. (Headquarters: Edmonds, Washington, USA), a vendor specialized in cryptography and network security, is excited to share updates regarding their products and technology at Black Hat 2022 this August 10 and 11 in Las Vegas, Nevada, at booth #1084

The first update is wolfCrypt, wolfSSLs embedded crypto engine, is on the CMVP MIP (Modules In Process) List for FIPS 140-3. wolfSSL is working with a testing lab to get validated as quickly as possible with the new FIPS standard from the NIST. wolfSSL is the first software library on the FIPS 140-3 MIP list for embedded systems and general purpose multi -platform use.

FIPS 140-3 involves significant changes, and wolfSSL endeavors to deliver the first and best implementation of FIPS 140-3. FIPS 140-3 is the replacement for FIPS 140-2, so it is always a good idea to switch over as soon as possible. Furthermore wolfSSLs FIPS 140-3 Certificate has advantages including:

For more information, please visit the FIPS page here.

The second exciting update is that wolfSSLs flagship product, a security library for embedded systems, supports post-quantum cryptography. As a result, users who use the wolfSSL library can communicate using post-quantum cryptography on TLS 1.3 (Transport Layer Security), a standard Internet security protocol, without having to make changes to their applications.

Once a quantum computer is built, attackers are able to decrypt communications protected by only non-quantum resistant cryptography. Thus, any information that wishes to remain confidential needs quantum resistant cryptography even before quantum computers exist. In communication protocols like TLS, digital signatures are used to authenticate the parties and key exchange is used to establish a shared secret, which can then be used in symmetric cryptography. This means that, for security against a future quantum adversary, authentication in todays secure channel establishment protocols can still rely on traditional primitives (such as RSA or elliptic curve signatures), but we should incorporate post-quantum key exchange to provide quantum-resistant long-term confidentiality. (https://eprint.iacr.org/2016/1017.pdf)

The era of quantum computing is becoming a reality, and ensuring secure network communication is beginning to appear as a real challenge. NIST (National Institute of Standards and Technology) in the competition for Post-Quantum Cryptography Standardization has announced the algorithms moving on to standardization. They are Kyber, Dilithium, Falcon, and SPHINCS+. We have already integrated OQS implemenations of Kyber and Falcon and are integrating the other two as well. We are working hard to craft our own implementations of these algorithms. Work for Kyber is already underway. Open Quantum Safe (OQS), an open source project, provides these finalist algorithms as a library, liboqs.

This post-quantum cryptography support for wolfSSL implements the algorithms provided by liboqs in wolfSSL, a TLS library product, and provides it as a product that can be used in embedded systems. This allows device manufacturers using wolfSSL to easily incorporate post-quantum cryptography protocols into their network connectivity capabilities without changing the structure or development environment of their products.

For those of you joining us at #BHUSA22, stop by our booth #1084 and talk to us about FIPS, Post Quantum Cryptography, SSH Daemon, TLS 1.3, DTLS 1.3, hardware crypto acceleration, DO-178, secure boot, Fuzz testing, and everything else that sets us apart as the most secure crypto out there. Customers win with wolfSSL, weve got the numbers to prove it.

If you are new to wolfSSL, here are some things you should know about us!

Email us at facts@wolfssl.com to book a meeting or register directly from Black Hats event site: https://www.blackhat.com/us-22/registration.html.

About wolfSSL

wolfSSL focuses on providing lightweight and embedded security solutions with an emphasis on speed, size, portability, features, and standards compliance. With its SSL/TLS products and crypto library, wolfSSL is supporting high security designs in automotive, avionics, and other industries. In avionics, wolfSSL has support for complete RTCA DO-178C level A certification. In automotive, it supports MISRA-C capabilities. For government consumers, wolfSSL has a strong history in FIPS 140-2, with upcoming Common Criteria support. wolfSSL supports industry standards up to the current TLS 1.3 and DTLS 1.3, is up to 20 times smaller than OpenSSL, offers a simple API, an OpenSSL compatibility layer, is backed by the robust wolfCrypt cryptography library, and much more. Our products are open source, giving customers the freedom to look under the hood.

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Explosive growth of faculty, courses and research signal new era for Computer Science at Yale – Yale University

August 5, 2022 | Author: admin

With numerous new courses, new faculty members, and a wider range of research fields, Computer Science (CS) at Yale is better positioned than ever to take on emerging challenges, and to meet the needs of students, interdisciplinary research on campus, and industry.

The CS department has recently hired nine tenure track faculty members and four teaching track lecturers to its ranks. These hires are in addition to an earlier round of 11 new tenure track faculty members and two lecturers hired in the last few years. The boost in hiring accomplishes a number of long-term goals, including expanding the department's areas of expertise. Also, as Computer Science has emerged as the second-most popular major (just behind economics) at Yale, it will go a long way toward meeting students' curriculum needs.

"Our new faculty members were chosen for the excellence of their research, as well as for their fields that they represent, all of which have been in high demand by both our students and faculty on campus as well as the industry," said Zhong Shao, the Thomas L. Kempner Professor of Computer Science and department chair. "The range of their expertise addresses some of the most critical challenges that we face today."

SEAS Dean Jeffrey Brock said the new faculty will be critical to realizing the ambitious goals set out in SEAS' Strategic Vision, particularly in the areas of artificial intelligence and robotics, while building in key areas like cybersecurity and distributed computing.

"This exciting cohort of new faculty stands to transform our CS department," Brock said. "During our recruiting season, they sensed Yale's momentum in CS and in engineering, ultimately turning down excellent offers at other top schools to join our faculty. Their presence will allow Yale CS to expand their course offerings, as well as to establish critical mass in core and cutting-edge research areas."

Many of the new faculty members, like Fan Zhang, cited the department's "fast growth in recent years." Others said that they were drawn by the collaborative environment at Yale, especially considering that Yale is ranked at or near the top in numerous research areas. Daniel Rakita, for instance, said he's looking forward to working with the Yale Medical School to see how his lab's robotics research can assist in hospital or home care settings, as well as working with the Wu Tsai Institute on Brain-Machine Interface technologies.

"Many people I spoke with indicated that there are no boundaries between departments at Yale, and interdisciplinary research is not just encouraged here, but is a 'way of life,'" Rakita said. Many of the new faculty have already engaged with key academic leaders around the campus, from medicine, to economics, to quantum computing.

As part of this boost in hiring, the department strategically targeted certain research areas, including artificial intelligence, trustworthy computing, robotics, quantum computing, and modeling.

The nine new tenure-track faculty hires, and their areas of research are below.

[We spoke to these new faculty members about their research, their motivations, potential collaborations, and much more. Click here to learn more about each of our latest faculty]

The four new teaching-track lecturer hires, and their areas of research are:

This hiring season marks the first since the changes in structure that made SEAS more independent, granting more faculty lines for growth.

"Our independence and ability to be opportunistic were key elements in our ability to realize this transformational growth of Computer Science at Yale," Brock said. "As CS plays such a critical role in an increasingly broad range of disciplines, the size and breadth of CS is critical to our strategy for SEAS. I'm thrilled to be able to take the first step in realizing that vision for a SEAS that is well integrated within its host University and aligned with its mission."

SEAS became independent from the Faculty of Arts and Sciences in July of 2022.

A curriculum to meet the needs of students and industry

Increasing the department's curriculum has also been in the planning stages for a while, a goal made possible by the recent hires of new faculty and lecturers. Shao said there was a concerted effort to meet the high demand in areas such as artificial intelligence, blockchain, machine learning, introductory programming and CS courses for non-majors.

"This has been on the to-do list for the department for many years, but we just didn't have the manpower," Shao said. "And finally, with the new faculty hires, we can actually offer these courses."

Ben Fisch, for instance, will be teaching a new course on blockchains for both graduate students and advanced undergraduates in computer science. Tesca Fitzgerald will introduce a new graduate-level seminar on Interactive Robot Learning. And Katerina Sotiraki will teach classes in theoretical and applied cryptography, at both the undergraduate and graduate level. These are just a few of the new courses that will be available.

Responding to industry needs, the department has also added courses focused on what's known as full stack web programming - that is, the set of skills needed to develop the interface as well as the coding behind building a complete web application. One of the department's most popular courses, on software engineering, will now be offered for both semesters of the year, instead of one. Both, Shao said, are specifically aimed at the needs of industry and students.

"As new challenges emerge, Computer Science at Yale will continue to adapt," Shao said. "We're excited about the future of our department, and these new additions to our faculty and our curriculum are going to be a major part of it."

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Global Artificial Intelligence of Things Solutions Market Report 2022: AIoT Market will Reach $83.6 Billion by 2027, Growing at 39.1% CAGR -…

August 3, 2022 | Author: admin

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence of Things Solutions by AIoT Market Applications and Services in and Industry Verticals 2022 - 2027" report has been added to ResearchAndMarkets.com's offering.

This AIoT market report provides an analysis of technologies, leading companies and solutions. The report also provides quantitative analysis including market sizing and forecasts for AIoT infrastructure, services, and specific solutions for the period 2022 through 2027. The report also provides an assessment of the impact of 5G upon AIoT (and vice versa) as well as blockchain and specific solutions such as Data as a Service, Decisions as a Service, and the market for AIoT in smart cities.

While it is no secret that AI is rapidly becoming integrated into many aspects of ICT, many do not understand the full extent of how it will transform communications, applications, content, and commerce. For example, the use of AI for decision-making in IoT and data analytics will be crucial for efficient and effective smart city solutions in terms of decision-making.

The convergence of AI and Internet of Things (IoT) technologies and solutions (AIoT) is leading to "thinking" networks and systems that are becoming increasingly more capable of solving a wide range of problems across a diverse number of industry verticals. AI adds value to IoT through machine learning and improved decision-making. IoT adds value to AI through connectivity, signaling, and data exchange.

AIoT is just beginning to become part of the ICT lexicon as the possibilities for the former adding value to the latter are only limited by the imagination. With AIoT, AI is embedded into infrastructure components, such as programs, chipsets and edge computing, all interconnected with IoT networks.APIs are then used to extend interoperability between components at the device level, software level and platform level. These units will focus primarily on optimizing system and network operations as well as extracting value from data.

While early AIoT solutions are rather monolithic, it is anticipated that AIoT integration within businesses and industries will ultimately lead to more sophisticated and valuable inter-business and cross-industry solutions. These solutions will focus primarily upon optimizing system and network operations as well as extracting value from industry data through dramatically improved analytics and decision-making processes.

Industry adoption for AIoT is gaining momentum. By way of example, Advantech partnered with Momenta Ventures to launch the AIoT Ecosystem Fund, a venture capital fund with a target of $50 million USD and a focus on the digital industry. KC Liu, CEO of Advantech, stated: "Advantech is committed to enabling an intelligent planet. This starts at the industrial edge with early innovators in energy, manufacturing, smart spaces and supply chain management."

The company launched Advantech Industrial Wireless solutions with Qualcomm, NXP, DEKRA, and E Ink. "We provide AIW industrial grade wireless modules and wireless design-in services to embedded customers. This one-stop shopping service helps customers acquire leading wireless enabled AIoT products and reduce their time to market," said Andy Lin, Advantech Senior ProductManager.

Many industry verticals will be transformed through AI integration with enterprise, industrial, and consumer product and service systems. It is destined to become an integral component of business operations including supply chains, sales and marketing processes, product and service delivery, and support models.

We see AIoT evolving to become more commonplace as a standard feature from big analytics companies in terms of digital transformation for the connected enterprise. This will be realized in infrastructure, software, andSaaS managed service offerings. Recent years have witnessed rapid growth for IoT data-as-a-service offerings to become AI-enabled decisions-as-a-service-solutions, customized on a per industry and company basis. Certain data-driven verticals such as the utility and energy service industries will lead the way.

As IoT networks proliferate throughout every major industry vertical, there will be an increasingly large amount of unstructured machine data. The growing amount of human-oriented and machine-generated data will drive substantial opportunities for AI support of unstructured data analytics solutions. Data generated from IoT-supported systems will become extremely valuable, both for internal corporate needs as well as for many customer-facing functions such as product life-cycle management.

The use of AI for decision-making in IoT and data analytics will be crucial for efficient and effective decision-making, especially in the area of streaming data and real-time analytics associated with edge computing networks. Real-time data will be a key value proposition for all use cases, segments, and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic.

In many cases, the data itself, and actionable information will be the service. AIoT infrastructure and services will, therefore, be leveraged to achieve more efficient IoT operations, improve human-machine interactions, and enhance data management and analytics, creating a foundation for IoT Data as a Service (IoTDaaS) and AI-based Decisions as a Service.

The fastest-growing 5G AIoT applications involve private networks. Accordingly, the 5GNR market for private wireless in industrial automation will reach $5.21B by 2027. Some of the largest market opportunities will be AIoT market IoTDaaS solutions. We see machine learning in edge computing as the key to realizing the full potential of IoT analytics.

Select Report Findings:

Key Topics Covered:

1.0 Executive Summary

2.0 Introduction

2.1 Defining AIoT

2.2 AI in IoT vs. AIoT

2.3 Artificial General Intelligence

2.4 IoT Network and Functional Structure

2.5 Ambient Intelligence and Smart Lifestyles

2.6 Economic and Social Impact

2.7 Enterprise Adoption and Investment

2.8 Market Drivers and Opportunities

2.9 Market Restraints and Challenges

2.10 AIoT Value Chain

2.10.1 Device Manufacturers

2.10.2 Equipment Manufacturers

2.10.3 Platform Providers

2.10.4 Software and Service Providers

2.10.5 User Communities

3.0 AIoT Technology and Market

3.1 AIoT Market

3.1.1 Equipment and Component

3.1.2 Cloud Equipment and Deployment

3.1.3 3D Sensing Technology

3.1.4 Software and Data Analytics

3.1.5 AIoT Platforms

3.1.6 Deployment and Services

3.2 AIoT Sub-Markets

3.2.1 Supporting Device and Connected Objects

3.2.2 IoT Data as a Service

3.2.3 AI Decisions as a Service

3.2.4 APIs and Interoperability

3.2.5 Smart Objects

3.2.6 Smart City Considerations

3.2.7 Industrial Transformation

3.2.8 Cognitive Computing and Computer Vision

3.2.9 Consumer Appliances

3.2.10 Domain-Specific Network Considerations

3.2.11 3D Sensing Applications

3.2.12 Predictive 3D Design

3.3 AIoT Supporting Technologies

3.3.1 Cognitive Computing

3.3.2 Computer Vision

3.3.3 Machine Learning Capabilities and APIs

3.3.4 Neural Networks

3.3.5 Context-Aware Processing

3.4 AIoT Enabling Technologies and Solutions

3.4.1 Edge Computing

3.4.2 Blockchain Networks

3.4.3 Cloud Technologies

3.4.4 5G Technologies

3.4.5 Digital Twin Technology and Solutions

3.4.6 Smart Machines

3.4.7 Cloud Robotics

3.4.8 Predictive Analytics and Real-Time Processing

3.4.8.1 All-Flash Array

3.4.8.2 Real-Time Operating Systems

3.4.9 Post Event Processing

3.4.10 Haptic Technology

4.0 AIoT Applications Analysis

4.1 Device Accessibility and Security

4.2 Gesture Control and Facial Recognition

4.3 Home Automation

4.4 Wearable Device

4.5 Fleet Management

4.6 Intelligent Robots

4.7 Augmented Reality Market

4.8 Drone Traffic Monitoring

4.9 Real-time Public Safety

4.10 Yield Monitoring and Soil Monitoring Market

4.11 HCM Operation

5.0 Analysis of Important AIoT Companies

5.1 Sharp

5.2 SAS

5.3 DT42

5.4 Chania Tech Giants: Baidu, Alibaba, and Tencent

5.4.1 Baidu

5.4.2 Alibaba

5.4.3 Tencent

5.5 Xiaomi Technology

5.6 NVidia

5.7 Intel Corporation

5.8 Qualcomm

5.9 Innodisk

5.10 Gopher Protocol

5.11 Micron Technology

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Global Artificial Intelligence in Healthcare Diagnosis Market Research Report 2022: Rising Adoption of Healthcare Artificial Intelligence in Research…

August 3, 2022 | Author: admin

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence in Healthcare Diagnosis Market Research Report by Technology, Component, Application, End User, Region - Global Forecast to 2026 - Cumulative Impact of COVID-19" report has been added to ResearchAndMarkets.com's offering.

The Global Artificial Intelligence in Healthcare Diagnosis Market size was estimated at USD 2,318.98 million in 2020, USD 2,725.72 million in 2021, and is projected to grow at a Compound Annual Growth Rate (CAGR) of 17.81% to reach USD 6,202.67 million by 2026.

Market Segmentation:

This research report categorizes the Artificial Intelligence in Healthcare Diagnosis to forecast the revenues and analyze the trends in each of the following sub-markets:

Competitive Strategic Window:

The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies to help the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. It describes the optimal or favorable fit for the vendors to adopt successive merger and acquisition strategies, geography expansion, research & development, and new product introduction strategies to execute further business expansion and growth during a forecast period.

FPNV Positioning Matrix:

The FPNV Positioning Matrix evaluates and categorizes the vendors in the Artificial Intelligence in Healthcare Diagnosis Market based on Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) that aids businesses in better decision making and understanding the competitive landscape.

Market Share Analysis:

The Market Share Analysis offers the analysis of vendors considering their contribution to the overall market. It provides the idea of its revenue generation into the overall market compared to other vendors in the space. It provides insights into how vendors are performing in terms of revenue generation and customer base compared to others. Knowing market share offers an idea of the size and competitiveness of the vendors for the base year. It reveals the market characteristics in terms of accumulation, fragmentation, dominance, and amalgamation traits.

Market Dynamics

Drivers

Restraints

Opportunities

Challenges

Key Topics Covered:

1. Preface

2. Research Methodology

3. Executive Summary

4. Market Overview

5. Market Insights

6. Artificial Intelligence in Healthcare Diagnosis Market, by Technology

7. Artificial Intelligence in Healthcare Diagnosis Market, by Component

8. Artificial Intelligence in Healthcare Diagnosis Market, by Application

9. Artificial Intelligence in Healthcare Diagnosis Market, by End User

10. Americas Artificial Intelligence in Healthcare Diagnosis Market

11. Asia-Pacific Artificial Intelligence in Healthcare Diagnosis Market

12. Europe, Middle East & Africa Artificial Intelligence in Healthcare Diagnosis Market

13. Competitive Landscape

14. Company Usability Profiles

15. Appendix

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/vgkht7

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Business leaders commemorate anniversary of EqualAI and its new leadership role on the National Artificial Intelligence Advisory Committee – PR…

August 3, 2022 | Author: admin

EqualAI's Miriam Vogel leads committee advising the President and National AI Initiative Office on a range of issues related to artificial intelligence

NEW YORK, Aug. 2, 2022 /PRNewswire/ -- LivePerson(Nasdaq: LPSN), a global leader in customer engagement solutions, joined business leaders in congratulating EqualAIon four years of progress fighting unconscious bias in AI.

EqualAI is an independent nonprofit organization and movement founded in 2018 to reduce unconscious bias in the development and use of artificial intelligence. It is supported by corporate members from the tech industry.

In addition to launching impactful initiatives including the EqualAI Pledgeand EqualAI Badge Program for Responsible AI Governance the organization's president, Miriam Vogel, was recently appointed as Chair of the National Artificial Intelligence Advisory Committee(NAIAC), which advises the US President and National AI Initiative Office on a range of issues related to artificial intelligence.

The NAIAC was established by the US Department of Commerce and consists of leaders with a broad and interdisciplinary range of AI-relevant expertise across academia, nonprofits, civil society, and the private sector.

LivePerson founder and CEO Rob LoCascio said, "In just four years, EqualAI has made an incredible impact on the trajectory of artificial intelligence, bending it toward more responsible and ethical outcomes for all. At LivePerson, we're proud to have played a key role investing in and spearheading these efforts as a founding member of EqualAI. As we celebrate Miriam Vogel's appointment to the NAIAC, we encourage organizations of all kinds to take the EqualAI Pledge and undertake the EqualAI Badge Program to make tangible steps toward responsible AI governance."

Arianna Huffington, founder and CEO of Thrive Global and a founding member of EqualAI, added, "From raising awareness to designing frameworks and initiatives that fight bias, EqualAI has pushed policymakers and business leaders to do more and do better when it comes to artificial intelligence. As AI continues to reshape our daily lives, it's more critical than ever that we come together to ensure it helps, not hurts, the well-being of all of our communities."

In addition to LoCascio and Huffington, EqualAI's leadership includes Karyn Temple, Senior EVP and Global General Counsel at Motion Picture Association; Monica Greenberg, EVP, Corporate Development, Strategic Alliances and General Counsel at LivePerson; Susan Gonzales, CEO of AIandYou; and Reggie Townsend, Director of Data Ethics at SAS. LivePerson, Verizon, and SAS support EqualAI through corporate membership.

To learn more about reducing bias in artificial intelligence, visit EqualAI's websiteand LivePerson's blog.

About LivePerson, Inc.

LivePerson (NASDAQ: LPSN) is a global leader in customer engagement solutions. We create AI-powered digital experiences that feel Curiously Human. Our customers including leading brands like HSBC, Orange, and GM Financial have conversations with millions of consumers as personally as they would with one. Our Conversational Cloud platform powers nearly a billion conversational interactions every month, providing a uniquely rich data set to build connections that reduce costs, increase revenue, and are anything but artificial. Fast Company named us the #1 Most Innovative AI Company in the world. To talk with us or our Conversational AI, please visit liveperson.com.

About EqualAI

EqualAIis a nonprofit organization that was created to reduce unconscious bias in the development and use of artificial intelligence (AI). AI is transforming our society enabling important and exciting developments that were unimaginable just a few years ago. With these immense benefits comes significant responsibility. Together with leaders and experts across industry, academia, technology, and government, EqualAI is developing standards and tools to increase awareness and reduce bias, as well as identifying regulatory and legislative solutions.

Contact:Mike Tague[emailprotected]

SOURCE LivePerson, Inc.

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Artificial Intelligence Regulation Updates: China, EU, and U.S – The National Law Review

August 3, 2022 | Author: admin

Wednesday, August 3, 2022

Artificial Intelligence (AI) systems are poised to drastically alter the way businesses and governments operate on a global scale, with significant changes already under way. This technology has manifested itself in multiple forms including natural language processing, machine learning, and autonomous systems, but with the proper inputs can be leveraged to make predictions, recommendations, and even decisions.

Accordingly,enterprises are increasingly embracing this dynamic technology. A2022 global study by IBMfound that 77% of companies are either currently using AI or exploring AI for future use, creating value by increasing productivity through automation, improved decision-making, and enhanced customer experience. Further, according to a2021 PwC studythe COVID-19 pandemic increased the pace of AI adoption for 52% of companies as they sought to mitigate the crises impact on workforce planning, supply chain resilience, and demand projection.

For these many businesses investing significant resources into AI, it is critical to understand the current and proposed legal frameworks regulating this novel technology. Specifically for businesses operating globally, the task of ensuring that their AI technology complies with applicable regulations will be complicated by the differing standards that are emerging from China, the European Union (EU), and the U.S.

China has taken the lead in moving AI regulations past the proposal stage. In March 2022, China passed aregulationgoverning companies use of algorithms in online recommendation systems, requiring that such services are moral, ethical, accountable, transparent, and disseminate positive energy. The regulation mandates companies notify users when an AI algorithm is playing a role in determining which information to display to them and give users the option to opt out of being targeted. Additionally, the regulation prohibits algorithms that use personal data to offer different prices to consumers. We expect these themes to manifest themselves in AI regulations throughout the world as they develop.

Meanwhile in the EU, the European Commission has published an overarchingregulatory framework proposaltitled the Artificial Intelligence Act which would have a much broader scope than Chinas enacted regulation. The proposal focuses on the risks created by AI, with applications sorted into categories of minimal risk, limited risk, high risk, or unacceptable risk. Depending on an applications designated risk level, there will be corresponding government action or obligations. So far, the proposed obligations focus on enhancing the security, transparency, and accountability of AI applications through human oversight and ongoing monitoring. Specifically, companies will be required to register stand-alone high-risk AI systems, such as remote biometric identification systems, in an EU database. If the proposed regulation is passed, the earliest date for compliance would be the second half of 2024 with potential fines for noncompliance ranging from 2-6% of a companys annual revenue.

Additionally, the previously enacted EU General Data Protection Regulation (GDPR) already carries implications for AI technology.Article 22prohibits decisions based on solely automated processes that produce legal consequences or similar effects for individuals unless the program gains the users explicit consent or meets other requirements.

In the United States there has been a fragmented approach to AI regulation thus far, with states enacting their own patchwork AI laws. Many of the enacted regulations focus on establishing various commissions to determine how state agencies can utilize AI technology and to study AIs potential impacts on the workforce and consumers. Common pending state initiatives go a step further and would regulate AI systems accountability and transparency when they process and make decisions based on consumer data.

On a national level, the U.S. Congress enacted theNational AI Initiative Actin January 2021, creating theNational AI Initiativethat provides an overarching framework to strengthen and coordinate AI research, development, demonstration, and education activities across all U.S. Departments and Agencies . . . . The Act created new offices and task forces aimed at implementing a national AI strategy, implicating a multitude of U.S. administrative agencies including the Federal Trade Commission (FTC), Department of Defense, Department of Agriculture, Department of Education, and the Department of Health and Human Services.

Pending national legislation includes theAlgorithmic Accountability Act of 2022, which was introduced in both houses of Congress in February 2022. In response to reports that AI systems can lead to biased and discriminatory outcomes, the proposed Act would direct the FTC to create regulations that mandate covered entities, including businesses meeting certain criteria, to perform impact assessments when using automated decision-making processes. This would specifically include those derived from AI or machine learning.

While the FTC has not promulgated AI-specific regulations, this technology is on the agencys radar. In April 2021 the FTC issued amemowhich apprised companies that using AI that produces discriminatory outcomes equates to a violation of Section 5 of the FTC Act, which prohibits unfair or deceptive practices. And the FTC may soon take this warning a step fartherin June 2022 theagency indicatedthat it will submit an Advanced Notice of Preliminary Rulemaking to ensure that algorithmic decision-making does not result in harmful discrimination with the public comment period ending in August 2022. The FTC also recently issued areportto Congress discussing how AI may be used to combat online harms, ranging from scams, deep fakes, and opioid sales, but advised against over-reliance on these tools, citing the technologys susceptibility to producing inaccurate, biased, and discriminatory outcomes.

Companies should carefully discern whether other non-AI specific regulations could subject them to potential liability for their use of AI technology. For example, the U.S. Equal Employment Opportunity Commission (EEOC) put forthguidancein May 2022 warning companies that their use of algorithmic decision-making tools to assess job applicants and employees could violate the Americans with Disabilities Act by, in part, intentionally or unintentionally screening out individuals with disabilities. Further analysis of the EEOCs guidance can be foundhere.

Many other U.S. agencies and offices are beginning to delve into the fray of AI. In November 2021, the White House Office of Science and Technology Policysolicited engagementfrom stakeholders across industries in an effort to develop a Bill of Rights for an Automated Society. Such a Bill of Rights could cover topics like AIs role in the criminal justice system, equal opportunities, consumer rights, and the healthcare system. Additionally, the National Institute of Standards and Technology (NIST), which falls under the U.S. Department of Commerce, is engaging with stakeholders todevelopa voluntary risk management framework for trustworthy AI systems. The output of this project may be analogous to the EUs proposed regulatory framework, but in a voluntary format.

The overall theme of enacted and pending AI regulations globally is maintaining the accountability, transparency, and fairness of AI. For companies leveraging AI technology, ensuring that their systems remain compliant with the various regulations intended to achieve these goals could be difficult and costly. Two aspects of AIs decision-making process make oversight particularly demanding:

Opaquenesswhere users can control data inputs and view outputs, but are often unable to explain how and with which data points the system made a decision.

Frequent adaptationwhere processes evolve over time as the system learns.

Therefore, it is important for regulators to avoid overburdening businesses to ensure that stakeholders may still leverage AI technologies great benefits in a cost-effective manner. The U.S. has the opportunity to observe the outcomes of the current regulatory action from China and the EU to determine whether their approaches strike a favorable balance. However, the U.S. should potentially accelerate its promulgation of similar laws so that it can play a role in setting the global tone for AI regulatory standards.

Thank you to co-author Lara Coole, a summer associate in Foley & Lardners Jacksonville office, for her contributions to this post.

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Artificial Intelligence is the Future of the Banking Industry Are You Prepared for It? – International Banker

August 3, 2022 | Author: admin

By Pritham Shetty, Consulting Director, Propel

Our world is moving at a fast pace. Though banks originally built their foundations to be run solely by humans, the time has come forartificial intelligence in the banking industry. In 2020, the global AI banking market was valued at $3.88 billion, andit is projected to reach $64.03 billion by the end of the decade,with a compound annual growth rate of 32.6%. However, when it comes to implementing even the best strategies, theapplication of artificial intelligence in the banking industryis susceptible to weak core tech and poor data backbones.

By my count, there were 20,000 new banking regulatory requirements created in 2015 alone. Chances are your business wont find a one-size-fits-all solution to dealing with this. The next-best option is to be nimble. You need to be able to break down the business process into small chunks. By doing so, you can come up with digital strategies that work with new and existing regulations.

AIcan take you a long way in this process, but you must know how to harness its power. Take originating home loans, for instance. This can be an important, sometimes tedious, process for the loan seeker and bank. With an AI solution, loan origination can happen quicker and be more beneficial to both parties.

As the world of banking moves toward AI, it is integral to note that the crucial working element for AI is data. The trick to using that data is to understand how to leverage it best for your business value. Data with no direction wont lead to progress, nor will it lead to the proper deployment of your AI. That is one of the top reasons it isso challenging to implement AI in banks there has to be a plan.

Even if you come up with a poor strategy, those mistakes can be course-corrected over time. It takes some time and effort, but it is doable. If you home in on how customer information can be used, you can utilize AI for banking services in a way that is scalable and actionable. Once you understand how to use the data you collect, you can develop technical solutions that work with each other, identify specific needs, and build data pipelines that will lead you down the road to AI.

How is artificial intelligence changing the banking sector?

Due to the increasingly digital world, customers have more access to their banking information than ever. Of course, this can lead to other problems. Because there is so much access to data, there are also prime opportunities for fraudulent activities, and this is one example ofhow AI is changing the banking sector. With AI, you can train systems to learn, understand, and recognize when these activities happen. In fact, there was a5% decrease in record exposure from 2020 to 2021.

AI also safeguards against data theft or abuse. Not only can AI recognize breaches from outside sources, but it can also recognize internal threats. Once an AI system is trained, it can identify these problems and even offer solutions to them. For instance, a customer support call center can have traffic directed by AI to assuage an influx of calls during high-volume fluctuations.

Another great example of this is the development ofconversational AI platforms. The ubiquity of social media and other online platforms can be used to tailor customer experiences directly led by AI. By using the data gathered from all sources, AI can greatly improve the customer experience overall.

For example, a loan might take anywhere from seven to 45 days to be granted. But with AI, the process can be expedited not only for the customer, but also the bank. By using AI in a situation such as this, your bank can assess the risk it is taking on by servicing loans. It can also make the process faster by performing underwriting, document scanning, and other manual processes previously associated with data collection. On top of all that, AI can gather and analyze data about your customers behaviors throughout their banking lives.

In the past, so much of this work was done solely by people. Although automation has certainly helped speed up and simplify tasks, it is used for tedium and doesnt have the complexity of AI. AI saves time and money by freeing up your employees to do other processes and provides valuable insights to your customers. And customers can budget better and have a clearer idea of where their money is going.

Even the most traditional banks will want to adopt AI to save time and money and allow employees more opportunities to have positive one-on-one relationships with customers. Look no further than fintech companies such as Credijusto, Nubank, and Monzo that have digitized traditional banking services through the power of cutting-edge tech.

Are you ready to put AI to work for your business?

Today, its not a question ofhow AI is impacting financial services. Now, its about how to implement it. That all starts with you. You must ask the right questions: What are your goals for implementing AI? Do you want to improve your internal processes? Simply provide a better customer service experience? If so, how should you implement AI for your banking services? Start with these strategies:

By making realistic short-term goals, you set yourself up for future success. These are the solutions that will be the building blocks for the type of AI everyone will aspire to use.

You want to ensure that you know how you currently use data and how you plan on using it in the future. Again, this sets your organization up for success in the long run. If you dont have the right practices now, you certainly wont going forward.

As you implement AI into your banking practices, you should know how exactlyyou generate data. Then, you must understand how you interpret it. What is the best use for it? After that, you can make decisions that will be scalable, useful, and seamless.

Technology has not only made the world around us move faster, but also better in so many ways. Traditional institutions such as banks might be slow to adopt, but weve already seenhow artificial intelligence is changing the banking sector. By taking the proper steps, you could be moving right along with it into the future.

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CSforALL Urges Greater Focus on AI and Data Science – Government Technology

August 3, 2022 | Author: admin

(TNS) If you're not in the know, artificial intelligence and data science may sound like especially nerdy subsets of the already pocket-protector infused field of computer science.

But anyone who is serious about expanding computer science educationa list that includes Fortune 500 company CEOs and policymakers on both sides of the aisleshould be thinking carefully about emphasizing AI, in which machines are trained to perform tasks that simulate some of what the human brain can do, and data science, in which students learn to record, store, and analyze data.

That means making sure kids have access to well-designed resources to learn those subjects, bolstering professional development for those who teach them, exposing career counselors to information about how to help students pursue jobs in those fields, and much more.

Leigh Ann DeLyser, CSforALL's co-founder and executive director, spoke with Education Week about some big picture ideas around the push for a greater focus on AI and data science within computer science education. Here are some key takeaways from that conversation.

Teaching computer scienceincluding AI and data sciencecan help the next generation grapple with big societal problems.

"Our world is complex and messy and full of big problems," DeLyser said. AI and data science are fast- growing areas when it comes to employment, but "they're also the fastest-growing tools that are being used by business people, nonprofits, and governments every single day. No matter what you do in life, if you want to tackle the big problems we have in the world, you're going to need to understand these things and how they can be used, even if you're not the programmer who is writing the code that makes them go."

Students from all different backgrounds must get grounding in computer science.

It's especially important to increase socioeconomic, racial, and gender diversity in the field.

"Research shows that teams that have different backgrounds are better problem solvers, because they think about problems from different ways," DeLyser said. "When everybody comes with the same perspective, you tend to miss out on some of the ideas or the big challenges that pop up along the way. ... We [want to] give equal access, no matter what ZIP code [students] grow up in, to those high-paying careers and opportunities later in their life."

There are already good models of how to teach AI and data-science.

It's possible to see school districts already experimenting with how to do this well, if you know where to look, DeLyser said. "Often, we frame [computer science access] as a deficit narrative. There's nothing happening in education, or education is failing."

But that's not the case, she added. For instance, the large Gwinnett County school district outside Atlanta, is getting ready to open a high school that will focus on artificial intelligence. And in Bentonville, Ark., where Walmart is headquartered, local high school students interning with the company get a first-hand look at how the retail giant uses AI to configure store layouts, with an eye towards maximizing profit.

It's never too early to start teaching artificial intelligence.

Believe it or not, kids as young as kindergarten or even preschool can become familiar with the basics of AI, DeLyser said.

"AI is pattern recognition. One of the most important pre skills for algebra and math development for kids in kindergarten, and even preschool, is pattern recognition. 'This is a circle, this is a square,'" DeLyser said. Teaching AI is "having them take that learning that they're doing for the pattern recognition just one step further. It's like, OK, 'I'm going to teach you, you're going to teach a friend. Now I'm going to teach a computer.' It's not that far off from the work that they're already doing."

2022 Education Week (Bethesda, Md.). Distributed by Tribune Content Agency, LLC.

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