![\"GenAI](\"https://www.pymnts.com/wp-content/uploads/2024/08/GenAI-uses-in-detecting-fraud.jpg\")
A bill in California would require artificial intelligence companies to conduct tests to prevent \u201ccatastrophic harm.\u201d
\nHowever, AI firms are trying to curtail the legislation, saying it would damage their industry, The Wall Street Journal (WSJ) reported Wednesday (Aug. 7).
\nThe bill, SB 1047, requires that makers of large AI models hold safety tests to reduce the risk of cyberattacks that result in mass casualties or cause at least $500 million in damage, per the report. In addition, companies would need to show that humans can turn off AIs that behave dangerously.
\nThe bill covers AI models meeting a certain computing power threshold and costing more than $100 million to train. That includes OpenAI\u2019s GPT-4, although any company doing business in California would need to comply, the report said.
\nWhile some AI industry figures have called for regulation, they want it to come from the federal government, according to the report. The sector argues the bill would require constraints that are too vague.
\n\u201cIf it were to go into effect as written, it would have a chilling effect on innovation in California,\u201d said Luther Lowe, who heads public policy at startup accelerator Y Combinator, per the report.
\nMeta and OpenAI raised concerns about the bill, the report said. Google, Anthropic and Microsoft all pitched extensive revisions.
\nThe bill, which still needs approval of the full California Assembly, was drafted by state Sen. Scott Weiner, according to the report.
\n\u201cThere are people in the tech sector who are opposed to any and all forms of regulation no matter what it is, even for something reasonable and light-touch,\u201d he said, per the report.
\nAt least 16 companies have signed onto the White House\u2019s voluntary commitment to safe AI development. In doing so, the companies agreed to a range of measures designed to further understand the risks and ethical implications of new technologies while offering greater transparency and restricting the potential for misuse.
\nLast month, the competition authorities from the United States, the United Kingdom and the European Union issued a rare joint statement outlining their concerns about market concentration and anti-competitive practices in the generative AI field.
\nFor all PYMNTS AI coverage, subscribe to the daily AI Newsletter.
\nThe post AI Sector Takes Aim at California Safety Bill appeared first on PYMNTS.com.
\n", "content_text": "A bill in California would require artificial intelligence companies to conduct tests to prevent \u201ccatastrophic harm.\u201d\nHowever, AI firms are trying to curtail the legislation, saying it would damage their industry, The Wall Street Journal (WSJ) reported Wednesday (Aug. 7).\nThe bill, SB 1047, requires that makers of large AI models hold safety tests to reduce the risk of cyberattacks that result in mass casualties or cause at least $500 million in damage, per the report. In addition, companies would need to show that humans can turn off AIs that behave dangerously.\nThe bill covers AI models meeting a certain computing power threshold and costing more than $100 million to train. That includes OpenAI\u2019s GPT-4, although any company doing business in California would need to comply, the report said.\nWhile some AI industry figures have called for regulation, they want it to come from the federal government, according to the report. The sector argues the bill would require constraints that are too vague.\n\u201cIf it were to go into effect as written, it would have a chilling effect on innovation in California,\u201d said Luther Lowe, who heads public policy at startup accelerator Y Combinator, per the report.\nMeta and OpenAI raised concerns about the bill, the report said. Google, Anthropic and Microsoft all pitched extensive revisions.\nThe bill, which still needs approval of the full California Assembly, was drafted by state Sen. Scott Weiner, according to the report.\n\u201cThere are people in the tech sector who are opposed to any and all forms of regulation no matter what it is, even for something reasonable and light-touch,\u201d he said, per the report.\nAt least 16 companies have signed onto the White House\u2019s voluntary commitment to safe AI development. In doing so, the companies agreed to a range of measures designed to further understand the risks and ethical implications of new technologies while offering greater transparency and restricting the potential for misuse.\nLast month, the competition authorities from the United States, the United Kingdom and the European Union issued a rare joint statement outlining their concerns about market concentration and anti-competitive practices in the generative AI field.\nFor all PYMNTS AI coverage, subscribe to the daily AI Newsletter.\nThe post AI Sector Takes Aim at California Safety Bill appeared first on PYMNTS.com.", "date_published": "2024-08-08T10:16:11-04:00", "date_modified": "2024-08-08T10:16:11-04:00", "authors": [ { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/f05cc0fdcc9e387e4f3570c17158c503?s=512&d=blank&r=g" } ], "author": { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/f05cc0fdcc9e387e4f3570c17158c503?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2024/08/AI-safety-California-Assembly.jpg", "tags": [ "Artificial Intelligence", "California", "Cybersecurity", "GenAI", "Government", "Innovation", "Legislation", "News", "PYMNTS News", "regulation", "Security", "Technology", "What's Hot" ] }, { "id": "https://www.pymnts.com/?p=2045343", "url": "https://www.pymnts.com/news/artificial-intelligence/2024/genai-tools-show-promise-of-reducing-payments-fraud-by-85/", "title": "GenAI Tools Show Promise of Reducing Payments Fraud by 85%", "content_html": "The landscape of payments fraud is undergoing a shift as traditional detection methods become increasingly inadequate against sophisticated fraud schemes.
\nConventional rules-based systems, relying on static rules and predefined patterns, are falling short in adapting to the dynamic tactics of modern fraudsters.
\nEnter generative artificial intelligence, a technology that promises to redefine fraud detection by uncovering subtle and evolving fraud patterns with unprecedented accuracy. This new approach not only enhances detection but also addresses issues such as privacy concerns and the high incidence of false positives.
\nThe PYMNTS Intelligence report \u201cCan Generative Al Break the Payments Fraud Cycle?\u201d provides an in-depth exploration of how generative AI is poised to revolutionize fraud detection and address the limitations of conventional systems.
\n
Traditional fraud detection systems are increasingly inadequate in addressing sophisticated fraud schemes. These systems demand frequent manual updates and suffer from high false-positive rates, causing inconvenience for legitimate customers and taxing resources. Generative AI employs unsupervised learning to uncover complex fraud patterns and anomalies that conventional systems often miss.
\nVisa\u2019s Visa Account Attack Intelligence Score uses generative AI to analyze transaction data in real time, achieving an 85% reduction in false positives compared to other models, for example. This advanced system enhances risk assessment for card-not-present transactions, improves decision-making for card issuers, and boosts consumer satisfaction while mitigating financial losses.
\nGenerative AI offers a solution to the challenges of fraud detection models that rely on real-world financial data, which often raises privacy and compliance concerns. By generating synthetic datasets that replicate actual transactions without exposing sensitive information, generative AI ensures adherence to privacy regulations while enhancing the robustness of fraud detection systems.
\nBunq, a European FinTech, demonstrates the efficacy of this approach, having integrated generative AI into its transaction-monitoring system. The innovation has boosted Bunq\u2019s data processing efficiency by more than five times and accelerated fraud detection model training by nearly 100 times compared to previous methods. Using synthetic data, Bunq continues to refine its fraud detection algorithms while upholding privacy standards.
\nGenerative AI is revolutionizing fraud detection by enhancing both speed and accuracy compared with traditional methods. Mastercard\u2019s deployment of generative AI has improved its fraud detection capabilities, achieving a twofold increase in the speed of identifying compromised cards and a 300% boost in the identification speed of at-risk merchants. These advancements allow for quicker response times and diminish the opportunity for fraudulent activities, thereby fortifying the digital payments ecosystem.
\nGenerative AI\u2019s ability to learn from extensive datasets and adapt in real time to new fraud schemes offers a more agile and effective approach to fraud prevention. This adaptability is crucial for countering the evolving tactics of fraudsters and ensuring a secure payments environment.
\nGenerative AI represents an advancement in combating payments fraud. By facilitating real-time adaptation, enhancing privacy through synthetic data, and improving both detection speed and accuracy, it can transform fraud prevention strategies.
\nAs financial institutions and businesses adopt this technology, they have the potential to boost fraud detection capabilities and reduce operational inefficiencies. The ongoing evolution of generative AI is set to play a role in protecting the integrity of the payments ecosystem against sophisticated fraud tactics.
\nThe post GenAI Tools Show Promise of Reducing Payments Fraud by 85% appeared first on PYMNTS.com.
\n", "content_text": "The landscape of payments fraud is undergoing a shift as traditional detection methods become increasingly inadequate against sophisticated fraud schemes.\nConventional rules-based systems, relying on static rules and predefined patterns, are falling short in adapting to the dynamic tactics of modern fraudsters.\nEnter generative artificial intelligence, a technology that promises to redefine fraud detection by uncovering subtle and evolving fraud patterns with unprecedented accuracy. This new approach not only enhances detection but also addresses issues such as privacy concerns and the high incidence of false positives.\nThe PYMNTS Intelligence report \u201cCan Generative Al Break the Payments Fraud Cycle?\u201d provides an in-depth exploration of how generative AI is poised to revolutionize fraud detection and address the limitations of conventional systems.\n\nGenerative AI Outperforms Traditional Systems\nTraditional fraud detection systems are increasingly inadequate in addressing sophisticated fraud schemes. These systems demand frequent manual updates and suffer from high false-positive rates, causing inconvenience for legitimate customers and taxing resources. Generative AI employs unsupervised learning to uncover complex fraud patterns and anomalies that conventional systems often miss.\nVisa\u2019s Visa Account Attack Intelligence Score uses generative AI to analyze transaction data in real time, achieving an 85% reduction in false positives compared to other models, for example. This advanced system enhances risk assessment for card-not-present transactions, improves decision-making for card issuers, and boosts consumer satisfaction while mitigating financial losses.\nEnhanced Privacy Through Synthetic Data\nGenerative AI offers a solution to the challenges of fraud detection models that rely on real-world financial data, which often raises privacy and compliance concerns. By generating synthetic datasets that replicate actual transactions without exposing sensitive information, generative AI ensures adherence to privacy regulations while enhancing the robustness of fraud detection systems.\nBunq, a European FinTech, demonstrates the efficacy of this approach, having integrated generative AI into its transaction-monitoring system. The innovation has boosted Bunq\u2019s data processing efficiency by more than five times and accelerated fraud detection model training by nearly 100 times compared to previous methods. Using synthetic data, Bunq continues to refine its fraud detection algorithms while upholding privacy standards.\nSpeed and Accuracy Improvements\nGenerative AI is revolutionizing fraud detection by enhancing both speed and accuracy compared with traditional methods. Mastercard\u2019s deployment of generative AI has improved its fraud detection capabilities, achieving a twofold increase in the speed of identifying compromised cards and a 300% boost in the identification speed of at-risk merchants. These advancements allow for quicker response times and diminish the opportunity for fraudulent activities, thereby fortifying the digital payments ecosystem.\nGenerative AI\u2019s ability to learn from extensive datasets and adapt in real time to new fraud schemes offers a more agile and effective approach to fraud prevention. This adaptability is crucial for countering the evolving tactics of fraudsters and ensuring a secure payments environment.\nGenerative AI represents an advancement in combating payments fraud. By facilitating real-time adaptation, enhancing privacy through synthetic data, and improving both detection speed and accuracy, it can transform fraud prevention strategies.\nAs financial institutions and businesses adopt this technology, they have the potential to boost fraud detection capabilities and reduce operational inefficiencies. The ongoing evolution of generative AI is set to play a role in protecting the integrity of the payments ecosystem against sophisticated fraud tactics.\nThe post GenAI Tools Show Promise of Reducing Payments Fraud by 85% appeared first on PYMNTS.com.", "date_published": "2024-08-08T04:00:24-04:00", "date_modified": "2024-08-07T21:26:22-04:00", "authors": [ { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/f05cc0fdcc9e387e4f3570c17158c503?s=512&d=blank&r=g" } ], "author": { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/f05cc0fdcc9e387e4f3570c17158c503?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2024/08/artificial-intelligence-AI-payments.jpg", "tags": [ "Artificial Intelligence", "bunq", "Can Generative Al Break the Payments Fraud Cycle?", "Featured News", "fraud", "GenAI", "Innovation", "MasterCard", "News", "privacy", "PYMNTS Intelligence", "PYMNTS News", "PYMNTS Study", "Security", "Technology", "Visa" ] }, { "id": "https://www.pymnts.com/?p=2038177", "url": "https://www.pymnts.com/news/artificial-intelligence/2024/zest-ai-launches-fraud-detection-solution/", "title": "Zest AI Launches Fraud Detection Solution", "content_html": "Zest AI unveiled a tool to identify fraudulent activity during the loan decisioning process.
\nZest Project is designed to use artificial intelligence to respond to the 69% increase in fraud cases \u2014 per the Federal Trade Commission \u2014 witnessed by community banks and credit unions in 2023, according to a Wednesday (Aug. 7) press release.
\n\u201cLenders need to outsmart fraud, including an increasing volume of AI-driven fraud in the industry with AI,\u201d said Adam Kleinman, head of strategy and client Success at Zest AI, in the release. \u201cOur team designed Zest Protect to create an efficient tool that can more accurately detect all types of fraud now and in the future, including AI-created fraud, with the ultimate goal of boosting lending confidence for our bank and credit union customers.\u201d
\nZest Protect employs machine learning technology to instantly detect first-party and third-party fraud, while also flagging income inconsistencies within the automated loan decisioning process, per the release.
\nThis lets lenders adjust \u201cspecific detection thresholds based on their risk tolerances and automation objectives,\u201d the release said. \u201cWith access to fraud prevention data and analytics, Zest AI can flag applications swiftly and protect against emerging threats.\u201d
\nAI is becoming the tool of choice for financial institutions that want to prevent illicit activity such as money laundering or bank fraud.
\nThe PYMNTS Intelligence report \u201cFinancial Institutions Revamping Technologies to Fight Financial Crimes\u201d found an uptick in financial crime, with more than 40% of financial institutions surveyed saying incidents of fraud are increasing, and 7 in 10 saying they now are using AI and machine learning to fend off fraudsters.
\n\u201cModern payments fraud demands real-time learning and adaptation at scale,\u201d PYMNTS wrote in June. \u201cGenerative AI offers the unprecedented advantage of continuous learning. It rapidly refines and adapts its understanding of patterns to distinguish between legitimate and fraudulent payments more accurately.\u201d
\nIn addition, generative AI can create synthetic datasets that mimic real-world financial data, allowing for robust model training without sacrificing privacy or compliance.
\nHowever, developing AI and ML tools can be costly, which could explain why just 14% of financial institutions said they build in-house fraud-fighting AI and ML technologies. Almost 30% said they rely entirely on third-party vendors to deliver these tools.
\nFor all PYMNTS AI coverage, subscribe to the daily AI Newsletter.
\nThe post Zest AI Launches Fraud Detection Solution appeared first on PYMNTS.com.
\n", "content_text": "Zest AI unveiled a tool to identify fraudulent activity during the loan decisioning process.\nZest Project is designed to use artificial intelligence to respond to the 69% increase in fraud cases \u2014 per the Federal Trade Commission \u2014 witnessed by community banks and credit unions in 2023, according to a Wednesday (Aug. 7) press release.\n\u201cLenders need to outsmart fraud, including an increasing volume of AI-driven fraud in the industry with AI,\u201d said Adam Kleinman, head of strategy and client Success at Zest AI, in the release. \u201cOur team designed Zest Protect to create an efficient tool that can more accurately detect all types of fraud now and in the future, including AI-created fraud, with the ultimate goal of boosting lending confidence for our bank and credit union customers.\u201d\nZest Protect employs machine learning technology to instantly detect first-party and third-party fraud, while also flagging income inconsistencies within the automated loan decisioning process, per the release.\nThis lets lenders adjust \u201cspecific detection thresholds based on their risk tolerances and automation objectives,\u201d the release said. \u201cWith access to fraud prevention data and analytics, Zest AI can flag applications swiftly and protect against emerging threats.\u201d\nAI is becoming the tool of choice for financial institutions that want to prevent illicit activity such as money laundering or bank fraud.\nThe PYMNTS Intelligence report \u201cFinancial Institutions Revamping Technologies to Fight Financial Crimes\u201d found an uptick in financial crime, with more than 40% of financial institutions surveyed saying incidents of fraud are increasing, and 7 in 10 saying they now are using AI and machine learning to fend off fraudsters.\n\u201cModern payments fraud demands real-time learning and adaptation at scale,\u201d PYMNTS wrote in June. \u201cGenerative AI offers the unprecedented advantage of continuous learning. It rapidly refines and adapts its understanding of patterns to distinguish between legitimate and fraudulent payments more accurately.\u201d\nIn addition, generative AI can create synthetic datasets that mimic real-world financial data, allowing for robust model training without sacrificing privacy or compliance.\nHowever, developing AI and ML tools can be costly, which could explain why just 14% of financial institutions said they build in-house fraud-fighting AI and ML technologies. Almost 30% said they rely entirely on third-party vendors to deliver these tools.\nFor all PYMNTS AI coverage, subscribe to the daily AI Newsletter.\nThe post Zest AI Launches Fraud Detection Solution appeared first on PYMNTS.com.", "date_published": "2024-08-07T12:59:52-04:00", "date_modified": "2024-08-07T12:59:52-04:00", "authors": [ { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/f05cc0fdcc9e387e4f3570c17158c503?s=512&d=blank&r=g" } ], "author": { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/f05cc0fdcc9e387e4f3570c17158c503?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2024/08/Zest-AI.png", "tags": [ "Artificial Intelligence", "Banks", "credit unions", "fraud", "GenAI", "Innovation", "Lending", "loans", "News", "PYMNTS News", "Security", "Technology", "What's Hot", "Zest AI" ] }, { "id": "https://www.pymnts.com/?p=2036514", "url": "https://www.pymnts.com/news/artificial-intelligence/2024/wendys-purchasing-co-op-deploy-palantir-ai-powered-supply-chain-solutions/", "title": "Wendy\u2019s Purchasing Co-op to Deploy Palantir\u2019s AI-Powered Supply Chain Solutions", "content_html": "Wendy\u2019s Quality Supply Chain Co-op (QSCC), a purchasing cooperative that services more than 6,400 Wendy\u2019s restaurants in the United States and Canada, teamed with Palantir Technologies to accelerate its digital transformation and adoption of artificial intelligence.
\nVia the partnership, Palantir, a provider of AI systems, will help QSCC develop an integrated supply chain network; implement AI-driven, automated workflows; and build a connected ecosystem of suppliers, distributors and restaurants, the companies said in a Wednesday (Aug. 7) press release.
\n\u201cTogether with Palantir, we\u2019re unlocking the inherent power of the supply chain ecosystem to drive new and compelling sales and operating efficiencies that will provide Wendy\u2019s with a distinctive edge in the industry,\u201d QSCC President and CEO Pete Suerken said in the release.
\nIn the first phase of the digital transformation, QSCC will move onto Palantir\u2019s Artificial Intelligence Platform (AIP), according to the release. The platform will enable the company to improve the scale and speed of decision-making by connecting disparate data sources.
\nIn the second phase, QSCC will use Palantir AIP for supply chain management and waste prevention, the release said. By doing so, the co-op will generate cost savings and efficiencies across the supply chain by deploying the capabilities of large language models and other AI systems.
\nTogether, these changes will benefit the quick service restaurant (QSR) company\u2019s restaurant operators, suppliers and distributors, Ted Mabrey, head of global commercial at Palantir, said in the release.
\n\u201cOur AI operating system powers many of America\u2019s most important companies, giving them a technology-driven competitive advantage, and we are excited to continue to grow in the QSR sector with this iconic brand and an ambitious vision,\u201d Mabrey said.
\nWendy\u2019s restaurants have already deployed AI systems in some customer-facing applications.
\nIn March 2023, the company unveiled an AI-based loyalty platform that analyzes customer data to create tailored offers and rewards. The system uses gamification, rewarding customers to encourage and recognize their loyalty.
\nIn May 2023, Wendy\u2019s and Google teamed to bring AI to the fast-food chain\u2019s drive-thrus, with the \u201cWendy\u2019s FreshAI\u201d system automating ordering. Wendy\u2019s said at the time that the AI system allows customers to have conversations with it that feel natural, receive quick answers to their questions, and be understood, \u201ceven if their order isn\u2019t phrased exactly as it appears on menus.\u201d
\nFor all PYMNTS AI coverage, subscribe to the daily AI Newsletter.
\nThe post Wendy\u2019s Purchasing Co-op to Deploy Palantir\u2019s AI-Powered Supply Chain Solutions appeared first on PYMNTS.com.
\n", "content_text": "Wendy\u2019s Quality Supply Chain Co-op (QSCC), a purchasing cooperative that services more than 6,400 Wendy\u2019s restaurants in the United States and Canada, teamed with Palantir Technologies to accelerate its digital transformation and adoption of artificial intelligence.\nVia the partnership, Palantir, a provider of AI systems, will help QSCC develop an integrated supply chain network; implement AI-driven, automated workflows; and build a connected ecosystem of suppliers, distributors and restaurants, the companies said in a Wednesday (Aug. 7) press release.\n\u201cTogether with Palantir, we\u2019re unlocking the inherent power of the supply chain ecosystem to drive new and compelling sales and operating efficiencies that will provide Wendy\u2019s with a distinctive edge in the industry,\u201d QSCC President and CEO Pete Suerken said in the release.\nIn the first phase of the digital transformation, QSCC will move onto Palantir\u2019s Artificial Intelligence Platform (AIP), according to the release. The platform will enable the company to improve the scale and speed of decision-making by connecting disparate data sources.\nIn the second phase, QSCC will use Palantir AIP for supply chain management and waste prevention, the release said. By doing so, the co-op will generate cost savings and efficiencies across the supply chain by deploying the capabilities of large language models and other AI systems.\nTogether, these changes will benefit the quick service restaurant (QSR) company\u2019s restaurant operators, suppliers and distributors, Ted Mabrey, head of global commercial at Palantir, said in the release.\n\u201cOur AI operating system powers many of America\u2019s most important companies, giving them a technology-driven competitive advantage, and we are excited to continue to grow in the QSR sector with this iconic brand and an ambitious vision,\u201d Mabrey said.\nWendy\u2019s restaurants have already deployed AI systems in some customer-facing applications.\nIn March 2023, the company unveiled an AI-based loyalty platform that analyzes customer data to create tailored offers and rewards. The system uses gamification, rewarding customers to encourage and recognize their loyalty.\nIn May 2023, Wendy\u2019s and Google teamed to bring AI to the fast-food chain\u2019s drive-thrus, with the \u201cWendy\u2019s FreshAI\u201d system automating ordering. Wendy\u2019s said at the time that the AI system allows customers to have conversations with it that feel natural, receive quick answers to their questions, and be understood, \u201ceven if their order isn\u2019t phrased exactly as it appears on menus.\u201d\nFor all PYMNTS AI coverage, subscribe to the daily AI Newsletter.\nThe post Wendy\u2019s Purchasing Co-op to Deploy Palantir\u2019s AI-Powered Supply Chain Solutions appeared first on PYMNTS.com.", "date_published": "2024-08-07T10:30:17-04:00", "date_modified": "2024-08-07T22:38:03-04:00", "authors": [ { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/f05cc0fdcc9e387e4f3570c17158c503?s=512&d=blank&r=g" } ], "author": { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/f05cc0fdcc9e387e4f3570c17158c503?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2023/05/Wendys-1.jpg", "tags": [ "Artificial Intelligence", "B2B", "B2B Payments", "commercial payments", "digital transformation", "food and beverage", "GenAI", "Innovation", "News", "Palantir Technologies", "partnerships", "PYMNTS News", "QSRs", "Restaurants", "supply chain management", "Technology", "wendy's", "Wendy\u2019s Quality Supply Chain Co-op", "What's Hot", "What's Hot In B2B" ] }, { "id": "https://www.pymnts.com/?p=2020475", "url": "https://www.pymnts.com/news/artificial-intelligence/2024/companies-assess-compliance-as-european-union-ai-act-takes-effect/", "title": "Companies Assess Compliance as EU\u2019s AI Act Takes Effect", "content_html": "The European Union\u2019s AI Act came into force Thursday (Aug. 1), establishing the world\u2019s first comprehensive regulatory framework for artificial intelligence and setting new compliance standards for businesses worldwide.
\nThe EU adopted the rules earlier this year after negotiations that gained urgency following the 2022 debut of ChatGPT. The chatbot\u2019s capabilities highlighted the potential and risks of generative AI systems, which can produce human-like text, images and other content.
\nThe new law classifies various types of AI based on risk and imposes different requirements and obligations on \u201climited risk\u201d and \u201chigh risk\u201d AI systems.
\nShaun Hurst of Smarsh, a digital communications compliance firm, emphasized new requirements for banks using high-risk AI technologies in a statement sent to PYMNTS.
\n\u201cBanks utilizing AI technologies categorized as high-risk must now adhere to stringent regulations focusing on system accuracy, robustness and cybersecurity, including registering in an EU database and comprehensive documentation to demonstrate adherence to the AI Act,\u201d Hurst said.
\nOther countries, including the United Kingdom, are also developing AI regulations. The U.K. is expected to unveil its proposal later this year.
\nThe AI Act is expected to have far-reaching effects on global commerce. Companies operating in or selling to the EU market must reassess their AI strategies and potentially redesign products to comply with the new regulations. This could lead to increased costs for research and development, compliance and legal consultation.
\nHowever, it may also spur innovation in responsible AI development and create new market opportunities for companies that can effectively navigate the regulatory landscape. Industries beyond finance, including healthcare, manufacturing and retail, must adapt their AI implementations to meet the EU\u2019s standards, potentially reshaping global AI adoption patterns.
\nUnilever implemented a Responsible AI Framework in anticipation of comprehensive regulations like the EU AI Act, according to a blog post. The company began addressing data and AI ethics in 2019, developing an assurance process for new AI projects.
\n\u201cTaking proof of concept projects using AI systems through a thorough assurance process at an early stage is enabling us to be more innovative and fully deploy trustworthy AI systems more quickly,\u201d Unilever Chief Data Officer Andy Hill said in the post.
\nUnilever views AI as a tool to \u201cdrive productivity, creativity and growth,\u201d he added in the post.
\nThe framework involves cross-functional expert reviews to manage risks and ensure compliance.
\n\u201cAlthough Unilever has developed legal and ethical guardrails for AI, risks around issues such as IP rights, data privacy, transparency, confidentiality and AI bias can remain, as legal frameworks can lag behind the rapidly evolving technology,\u201d Unilever Chief Privacy Officer Christine Lee said in the post.
\nUnilever operates over 500 AI systems globally, spanning R&D, stock control and marketing, the post said. The company\u2019s approach includes ongoing monitoring and adaptable processes to keep pace with evolving regulations.
\n\u201cWe will continue to ensure Unilever stays in step with legal developments that affect our business and brands \u2014 from copyright ownership in AI-generated materials to data privacy laws and advertising regulations,\u201d Hill said in the post.
\nEuropean Commission President Ursula von der Leyen said the act creates \u201cguardrails\u201d to protect people while providing businesses with regulatory clarity. The law follows a risk-based approach, imposing stricter obligations on high-risk AI systems that could impact citizens\u2019 rights or health.
\nCompanies must comply by 2026, with rules for AI models like ChatGPT taking effect in 12 months. Bans on certain AI uses, such as predictive policing based on profiling and systems inferring personal characteristics from biometric data, will apply in six months.
\nViolations of banned practices or data requirements may result in fines of up to 7% of global annual revenue, a deterrent for non-compliance. The EU has established an AI Office staffed with technology experts to oversee the law\u2019s implementation.
\nThe regulations are expected to drive investment in compliance technologies within various industries, particularly financial services. Companies adept at navigating the new rules may gain advantages in AI-enabled markets.
\nThis development marks a shift in the global AI regulatory landscape. The EU\u2019s first-mover status in comprehensive AI regulation could influence approaches in other jurisdictions, potentially setting a benchmark for future AI governance worldwide.
\nThe AI Act\u2019s broad scope extends beyond EU-based companies, affecting organizations with EU business connections or customers. This extraterritorial reach underscores the law\u2019s potential to shape global AI development and deployment practices.
\nFor all PYMNTS AI coverage, subscribe to the daily AI Newsletter.
\nThe post Companies Assess Compliance as EU\u2019s AI Act Takes Effect appeared first on PYMNTS.com.
\n", "content_text": "The European Union\u2019s AI Act came into force Thursday (Aug. 1), establishing the world\u2019s first comprehensive regulatory framework for artificial intelligence and setting new compliance standards for businesses worldwide.\nThe EU adopted the rules earlier this year after negotiations that gained urgency following the 2022 debut of ChatGPT. The chatbot\u2019s capabilities highlighted the potential and risks of generative AI systems, which can produce human-like text, images and other content.\nThe new law classifies various types of AI based on risk and imposes different requirements and obligations on \u201climited risk\u201d and \u201chigh risk\u201d AI systems.\nShaun Hurst of Smarsh, a digital communications compliance firm, emphasized new requirements for banks using high-risk AI technologies in a statement sent to PYMNTS.\n\u201cBanks utilizing AI technologies categorized as high-risk must now adhere to stringent regulations focusing on system accuracy, robustness and cybersecurity, including registering in an EU database and comprehensive documentation to demonstrate adherence to the AI Act,\u201d Hurst said.\nOther countries, including the United Kingdom, are also developing AI regulations. The U.K. is expected to unveil its proposal later this year.\nCompanies Brace for New Compliance Measures\nThe AI Act is expected to have far-reaching effects on global commerce. Companies operating in or selling to the EU market must reassess their AI strategies and potentially redesign products to comply with the new regulations. This could lead to increased costs for research and development, compliance and legal consultation.\nHowever, it may also spur innovation in responsible AI development and create new market opportunities for companies that can effectively navigate the regulatory landscape. Industries beyond finance, including healthcare, manufacturing and retail, must adapt their AI implementations to meet the EU\u2019s standards, potentially reshaping global AI adoption patterns.\nUnilever implemented a Responsible AI Framework in anticipation of comprehensive regulations like the EU AI Act, according to a blog post. The company began addressing data and AI ethics in 2019, developing an assurance process for new AI projects.\n\u201cTaking proof of concept projects using AI systems through a thorough assurance process at an early stage is enabling us to be more innovative and fully deploy trustworthy AI systems more quickly,\u201d Unilever Chief Data Officer Andy Hill said in the post.\nUnilever views AI as a tool to \u201cdrive productivity, creativity and growth,\u201d he added in the post.\nThe framework involves cross-functional expert reviews to manage risks and ensure compliance.\n\u201cAlthough Unilever has developed legal and ethical guardrails for AI, risks around issues such as IP rights, data privacy, transparency, confidentiality and AI bias can remain, as legal frameworks can lag behind the rapidly evolving technology,\u201d Unilever Chief Privacy Officer Christine Lee said in the post.\nUnilever operates over 500 AI systems globally, spanning R&D, stock control and marketing, the post said. The company\u2019s approach includes ongoing monitoring and adaptable processes to keep pace with evolving regulations.\n\u201cWe will continue to ensure Unilever stays in step with legal developments that affect our business and brands \u2014 from copyright ownership in AI-generated materials to data privacy laws and advertising regulations,\u201d Hill said in the post.\nFinancial Sector Braces for New Compliance Measures\nEuropean Commission President Ursula von der Leyen said the act creates \u201cguardrails\u201d to protect people while providing businesses with regulatory clarity. The law follows a risk-based approach, imposing stricter obligations on high-risk AI systems that could impact citizens\u2019 rights or health.\nCompanies must comply by 2026, with rules for AI models like ChatGPT taking effect in 12 months. Bans on certain AI uses, such as predictive policing based on profiling and systems inferring personal characteristics from biometric data, will apply in six months.\nViolations of banned practices or data requirements may result in fines of up to 7% of global annual revenue, a deterrent for non-compliance. The EU has established an AI Office staffed with technology experts to oversee the law\u2019s implementation.\nThe regulations are expected to drive investment in compliance technologies within various industries, particularly financial services. Companies adept at navigating the new rules may gain advantages in AI-enabled markets.\nThis development marks a shift in the global AI regulatory landscape. The EU\u2019s first-mover status in comprehensive AI regulation could influence approaches in other jurisdictions, potentially setting a benchmark for future AI governance worldwide.\nThe AI Act\u2019s broad scope extends beyond EU-based companies, affecting organizations with EU business connections or customers. This extraterritorial reach underscores the law\u2019s potential to shape global AI development and deployment practices.\nFor all PYMNTS AI coverage, subscribe to the daily AI Newsletter.\nThe post Companies Assess Compliance as EU\u2019s AI Act Takes Effect appeared first on PYMNTS.com.", "date_published": "2024-08-01T12:38:11-04:00", "date_modified": "2024-08-01T12:38:11-04:00", "authors": [ { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/f05cc0fdcc9e387e4f3570c17158c503?s=512&d=blank&r=g" } ], "author": { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/f05cc0fdcc9e387e4f3570c17158c503?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2023/10/European-Union-EU.jpg", "tags": [ "AI Act", "Artificial Intelligence", "chatbots", "ChatGPT", "EU", "GenAI", "Innovation", "international", "Legislation", "News", "privacy", "PYMNTS News", "regulations", "Technology", "unilever" ] }, { "id": "https://www.pymnts.com/?p=2019835", "url": "https://www.pymnts.com/news/artificial-intelligence/2024/nvidia-tools-aim-speed-development-humanoid-robots/", "title": "Nvidia Tools Aim to Speed Up Development of Humanoid Robots", "content_html": "Nvidia announced this week a suite of new services and tools aimed at speeding up the development of humanoid robots as the company seeks to position itself at the forefront of this emerging technology.
\nThe chipmaker introduced new microservices for robot simulation, a cloud computing orchestration service, and an AI-enabled workflow for capturing human movements to train robots. These offerings are designed to help robotics companies, AI developers and software makers create more advanced humanoid robots quicker.
\nExperts say the development of humanoid robots could have implications for commerce and industry. These robots, with their human-like form and potential for complex movements, could revolutionize manufacturing, warehousing and customer service sectors. They may be able to perform tasks that are challenging for traditional robots, such as navigating cluttered environments or manipulating objects with human-like dexterity.
\n\u201cAI-powered industrial and collaborative robots are significantly increasing efficiency, productivity and safety today compared to current manual processes,\u201d Plus One Robotics CEO and co-founder Erik Nieves told PYMNTS. \u201cThey can take over repetitive, strenuous physical tasks like picking, moving and placing objects, freeing up human workers for higher-level roles. This can increase warehouse throughput and get products to consumers faster.\u201d
\n\u201cThe next wave of AI is robotics, and one of the most exciting developments is humanoid robots,\u201d said Nvidia founder and CEO Jensen Huang in a statement. \u201cWe\u2019re advancing the entire Nvidia robotics stack, opening access for worldwide humanoid developers and companies to use the platforms, acceleration libraries and AI models best suited for their needs.\u201d
\nOne key challenge in humanoid robotics is the vast amount of data required to train these machines. Nvidia\u2019s new teleoperation workflow aims to address this by allowing developers to generate large amounts of synthetic data from a small number of human demonstrations.
\n\u201cDeveloping humanoid robots is extremely complex \u2014 requiring an incredible amount of real data, tediously captured from the real world,\u201d Alex Gu, CEO of Fourier, a general-purpose robot platform company, said in a statement. \u201cNvidia\u2019s new simulation and generative AI developer tools will help bootstrap and accelerate our model development workflows.\u201d
\nNvidia\u2019s move comes as several companies are making strides in humanoid robotics. Tesla unveiled its Optimus robot, which is designed for general-purpose use. Tesla CEO Elon Musk announced plans to deploy humanoid robots within the company\u2019s operations as early as next year.
\nIn a post on social platform X, Musk said: \u201cTesla will have genuinely useful humanoid robots in low production for Tesla internal use next year and, hopefully, high production for other companies in 2026.\u201d
\n\n\n\nTesla will have genuinely useful humanoid robots in low production for Tesla internal use next year and, hopefully, high production for other companies in 2026
\n\u2014 Elon Musk (@elonmusk) July 22, 2024
The declaration signals Tesla\u2019s ambition to integrate advanced robotics into its manufacturing processes, potentially reshaping labor practices in the automotive industry and beyond.
\nMeanwhile, Boston Dynamics\u2019 Atlas robot demonstrated agility and balance. Agility Robotics\u2019 Digit is being tested for warehouse operations by companies like Amazon. These developments underscore the growing interest and potential applications for humanoid robots across various industries.
\n\u201cBoston Dynamics and Nvidia have a long history of close collaboration to push the boundaries of what\u2019s possible in robotics, Boston Dynamics Chief Technology Officer Aaron Saunders said in a statement. \u201cWe\u2019re really excited to see the fruits of this work accelerating the industry at large, and the early-access program is a fantastic way to access best-in-class technology.\u201d
\nHowever, Nieves cautioned against overestimating the near-term impact of humanoid robots specifically.
\n\u201cGeneralized applications of humanoid AI robots are gaining interest, but for the foreseeable future, they\u2019re far too impractical and expensive to replace warehouse workers,\u201d he said. \u201cAny and all robots working on variable tasks will routinely experience circumstances outside of their realm of expertise that necessitate intervention or cooperation from a human co-worker.\u201d
\nLooking to the future, Nieves said he sees a transformative potential in combining AI capabilities with increasingly dexterous robotic systems.
\n\u201cWe\u2019re witnessing an exciting period of rapid adoption for AI-powered robotics across many sectors, but warehousing and logistics are really leading the charge,\u201d he said. \u201cAutonomous mobile robots and intelligent picking arms are being deployed to handle a wide range of tasks like transporting inventory, loading and unloading, and processing parcels.\u201d
\nNvidia is also launching a Humanoid Robot Developer Program, which will offer early access to the new tools and the company\u2019s existing robotics platforms. Several prominent robotics companies, including Boston Dynamics, Figure and ByteDance Research, have already joined the program.
\nFor all PYMNTS AI coverage, subscribe to the daily AI Newsletter.
\nThe post Nvidia Tools Aim to Speed Up Development of Humanoid Robots appeared first on PYMNTS.com.
\n", "content_text": "Nvidia announced this week a suite of new services and tools aimed at speeding up the development of humanoid robots as the company seeks to position itself at the forefront of this emerging technology.\nThe chipmaker introduced new microservices for robot simulation, a cloud computing orchestration service, and an AI-enabled workflow for capturing human movements to train robots. These offerings are designed to help robotics companies, AI developers and software makers create more advanced humanoid robots quicker.\nExperts say the development of humanoid robots could have implications for commerce and industry. These robots, with their human-like form and potential for complex movements, could revolutionize manufacturing, warehousing and customer service sectors. They may be able to perform tasks that are challenging for traditional robots, such as navigating cluttered environments or manipulating objects with human-like dexterity.\n\u201cAI-powered industrial and collaborative robots are significantly increasing efficiency, productivity and safety today compared to current manual processes,\u201d Plus One Robotics CEO and co-founder Erik Nieves told PYMNTS. \u201cThey can take over repetitive, strenuous physical tasks like picking, moving and placing objects, freeing up human workers for higher-level roles. This can increase warehouse throughput and get products to consumers faster.\u201d\nAdvancing the Field of Humanoid Robotics\n\u201cThe next wave of AI is robotics, and one of the most exciting developments is humanoid robots,\u201d said Nvidia founder and CEO Jensen Huang in a statement. \u201cWe\u2019re advancing the entire Nvidia robotics stack, opening access for worldwide humanoid developers and companies to use the platforms, acceleration libraries and AI models best suited for their needs.\u201d\nOne key challenge in humanoid robotics is the vast amount of data required to train these machines. Nvidia\u2019s new teleoperation workflow aims to address this by allowing developers to generate large amounts of synthetic data from a small number of human demonstrations.\n\u201cDeveloping humanoid robots is extremely complex \u2014 requiring an incredible amount of real data, tediously captured from the real world,\u201d Alex Gu, CEO of Fourier, a general-purpose robot platform company, said in a statement. \u201cNvidia\u2019s new simulation and generative AI developer tools will help bootstrap and accelerate our model development workflows.\u201d\nNvidia\u2019s move comes as several companies are making strides in humanoid robotics. Tesla unveiled its Optimus robot, which is designed for general-purpose use. Tesla CEO Elon Musk announced plans to deploy humanoid robots within the company\u2019s operations as early as next year.\nIn a post on social platform X, Musk said: \u201cTesla will have genuinely useful humanoid robots in low production for Tesla internal use next year and, hopefully, high production for other companies in 2026.\u201d\n\nTesla will have genuinely useful humanoid robots in low production for Tesla internal use next year and, hopefully, high production for other companies in 2026\n\u2014 Elon Musk (@elonmusk) July 22, 2024\n\nThe declaration signals Tesla\u2019s ambition to integrate advanced robotics into its manufacturing processes, potentially reshaping labor practices in the automotive industry and beyond.\nMeanwhile, Boston Dynamics\u2019 Atlas robot demonstrated agility and balance. Agility Robotics\u2019 Digit is being tested for warehouse operations by companies like Amazon. These developments underscore the growing interest and potential applications for humanoid robots across various industries.\n\u201cBoston Dynamics and Nvidia have a long history of close collaboration to push the boundaries of what\u2019s possible in robotics, Boston Dynamics Chief Technology Officer Aaron Saunders said in a statement. \u201cWe\u2019re really excited to see the fruits of this work accelerating the industry at large, and the early-access program is a fantastic way to access best-in-class technology.\u201d\nThe Impact on Commerce and Industry\nHowever, Nieves cautioned against overestimating the near-term impact of humanoid robots specifically.\n\u201cGeneralized applications of humanoid AI robots are gaining interest, but for the foreseeable future, they\u2019re far too impractical and expensive to replace warehouse workers,\u201d he said. \u201cAny and all robots working on variable tasks will routinely experience circumstances outside of their realm of expertise that necessitate intervention or cooperation from a human co-worker.\u201d\nLooking to the future, Nieves said he sees a transformative potential in combining AI capabilities with increasingly dexterous robotic systems.\n\u201cWe\u2019re witnessing an exciting period of rapid adoption for AI-powered robotics across many sectors, but warehousing and logistics are really leading the charge,\u201d he said. \u201cAutonomous mobile robots and intelligent picking arms are being deployed to handle a wide range of tasks like transporting inventory, loading and unloading, and processing parcels.\u201d\nNvidia is also launching a Humanoid Robot Developer Program, which will offer early access to the new tools and the company\u2019s existing robotics platforms. Several prominent robotics companies, including Boston Dynamics, Figure and ByteDance Research, have already joined the program.\nFor all PYMNTS AI coverage, subscribe to the daily AI Newsletter.\nThe post Nvidia Tools Aim to Speed Up Development of Humanoid Robots appeared first on PYMNTS.com.", "date_published": "2024-07-31T12:45:11-04:00", "date_modified": "2024-07-31T12:45:11-04:00", "authors": [ { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/f05cc0fdcc9e387e4f3570c17158c503?s=512&d=blank&r=g" } ], "author": { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/f05cc0fdcc9e387e4f3570c17158c503?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2024/07/humanoid-robots-artificial-intelligence.jpg", "tags": [ "Agility Robotics", "Artificial Intelligence", "boston dynamics", "Erik Nieves", "GenAI", "Innovation", "News", "NVIDIA", "Plus One Robotics", "PYMNTS News", "Robots", "Technology", "Tesla" ] }, { "id": "https://www.pymnts.com/?post_type=tracker_posts&p=2018675", "url": "https://www.pymnts.com/tracker_posts/how-generative-ai-is-boosting-innovation-for-carmakers-and-drivers/", "title": "How Generative AI Is Boosting Innovation for Carmakers and Drivers", "content_html": "The automotive industry, like so many others, is undergoing a technological awakening with the advent of generative artificial intelligence (AI). From streamlining research and development (R&D) to offering in-car experiences that were once the domain of science fiction, generative AI is unlocking potential for the industry at every turn. The technology has the capacity to transform the sector across vehicle design, manufacturing and customer experience. By enabling rapid design iterations, virtual testing and optimization of manufacturing processes, generative AI could significantly reduce time to market. It can also enhance personalization, improve safety features and support the development of autonomous vehicles.
\nHowever, widespread adoption is not without its challenges. These include accurately predicting performance metrics and ensuring the manufacturability of AI-generated designs. Perhaps most pressing, automakers must steer through a nascent ethical and regulatory environment around the technology\u2019s data privacy and security concerns. Equally challenging is building expertise and an AI-ready organizational culture. Nevertheless, what seems increasingly certain is that a new metric may soon determine the success of an automotive product line: not necessarily how well a car performs on the road, but how effectively it learns from it.
\nGenerative AI has the potential to become the engine of innovation in the automotive world. Investment is pouring in, and tremendous growth is on the horizon.
\nGenerative AI is revolutionizing the auto industry. The technology offers almost limitless potential to fine-tune everything from automotive design to driver experience, whether for more closely meeting consumer preferences, elevating vehicle safety or engineering more environmentally sustainable automobiles. The impact of this technology is expected to grow significantly, driving innovation and competitiveness in the sector.
\nof automotive industry stakeholders believe generative AI is an industry game-changer, and 75% plan to adopt it this year.
\nThe generative AI market in the automotive segment is expected to skyrocket from $335 million in 2023 to $2.6 billion by 2033. This increase represents a compound annual growth rate (CAGR) of 23%. Fueling this growth is widespread industry buy-in among R&D departments. A remarkable 69% of decision-makers in these departments are prioritizing early adoption of the technology.
\nAlthough North America commands more than 42% of the current global market in generative AI for the auto segment, 93% of stakeholders in European, North American and Asian markets say that this technology is a game-changer for the industry. For example, generative AI-driven personalization in automotive innovation is expected to manage 75% of customer interactions. This innovation will boost sales by 15% and customer satisfaction by 20%.
\nEfficiency gains and accelerated product development timelines are two key factors leading early integration of this technology in automotive R&D and manufacturing. Testing processes \u2014 the nuts and bolts of making sure a vehicle passes regulatory muster and is prime for market approval \u2014 are seeing 20% to 30% efficiency gains through AI-driven automation of reporting and scenario simulations. One German supplier for the industry has reported a 70% uptick in productivity in test vector generation owing to the use of the technology. The benefits extend to engineering teams also, with reports of 30% productivity gains when using this technology to draft initial stakeholder requirements.
\nGiven these gains, this technology is set to change the pace and the precision of product development in automotive R&D. Already, 75% of European automotive companies are actively test-driving at least one application. In the design segment, early use cases of generative AI show high promise, with executives estimating a 10% to 20% improvement in R&D processes.
\nDesign Innovation: Generative AI can rapidly generate multiple design options for complex automotive systems, accelerating the design process and optimizing vehicle performance, safety and efficiency.
\nResearch and Development: The technology can assist engineers in making data-driven decisions, pinpointing optimal materials, designs and technologies to enhance vehicle performance and safety as well as streamlining the innovation process.
\nVirtual Testing and Simulation: Generative AI creates detailed, realistic models of cars and their components for virtual trials, including crash simulations and performance in different weather conditions, accelerating development by reducing the need for costly physical prototypes.
\nPersonalized Driving Experiences and Customer Interaction: The technology can create personalized driving experiences that adapt to individual preferences and needs, including changing vehicle aesthetics, displays and controls to align with user preferences.
\nPredictive Maintenance: Generative AI enhances operations within auto manufacturing by predicting maintenance needs, reducing equipment failure and improving productivity.
\nThe auto industry\u2019s roadmap for generative AI reveals a near future in which driver experiences transcend horsepower and handling to feature personalized interactions and vehicles that anticipate needs.
\n12
\nNumber of languages cars speak in the new Stellantis ChatGPT-powered voice assistance system to be used in 17 countries
\nIn-vehicle interfaces will soon be interactive powerhouses with the integration of generative AI. General Motors (GM) recently announced an initiative to use Microsoft Azure and OpenAI technologies to develop a chatbot capable of helping with real-time vehicle issues. GM looks to offer drivers access to step-by-step instructions for solving problems. These include tire changes and explanations of vehicle alerts, even potentially scheduling maintenance visits, all communicated through natural conversation. Similarly, auto industry AI stalwart Cerence is working with Nvidia to create an automotive-specific large language model (LLM). This LLM aims to achieve more intuitive, real-time human-vehicle interactions highly contextualized to the automobile experience.
\nThis technology is turning the long sought-after goal of personalizing driver experiences into reality. Audi\u2019s integration of Cerence\u2019s Chat Pro, an AI assistant powered by ChatGPT, across its product lineup aims to enhance the in-car experience through advanced conversational interfaces, showing the technology\u2019s immediate viability. Stellantis, too, is rapidly scaling its generative AI use across its European brands by adding ChatGPT to its SoundHound Chat AI voice assistance system, with rollout aimed to span 17 countries and 12 languages by the end of July 2024.
\nGenerative AI-powered personalization stands to profoundly influence the driver-vehicle \u201crelationship.\u201d As generative AI automotive systems evolve, they could lead to new products that seamlessly connect with other areas of drivers\u2019 digital lives. In the longer term, these adaptive tools could even learn from individual driver behaviors, potentially enhancing both safety and efficiency.
\nMajor challenges to generative AI adoption in the auto industry include building an AI-ready skill set and organizational culture as well as addressing ethical, data privacy and security concerns around the technology\u2019s use.
\nThe road to widespread adoption of this technology in the auto industry is still under construction. Industry stakeholders cite a lack of skilled staff (63%), data privacy concerns (53%) and complex or ambiguous regulatory regimes (41%) as the top three critical obstacles to implementing generative AI solutions within their organizations.
\nof automotive industry stakeholders cite a lack of skilled staff as a critical obstacle to using generative AI solutions within their organizations.
\nA shortage of professionals with expertise in both automotive engineering and advanced AI technologies makes it difficult for companies to build and maintain generative AI systems. Implementing generative AI solutions often requires integrating them with legacy systems and processes, which can be complex and time-consuming. Moreover, with many auto companies still in the experimental stages with the technology, building an AI-ready organizational culture and overcoming resistance to change can be significant hurdles.
\nEthical, data privacy and security concerns represent important \u2014 and as yet unknown \u2014 risks that will need careful management for effective implementation of the technology for the industry. These concerns are particularly crucial due to the safety-critical nature of vehicles and the large amounts of personal data involved. Ensuring that generative AI systems are trustworthy, protect user privacy and are secure against potential attacks or misuse is a major challenge that automotive companies must overcome to successfully implement this technology at scale. Without handling these challenges, the industry could fail to fully realize the potential benefits of generative AI across design, manufacturing and other key areas.
\nThe post How Generative AI Is Boosting Innovation for Carmakers and Drivers appeared first on PYMNTS.com.
\n", "content_text": "The automotive industry, like so many others, is undergoing a technological awakening with the advent of generative artificial intelligence (AI). From streamlining research and development (R&D) to offering in-car experiences that were once the domain of science fiction, generative AI is unlocking potential for the industry at every turn. The technology has the capacity to transform the sector across vehicle design, manufacturing and customer experience. By enabling rapid design iterations, virtual testing and optimization of manufacturing processes, generative AI could significantly reduce time to market. It can also enhance personalization, improve safety features and support the development of autonomous vehicles.\nHowever, widespread adoption is not without its challenges. These include accurately predicting performance metrics and ensuring the manufacturability of AI-generated designs. Perhaps most pressing, automakers must steer through a nascent ethical and regulatory environment around the technology\u2019s data privacy and security concerns. Equally challenging is building expertise and an AI-ready organizational culture. Nevertheless, what seems increasingly certain is that a new metric may soon determine the success of an automotive product line: not necessarily how well a car performs on the road, but how effectively it learns from it.\n\nRevving Up Innovation With Generative AI\nCo-Piloting the Driving Experience With Generative AI\nNavigating Challenges to Generative AI in the Automotive Industry\n\nRevving Up Innovation With Generative AI\nGenerative AI has the potential to become the engine of innovation in the automotive world. Investment is pouring in, and tremendous growth is on the horizon.\n\n\n\nGenerative AI marks a milestone in the history of automotive innovation.\nGenerative AI is revolutionizing the auto industry. The technology offers almost limitless potential to fine-tune everything from automotive design to driver experience, whether for more closely meeting consumer preferences, elevating vehicle safety or engineering more environmentally sustainable automobiles. The impact of this technology is expected to grow significantly, driving innovation and competitiveness in the sector.\n\n\n93%\nof automotive industry stakeholders believe generative AI is an industry game-changer, and 75% plan to adopt it this year.\n\n\n\nBuy-in for generative AI is set to gain considerable momentum in the next 10 years.\nThe generative AI market in the automotive segment is expected to skyrocket from $335 million in 2023 to $2.6 billion by 2033. This increase represents a compound annual growth rate (CAGR) of 23%. Fueling this growth is widespread industry buy-in among R&D departments. A remarkable 69% of decision-makers in these departments are prioritizing early adoption of the technology.\nAlthough North America commands more than 42% of the current global market in generative AI for the auto segment, 93% of stakeholders in European, North American and Asian markets say that this technology is a game-changer for the industry. For example, generative AI-driven personalization in automotive innovation is expected to manage 75% of customer interactions. This innovation will boost sales by 15% and customer satisfaction by 20%.\nGenerative AI offers a powerful R&D engine for automotive innovation.\nEfficiency gains and accelerated product development timelines are two key factors leading early integration of this technology in automotive R&D and manufacturing. Testing processes \u2014 the nuts and bolts of making sure a vehicle passes regulatory muster and is prime for market approval \u2014 are seeing 20% to 30% efficiency gains through AI-driven automation of reporting and scenario simulations. One German supplier for the industry has reported a 70% uptick in productivity in test vector generation owing to the use of the technology. The benefits extend to engineering teams also, with reports of 30% productivity gains when using this technology to draft initial stakeholder requirements.\nGiven these gains, this technology is set to change the pace and the precision of product development in automotive R&D. Already, 75% of European automotive companies are actively test-driving at least one application. In the design segment, early use cases of generative AI show high promise, with executives estimating a 10% to 20% improvement in R&D processes.\n\nGenerative AI\u2019s Applications in the Auto Industry\nDesign Innovation: Generative AI can rapidly generate multiple design options for complex automotive systems, accelerating the design process and optimizing vehicle performance, safety and efficiency.\n Research and Development: The technology can assist engineers in making data-driven decisions, pinpointing optimal materials, designs and technologies to enhance vehicle performance and safety as well as streamlining the innovation process.\n Virtual Testing and Simulation: Generative AI creates detailed, realistic models of cars and their components for virtual trials, including crash simulations and performance in different weather conditions, accelerating development by reducing the need for costly physical prototypes.\n Personalized Driving Experiences and Customer Interaction: The technology can create personalized driving experiences that adapt to individual preferences and needs, including changing vehicle aesthetics, displays and controls to align with user preferences.\nPredictive Maintenance: Generative AI enhances operations within auto manufacturing by predicting maintenance needs, reducing equipment failure and improving productivity.\n\nCo-Piloting the Driving Experience With Generative AI\nThe auto industry\u2019s roadmap for generative AI reveals a near future in which driver experiences transcend horsepower and handling to feature personalized interactions and vehicles that anticipate needs.\nDashboards are set to become generative AI command centers.\n\n\n\n12\nNumber of languages cars speak in the new Stellantis ChatGPT-powered voice assistance system to be used in 17 countries\n\n\nIn-vehicle interfaces will soon be interactive powerhouses with the integration of generative AI. General Motors (GM) recently announced an initiative to use Microsoft Azure and OpenAI technologies to develop a chatbot capable of helping with real-time vehicle issues. GM looks to offer drivers access to step-by-step instructions for solving problems. These include tire changes and explanations of vehicle alerts, even potentially scheduling maintenance visits, all communicated through natural conversation. Similarly, auto industry AI stalwart Cerence is working with Nvidia to create an automotive-specific large language model (LLM). This LLM aims to achieve more intuitive, real-time human-vehicle interactions highly contextualized to the automobile experience.\n\n\n\nGenerative AI could write a new chapter on driver-vehicle relationships.\nThis technology is turning the long sought-after goal of personalizing driver experiences into reality. Audi\u2019s integration of Cerence\u2019s Chat Pro, an AI assistant powered by ChatGPT, across its product lineup aims to enhance the in-car experience through advanced conversational interfaces, showing the technology\u2019s immediate viability. Stellantis, too, is rapidly scaling its generative AI use across its European brands by adding ChatGPT to its SoundHound Chat AI voice assistance system, with rollout aimed to span 17 countries and 12 languages by the end of July 2024.\nGenerative AI-powered personalization stands to profoundly influence the driver-vehicle \u201crelationship.\u201d As generative AI automotive systems evolve, they could lead to new products that seamlessly connect with other areas of drivers\u2019 digital lives. In the longer term, these adaptive tools could even learn from individual driver behaviors, potentially enhancing both safety and efficiency.\nNavigating Challenges to Generative AI in the Automotive Industry\nMajor challenges to generative AI adoption in the auto industry include building an AI-ready skill set and organizational culture as well as addressing ethical, data privacy and security concerns around the technology\u2019s use.\n\n\n\nA critical skills gap challenges implementation in the auto industry.\nThe road to widespread adoption of this technology in the auto industry is still under construction. Industry stakeholders cite a lack of skilled staff (63%), data privacy concerns (53%) and complex or ambiguous regulatory regimes (41%) as the top three critical obstacles to implementing generative AI solutions within their organizations.\n\n\n63%\nof automotive industry stakeholders cite a lack of skilled staff as a critical obstacle to using generative AI solutions within their organizations.\n\n\n\nA shortage of professionals with expertise in both automotive engineering and advanced AI technologies makes it difficult for companies to build and maintain generative AI systems. Implementing generative AI solutions often requires integrating them with legacy systems and processes, which can be complex and time-consuming. Moreover, with many auto companies still in the experimental stages with the technology, building an AI-ready organizational culture and overcoming resistance to change can be significant hurdles.\nEthical, data privacy and security concerns remain the number-one challenge in applying the technology.\nEthical, data privacy and security concerns represent important \u2014 and as yet unknown \u2014 risks that will need careful management for effective implementation of the technology for the industry. These concerns are particularly crucial due to the safety-critical nature of vehicles and the large amounts of personal data involved. Ensuring that generative AI systems are trustworthy, protect user privacy and are secure against potential attacks or misuse is a major challenge that automotive companies must overcome to successfully implement this technology at scale. Without handling these challenges, the industry could fail to fully realize the potential benefits of generative AI across design, manufacturing and other key areas.\nThe post How Generative AI Is Boosting Innovation for Carmakers and Drivers appeared first on PYMNTS.com.", "date_published": "2024-07-31T04:03:54-04:00", "date_modified": "2024-07-30T21:47:47-04:00", "authors": [ { "name": "Ashley McLeod", "url": "https://www.pymnts.com/author/amcleod/", "avatar": "https://secure.gravatar.com/avatar/5fcbfee0fc4dc81613187d709d661036?s=512&d=blank&r=g" } ], "author": { "name": "Ashley McLeod", "url": "https://www.pymnts.com/author/amcleod/", "avatar": "https://secure.gravatar.com/avatar/5fcbfee0fc4dc81613187d709d661036?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2024/07/generative-ai-automotive-industry.jpg", "tags": [ "Artificial Intelligence", "automotive industry", "Featured News", "GenAI", "generative AI", "Innovation", "Merchant Innovation", "News", "PYMNTS Intelligence", "PYMNTS News", "regulation", "Technology", "Tracker Series" ] }, { "id": "https://www.pymnts.com/?p=2011668", "url": "https://www.pymnts.com/news/artificial-intelligence/2024/eye-scans-drug-design-ai-healthcare/", "title": "From Eye Scans to Drug Design, AI Takes on Healthcare", "content_html": "Artificial intelligence is making waves across the medical field, with new studies showing promise in predicting eye treatment complications, analyzing heart MRIs, and even designing RNA-based drugs.
\nWhile specialized healthcare AI models demonstrate potential, research also cautions against relying on general-purpose AI chatbots for clinical decision-making, highlighting the need for tailored solutions in critical medical applications.
\nA new study suggested AI could help predict complications from treatments for age-related macular degeneration (AMD), a leading cause of vision loss affecting millions of Americans. Researchers from Emory University and Cleveland Clinic developed a machine-learning model that analyzes eye scans to identify patients at risk of inflammatory responses to common AMD treatments.
\nThe study, published in the journal Heliyon, focused on neovascular AMD (nAMD), a severe form of the disease typically treated with anti-VEGF drugs, according to a press release. While effective, these treatments can cause severe eye inflammation in some patients. The AI model, which analyzed optical coherence tomography (OCT) scans, demonstrated accuracy rates of up to 81% in identifying patients likely to develop this complication.
\n\u201cOur study provides valuable data for clinicians to make better treatment decisions, potentially reducing the dosage or combining these agents with anti-inflammatory drugs to prevent severe complications,\u201d Anant Madabhushi, executive director of Emory AI.Health and principal investigator of the study, said in the release.
\nThe research team analyzed images from 67 eyes in a retrospective clinical trial. While the results were promising, more extensive prospective studies will be necessary to validate the model\u2019s effectiveness in clinical settings. The researchers aim to integrate their algorithms into future clinical trials to test real-time identification of at-risk patients, per the release.
\nHealthcare AI firm Atropos\u2019 study revealed popular chatbots like ChatGPT falter in clinical decision-making, underscoring the need for specialized AI in critical medical applications.
\nAccording to the company\u2019s paper, published on the open-access platform Arxiv, Atropos tested five large language models \u2014 including general-purpose models, a healthcare-specific model, and Atropos\u2019 own ChatRWD beta \u2014 on 50 healthcare questions. Nine independent clinicians assessed the models\u2019 responses based on relevance, reliability and actionability.
\nResults showed that general-purpose LLMs provided relevant information only 2% to 10% of the time. A healthcare-focused model performed slightly better at 24%. Atropos\u2019 ChatRWD, which uses 160 million de-identified patient records, outperformed competitors by providing relevant insights 58% of the time.
\nThe study also tested the models\u2019 ability to answer novel questions. While most LLMs struggled, answering 0% to 9% of such queries, ChatRWD addressed 65% of them. These findings raise questions about the appropriate use of AI in healthcare settings and highlight the potential advantages of specialized models in critical fields like medicine.
\nResearchers developed an AI model to analyze heart MRI scans in seconds. The study, published in European Radiology Experimental, demonstrated how AI could reduce the time and resources required for hospital heart image analysis.
\nThe AI was trained on data from 814 patients across multiple NHS trusts and tested on 101 patients from a separate hospital, per a press release. The diverse dataset enhanced the model\u2019s potential for widespread application.
\n\u201cThe AI model precisely determined the size and function of the heart\u2019s chambers and demonstrated outcomes comparable to those acquired by doctors manually but much quicker,\u201d Pankaj Garg, lead researcher from the University of East Anglia, said in the release. \u201cUnlike a standard manual MRI analysis, which can take up to 45 minutes or more, the new AI model takes just a few seconds.\u201d
\nPostgraduate researcher Hosamadin Assadi emphasized the broader implications.
\n\u201cThis innovation could lead to more efficient diagnoses, better treatment decisions, and ultimately, improved outcomes for patients with heart conditions,\u201d Assadi said in the release.
\nWhile the results were promising, the researchers suggested further testing with larger, more diverse patient groups to validate the model\u2019s effectiveness in various real-world scenarios.
\nJakob Uszkoreit, a figure behind the transformer architecture powering modern AI, shifted his focus to advancing drug development.
\nAs co-founder of biotech startup Inceptive, Uszkoreit is applying generative AI to create more effective, biologically harmonious medicines, CNBC reported.
\nUszkoreit, who left Google in 2021, was part of the team that published the seminal \u201cAttention Is All You Need\u201d paper in 2017, laying the groundwork for today\u2019s AI boom.
\n\u201cThere are actually applications … where transformers have been deployed in production long before but to much, much less fanfare,\u201d Uszkoreit said, per the report.
\nLast year, Inceptive secured $100 million in funding led by Andreessen Horowitz and Nvidia. The company aims to design RNA molecules using AI that can exhibit specific behaviors within biological systems.
\n\u201cThere\u2019s actually this promise of a flavor of medicine that is in much greater harmony with living systems than most existing medicines,\u201d Uszkoreit said, per CNBC, highlighting the potential for advancements in pharmaceutical research and development.
\nFor all PYMNTS AI coverage, subscribe to the daily AI Newsletter.
\nThe post From Eye Scans to Drug Design, AI Takes on Healthcare appeared first on PYMNTS.com.
\n", "content_text": "Artificial intelligence is making waves across the medical field, with new studies showing promise in predicting eye treatment complications, analyzing heart MRIs, and even designing RNA-based drugs.\nWhile specialized healthcare AI models demonstrate potential, research also cautions against relying on general-purpose AI chatbots for clinical decision-making, highlighting the need for tailored solutions in critical medical applications.\nAI Model Shows Promise in Predicting Eye Treatment Complications\nA new study suggested AI could help predict complications from treatments for age-related macular degeneration (AMD), a leading cause of vision loss affecting millions of Americans. Researchers from Emory University and Cleveland Clinic developed a machine-learning model that analyzes eye scans to identify patients at risk of inflammatory responses to common AMD treatments.\nThe study, published in the journal Heliyon, focused on neovascular AMD (nAMD), a severe form of the disease typically treated with anti-VEGF drugs, according to a press release. While effective, these treatments can cause severe eye inflammation in some patients. The AI model, which analyzed optical coherence tomography (OCT) scans, demonstrated accuracy rates of up to 81% in identifying patients likely to develop this complication.\n\u201cOur study provides valuable data for clinicians to make better treatment decisions, potentially reducing the dosage or combining these agents with anti-inflammatory drugs to prevent severe complications,\u201d Anant Madabhushi, executive director of Emory AI.Health and principal investigator of the study, said in the release.\nThe research team analyzed images from 67 eyes in a retrospective clinical trial. While the results were promising, more extensive prospective studies will be necessary to validate the model\u2019s effectiveness in clinical settings. The researchers aim to integrate their algorithms into future clinical trials to test real-time identification of at-risk patients, per the release.\nStudy Finds General AI Chatbots Unsuitable for Clinical Decision-Making\nHealthcare AI firm Atropos\u2019 study revealed popular chatbots like ChatGPT falter in clinical decision-making, underscoring the need for specialized AI in critical medical applications.\nAccording to the company\u2019s paper, published on the open-access platform Arxiv, Atropos tested five large language models \u2014 including general-purpose models, a healthcare-specific model, and Atropos\u2019 own ChatRWD beta \u2014 on 50 healthcare questions. Nine independent clinicians assessed the models\u2019 responses based on relevance, reliability and actionability.\nResults showed that general-purpose LLMs provided relevant information only 2% to 10% of the time. A healthcare-focused model performed slightly better at 24%. Atropos\u2019 ChatRWD, which uses 160 million de-identified patient records, outperformed competitors by providing relevant insights 58% of the time.\nThe study also tested the models\u2019 ability to answer novel questions. While most LLMs struggled, answering 0% to 9% of such queries, ChatRWD addressed 65% of them. These findings raise questions about the appropriate use of AI in healthcare settings and highlight the potential advantages of specialized models in critical fields like medicine.\nAI Heart MRI Analysis Shows Promise\nResearchers developed an AI model to analyze heart MRI scans in seconds. The study, published in European Radiology Experimental, demonstrated how AI could reduce the time and resources required for hospital heart image analysis.\nThe AI was trained on data from 814 patients across multiple NHS trusts and tested on 101 patients from a separate hospital, per a press release. The diverse dataset enhanced the model\u2019s potential for widespread application.\n\u201cThe AI model precisely determined the size and function of the heart\u2019s chambers and demonstrated outcomes comparable to those acquired by doctors manually but much quicker,\u201d Pankaj Garg, lead researcher from the University of East Anglia, said in the release. \u201cUnlike a standard manual MRI analysis, which can take up to 45 minutes or more, the new AI model takes just a few seconds.\u201d\nPostgraduate researcher Hosamadin Assadi emphasized the broader implications.\n\u201cThis innovation could lead to more efficient diagnoses, better treatment decisions, and ultimately, improved outcomes for patients with heart conditions,\u201d Assadi said in the release.\nWhile the results were promising, the researchers suggested further testing with larger, more diverse patient groups to validate the model\u2019s effectiveness in various real-world scenarios.\nAI Pioneer\u2019s New Venture Targets Breakthrough in Drug Development\nJakob Uszkoreit, a figure behind the transformer architecture powering modern AI, shifted his focus to advancing drug development.\nAs co-founder of biotech startup Inceptive, Uszkoreit is applying generative AI to create more effective, biologically harmonious medicines, CNBC reported.\nUszkoreit, who left Google in 2021, was part of the team that published the seminal \u201cAttention Is All You Need\u201d paper in 2017, laying the groundwork for today\u2019s AI boom.\n\u201cThere are actually applications … where transformers have been deployed in production long before but to much, much less fanfare,\u201d Uszkoreit said, per the report.\nLast year, Inceptive secured $100 million in funding led by Andreessen Horowitz and Nvidia. The company aims to design RNA molecules using AI that can exhibit specific behaviors within biological systems.\n\u201cThere\u2019s actually this promise of a flavor of medicine that is in much greater harmony with living systems than most existing medicines,\u201d Uszkoreit said, per CNBC, highlighting the potential for advancements in pharmaceutical research and development.\nFor all PYMNTS AI coverage, subscribe to the daily AI Newsletter.\nThe post From Eye Scans to Drug Design, AI Takes on Healthcare appeared first on PYMNTS.com.", "date_published": "2024-07-16T12:52:47-04:00", "date_modified": "2024-07-16T12:52:47-04:00", "authors": [ { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/f05cc0fdcc9e387e4f3570c17158c503?s=512&d=blank&r=g" } ], "author": { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/f05cc0fdcc9e387e4f3570c17158c503?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2024/07/AI-healthcare-eye-scan.jpg", "tags": [ "Artificial Intelligence", "Atropos", "chatbots", "GenAI", "Healthcare", "Inceptive", "Innovation", "News", "PYMNTS News", "Technology" ] }, { "id": "https://www.pymnts.com/?p=2011034", "url": "https://www.pymnts.com/news/artificial-intelligence/2024/c3-debuts-ai-powered-application-government-programs/", "title": "C3 Debuts AI-Powered Application for Government Programs", "content_html": "Artificial intelligence software company C3 AI has a new application designed for government agencies.
\nC3 Generative AI for Government Programs uses generative AI to help federal, state and local governments inform the public about a variety of programs, according to a Monday (July 15) press release.
\n\u201cUnderstanding and accessing vital services and benefits programs can be an overwhelming process for many, and C3 Generative AI for Government Programs provides an easy way to get clear and quick answers to questions about the intricacies of these services,\u201d C3 AI CEO Thomas M. Siebel said in the release. \u201cGovernment programs and services make the most impact when those who need them can smoothly use them.\u201d
\nThe offering lets government agencies eliminate service delays, reduce wait times and make contact centers more effective, letting service representatives spend more time on complex cases and inquiries, per the release.
\nThe application understands the nuances of a range of programs and offers clear, step-by-step instructions when applicable, the release said. It supports queries and responses in more than 130 languages.
\n\u201cFor example, a user could ask, \u2018What are the enrollment steps for the Affordable Care Act?\u2019 and C3 Generative AI for Government Programs will provide a response that clearly details each step a potential enrollee would need to take to apply for and secure an ACA-compliant health insurance plan,\u201d the release said.
\nIn other AI news, PYMNTS examined the technology\u2019s role in payments in a conversation Monday with Kate Lybarger, head of payments innovation at Discover\u00ae Global Network.
\n\u201cGenerative AI\u2019s democratization means anyone with internet access can become a consumer, creator and builder using this technology \u2026 the lift required to clean up your data in order to use AI doesn\u2019t exist in the same capacity as it once did,\u201d Lybarger said.
\nThis widespread access opens new opportunities for innovation while also presenting new challenges.
\n\u201cWe\u2019re at the beginning of the maturity curve with the deployments of generative AI at scale,\u201d Lybarger added, noting AI\u2019s capacity to \u201cease a lot of burden across those who are participating in the payments value chain.\u201d
\nFor all PYMNTS AI coverage, subscribe to the daily AI Newsletter.
\nThe post C3 Debuts AI-Powered Application for Government Programs appeared first on PYMNTS.com.
\n", "content_text": "Artificial intelligence software company C3 AI has a new application designed for government agencies.\nC3 Generative AI for Government Programs uses generative AI to help federal, state and local governments inform the public about a variety of programs, according to a Monday (July 15) press release.\n\u201cUnderstanding and accessing vital services and benefits programs can be an overwhelming process for many, and C3 Generative AI for Government Programs provides an easy way to get clear and quick answers to questions about the intricacies of these services,\u201d C3 AI CEO Thomas M. Siebel said in the release. \u201cGovernment programs and services make the most impact when those who need them can smoothly use them.\u201d\nThe offering lets government agencies eliminate service delays, reduce wait times and make contact centers more effective, letting service representatives spend more time on complex cases and inquiries, per the release.\nThe application understands the nuances of a range of programs and offers clear, step-by-step instructions when applicable, the release said. It supports queries and responses in more than 130 languages.\n\u201cFor example, a user could ask, \u2018What are the enrollment steps for the Affordable Care Act?\u2019 and C3 Generative AI for Government Programs will provide a response that clearly details each step a potential enrollee would need to take to apply for and secure an ACA-compliant health insurance plan,\u201d the release said.\nIn other AI news, PYMNTS examined the technology\u2019s role in payments in a conversation Monday with Kate Lybarger, head of payments innovation at Discover\u00ae Global Network.\n\u201cGenerative AI\u2019s democratization means anyone with internet access can become a consumer, creator and builder using this technology \u2026 the lift required to clean up your data in order to use AI doesn\u2019t exist in the same capacity as it once did,\u201d Lybarger said.\nThis widespread access opens new opportunities for innovation while also presenting new challenges.\n\u201cWe\u2019re at the beginning of the maturity curve with the deployments of generative AI at scale,\u201d Lybarger added, noting AI\u2019s capacity to \u201cease a lot of burden across those who are participating in the payments value chain.\u201d\nFor all PYMNTS AI coverage, subscribe to the daily AI Newsletter.\nThe post C3 Debuts AI-Powered Application for Government Programs appeared first on PYMNTS.com.", "date_published": "2024-07-15T15:44:03-04:00", "date_modified": "2024-07-15T15:44:03-04:00", "authors": [ { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/f05cc0fdcc9e387e4f3570c17158c503?s=512&d=blank&r=g" } ], "author": { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/f05cc0fdcc9e387e4f3570c17158c503?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2024/01/artificial-intelligence-AI_4591f9.jpg", "tags": [ "Artificial Intelligence", "C3.ai", "GenAI", "Government", "Innovation", "News", "PYMNTS News", "software", "Technology", "What's Hot" ] }, { "id": "https://www.pymnts.com/?p=1975669", "url": "https://www.pymnts.com/news/artificial-intelligence/2024/amazon-makes-generative-ai-powered-shopping-assistant-available-all-united-states-customers/", "title": "Rufus, Amazon\u2019s AI Shopping Companion, Rolls Out Nationwide", "content_html": "Amazon\u2019s generative artificial intelligence-powered conversational shopping assistant, Rufus, is now available to all U.S. customers in the Amazon Shopping app.
\nIntroduced in February, Rufus answers questions on various shopping needs and products, the company said in a Friday (July 12) press release.
\n\u201cSince introducing Rufus, we\u2019ve been thrilled to hear directly from customers how Rufus has helped them with broad-range and specific shopping questions, and everything in between,\u201d Rajiv Mehta, vice president of search and conversational shopping at Amazon, said in the release. \u201cCustomers have already asked Rufus tens of millions of questions, and we\u2019ve appreciated their feedback so far.\u201d
\nAmazon beta launched the shopping assistant in February, saying Rufus would initially be available to a small subset of customers using the company\u2019s mobile app and then would be rolled out to more customers in the U.S. in the following weeks.
\nThe AI-powered tool is trained on Amazon\u2019s product catalog, customer reviews, community Q&As and information from across the web, the company said at the time.
\nIn its Friday press release, Amazon said it has found that customers are asking Rufus specific product questions; receiving answers based on the data on which it has been trained; and then clicking on related questions that Rufus surfaces to learn more.
\nThey are also asking for product recommendations and finding that Rufus surfaces products with features relevant to their needs, the release said.
\nCustomers are also using Rufus to compare the features of different products; keep up-to-date with new products and trends; access information about their current or past orders; and learn things tangentially related to shopping, such as the cookware they would need to make a souffl\u00e9 or the products they would need for a summer party, per the release.
\n\u201cWhile it\u2019s still early days for both generative AI and Rufus, we\u2019re excited to hear customers are using Rufus to help them make more informed shopping decisions,\u201d Mehta said in the release. \u201cAs we continue to grow and improve upon Rufus, we\u2019re looking forward to seeing how customers continue to use it to find exactly what they need or want in our store.\u201d
\nFor all PYMNTS retail and AI coverage, subscribe to the daily Retail and AI newsletters.
\nThe post Rufus, Amazon’s AI Shopping Companion, Rolls Out Nationwide appeared first on PYMNTS.com.
\n", "content_text": "Amazon\u2019s generative artificial intelligence-powered conversational shopping assistant, Rufus, is now available to all U.S. customers in the Amazon Shopping app.\nIntroduced in February, Rufus answers questions on various shopping needs and products, the company said in a Friday (July 12) press release.\n\u201cSince introducing Rufus, we\u2019ve been thrilled to hear directly from customers how Rufus has helped them with broad-range and specific shopping questions, and everything in between,\u201d Rajiv Mehta, vice president of search and conversational shopping at Amazon, said in the release. \u201cCustomers have already asked Rufus tens of millions of questions, and we\u2019ve appreciated their feedback so far.\u201d\nAmazon beta launched the shopping assistant in February, saying Rufus would initially be available to a small subset of customers using the company\u2019s mobile app and then would be rolled out to more customers in the U.S. in the following weeks.\nThe AI-powered tool is trained on Amazon\u2019s product catalog, customer reviews, community Q&As and information from across the web, the company said at the time.\nIn its Friday press release, Amazon said it has found that customers are asking Rufus specific product questions; receiving answers based on the data on which it has been trained; and then clicking on related questions that Rufus surfaces to learn more.\nThey are also asking for product recommendations and finding that Rufus surfaces products with features relevant to their needs, the release said.\nCustomers are also using Rufus to compare the features of different products; keep up-to-date with new products and trends; access information about their current or past orders; and learn things tangentially related to shopping, such as the cookware they would need to make a souffl\u00e9 or the products they would need for a summer party, per the release.\n\u201cWhile it\u2019s still early days for both generative AI and Rufus, we\u2019re excited to hear customers are using Rufus to help them make more informed shopping decisions,\u201d Mehta said in the release. \u201cAs we continue to grow and improve upon Rufus, we\u2019re looking forward to seeing how customers continue to use it to find exactly what they need or want in our store.\u201d\nFor all PYMNTS retail and AI coverage, subscribe to the daily Retail and AI newsletters.\nThe post Rufus, Amazon’s AI Shopping Companion, Rolls Out Nationwide appeared first on PYMNTS.com.", "date_published": "2024-07-12T12:21:21-04:00", "date_modified": "2024-07-14T20:36:52-04:00", "authors": [ { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/f05cc0fdcc9e387e4f3570c17158c503?s=512&d=blank&r=g" } ], "author": { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/f05cc0fdcc9e387e4f3570c17158c503?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2023/11/amazon-5.jpg", "tags": [ "Amazon", "Artificial Intelligence", "chatbots", "ecommerce", "GenAI", "Innovation", "News", "PYMNTS News", "Retail", "Rufus", "Technology", "What's Hot" ] } ] }