However, AI firms are trying to curtail the legislation, saying it would damage their industry, The Wall Street Journal (WSJ) reported Wednesday (Aug. 7).

The 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.

The bill covers AI models meeting a certain computing power threshold and costing more than $100 million to train. That includes OpenAI’s GPT-4, although any company doing business in California would need to comply, the report said.

While 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.

“If it were to go into effect as written, it would have a chilling effect on innovation in California,” said Luther Lowe, who heads public policy at startup accelerator Y Combinator, per the report.

Meta and OpenAI raised concerns about the bill, the report said. Google, Anthropic and Microsoft all pitched extensive revisions.

The bill, which still needs approval of the full California Assembly, was drafted by state Sen. Scott Weiner, according to the report.

“There 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,” he said, per the report.

At least 16 companies have signed onto the White House’s 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.

Last 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.

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Conventional rules-based systems, relying on static rules and predefined patterns, are falling short in adapting to the dynamic tactics of modern fraudsters.

Enter 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.

The PYMNTS Intelligence report “Can Generative Al Break the Payments Fraud Cycle?” provides an in-depth exploration of how generative AI is poised to revolutionize fraud detection and address the limitations of conventional systems.

Generative AI Outperforms Traditional Systems

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.

Visa’s 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.

Enhanced Privacy Through Synthetic Data

Generative 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.

Bunq, a European FinTech, demonstrates the efficacy of this approach, having integrated generative AI into its transaction-monitoring system. The innovation has boosted Bunq’s 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.

Speed and Accuracy Improvements

Generative AI is revolutionizing fraud detection by enhancing both speed and accuracy compared with traditional methods. Mastercard’s 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.

Generative AI’s 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.

Generative 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.

As 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.

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Zest Project is designed to use artificial intelligence to respond to the 69% increase in fraud cases — per the Federal Trade Commission — witnessed by community banks and credit unions in 2023, according to a Wednesday (Aug. 7) press release.

“Lenders need to outsmart fraud, including an increasing volume of AI-driven fraud in the industry with AI,” said Adam Kleinman, head of strategy and client Success at Zest AI, in the release. “Our 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.”

Zest 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.

This lets lenders adjust “specific detection thresholds based on their risk tolerances and automation objectives,” the release said. “With access to fraud prevention data and analytics, Zest AI can flag applications swiftly and protect against emerging threats.”

AI is becoming the tool of choice for financial institutions that want to prevent illicit activity such as money laundering or bank fraud.

The PYMNTS Intelligence report “Financial Institutions Revamping Technologies to Fight Financial Crimes” 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.

“Modern payments fraud demands real-time learning and adaptation at scale,” PYMNTS wrote in June. “Generative 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.”

In addition, generative AI can create synthetic datasets that mimic real-world financial data, allowing for robust model training without sacrificing privacy or compliance.

However, 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.

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Via 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.

“Together with Palantir, we’re unlocking the inherent power of the supply chain ecosystem to drive new and compelling sales and operating efficiencies that will provide Wendy’s with a distinctive edge in the industry,” QSCC President and CEO Pete Suerken said in the release.

In the first phase of the digital transformation, QSCC will move onto Palantir’s 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.

In 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.

Together, these changes will benefit the quick service restaurant (QSR) company’s restaurant operators, suppliers and distributors, Ted Mabrey, head of global commercial at Palantir, said in the release.

“Our AI operating system powers many of America’s 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,” Mabrey said.

Wendy’s restaurants have already deployed AI systems in some customer-facing applications.

In 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.

In May 2023, Wendy’s and Google teamed to bring AI to the fast-food chain’s drive-thrus, with the “Wendy’s FreshAI” system automating ordering. Wendy’s 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, “even if their order isn’t phrased exactly as it appears on menus.”

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The EU adopted the rules earlier this year after negotiations that gained urgency following the 2022 debut of ChatGPT. The chatbot’s capabilities highlighted the potential and risks of generative AI systems, which can produce human-like text, images and other content.

The new law classifies various types of AI based on risk and imposes different requirements and obligations on “limited risk” and “high risk” AI systems.

Shaun Hurst of Smarsh, a digital communications compliance firm, emphasized new requirements for banks using high-risk AI technologies in a statement sent to PYMNTS.

“Banks 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,” Hurst said.

Other countries, including the United Kingdom, are also developing AI regulations. The U.K. is expected to unveil its proposal later this year.

Companies Brace for New Compliance Measures

The 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.

However, 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’s standards, potentially reshaping global AI adoption patterns.

Unilever 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.

“Taking 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,” Unilever Chief Data Officer Andy Hill said in the post.

Unilever views AI as a tool to “drive productivity, creativity and growth,” he added in the post.

The framework involves cross-functional expert reviews to manage risks and ensure compliance.

“Although 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,” Unilever Chief Privacy Officer Christine Lee said in the post.

Unilever operates over 500 AI systems globally, spanning R&D, stock control and marketing, the post said. The company’s approach includes ongoing monitoring and adaptable processes to keep pace with evolving regulations.

“We will continue to ensure Unilever stays in step with legal developments that affect our business and brands — from copyright ownership in AI-generated materials to data privacy laws and advertising regulations,” Hill said in the post.

Financial Sector Braces for New Compliance Measures

European Commission President Ursula von der Leyen said the act creates “guardrails” 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’ rights or health.

Companies 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.

Violations 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’s implementation.

The 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.

This development marks a shift in the global AI regulatory landscape. The EU’s first-mover status in comprehensive AI regulation could influence approaches in other jurisdictions, potentially setting a benchmark for future AI governance worldwide.

The AI Act’s broad scope extends beyond EU-based companies, affecting organizations with EU business connections or customers. This extraterritorial reach underscores the law’s potential to shape global AI development and deployment practices.

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The 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.

Experts 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.

“AI-powered industrial and collaborative robots are significantly increasing efficiency, productivity and safety today compared to current manual processes,” Plus One Robotics CEO and co-founder Erik Nieves told PYMNTS. “They 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.”

Advancing the Field of Humanoid Robotics

“The next wave of AI is robotics, and one of the most exciting developments is humanoid robots,” said Nvidia founder and CEO Jensen Huang in a statement. “We’re 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.”

One key challenge in humanoid robotics is the vast amount of data required to train these machines. Nvidia’s new teleoperation workflow aims to address this by allowing developers to generate large amounts of synthetic data from a small number of human demonstrations.

“Developing humanoid robots is extremely complex — requiring an incredible amount of real data, tediously captured from the real world,” Alex Gu, CEO of Fourier, a general-purpose robot platform company, said in a statement. “Nvidia’s new simulation and generative AI developer tools will help bootstrap and accelerate our model development workflows.”

Nvidia’s 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’s operations as early as next year.

In a post on social platform X, Musk said: “Tesla will have genuinely useful humanoid robots in low production for Tesla internal use next year and, hopefully, high production for other companies in 2026.”

Tesla will have genuinely useful humanoid robots in low production for Tesla internal use next year and, hopefully, high production for other companies in 2026

— Elon Musk (@elonmusk) July 22, 2024

The declaration signals Tesla’s ambition to integrate advanced robotics into its manufacturing processes, potentially reshaping labor practices in the automotive industry and beyond.

Meanwhile, Boston Dynamics’ Atlas robot demonstrated agility and balance. Agility Robotics’ 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.

“Boston Dynamics and Nvidia have a long history of close collaboration to push the boundaries of what’s possible in robotics, Boston Dynamics Chief Technology Officer Aaron Saunders said in a statement. “We’re 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.”

The Impact on Commerce and Industry

However, Nieves cautioned against overestimating the near-term impact of humanoid robots specifically.

“Generalized applications of humanoid AI robots are gaining interest, but for the foreseeable future, they’re far too impractical and expensive to replace warehouse workers,” he said. “Any 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.”

Looking to the future, Nieves said he sees a transformative potential in combining AI capabilities with increasingly dexterous robotic systems.

“We’re witnessing an exciting period of rapid adoption for AI-powered robotics across many sectors, but warehousing and logistics are really leading the charge,” he said. “Autonomous 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.”

Nvidia is also launching a Humanoid Robot Developer Program, which will offer early access to the new tools and the company’s existing robotics platforms. Several prominent robotics companies, including Boston Dynamics, Figure and ByteDance Research, have already joined the program.

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However, 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’s 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.

• Revving Up Innovation With Generative AI
• Co-Piloting the Driving Experience With Generative AI
• Navigating Challenges to Generative AI in the Automotive Industry

Revving Up Innovation With Generative AI

Generative 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.

Buy-in for generative AI is set to gain considerable momentum in the next 10 years.

The 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.

Although 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%.

Generative AI offers a powerful R&D engine for automotive innovation.

Efficiency gains and accelerated product development timelines are two key factors leading early integration of this technology in automotive R&D and manufacturing. Testing processes — the nuts and bolts of making sure a vehicle passes regulatory muster and is prime for market approval — 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.

Given 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.

Co-Piloting the Driving Experience With Generative AI

The auto industry’s 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.

Dashboards are set to become generative AI command centers.

Generative AI could write a new chapter on driver-vehicle relationships.

This technology is turning the long sought-after goal of personalizing driver experiences into reality. Audi’s integration of Cerence’s 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’s 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.

Generative AI-powered personalization stands to profoundly influence the driver-vehicle “relationship.” As generative AI automotive systems evolve, they could lead to new products that seamlessly connect with other areas of drivers’ digital lives. In the longer term, these adaptive tools could even learn from individual driver behaviors, potentially enhancing both safety and efficiency.

Navigating Challenges to Generative AI in the Automotive Industry

Major 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’s use.

A 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.

Ethical, data privacy and security concerns remain the number-one challenge in applying the technology.

Ethical, data privacy and security concerns represent important — and as yet unknown — 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.

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While 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.

AI Model Shows Promise in Predicting Eye Treatment Complications

A 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.

The 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.

“Our 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,” Anant Madabhushi, executive director of Emory AI.Health and principal investigator of the study, said in the release.

The 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’s 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.

Study Finds General AI Chatbots Unsuitable for Clinical Decision-Making

Healthcare AI firm Atropos’ study revealed popular chatbots like ChatGPT falter in clinical decision-making, underscoring the need for specialized AI in critical medical applications.

According to the company’s paper, published on the open-access platform Arxiv, Atropos tested five large language models — including general-purpose models, a healthcare-specific model, and Atropos’ own ChatRWD beta — on 50 healthcare questions. Nine independent clinicians assessed the models’ responses based on relevance, reliability and actionability.

Results 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’ ChatRWD, which uses 160 million de-identified patient records, outperformed competitors by providing relevant insights 58% of the time.

The study also tested the models’ 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.

AI Heart MRI Analysis Shows Promise

Researchers 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.

The 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’s potential for widespread application.

“The AI model precisely determined the size and function of the heart’s chambers and demonstrated outcomes comparable to those acquired by doctors manually but much quicker,” Pankaj Garg, lead researcher from the University of East Anglia, said in the release. “Unlike a standard manual MRI analysis, which can take up to 45 minutes or more, the new AI model takes just a few seconds.”

Postgraduate researcher Hosamadin Assadi emphasized the broader implications.

“This innovation could lead to more efficient diagnoses, better treatment decisions, and ultimately, improved outcomes for patients with heart conditions,” Assadi said in the release.

While the results were promising, the researchers suggested further testing with larger, more diverse patient groups to validate the model’s effectiveness in various real-world scenarios.

AI Pioneer’s New Venture Targets Breakthrough in Drug Development

Jakob Uszkoreit, a figure behind the transformer architecture powering modern AI, shifted his focus to advancing drug development.

As co-founder of biotech startup Inceptive, Uszkoreit is applying generative AI to create more effective, biologically harmonious medicines, CNBC reported.

Uszkoreit, who left Google in 2021, was part of the team that published the seminal “Attention Is All You Need” paper in 2017, laying the groundwork for today’s AI boom.

“There are actually applications … where transformers have been deployed in production long before but to much, much less fanfare,” Uszkoreit said, per the report.

Last 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.

“There’s actually this promise of a flavor of medicine that is in much greater harmony with living systems than most existing medicines,” Uszkoreit said, per CNBC, highlighting the potential for advancements in pharmaceutical research and development.

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C3 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.

“Understanding 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,” C3 AI CEO Thomas M. Siebel said in the release. “Government programs and services make the most impact when those who need them can smoothly use them.”

The 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.

The 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.

“For example, a user could ask, ‘What are the enrollment steps for the Affordable Care Act?’ 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,” the release said.

In other AI news, PYMNTS examined the technology’s role in payments in a conversation Monday with Kate Lybarger, head of payments innovation at Discover® Global Network.

“Generative AI’s democratization means anyone with internet access can become a consumer, creator and builder using this technology … the lift required to clean up your data in order to use AI doesn’t exist in the same capacity as it once did,” Lybarger said.

This widespread access opens new opportunities for innovation while also presenting new challenges.

“We’re at the beginning of the maturity curve with the deployments of generative AI at scale,” Lybarger added, noting AI’s capacity to “ease a lot of burden across those who are participating in the payments value chain.”

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Introduced in February, Rufus answers questions on various shopping needs and products, the company said in a Friday (July 12) press release.

“Since introducing Rufus, we’ve been thrilled to hear directly from customers how Rufus has helped them with broad-range and specific shopping questions, and everything in between,” Rajiv Mehta, vice president of search and conversational shopping at Amazon, said in the release. “Customers have already asked Rufus tens of millions of questions, and we’ve appreciated their feedback so far.”

Amazon beta launched the shopping assistant in February, saying Rufus would initially be available to a small subset of customers using the company’s mobile app and then would be rolled out to more customers in the U.S. in the following weeks.

The AI-powered tool is trained on Amazon’s product catalog, customer reviews, community Q&As and information from across the web, the company said at the time.

In 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.

They are also asking for product recommendations and finding that Rufus surfaces products with features relevant to their needs, the release said.

Customers 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 soufflé or the products they would need for a summer party, per the release.

“While it’s still early days for both generative AI and Rufus, we’re excited to hear customers are using Rufus to help them make more informed shopping decisions,” Mehta said in the release. “As we continue to grow and improve upon Rufus, we’re looking forward to seeing how customers continue to use it to find exactly what they need or want in our store.”

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