Latest news with #machineLearning
Forbes
2 hours ago
- Business
- Forbes
What Video Games Can Teach Us About Global Connectivity
Could a common gaming challenge lead to more web connectivity for all? California drivers are a different breed. Especially if they've ever lived in Los Angeles. Maneuvering through L.A.'s congested streets is not for the faint of heart. It requires eternal vigilance and a willingness to be aggressive. When that light switches to yellow, you don't slow down. You go, hopefully making it through before it flashes red. Yet even the most strident of California motorists knows that white-knuckling it through endless stop and go mayhem will only take you so far. You need a layup from technology if you want to combat bumper-to-bumper traffic and actually get to your destination on time. Personally, I like Maps for its simple interface. It's my go-to when nothing's on the line and I can take my sweet time to get to my destination. But when the chips are down, when I need to say, get from south O.C. to Century City for a 7 AM meeting, I bring in the big guns: Waze. The navigation app leverages machine learning and crowdsourced intelligence, specifically other drivers' GPS data, to plot the fastest route in real-time. When a hazard or accident forces cars to slow down, Waze takes note. It then reroutes you through side streets, back alleys, maybe even a mysterious cobblestone bridge through the Shire to reach your destination. Now, unless you're some directional wizard, you would've never dreamed of trying these detours on your own. That's the magic of dynamic routing, a boon that's gotten me out of more traffic pickles than I can count. But imagine if you had a different, yet related problem. Due to where you live, an underserved/emerging market, your WIFI is slow. We're talking interminably slow. Web pages take ages to load. Zoom calls keep dropping or freezing. All this endless lagging hurts your business. After all, time is money. What if you could use a similar solution to solve this second challenge: real-time rerouting. Only this time, instead of moving your car, you're moving data, millions of packets of it, through a clogged internet highway. This is what a company called ExitLag set out to do. How? By combining AI technology pioneered through … video gaming. 'Honestly, we didn't set out to solve the world's internet inequality problem,' said Lucas Stolze, ExitLag's CEO when I sat down with him. 'Our business model initially began with a simple yet urgent issue: gamers hate lag.' He's onto something. In fast-paced online games, milliseconds matter. A poor connection can cost you the winning shot in a multi-person sports match. These virtual nailbiter games mirror the competitive flavor of the physical variety. Often spread out across the world, players rely on split-second timing to make shots or passes. Even a minor latency spike can result in 'input delay,' meaning a command is executed too late, disrupting synchronization between devices, players, and ultimately, outcomes. 'We built a solution to address this common gaming challenge,' said Stolze. 'Our platform uses real-time traffic optimization and AI-powered predictive routing to secure the most optimal path through the web—just like Waze finds the quickest route through city traffic.' Core to ExitLag's model is one central difference: Rather than relying on default internet routing, which can be chaotic and outdated, ExitLag acts autonomously. Using intelligence at scale, AI charts its own connectivity course, capitalizing on a network of local servers and cloud integrations to move data based on dynamic conditions as they develop. 'Though we initially created this to help gamers, it now has much wider societal implications,' said Stolze. To grok the underlying technology, it's helpful to imagine the internet as a vast freeway system, not unlike the 405. At any given moment, billions of devices are trying to move data across it. This leads to bottlenecks, especially if and when network disruptions and server outages exacerbate flow. ExitLag employs AI to seek out the best route to counter such obstacles. If one path is blocked, AI doesn't have to idle like a Prius stuck in the carpool lane at 5 PM. It can resort to multipath routing, algorithms capable of transporting data to several routes at once. Whenever one path isn't viable, yet another might be, slashing lag time. Again, this is all automatic, the function of computers talking to each other with little to no direct human intervention. As discussed, a slow internet connection is frustrating for gamers. But connectivity stakes can be more drastic for underserved communities. They might even be life or death. Many suffer from unstable internet connections in developing countries like Vietnam or the Philippines, even in rural parts of America. The usual culprits behind connectivity hindrance tend to be outdated infrastructure and limited fiber cables. Knowing this, imagine you're the head of a Sub-Saharan Africa hospital. One day an 8-year-old girl is rushed to your operating room suffering from a ruptured appendix. Without immediate surgery she may go septic and die. Unfortunately, your facility lacks a specialist surgeon at this precise moment. In the past you've relied on remote surgery, connecting you to a world-class pediatric medical center in Philadelphia. In order to successfully accomplish robot-assisted laparoscopic surgery via high-precision instruments controlled over the internet you need an ultra-low-latency connection. This is because every micro-adjustment of a robotic arm must translate in real time. Without such consistent transmission, your young patient could very well die. Smart multipath routing systems can save the child by tapping into cloud networks from the likes of Amazon and pairing it with local infrastructure. It all comes together by building a kind of internet scaffolding to patch connectivity gaps. ExitLag isn't alone in using AI to mitigate connectivity challenges, especially in the remote medical care space. Proximie, a cloud-based software platform, also utilizes AI and video compression to enable real-time telepresence, even in limited bandwidth environments. Access to fast, lag-free connectivity is no longer some nicety. Increasingly, it's becoming a must-have in our complex society. Being locked out of the internet, even for nanoseconds at a time, can set back people and businesses. By applying machine learning to a real, daily frustration it's clear to see how AI can boost more than productivity. It can serve as a bridge, offering the underserved unprecedented web access, and in the process, enable greater equality. A true game changer, it speaks to technology's real promise: imagining a brighter future for everyone to move through.
South China Morning Post
9 hours ago
- Science
- South China Morning Post
Mathematician Zhong Xiao leaves Finland for China with award-winning work critical for AI
Award-winning mathematician Professor Zhong Xiao has departed Finland after nearly three decades, bringing his foundational research critical to artificial intelligence back to China Zhong, a fellow of Finland 's Academy of Science and Humanities and recent recipient of its prestigious Väisälä Prize, joined Sun Yat-sen University full-time in April, according to information on the university's website. His groundbreaking work on the Poincaré inequality provides essential mathematical underpinnings for modern machine learning algorithms. Zhong was born in Changsha, Hunan province. In 1985, he was admitted to the top university, the University of Science and Technology of China. In 1995, he graduated with a master's degree from the Wuhan Institute of Mathematics and Physics, affiliated with the Chinese Academy of Sciences. After working for one year, he went abroad in 1996 to study at the University of Jyväskylä in Finland under the mentorship of the renowned Finnish mathematician Tero Kilpeläinen.
Forbes
a day ago
- Health
- Forbes
Stanford Initiative Leverages AI To Robustly Transform Mental Health Research And Therapy
In today's column, I explore the latest efforts to transform mental health research and therapy into being less subjective and more objective in its ongoing pursuits. This kind of transformation is especially spurred via the use of advanced AI, including leveraging deep learning (DL), machine learning (ML), artificial neural networks (ANN), generative AI, and large language models (LLMs). It is a vital pursuit well worth undertaking. Expectations are strong that great results and new insights will be gleaned accordingly. Let's talk about it. This analysis of AI breakthroughs is part of my ongoing Forbes column coverage on the latest in AI, including identifying and explaining various impactful AI complexities (see the link here). As a quick background, I've been extensively covering and analyzing a myriad of facets regarding the advent of modern-era AI that produces mental health advice and performs AI-driven therapy. This rising use of AI has principally been spurred by the evolving advances and widespread adoption of generative AI. For a quick summary of some of my posted columns on this evolving topic, see the link here, which briefly recaps about forty of the over one hundred column postings that I've made on the subject. There is little doubt that this is a rapidly developing field and that there are tremendous upsides to be had, but at the same time, regrettably, hidden risks and outright gotchas come into these endeavors too. I frequently speak up about these pressing matters, including in an appearance last year on an episode of CBS 60 Minutes, see the link here. If you are new to the topic of AI for mental health, you might want to consider reading my recent analysis that also recounts a highly innovative initiative at the Stanford University Department of Psychiatry and Behavioral Sciences called AI4MH, see the link here. Indeed, today's discussion is substantively shaped around a recent seminar conducted by AI4MH. Let's begin with a cursory unpacking of what is generally thought of as a type of rivalry or balance amid being subjective versus objective. The conceptualization of 'objective' consists of a quality or property intended to convey that there are hard facts, proven principles and precepts, clear-cut observations, reproducible results, and other highly tangible systemic elements at play. In contrast, 'subjective' is characterized as largely speculative, sentiment-based, open to interpretation, and otherwise less ironclad. Where does the consideration of subjective vs. objective often arise? You might be surprised to know that the question of subjective versus objective has a longstanding root in two fields of endeavor, namely psychology and physics. Yes, it turns out that psychology and physics have historically been domains that richly illuminate the dialogue regarding subjective versus objective. The general sense is that people perceive physics as tilted more toward the objective and less toward the subjective, while the perception of psychology is that it is a field angled toward the subjective side more so than the objective. Turn back the clock to the 1890s, in which the famed Danish professor Harald Hoffding made these notable points about psychology and physics (source: 'Outlines of Psychology' by Harald Hoffding, London Macmillan, 1891): You might notice the rather stunning point that psychology and physics are themselves inclusive of everything that could potentially be the subject of human research. That's amazingly alluring to those in the psychology and physics fields, while perhaps not quite as affable for all other domains. In any case, on the thorny matter of subjective versus objective in the psychology realm, we can recall Pavlov's remarks made in 1930: Pavlov's comments reflect a longstanding aspiration of the field of psychology to ascertain and verify bona fide means to augment the subjective aspects of mental health analysis with more exacting objective measures and precepts. The final word on this goes to Albert Einstein as to the heady matter: It's always an uphill battle to refute remarks made by Einstein, so let's take them as they are. Shifting gears, the topic of psychology and the challenging properties of subjective vs. objective was a major theme during a recent seminar undertaken by Stanford University on May 28, 2025, at the Stanford campus. Conducted by the initiative known as AI4MH (Artificial Intelligence for Mental Health), see the link here, within the Stanford School of Medicine, Department of Psychiatry and Behavioral Sciences, the session was entitled 'Insights from AI4MH Faculty: Transforming Mental Health Research with AI' and a video recording of the session can be found at the link here. The moderator and the three speakers consisted of: I attended the session and will provide a recap and analysis here. In addition, I opted to look at various research papers by the speakers. I encompass selected aspects from the papers to further whet your appetite for learning more about the weighty insights provided during the seminar and based on their respective in-depth research studies. I'll proceed next in the same sequence as occurred during the seminar, covering each speaker one at a time, and then offer some concluding thoughts. The human brain consists of around 86 billion neurons and approximately 100 trillion synapses. This elaborate organ in our noggin is often referred to in the AI field as the said-to-be wetware of humans. That's a cheeky sendoff of computer-based hardware and software. Somehow, in ways that we still aren't quite sure, the human brain or wetware gives rise to our minds and our ability to think. In turn, we are guided in what we do and how we act via the miracle of what's happening in our minds. For my related discussion about the Theory of Mind (ToM) and its relationship to the AI realm, see the link here. In the presentation by Dr. Kaustubh Supekar, he keenly pointed out that the brain-mind indubitably is the source of our mental health and ought to be closely studied when trying to ascertain the causes of mental disorders. He and his team are using AI to derive brain fingerprints that can be associated with mental disorders. It's quite exciting to envision that we could eventually end up with a tight mapping between the inner workings of the brain-mind and how mental disorders manifest within the brain-mind. Imagine the incredible possibilities of anticipating, remedying, or at least aiding those incurring mental disorders. In case you aren't familiar with the formal definition of what mental disorders consist of, I covered the DSM-5 guidelines in a posting on AI-driven therapy using DSM-5, see the link here, and included this definition from the well-known manual: DSM-5 is a widely accepted standard and is an acronym for the Diagnostic and Statistical Manual of Mental Disorders fifth edition, which is promulgated by the American Psychiatric Association (APA). The DSM-5 guidebook or manual serves as a venerated professional reference for practicing mental health professionals. In a recent research article that Dr. Kaustubh Supekar was the lead author of, entitled 'Robust And Replicable Functional Brain Signatures Of 22q11.2 Deletion Syndrome And Associated Psychosis: A Deep Neural Network-Based Multi-Cohort Study' by Kaustubh Supekar, Carlo de los Angeles, Sikanth Ryali, Leila Kushan, Charlie Schleifer, Gabriela Repetto, Nicolas Crossley, Tony Simon, Carrie Bearden, and Vinod Meno, Molecular Psychiatry, April 2024, these salient points were made (excerpts): The study aimed to find relationships between those having a particular chromosomal omission, known as DiGeorge syndrome or technically as 22q11.2 deletion syndrome (DS), and linking the brain patterns of those individuals to common psychosis symptoms. The brain-related data was examined via the use of an AI-based artificial neural network (a specialized version involving space-time or spatiotemporal analyses underlying the data, referred to as stDNN). This and other such studies are significant steps in the erstwhile direction of mapping brain-mind formulations to the nature of mental disorders. Faithful readers might recall my prediction that ambient intelligence (AmI) would be a rapidly expanding field and will dramatically inevitably change the nature of our lives, see the link here. What is ambient intelligence? Simply stated, it is a mishmash term depicting the use of AI to bring together data from electronic devices and do so with a focus on detecting and reacting to human presence. This catchphrase got its start in the 1990s when it was considered state-of-the-art to have mobile devices and the Internet of Things (IoT) was gaining prominence. It is a crucial aspect of ubiquitous computing. Ambient intelligence has made strong strides due to advances in AI and advances in ubiquitous technologies. Costs are getting lower and lower. Embedded devices are here and there, along with the devices seemingly invisible to those within their scope. The AI enables adaptability and personalization. In the second presentation of the AI4MH seminar, Dr. Ehsan Adeli notably pointed out that we can make use of exhibited behaviors to try and aid the detection and mitigation of mental health issues. But how can we capture exhibited behavior? One strident answer is to lean into ambient intelligence. In a research article that he served as a co-author, entitled 'Ethical Issues In Using Ambient Intelligence In Healthcare Settings' by Nicole Martinez-Martin, Zelun Luo, Amit Kaushal, Ehsan Adeli, Albert Haque, Sara S Kelly, Sarah Wieten, Mildred K Cho, David Magnus, Li Fei-Fei, Kevin Schulman, and Arnold Milstein, Lancet Digital Health, December 2020, these salient points were made (excerpts): The idea is that by observing the exhibited behavior of a person, we can potentially link this to their mental health status. Furthermore, via the appropriate use of AI, the AI might be able to detect when someone is having mental health difficulties or perhaps incurring an actual mental health disorder. The AI could in turn notify clinicians or others, including the person themselves, as suitably determined. In a sense, this opens the door to undertaking continuous assessment of neuropsychiatric symptoms (NPS). Of course, as directly noted by Dr. Ehsan Adeli, the enabling of AmI for this purpose brings with it the importance of considering aspects of privacy and other AI ethics and patient ethics caveats underlying when to best use these growing capabilities. Being evidence-based is a hot topic, aptly so. The trend toward evidence-based medicine and healthcare has been ongoing and aims to improve both research and practice, doing so in a classic less subjective, and more objective systematic way. The American Psychological Association (APA) defines evidence-based practice in psychology (EBPP) as 'the integration of the best available research with clinical expertise in the context of patient characteristics, culture, and preferences.' The third speaker in the AI4MH seminar was Dr. Shannon Wiltsey Stirman, a top researcher with a focus on how to facilitate the high-quality delivery of evidence-based psychosocial (EBPs) interventions. Among her research work is a framework for identifying and classifying adaptations made to EBPs in routine care. On the matter of frameworks, Dr. Stirman's presentation included a discussion about a newly formulated framework associated with evaluating AI-based mental health apps. The innovative and well-needed framework had been devised with several of her fellow researchers. In a co-authored paper entitled 'Readiness Evaluation for Artificial Intelligence-Mental Health Deployment and Implementation (READI): A Review and Proposed Framework' by Elizabeth Stade, Johannes Eichstaedt, Jane Kim, and Shannon Wiltsey Stirman, Technology, Mind, and Behavior, March 2025, these salient points were made (excerpts): Longtime readers know that I have been calling for an assessment framework like this for quite a while. For example, when OpenAI first allowed ChatGPT users to craft customized GPTs, there was a sudden surge in GPT-based applets that purportedly performed mental health therapy via the use of ChatGPT. In my review of those GPTs, I pointed out that many were not only vacuous, but they were at times dangerous in the sense that the advice being dispensed by these wantonly shaped ChatGPT applets was erroneous and misguided (see my extensive coverage at the link here and the link here). I have also repeatedly applauded the FTC for going after those who tout false claims about their AI for mental health apps (see my indication at the link here). Just about anyone can readily stand up a generative AI app that they claim is suitable for mental health therapy. They might have zero experience, zero training, and otherwise be completely absent from any credentials associated with a mental health professional. Meanwhile, consumers are at a loss to know which mental health apps are prudent and useful and which ones are problematic and ought to be avoided. It is for this reason that I have sought a kind of Consumer Reports scoring that might be used to differentiate AI mental health apps (see my discussion at the link here). The new READI framework is a substantial step in that profoundly needed direction. Moving the needle on the subjective vs. objective preponderance in psychology is going to take earnest and undeterred energy and attention. Newbie researchers especially are encouraged to pursue these novel efforts. Seasoned researchers might consider adjusting their usual methods to also incorporate AI, when suitable. The use of AI can be a handy tool and demonstrative aid. I've delineated the many ways that AI has already inspired and assisted psychology, and likewise, how psychology has aided and inspired advances in AI, see the link here for that discussion. There is a great deal at stake in terms of transforming psychology and the behavioral sciences as far forward as we can aim to achieve. Besides bolstering mental health, which certainly is crucial and laudable, Charles Darwin made an even grander point in his 'On the Origin of Species by Means of Natural Selection' in 1859: You see, the stakes also include revealing the origins of humankind and our storied history. Boom, drop the mic. Some might say it is ironic that AI as a computing machine would potentially have a hand in the discovery of that origin, but it isn't that far-fetched since AI is in fact made by the hand of humanity. It's our self-devised tool in an expanding toolkit to understand the world. And which gladly includes two very favored domains, e.g., the close and dear cousins of psychology and physics.
Forbes
5 days ago
- Business
- Forbes
Snorkel AI Raises $100 Million To Build Better Evaluators For AI Models
Snorkel AI CEO Alex Ratner said his company is placing more emphasis on helping subject matter experts build datasets and models for evaluating AI systems. Alex Ratner, CEO of Snorkel AI remembers a time when data labeling —the grueling task of adding context to swathes of raw data and grading an AI model's response— was considered 'janitorial' work among AI researchers. But that quickly changed when ChatGPT stunned the world in 2022 and breathed new life (and billions of dollars) into a string of startups rushing to supply human-labeled data to the likes of OpenAI and Anthropic to train capable models. Now, the crowded field of data labelling appears to be undergoing another shift. Fewer companies are training large language models from scratch, leaving that task instead to the tech giants. Instead, they are fine-tuning models and building applications in areas like software development, healthcare and finance, creating demand for specialized data. AI chatbots no longer just write essays and haikus; they're being tasked with high stakes jobs like helping physicians make diagnoses or screening loan applications, and they're making more mistakes. Assessing a model's performance has become crucial for businesses to trust and ultimately adopt AI, Ratner said. 'Evaluation has become the new entry point,' he told Forbes. That urgency for measuring AI's abilities across very specific use cases has sparked a new direction for Snorkel AI, which is shifting gears to help enterprises create evaluation systems and datasets to test their AI models and adjust them accordingly. Data scientists and subject matter experts within an enterprise use Snorkel's software to curate and generate thousands of prompt and response pairs as examples of what a correct answer looks like to a query. The AI model is then evaluated according to that dataset, and trained on it to improve overall quality. The company has now raised $100 million in a Series D funding round led by New York-based VC firm Addition at a $1.3 billion valuation— a 30% increase from its $1 billion valuation in 2021. The relatively small change in valuation could be a sign that the company hasn't grown as investors expected, but Ratner said it's a result of a 'healthy correction in the broader market.' Snorkel AI declined to disclose revenue. Customer support experts at a large telecommunication company have used Snorkel AI to evaluate and fine tune its chatbot to answer billing related questions and schedule appointments, Ratner told Forbes. Loan officers at one of the top three U.S. banks have used Snorkel to train an AI system that mined databases to answer questions about large institutional customers, improving its accuracy from 25% to 93%, Ratner said. For nascent AI startup Rox that didn't have the manpower or time to evaluate its AI system for salespeople, Snorkel helped improve the accuracy by between 10% to 12%, Rox cofounder Sriram Sridharan told Forbes. It's a new focus for the once-buzzy company, which spun out of the Stanford Artificial Intelligence Lab in 2019 with a product that helped experts classify thousands of images and text. But since the launch of ChatGPT in 2022, the startup has been largely overshadowed by bigger rivals as more companies flooded the data labelling space. Scale AI, which also offers data labeling and evaluation services, is reportedly in talks to finalize a share sale at a $25 billion valuation, up from its $13.8 billion valuation a year ago. Other competitors include Turing, which doubled its valuation to $2.2 billion from 2021, and Invisible Technologies, which booked $134 million in 2024 revenue without raising much from VCs at all. Snorkel has faced macro challenges too: As AI models like those powering ChatGPT got better, they could label data on a massive scale for free, shrinking the size of the market further. Ratner acknowledged that Snorkel saw a brief period of slow growth right after OpenAI launched ChatGPT and said enterprises had paused pilots with some vendors to consider using AI models for labelling directly. But he said Snorkel's business bounced back in 2023 and has grown since. Ratner said Snorkel's differentiator is its emphasis on bringing in subject matter experts — either its own or those within a company– and using a proprietary method called 'programmatic labelling,' to automatically assign labels to massive troves of data through simple keywords or bits of code as opposed to doing it manually. The aim is to help time-crunched experts like doctors and lawyers label data faster and more economically. As it leans into evaluation, which also requires data generation, Snorkel has started hiring tens of thousands of skilled contractors like STEM professors, lawyers, accountants and fiction writers to create specialized datasets for multiple AI developers, who then use the datasets to evaluate their models (he declined to say which frontier AI labs Snorkel works with). They can also use this data to add new functionality to their chatbots, like the ability to break down and 'reason' about a difficult query or conduct in-depth research on a topic, Ratner said. But even when it comes to building specialized evaluations, Snorkel faces fierce competition— new and old. The top AI companies have released a number of public benchmarks and open source datasets to evaluate their models. LMArena, a popular leaderboard for evaluating AI model performance, recently spun out as a new company and raised $100 million in seed funding from top investors at a hefty $600 million valuation, according to Bloomberg. Plus, companies like Scale, Turing and Invisible, all offer evaluation services. But Ratner said that unlike its rivals, Snorkel was built around human experts right from the start. Saam Motamedi, a partner at Greylock who participated in the round, said these new specialized dataset services are a fast-growing part of Snorkel's business as the industry shifts to what's called 'post training' — the process of tweaking the model's performance for certain applications. AI has already soaked up most of the internet data, making datasets custom-made by domain experts even more valuable. 'I think that market tailwind has proven to be a really good one for Snorkel,' he said. MORE FROM FORBES
TechCrunch
5 days ago
- Automotive
- TechCrunch
From Insights to Action: Advancing Agentic AI
The frontier of AI is rapidly advancing, but among the public and even in the enterprise, an understanding of its capabilities hasn't always kept pace. The widely held view of AI often gets stuck on the image of a sophisticated chatbot, capable of engaging in a conversation with a user and providing a response to prompts. But this way of thinking is also a limitation, boxing the technology into something as simple as trading messages and images. It misses both the nuance and full potential of state-of-the-art, real-world applications. As businesses, societies, and technical practitioners alike seek to unlock the value of AI, tapping an expanded set of capabilities has become a top priority. Capital One has delivered a recent breakthrough by building a new multi-agentic conversational AI assistant for car buyers. Capital One has a long history of using data, technology, and analytics to deliver superior financial services products and services for millions of customers. For over a decade, the business has been on a technology transformation journey to rebuild its tech stack, scale its technology workforce, and extend machine learning across the business. This dedication to innovation has positioned the company at the forefront of enterprises creating industry-leading AI advances today. 'We are continually exploring ways to enhance the customer experience at the frontier of AI. As we dug into new ways to improve the shopping experience with AI, we were looking at how to provide natural and satisfying interactions based on the way humans interact and reason,' says Dr. Milind Naphade, SVP of Technology, AI Foundations at Capital One. 'We wanted to transform the customer experience by replacing the previous generation of conversational AI technology with an agentic approach that leverages large language models (LLMs). We knew we needed to build a solution that would be able to really interact with a customer, understand their needs, and take actions on their behalf while they searched for a new vehicle.' The result: Chat Concierge from Capital One. The proprietary multi-agentic conversational AI assistant is custom-built to enhance the experience for car buyers and dealers alike. But answering questions and organizing information is only one part of what Chat Concierge can do. Model advances have enabled the dawn of AI agents that are trained to work together and tackle a series of complex tasks. Each AI agent performs a specific duty based on the user's request. Breaking a given workflow into discrete tasks and assigning each task to an AI agent can help ease the cognitive load of the user and create a more streamlined, satisfying experience. It's almost like building a dream team where each member is assigned to a role fitting their strengths. With Chat Concierge, multiple AI agents work together to not only provide information to the customer, but to take specific actions based on the customer's preferences and needs. For example, one agent communicates with the customer. Another creates an action plan based on business rules and the tools it is allowed to use. A third agent evaluates the accuracy of the first two, and a fourth agent that explains and validates the action plan with the user. In a single conversation, Chat Concierge can present information like vehicle comparisons and specifications, then take the next step by scheduling appointments and test drives with a sales team. 'There is a complex workflow that is getting executed behind the scenes, but it's all happening behind the scenes,' Naphade explains. These advances come as a logical progression from generative AI to AI agents that understand their environment, make decisions, and take actions. This requires an underlying infrastructure where the data and application programming interfaces (APIs) are AI-ready. 'We are standing on the shoulders of all the giant systems Capital One has built so far,' Naphade says. 'For example, we are one of the only banks that has fully committed to a public cloud. The data-driven, machine learning heritage of Capital One precedes us.' The possibilities for agentic AI–and future advances in the field–continue to evolve at a rapid clip. State of the art reasoning models are now designed to handle complex tasks by thinking through multiple steps and reasoning logically. Using these models to create AI agents brings the potential to help people turn insights into action for a range of sophisticated tasks that were never possible before. For instance, they have the potential to help solve real-world challenges like working together to tackle complex, PhD-level research problems; work with a company's developers to autonomously support the entire software development lifecycle, from planning to deployment; or even help a new business create a business plan and financial models along with a logo, website, and marketing plans. While the pace of AI innovation excels, so does the need for thoughtful approaches that balance speed with risk management. Capital One has tested, learned, and adapted its multi-agentic conversational AI workflow to create a great customer experience, in real-time, while also mitigating hallucination and errors through strong guardrails. In continuing to advance the state of the art in AI, Capital One isn't just inventing new tools and technology. It's delivering the right help at the right time—with intelligent, dynamically adaptive approaches—for more than 100 million customers. Learn more about AI at Capital One here.
