You’re busy—but are you actually growing? In this episode, Nata Salvatori exposes a trap that’s costing service providers time, money, and sanity: chasing busywork that feels productive but doesn’t move the needle. She walks through a clear, five-step growth path—from clarifying your offer, validating through real sales, delivering sustainably, building repeatable systems, to scaling confidently. You’ll learn: How to spot and ditch “fake work” Why clarity beats complexity every time How to use real feedback to validate your offers Delivery tips that prevent burnout System creation that enables scaling How to honor your current phase of growth 📌 Ready to stop spinning your wheels and make real moves? Map your phase, pick your next action, and don’t be afraid to ask for help: 👉 accidentalceo.co/coaching Support the show…
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Exploring Nvidia’s approach to AI factories and AI supercomputers
Manage episode 486633210 series 3493557
محتوای ارائه شده توسط Informa TechTarget. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Informa TechTarget یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
Nvidia's hardware strategies are powering AI technologies. Recently, networking has become the critical backbone of modern AI systems. In today’s episode, Kevin Deierling provides practical insights for enterprises looking to implement AI technologies effectively. Deierling contrasts traditional data centers with the emerging concept of AI factories, revealing how these specialized environments are reshaping enterprise computing.
Featuring: Kevin Deierling, senior vice president of networking, Nvidia.
In today's episode, we cover:
- Nvidia hardware and software approach
- AI factories and data centers
- Agentic AI and the shift toward complex reasoning
and more.
To learn more about AI and Nvidia, check out SearchEnterprise AI.
To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.
References:
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اشتراک گذاریManage episode 486633210 series 3493557
محتوای ارائه شده توسط Informa TechTarget. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Informa TechTarget یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
Nvidia's hardware strategies are powering AI technologies. Recently, networking has become the critical backbone of modern AI systems. In today’s episode, Kevin Deierling provides practical insights for enterprises looking to implement AI technologies effectively. Deierling contrasts traditional data centers with the emerging concept of AI factories, revealing how these specialized environments are reshaping enterprise computing.
Featuring: Kevin Deierling, senior vice president of networking, Nvidia.
In today's episode, we cover:
- Nvidia hardware and software approach
- AI factories and data centers
- Agentic AI and the shift toward complex reasoning
and more.
To learn more about AI and Nvidia, check out SearchEnterprise AI.
To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.
References:
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In this episode of the Targeting AI podcast from Informa TechTarget, Ben Flast, of NoSQL database vendor MongoDB, discusses the company's rapid integration of generative AI technologies, including vector search and real-time updates through Atlas Stream Processing. He emphasizes the importance of community engagement and the role of agentic AI in enhancing developer productivity. The conversation also explores the differences between open source and proprietary models, the impact of model sizes on performance, and MongoDB's approach to AI governance. Flast shares customer applications that highlight the transformative potential of AI in various industries and concludes with insights into future innovations at MongoDB. Featuring: Ben Flast, director of product management at MongoDB In today's episode, we cover these topics: Vector search enhances the capabilities of GenAI applications. Agentic AI represents a new application pattern for AI capabilities. Model size affects performance and cost for developers. To learn more about AI and the importance of GenAI in database platforms, check out SearchEnterprise AI and SearchDataManagement . To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech . References: Atlas Stream Processing MongoDB vector search Model Context Protocol GenAI standard…
The lack of diversity in AI systems has been an issue since the birth of the technology. In this episode of the Targeting AI podcast from Informa TechTarget, Karen Panetta discusses the importance of diversity in tech, and the ethical implications of AI. She emphasizes the need for inclusive design in engineering and AI systems, the role of digital twins in education, and the challenges of AI bias. Featuring: Karen Panetta, an IEEE fellow and dean of graduate engineering education at Tufts University In today's episode, we cover these topics: AI should focus on solving real-world problems rather than being applied indiscriminately. Ethical AI must prioritize the principle of “do no harm” to individuals and communities. AI bias can lead to significant real-world consequences, especially in healthcare and hiring. and more. To learn more about AI and the importance of diversity in AI systems, check out SearchEnterprise AI . To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech . References: Generative AI will force diversity in AI systems Federal report focuses on AI diversity and ethics Diversity in hiring a key to eradicating AI bias…
T
Targeting AI
1 Tech pioneer Alan Trefler of Pegasystems: GenAI added creativity, speed and scale to workflows 33:04
In this episode of the Targeting AI podcast, Shaun Sutner and Esther Ajao interview Alan Trefler, founder and CEO of Pegasystems, discussing the evolution of AI technology, particularly generative AI, and its integration into business processes. Trefler shares insights on the differences between design time and runtime applications of AI, the importance of workflow engines, and the challenges of AI safety and reliability. He emphasizes the need for collaboration between AI and human expertise, and outlines Pegasystems' roadmap for effectively using AI in business process automation and legacy transformation. Featuring: Alan Trefler , founder and CEO, Pegasystems. In today's episode, we cover how: GenAI has significantly advanced Pegasystems' offerings. GenAI coaches differ from traditional generative AI assistants by focusing on design time. Design time is crucial for ensuring reliable AI outcomes in business settings. GenAI can enhance business process automation by streamlining workflows. References: Pegasystems expands agentic AI for business automation | TechTarget Pegasystems expands generative AI in CX, BPA cloud platform | TechTarget Pegasystems unveils AI assistant for knowledge management | TechTarget CRM and BPM vendor Pegasystems adds new AI features | TechTarget To learn more about AI and Pegasystems, check out Informa TechTarget news sites, including SearchCustomerExperience and SeachEnterpriseAI To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech .…
Nvidia's hardware strategies are powering AI technologies. Recently, networking has become the critical backbone of modern AI systems. In today’s episode, Kevin Deierling provides practical insights for enterprises looking to implement AI technologies effectively. Deierling contrasts traditional data centers with the emerging concept of AI factories, revealing how these specialized environments are reshaping enterprise computing. Featuring: Kevin Deierling, senior vice president of networking, Nvidia. In today's episode, we cover: Nvidia hardware and software approach AI factories and data centers Agentic AI and the shift toward complex reasoning and more. To learn more about AI and Nvidia, check out SearchEnterprise AI . To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech . References: Nvidia AI platform for cloud GPU providers widens supply Nvidia, AMD and others tout partnership with Saudi Arabia Nvidia aims at agents, physical AI with reasoning models…
Generative AI has led to many fears about the workforce. However, for work management platform vendor Asana, GenAI and agentic AI can be effective tools in the workforce. Instead of replacing humans, AI technology can work alongside humans. Despite the potential for collaboration, not all tasks require the use of AI technology. Featuring: Saket Srivastava, CIO of work management platform, Asana. In today's episode, we cover: The collaboration between AI technology and humans Employees need training and support in AI How GenAI can significantly improve project management tasks and more. To learn more about AI and Asana, check out SearchEnterpriseAI . To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech . References: Project management vendor Asana brings AI to Work Graph 6 of the top change management applications Connected workspace apps improve collaboration management…
As an AI writing assistant, Grammarly has used AI technology from its inception. The popularity of large language models has led to a shift in which the writing assistant vendor moved from natural language processing to including large language models to help enterprise employees improve their writing as they work. This has led Grammarly to see a possibility in the part it can play in transforming the future of work. Featuring: Luke Behnke , head of Enterprise Product at Grammarly, an AI-powered assistant writing platform. In today’s episode, we cover: Grammarly’s AI evolution Agentic AI and the future of work AI technology as an assistant and not a replacement for work and more. To learn more about AI and Grammarly, check out SearchEnterprise AI . To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech . References: Grammarly AI and an update to the writing tool What will be the future of the workplace? Top 4 AI writing tools for improved business efficiency…
A key truth about AI is that regulation has long lagged innovation. However, this has not removed the responsibility of enterprises to deploy AI systems responsibly or for AI vendors to create responsible systems. What are the key metrics to understanding a safe AI system? Featuring: Stuart Battersby , CTO at Chatterbox Labs, vendor of a quantitative AI risk metrics platform, and Danny Coleman , CEO at Chatterbox. In today’s episode, we cover: The difference between AI safety and responsible AI The need for standards in AI safety The future of AI safety in Enterprises and more. To learn more about responsible AI, check out SearchEnterprise AI . To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech . References: Assessing if DeepSeek is safe to use in the enterprise EU, U.S. at odds on AI safety regulations Responsible AI vs. ethical AI: What's the difference?…
Industrial AI is less familiar than consumer AI, but represents a critical and growing sector within AI’s influence. What unique AI applications are surfacing in this area? Featuring: Olympia Brikis , director of Industrial AI research at Siemens In today’s episode, we’ll cover… Understanding Industrial AI and its distinctions from consumer AI AI and, specifically, generative AI adoption at Siemens The role of digital twins in testing AI recommendations and more. To learn more about AI in healthcare, check out Search Enterprise AI . To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech . References: CES 2024: Siemens eyes up immersive tech, AI to enable industrial metaverse How businesses are using AI in the construction industry Siemens forges digital twin deal with Nvidia for metaverse…
Traditional, generative, agentic—in the past couple of decades, AI metamorphosed into an indisposable tool for enterprises wanting to streamline their processes and improve their impact. In this episode, we dive into the different types of AI, best practices for implementation, and the challenges faced in the industry. Featuring: Deepak Singh , Vice President at AWS In today’s episode, we’ll cover… The difference between traditional AI, generative AI, and agentic AI The role of agentic AI in software development Best practices for implementing agentic AI and more! To learn more about agentic AI, check out Search Enterprise AI . To watch the video version our podcast, subscribe to our YouTube channel, @EyeOnTech . References: AWS intros new foundation model line and tools for Bedrock Amazon Q, Bedrock updates make case for cloud in agentic AI Amazon to spend $100B on AWS AI infrastructure…
In the couple of years since the popularization of ChatGPT , generative AI technology has quickly taken hold in the legal profession. It has backfired in some cases, such as when an attorney filed a legal brief written with ChatGPT's help and the AI platform hallucinated some of the cases in the brief. That case and others have led some law firms to block general access to AI tools. Most recently, Hill Dickinson, a law firm in the U.K., asked its staff not to use generative AI tools like ChatGPT. Many law firms are using generative AI tools, and some even market their own AI systems. AI vendors are also partnering with law firms and companies in the legal profession. In February, LexisNexis and OpenAI agreed to integrate OpenAI's large language models across its products. The success, and uncertainty, surrounding AI tools in the legal profession led James M. Cooper and Kashyap Kompella to write the book A Short and Happy Guide for Artificial Intelligence for Lawyers . Cooper is a law professor at California Western School of Law, while Kompella is CEO of AI analyst firm RPA2AI Research. In the book , Cooper and Kompella explore how lawyers can understand and use AI technology. "We saw an urgent need to upskill lawyers on AI," Kompella said on the latest episode of Informa TechTarget's Targeting A I podcast. "How do you move AI ethics and responsible AI into practice? You have to move them through lawyers. Lawyers are a big part of that equation." Kompella and Cooper argue that while numerous books for lawyers about AI exist, few focus on using the technology ethically. The authors also argue that while the legal profession has traditionally been slow to adopt new technologies, it can benefit from AI for several reasons. For example, AI technology can provide access to legal services for those in underserved areas like rural communities in the United States, Cooper said. "AI can be a game changer in terms of provision of legal services," he said. However, providing more education is the key to helping legal professionals understand AI technology . "The law school curriculum is not teaching AI or any technologies to the students, so there is a huge skill gap," Kompella said. Cooper added, "The skill sets of prompt engineering, of knowing how to use these AI tools and the dangers that come with them, should be rote in law schools now right from the first year. Those law schools around the world that embrace this idea are future-proofing their students. They're not going to have to play catch up." Esther Shittu is an Informa TechTarget news writer and podcast host covering artificial intelligence software and systems. Shaun Sutner is senior news director for Informa TechTarget's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. Together, they host the Targeting AI podcast series…
Without a good data strategy, generative AI becomes unusable technology for enterprises. This was true when ChatGPT started becoming popular, and it is even more accurate years later. The most recent example is the AI Chinese startup DeepSeek . While most AI cloud providers like Google, AWS and Microsoft now offer the DeepSeek-R1 reasoning model, many AI experts believe that enterprises might be hesitant to use it due to the data it was trained on. Despite DeepSeek's R1's innovation, it all comes down to the foundation, said Michelle Bonat , chief AI officer at AI Squared, an AI and data integration platform. "As GenAI expands and expands ... the fundamentals are the fundamentals," Bonat said on the latest episode of Informa TechTarget's Targeting AI podcast. She added that while many organizations may have started with GenAI by just putting up a chatbot, many have found that if they do not have good quality data, they might have to pause their GenAI initiatives. The reason is that the nature of generative AI systems is to produce responses. Therefore, if they do not have good-quality data , they tend to hallucinate. Thus, Bonat said the growth in GenAI initiatives across organizations has also led to an increase in conversation around data strategy, data quality and data cleanliness. "They're very much connected," she said. "GenAI has become important in the conversation that connects with data strategy, data quality, data cleanliness and also, ultimately, in responsible AI and governance within the organization." She added that enterprises should pay attention to data and responsible AI because it benefits their businesses. "It's a competitive advantage to have responsible AI," she continued. "Customers want AI systems they can trust. ... Being transparent and having responsible AI helps increase your brand reputation." Esther Shittu is an Informa TechTarget news writer and podcast host covering artificial intelligence software and systems. Shaun Sutner is senior news director for Informa TechTarget's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. Together, they host the Targeting AI podcast series.…
While some vendors are working to ensure large language models become better at reasoning, other AI vendors are making them compatible in multiple languages. Writer is a provider of a full-stack generative AI platform for enterprises. While the vendor provides a generative AI platform that enterprises can use to build generative AI capabilities into their workflows , it also offers a family of LLMs: Palmyra . The models support text generation and translation in numerous languages, including Spanish, French, Hindi and Russian. "Multilingual training data and models that can be as good in dozens of other languages as they are in English is something everybody should strive for," said Writer cofounder and CEO May Habib on a recent episode of Informa TechTarget's Targeting AI Podcast. Writer also uses large volumes of synthetic data to help build legal confidence in generative AI technology, Habib said. Writer also publishes data on how its models score for bias and toxicity. "We really want to make sure that we are compliant with folks' ESG [equity, sustainability and governance] guardrails and guidelines," Habib said. Writer recently raised $200 million in series C funding, bringing its valuation to $1.9 billion. Esther Shittu is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems.…
The contact center world is a difficult place, packed with frustration and stress. Digital communications giant Cisco sees its mission as easing that experience for human contact center workers and the customers they deal with every day. For that undertaking, the vendor has seized on generative AI and agentic AI as the vehicles to both automate and augment the work of humans, in essence, smartening up the traditional chatbots that have long helped companies interact with their customers. "We're to see a lot more of what I call event-based communication, proactive communication outbound that we do particularly well, powered by AI," said Jay Patel, senior vice president and general manager for customer experience at Cisco Webex, on the Targeting AI podcast from Informa TechTarget. "And then the response path to that is we think there will be AI agents involved in some of the more simple use cases. "For example, if you haven't paid a bill, they can obviously call you in the outbound call center, but probably a better way of doing it is probably to send you a message with a link to then basically make the payment," Patel continued. Like many other big tech vendors, Cisco deploys large language models (LLMs) from a variety of specialist vendors, including OpenAI and Microsoft. It also uses open models from independent generative AI vendor Mistral , as well as its own AI technology developed in-house or acquired by acquisition. "Fundamentally, what we are looking at is the idea of an AI engine for each use case, and within the AI engine you would have a particular LLM," Patel said. Among the generative AI-powered tools Cisco has assembled are Webex AI Assistant and Agent Wellness, to tend to the psyches of busy contact center human workers . "Customers call very frustrated; they may shout at somebody. And then if you've had a difficult call, the agent wellness feature will mean that the supervisor knows that this set of agents has had a set of difficult calls," Patel said. "Maybe they're the ones who need a break now. So, there are ways of improving employee experience inside the contact center that we think we can … use AI for." Shaun Sutner is senior news director for Informa TechTarget's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. He is a veteran journalist with more than 35 years of news experience. Esther Shittu is an Informa TechTarget news writer and podcast host covering artificial intelligence software and systems. Together, they host the Targeting AI podcast.…
Box has been in the AI game for a long time. But when generative AI mushroomed into a transformative force in the tech world, the cloud content management vendor opted to turn to specialists in the new and fast-growing technology to power the arsenal of tools in its platform. "We've been doing AI for many years. But the really cool thing that happened … AI got to the point where the generative AI models understood content," said Ben Kus, CTO at Box, on the Targeting AI podcast from Informa TechTarget. "For us, this whole generative AI revolution has been this great gift to everybody who deals with content. It's almost like having a very dedicated, very intelligent person who stands next to you, ready to do what you want." When generative AI exploded with OpenAI's release of ChatGPT in November 2022, Box turned to OpenAI for its first batch of generative AI tools. Box CEO Aaron Levie had known OpenAI CEO and co-founder Sam Altman for many years. However, when a passel of other independent generative AI vendors sprang up and the tech giants started releasing their own powerful large language models (LLMs) and multimodal models, Box decided to broaden its generative AI palette. "Azure and OpenAI are partners of ours and we think they have great models, but we are not at all dedicated to any one model," Kus said. "In fact, at Box, one of our goals is to provide you with all of the major models that you might want." These include generative AI models from Google, IBM, Anthropic and Amazon . One example of how Box uses an outside model is Anthropic's 3.5 Sonnet LLM, which Kus called "one of the best models out there right now." One application is at a financial firm that deals with long bond offerings . The company needs to analyze many of these complex financial vehicles to evaluate which bonds in which it wants to invest. "They use [the model] to extract key info. It takes the [job] of looking through these bonds. From hours or days to … hopefully, minutes," Kus said. "If the model is very good, it can give you very good answers. If it's not as smart, then it can be off a little bit. So, this particular company really wants to have the best models so they can get the best sort of use of this kind of AI." Shaun Sutner is senior news director for Informa TechTarget's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. He is a veteran journalist with more than 30 years of news experience. Esther Shittu is an Informa TechTarget news writer and podcast host covering artificial intelligence software and systems.…
This is the year of AI agents. The last few months of 2024 brought much talk about and expectations for AI agents that can operate autonomously and semi-autonomously. Many vendors have capitalized on the enthusiasm to introduce new agentic products: Salesforce came out with Agentforce, and Microsoft introduced Copilot agents. With 2025 here, questions about whether the momentum on agents will continue. Some see the agentic hype, and real progress, persisting this year. Craig Le Clair, a Forrester Research analyst and author of the soon-to-be-published book Random Acts of Automation , is among those who think AI agents will continue to gain momentum in the new year. "It's the biggest change toward AGI [artificial general intelligence] that I've seen," Le Clair said on the latest episode of Informa TechTarget's Targeting AI podcast, referring to the concept of AI that is as smart or smarter than human intelligence. Enterprises will likely adjust the ways they use applications that use AI agents as copilots to augment humans, because many of those applications are not profitable, he said. However, AI agents will be the driving force in helping enterprises build platforms that use generative AI technology to spur business value, he said. "When you really start to turn piles of data into conversations with people ... that's the opportunity for this," Le Clair said. "For an employee to have a conversation with standard operating procedures to get advice on what to do, or for standard operating procedures to be taken out of that PDF repository and actually put into a prompt and generate tasks that are then followed by an agent to get something done -- the potential is really there." As with all new technology, AI agents involve a trust issue. Enterprises still do not trust the technology to be fully autonomous and perform tasks from start to finish all on its own, Le Clair said. However, organizations can rely on AI agents to perform part of the work with the assistance of a human in the loop. With the speed of the technology's maturation, progress toward fully autonomous agents by 2028 is likely, Le Clair predicted. Esther Shittu is an Informa TechTarget news writer and podcast host covering artificial intelligence software and systems. Shaun Sutner is senior news director for Informa TechTarget's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. Together, they host the Targeting AI podcast series.…
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