BERT Archives - gettectonic.com

01Dec

Enhancing OR Efficiency with Ambient Sensor Technology

Implementing ambient sensors in ORs can be challenging, as clinicians may feel uneasy about being recorded. Schwartz noted that emphasizing the benefits of the technology—such as improved accuracy and streamlined communication—has been essential in gaining clinician acceptance. DeDominico highlighted that the AI’s ability to send clinicians relevant updates, such as when a patient is ready for surgery, has increased clinician satisfaction by reducing unnecessary waiting.

December 1, 2024in AI Tools, Data, Technology0 Comments

26Nov

Cyber Insurance

Navigating Cyber Insurance in an Evolving Threat Landscape The rapidly shifting cyberthreat landscape presents unique challenges for healthcare organizations and underwriters navigating cyber insurance coverage decisions. Cyber liability insurance plays a crucial role in shielding healthcare providers from the mounting costs associated with data breaches and cyberattacks, which now average $9.77 million per incident in the healthcare sector, according to IBM. The Challenges of Retaining Cyber Insurance Healthcare, among other heavily targeted sectors, faces difficulties in securing and maintaining affordable cyber insurance. The constantly evolving threat landscape impacts risk profiles, which drives up premiums and complicates coverage retention. Although year-over-year premium growth plateaued in the U.S. in 2023, 79% of respondents in a Delinea survey still reported increased insurance costs, with 67% experiencing premium hikes between 50% and 100%. As high-profile healthcare cyberattacks and increasing cyber risks persist, navigating the insurance landscape remains a significant challenge. Additionally, the lag in processing claims makes it difficult to anticipate how underwriters will respond to these changing threats. How the Evolving Threat Landscape Impacts Cyber Insurance Obtaining adequate cyber insurance coverage can be challenging in today’s risk-heavy environment. Unlike traditional insurance, where risks remain static, cyber risks constantly evolve to counteract security controls. “Cyber insurance risk adjusters face a unique challenge; unlike fires, which aren’t actively trying to burn you in new ways, cyberthreats are constantly adapting to bypass existing protections,” said Christopher Henderson, senior director of threat operations at Huntress. This continuous adaptation often means that by the time underwriting is complete, a risk assessment may already be outdated. Shifts in the threat landscape are driving changes in cyber insurance questionnaires. While in 2023 insurers focused on remote access tools, vulnerability management, and administrative access controls, the focus in 2024 shifted to include multifactor authentication (MFA) and identity-based attack prevention. This shift highlights the need for organizations to adapt to new requirements in cyber insurance as cybercriminals add new tactics to their playbooks. Adapting Insurance to Emerging Threats As cyberthreat tactics evolve, insurers may adjust policy terms to keep pace with the latest risks. Henderson suggests that insurers could move toward shorter underwriting cycles, possibly even six-month periods, to better align with the rapidly shifting cyber landscape. Mitigating Risk and Controlling Cyber Costs Several factors influence cyber insurance premiums, including organizational size and security posture. For healthcare providers, adhering to industry standards like SOC 2 and ISO 27001 can demonstrate compliance with best practices, improving coverage terms and potentially lowering premiums. Healthcare organizations using the NIST Cybersecurity Framework (CSF) as their primary security standard reported lower premium increases compared to those without this framework, according to a 2024 report by KLAS Research, Censinet, and the American Hospital Association. Henderson emphasizes the importance of layering new strategies with proven, traditional ones: “While staying vigilant against newer tactics like social engineering and identity-based attacks, maintaining consistent, auditable identity verification and MFA protocols remains crucial.” Despite upfront costs, cyber insurance can significantly reduce financial impact during cybersecurity incidents. For example, a 2024 Sophos report found that organizations with cyber insurance saw an average ransomware recovery cost of $2.94 million compared to $3.48 million for those without coverage. Navigating cyber insurance can be complex for healthcare organizations, but careful attention to risks and proactive security measures can help them secure the right coverage at sustainable rates. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

November 26, 2024in Data0 Comments

09Nov

Where LLMs Fall Short

Large Language Models (LLMs) have transformed natural language processing, showcasing exceptional abilities in text generation, translation, and various language tasks. Models like GPT-4, BERT, and T5 are based on transformer architectures, which enable them to predict the next word in a sequence by training on vast text datasets. How LLMs Function LLMs process input text through multiple layers of attention mechanisms, capturing complex relationships between words and phrases. Here’s an overview of the process: Tokenization and Embedding Initially, the input text is broken down into smaller units, typically words or subwords, through tokenization. Each token is then converted into a numerical representation known as an embedding. For instance, the sentence “The cat sat on the mat” could be tokenized into [“The”, “cat”, “sat”, “on”, “the”, “mat”], each assigned a unique vector. Multi-Layer Processing The embedded tokens are passed through multiple transformer layers, each containing self-attention mechanisms and feed-forward neural networks. Contextual Understanding As the input progresses through layers, the model develops a deeper understanding of the text, capturing both local and global context. This enables the model to comprehend relationships such as: Training and Pattern Recognition During training, LLMs are exposed to vast datasets, learning patterns related to grammar, syntax, and semantics: Generating Responses When generating text, the LLM predicts the next word or token based on its learned patterns. This process is iterative, where each generated token influences the next. For example, if prompted with “The Eiffel Tower is located in,” the model would likely generate “Paris,” given its learned associations between these terms. Limitations in Reasoning and Planning Despite their capabilities, LLMs face challenges in areas like reasoning and planning. Research by Subbarao Kambhampati highlights several limitations: Lack of Causal Understanding LLMs struggle with causal reasoning, which is crucial for understanding how events and actions relate in the real world. Difficulty with Multi-Step Planning LLMs often struggle to break down tasks into a logical sequence of actions. Blocksworld Problem Kambhampati’s research on the Blocksworld problem, which involves stacking and unstacking blocks, shows that LLMs like GPT-3 struggle with even simple planning tasks. When tested on 600 Blocksworld instances, GPT-3 solved only 12.5% of them using natural language prompts. Even after fine-tuning, the model solved only 20% of the instances, highlighting the model’s reliance on pattern recognition rather than true understanding of the planning task. Performance on GPT-4 Temporal and Counterfactual Reasoning LLMs also struggle with temporal reasoning (e.g., understanding the sequence of events) and counterfactual reasoning (e.g., constructing hypothetical scenarios). Token and Numerical Errors LLMs also exhibit errors in numerical reasoning due to inconsistencies in tokenization and their lack of true numerical understanding. Tokenization and Numerical Representation Numbers are often tokenized inconsistently. For example, “380” might be one token, while “381” might split into two tokens (“38” and “1”), leading to confusion in numerical interpretation. Decimal Comparison Errors LLMs can struggle with decimal comparisons. For example, comparing 9.9 and 9.11 may result in incorrect conclusions due to how the model processes these numbers as strings rather than numerically. Examples of Numerical Errors Hallucinations and Biases Hallucinations LLMs are prone to generating false or nonsensical content, known as hallucinations. This can happen when the model produces irrelevant or fabricated information. Biases LLMs can perpetuate biases present in their training data, which can lead to the generation of biased or stereotypical content. Inconsistencies and Context Drift LLMs often struggle to maintain consistency over long sequences of text or tasks. As the input grows, the model may prioritize more recent information, leading to contradictions or neglect of earlier context. This is particularly problematic in multi-turn conversations or tasks requiring persistence. Conclusion While LLMs have advanced the field of natural language processing, they still face significant challenges in reasoning, planning, and maintaining contextual accuracy. These limitations highlight the need for further research and development of hybrid AI systems that integrate LLMs with other techniques to improve reasoning, consistency, and overall performance. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

November 9, 2024in Data

04Nov

Road for AI Regulation

The concept of artificial intelligence, or synthetic minds capable of thinking and reasoning like humans, has been around for centuries. Ancient cultures often expressed ideas and pursued goals similar to AI, and in the early 20th century, science fiction brought these notions to modern audiences. Works like The Wizard of Oz and films such as Metropolis resonated globally, laying the groundwork for contemporary AI discussions.

November 4, 2024in Generative AI

25Oct

Transform Customer Experience

In today’s AI-driven business environment, customer experience (CX) has evolved from being a buzzword to a critical factor in determining success. It’s no longer enough for businesses to offer high-quality products or excellent service alone—today’s customers are always online, engaged, and seeking the most convenient, relevant, and enjoyable experiences. This is where Salesforce Data Cloud becomes a game-changer, providing the tools needed to meet modern customer expectations. Transforming Customer Experience with Salesforce Data Cloud Salesforce enables businesses to collect, integrate, and leverage critical customer information within its ecosystem, offering an all-encompassing view of each customer. This unified customer data allows organizations to forecast visitor trends, assess marketing impact, and predict customer behavior. As data-driven decision-making becomes increasingly central to business strategy, Salesforce Data Cloud and its Customer Data Platform (CDP) features provide a significant competitive edge—whether in e-commerce, fintech, or B2B industries. Data Cloud is more than just your traditional CDP. It’s the only data platform native to the world‘s #1 AI CRM. This means that marketers can quickly access and easily action on unified data – from across the entire business – to drive growth and increase customer lifetime value. Data Cloud’s Role in Enhancing CX By unifying data in one place, Salesforce Data Cloud enables organizations to access real-time customer insights. This empowers them to track customer activity across channels like email, social media, and online sales, facilitating targeted marketing strategies. Businesses can analyze customer behavior and deliver personalized messaging, aligning marketing, sales, and customer service efforts to ensure consistency. With these capabilities, Salesforce customers can elevate the CX by delivering the right content, at the right time, to the right audience, ultimately driving customer satisfaction and growth. New Features of Salesforce Data Cloud Salesforce continues to evolve, introducing cutting-edge features that reshape customer interaction: To fully maximize these features, partnering with a Salesforce Data Cloud consultant can help businesses unlock the platform’s full potential and refine their customer engagement strategies. Agentic AI Set to Supercharge Business Processes Salesforce’s vision extends beyond customer relationship management with the integration of Agentic AI through its Customer 360 platform. According to theCUBE Research analysts, this signals a shift toward using AI agents to automate complex business processes. These AI agents, built on Salesforce’s vast data resources, promise to revolutionize how companies operate, offering customized, AI-driven business tools. “If they can pull this off, where it becomes a more dynamic app platform, more personalized, really focused on those processes all the way back to the data, it’s going to be a clear win for them,” said Strechay. “They’re sitting on cloud; they’re sitting on IaaS. That’s a huge win from that perspective.” AI agents create a network of microservices that think and act independently, involving human intervention only when necessary. This division of labor allows businesses to capture expertise in routine tasks while freeing human workers to focus on more complex decision-making. However, the success of these AI agents depends on access to accurate and reliable data. As Gilbert explained, “Agents can call on other agents, and when they’re not confident of a step in a process or an outcome, they can then bounce up to an inbox for a human to supervise.” The goal isn’t to eliminate humans but to capture their expertise for simpler processes. Empowering Developers and Citizen Creators At the core of this AI-driven transformation is Salesforce’s focus on developers. The platform’s low-code tools allow businesses to easily customize AI agents and automate business processes, empowering both experienced developers and citizen creators. With simple language commands or goal-setting, companies can build and train these AI agents, streamlining operations. “It’s always going to be about good data—that’s the constant,” Bertrand said. “The second challenge is how to train agents and humans to work together effectively. While some entry-level jobs may be replaced, AI will continue to evolve, creating new opportunities in the future.” Is Salesforce Data Cloud the Right Fit for Your Business? Salesforce Data Cloud offers comprehensive capabilities for businesses of all sizes, but it’s essential to assess whether it aligns with your specific needs. The platform is particularly valuable for: For businesses that fit these scenarios, working with Salesforce’s partner ecosystem or a Data Cloud consultant can help ensure successful integration and optimization. What’s New in Salesforce’s Latest Release? The latest Salesforce Spring Release introduced several exciting features, further enhancing Salesforce Data Cloud: These updates reflect Salesforce’s commitment to providing innovative, data-driven solutions that enhance customer experiences and drive business success. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

October 25, 2024in AI Tools, Data, Generative AI, Salesforce, Salesforce Data Cloud

18Oct

Customer Engagement with AI

Funlab Explores AI to Boost Customer Engagement in Leisure Venues In a push to enhance customer experiences across its “leisure-tainment” venues, Funlab has begun experimenting with artificial intelligence. Speaking at a Salesforce Agentforce event in Sydney, Funlab’s Head of Customer Relationships and Retention, Tracy Tanti, shared that the company is “excited to be able to start experimenting” with AI. Agentforce, a Salesforce platform designed to create autonomous agents for supporting employees and customers, serves as a key part of Funlab’s AI exploration efforts. According to Tanti, Funlab has a range of AI-focused projects on its roadmap, with the goal of blending digital experiences into real-life interactions and supporting both venue and corporate teams with AI-driven tools. Reflecting the company’s dedication to careful planning, Tanti described how Salesforce connected Funlab with another customer, Norths Collective, to discuss its own AI implementation journey. Robert Lopez, Chief Marketing and Innovation Officer at Norths Collective, has seen success with enhanced personalization and analytics, which have contributed to increased membership and engagement. Tanti noted that Norths Collective’s transformation work would provide valuable insights for Funlab as it optimizes its data in preparation for AI adoption. Currently, Funlab is in a post-digital transformation phase, refining its processes to deliver more connected and personalized guest experiences throughout the customer lifecycle. With ongoing expansion into the U.S. market—including recent openings of Holey Moley venues—Funlab is also focusing on building robust support infrastructure and engaging local audiences through Salesforce. Tanti highlighted the company’s vision for the U.S. to become a significant portion of total revenues and emphasized how Salesforce will help Funlab nurture a strong customer database in this new market. Additionally, Funlab is leveraging Salesforce to grow its event and function sales, which are projected to reach 39% of total online revenue by year’s end, up from 23% earlier this year. This expansion underscores Funlab’s commitment to using AI and data-driven insights to fuel growth and deepen customer engagement across all its markets and venues. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

October 18, 2024in Artificial Intelligence, Data, Salesforce

08Oct

Veeam Latest Acquisition

Veeam continues its acquisition strategy with the purchase of Alcion, bolstering its capabilities in AI and as-a-service offerings. This acquisition follows Veeam’s investment in Microsoft 365 backup-as-a-service provider Alcion last year, and brings in a team of AI and security specialists. Analysts and Veeam executives see this move as a key step in expanding Veeam’s as-a-service offerings. Earlier this year, the company launched Veeam Data Cloud, a backup-as-a-service solution for Microsoft 365 and Azure workloads. “After years of resisting, Veeam has fully embraced the as-a-service model,” said Christophe Bertrand, an analyst at TheCube Research. Veeam Latest Acquisition The acquisition, which closed in mid-September, marks the second time Veeam has purchased a company founded by Niraj Tolia and Vaibhav Kamra. In 2020, Veeam acquired Kasten, their Kubernetes backup provider. A year ago, Veeam led a million funding round for Alcion, which has since developed AI-driven data protection solutions. Veeam has been active in acquisitions, joining a broader trend in the data protection market. Recently, Commvault acquired Clumio, Cohesity merged with Veritas, and Veeam itself bought Cirrus from CT4, which later became part of the Veeam Data Cloud. Earlier this year, Veeam also acquired Coveware, an incident response vendor. “Veeam hasn’t traditionally been an acquisition-heavy company, but that has changed in recent years,” said Rick Vanover, Veeam’s VP of product strategy. “I expect this trend to continue.” Alcion’s Role at Veeam This acquisition strengthens Veeam’s expertise in the fast-growing as-a-service market. Alcion’s team of fewer than 50 employees, including founders Niraj Tolia and Vaibhav Kamra, joins Veeam, with Tolia stepping in as Veeam’s new CTO. Tolia will lead product strategy and engineering for Veeam Data Cloud, succeeding Danny Allan, who recently became CTO at cybersecurity company Snyk. Alcion, which has hundreds of customers, will offer those customers the opportunity to transition to Veeam Data Cloud. However, Veeam has not finalized the future of Alcion’s product or established a timeline for its integration. “This acquisition brings incredible talent and thought leadership to Veeam, especially from Niraj and the Alcion team,” said Brandt Urban, Veeam’s senior VP of worldwide cloud sales. “Their expertise will help us rapidly enhance Veeam Data Cloud, adding more capabilities and expanding workload coverage.” Analysts, like Bertrand, expect Veeam to broaden its data protection offerings for additional SaaS platforms beyond Microsoft 365, looking toward collaboration and DevOps tools as potential areas for growth. AI and Security at the Forefront Alcion’s AI-powered features allow administrators to optimize backups, detect malware, and respond proactively to threats. According to Krista Case, an analyst at The Futurum Group, Alcion uses AI strategically to adapt backup schedules based on data modification patterns, trigger backups when potential threats are identified, and recommend the best recovery points. “When practitioners talk about cyber resilience, they’re focused on minimizing data loss and downtime—Alcion’s AI capabilities directly address these concerns,” said Case. Veeam has also been integrating AI into its existing products, offering inline malware detection and an Intelligent Diagnostics service. A forthcoming Copilot feature for Microsoft 365 backups will further enhance AI-driven data protection. Veeam Latest Acquisition “AI is a real asset when applied thoughtfully—it’s not just hype,” said Bertrand, adding that users are more interested in AI’s ability to drive outcomes, like detecting threats that could otherwise go unnoticed. Veeam executives echoed the importance of delivering clear, tangible AI benefits. “We keep user outcomes front and center because, otherwise, AI becomes an expensive experiment,” Vanover said. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

October 8, 2024in Data

03Oct

Marc Benioff Interviewed

Salesforce, now one of the most influential companies in tech history, was founded by Marc Benioff after his successful tenure at Oracle. Having witnessed the potential of the internet, Benioff realized it would usher in a new era for delivering enterprise software through the cloud. However, this new approach demanded a complete overhaul of traditional business models, marketing strategies, and software development practices. Salesforce would embody this transformation, evolving over the decades into a platform supporting a diverse array of enterprise applications. Today, Salesforce’s flagship initiative is Agentforce, a pioneering wave of AI agents built on the Salesforce platform. This initiative, which I covered in detail earlier this month, exemplifies Benioff’s forward-thinking vision for AI. He passionately argues that Salesforce is well-positioned to lead in this space, and his insights on AI and its potential are as compelling as they are thought-provoking. Before diving into Agentforce and Benioff’s views on AI, it’s important to explore his early life and the founding of Salesforce. Benioff’s path to becoming the CEO of one of the most powerful software companies in the world was shaped by an early interest in technology. His first foray into software development was creating an app called “How to Juggle,” which he sold for $75 to CLOAD Magazine in Goleta, California. This early success marked the beginning of a long career in tech. Benioff went on to establish an Atari game company called Liberty Software and later wrote assembly code for the Mac as part of an internship. Despite his success as a software developer, he transitioned to a leadership role after realizing his interest in the broader business of technology. He credits his time at Oracle, where he learned the intricacies of sales, marketing, and customer communication, as critical to shaping his vision for Salesforce. Oracle, during Larry Ellison’s prime, was an ideal place to hone these business skills, which Benioff would later apply to revolutionize the enterprise software space. With this foundation, Benioff founded Salesforce, a company that has fundamentally changed the way businesses engage with technology. The company’s success lies not just in its cloud-based software, but in its ongoing evolution to address new challenges, including the AI-powered Agentforce initiative, which stands at the forefront of Salesforce’s future growth. Benioff’s journey, from a young programmer to the CEO of a global tech giant, is a testament to his visionary approach and adaptability in an ever-changing tech landscape. Marc Benioff’s early entrepreneurial instincts were evident even before he fully embraced the title of entrepreneur. From a young age, he knew he wanted to build something of his own, though he hadn’t yet honed the full set of skills needed to succeed in business. He had a knack for technology, excelling as a programmer working in assembly code, but his mentors, including university professors and colleagues at Apple, recognized that being an entrepreneur required more than just technical ability. They encouraged him to develop his sales, marketing, and business acumen. His early exposure to these lessons was pivotal in shaping his future trajectory. Benioff’s transition from a programmer to a business leader began when he sought advice from Bill Jost at Apple, who connected him with Gary Gibson, an Oracle executive. After a memorable and unexpected interview, Benioff joined Oracle, starting with a role answering calls at the company’s 800-number. He quickly moved into sales and marketing, eventually working directly with Larry Ellison, who would become a significant mentor over the next 13 years. Benioff’s success at Oracle set the stage for his eventual departure to start Salesforce in 1999. In the mid-1990s, while at Oracle, Benioff had an epiphany while using a web browser. The internet was transforming how software could be delivered, and Benioff saw the potential for a new business model based on cloud computing. At this time, Oracle was focused on the network computer—a vision Benioff did not fully share. After discussing his ideas with Larry Ellison and taking some time off for reflection and research, Benioff recognized that the world was on the cusp of a major shift in technology. He felt compelled to create something new that would leverage the internet, offering software over the web through a subscription model. During his time off, Benioff traveled to Hawaii, reflecting on the future of the tech industry and contemplating ideas. He also experimented with domain names, purchasing You.com and later gifting it to a friend who turned it into a search and AI company. Benioff returned to Larry Ellison to share his vision of a cloud-based business model and later found inspiration from spiritual gurus during a trip to India, who reminded him that success in business should also be grounded in positive values. Benioff’s experience led to the founding of Salesforce with its revolutionary 1-1-1 Model—dedicating 1% of equity, profit, and employee time to philanthropy. Salesforce also embraced emerging technologies such as social, mobile, and AI, and created a new business model through subscription-based services. This combination of technological innovation and a strong sense of social responsibility has set Salesforce apart, attracting both admiration and emulation from other companies. Benioff’s Career Benioff’s career is a testament to the power of surrounding oneself with great people, continuing to learn, and having a clear vision. His leadership at Salesforce has been guided by values he believes are crucial to the company’s success and its ability to make a positive impact on the world. His insights on the Innovator’s Dilemma, a concept popularized by Clayton Christensen, highlight the challenges companies face as they evolve with changing technologies. While Salesforce’s core principles remain intact, the company has continued to adapt to new technology paradigms, ensuring it remains at the forefront of innovation in the software industry. Marc Benioff (MB) recently shared his excitement about the future of AI and its potential to revolutionize business and technology. He explained that, from a business perspective, this is the most thrilling moment in his career. AI, according to him, will transform how enterprises operate, making them more

October 3, 2024in Agentforce Platform, Data, Enterprise, Google, Hyperforce, Salesforce, Salesforce Data Cloud, Snowflake, Technology

23Sep

Dreamforce 24 Insights

Three Key Insights You Might Have Missed from Dreamforce ’24 In today’s digital-driven world, interconnected systems are commonplace and essential, making platform integration and unified operations critical. As AI becomes more central, technologies like Salesforce Agentforce AI are drawing increased attention. At Dreamforce ’24, automation and AI were the event’s stars, particularly Salesforce’s plans for Agentforce AI. Dreamforce 24 Insights. Here are three key insights from Dreamforce ’24 that you might have missed: 1. Salesforce’s Automation Plans Could Reshape Its Future Salesforce has a solid reputation for business automation, but now, with agentic systems entering the picture, the company is looking at a transformative opportunity. John Furrier of theCUBE noted during Dreamforce, “Salesforce is positioned to use generative AI to simplify complexity and reduce the steps required to get things done.” As Salesforce integrates generative AI, the emphasis on securing and utilizing data becomes paramount. Christophe Bertrand of theCUBE pointed out that many organizations are not fully utilizing their data. The introduction of Agentforce AI, which aims to leverage this untapped potential, could bring automation to new heights and fundamentally transform how businesses operate. 2. Salesforce Agentforce AI Aims to Integrate Seamlessly Into Business Workflows A major focus of Dreamforce was Salesforce’s new AI offering—Agentforce. According to Muralidhar Krishnaprasad, Salesforce’s CTO, this represents the next stage of AI for the company. While earlier efforts focused on predictive AI (Einstein) and generative AI copilots, Agentforce moves toward more autonomous AI agents. “Our platform will be one of the most comprehensive for agent development,” Krishnaprasad explained. He highlighted that Agentforce will allow businesses to deploy AI agents across various functions—advertising, sales, service, and analytics—creating a seamless AI-driven ecosystem within the Salesforce platform. David Schmaier, president and CPO of Salesforce, added that Agentforce will transform customer interactions by integrating AI agents with Salesforce Data Cloud to deliver more personalized and efficient experiences. 3. Strategic Partnerships Are Streamlining Business and Enhancing Customer Solutions At Dreamforce, partnerships played a key role in Salesforce’s strategy for the future. A collaboration between Salesforce and AWS is streamlining procurement for joint customers through AWS Marketplace. This partnership allows companies to optimize their spend management and simplify the purchasing process for Salesforce products. IBM is also leveraging Agentforce to drive new outcomes through watsonx Orchestrate, as Nick Otto, IBM’s head of global strategic partnerships, explained. Automation and orchestration have been focal points for both IBM and Salesforce. Another partnership with Canva showcased AI-driven data autofill capabilities that integrate with Salesforce CRM. This allows sales teams to create personalized presentations at scale, automating workflows and increasing efficiency, as noted by Canva’s Chief Customer Officer, Rob Giglio. These insights from Dreamforce ’24 highlight the growing importance of AI, automation, and strategic partnerships in shaping the future of business operations with Salesforce at the forefront. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 23, 2024in Data, Generative AI, Salesforce, Salesforce Data Cloud

14Sep

Liberty Bank and Salesforce

Liberty Bank, based in Middletown, Connecticut, announced on September 5th an expanded partnership with Salesforce, the world’s leading AI-powered CRM platform, to enhance its customer engagement efforts. Liberty Bank and Salesforce. By integrating Salesforce’s Financial Services Cloud, Marketing Cloud, MuleSoft, and Salesforce Shield, Liberty Bank aims to deliver more personalized, efficient, and enriched services. This strategic investment will further position Liberty Bank as a leader in customer satisfaction and loyalty within the community banking sector. “We set out to find a strategic partner that truly understands the unique nature of banking and puts the customer first,” said David W. Glidden, Liberty Bank President and CEO. “As we continue our mission to ‘Build the Community Bank of the Future,’ having the best partners is crucial to elevating our customer experience. With Salesforce’s innovative CRM solutions, we’re investing in the future to meet the evolving needs of our customers, team members, and communities, and to exceed their expectations.” Salesforce’s platform will enable Liberty Bank to streamline operations and gain deeper insights into customers’ financial journeys, ensuring a seamless and personalized banking experience. The Financial Services Cloud offers tools specifically tailored to the banking industry, allowing for faster time-to-value. Set to roll out next year, this transformation will allow Liberty Bank to prioritize customer financial goals while maintaining a high level of service and support. “Banks of all sizes are under pressure to innovate and deliver more personalized experiences. By leveraging CRM, data, and AI, Liberty Bank will gain a comprehensive view of its customers, enabling its teams to build stronger relationships and improve overall productivity.”Greg Jacobi, VP & GM of Banking and Lending at Salesforce. About Liberty Bank Founded in 1825, Liberty Bank is the nation’s oldest and largest independent mutual bank. With nearly $8 billion in assets, Liberty operates 56 branches across Connecticut and two in Massachusetts. It provides a full range of services, including consumer and commercial banking, cash management, home mortgages, business loans, insurance, and investment services. The bank has been named a ‘Top Workplace’ by the Hartford Courant every year since 2012 and recognized as a Best-In-State Bank in Connecticut by Forbes in 2021, 2022, and 2023. For more information, visit www.liberty-bank.com. Liberty Bank and Salesforce. Interested in discussing Salesforce for your financial institution? Contact Tectonic today. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 14, 2024in Mulesoft, Salesforce Financial Services Solutions, Salesforce Implementation Services, Salesforce Managed Services, Salesforce Optimization, Salesforce Shield

13Sep

Large and Small Language Models

Understanding Language Models in AI Language models are sophisticated AI systems designed to generate natural human language, a task that is far from simple. These models operate as probabilistic machine learning systems, predicting the likelihood of word sequences to emulate human-like intelligence. In the scientific realm, the focus of language models has been twofold: While today’s cutting-edge AI models in Natural Language Processing (NLP) are impressive, they have not yet fully passed the Turing Test—a benchmark where a machine’s communication is indistinguishable from that of a human. The Emergence of Language Models We are approaching this milestone with advancements in Large Language Models (LLMs) and the promising but less discussed Small Language Models (SLMs). Large Language Models compared to Small Language Models LLMs like ChatGPT have garnered significant attention due to their ability to handle complex interactions and provide insightful responses. These models distill vast amounts of internet data into concise and relevant information, offering an alternative to traditional search methods. Conversely, SLMs, such as Mistral 7B, while less flashy, are valuable for specific applications. They typically contain fewer parameters and focus on specialized domains, providing targeted expertise without the broad capabilities of LLMs. How LLMs Work Comparing LLMs and SLMs Choosing the Right Language Model The decision between LLMs and SLMs depends on your specific needs and available resources. LLMs are well-suited for broad applications like chatbots and customer support. In contrast, SLMs are ideal for specialized tasks in fields such as medicine, law, and finance, where domain-specific knowledge is crucial. Large and Small Language Models’ Roles Language models are powerful tools that, depending on their size and focus, can either provide broad capabilities or specialized expertise. Understanding their strengths and limitations helps in selecting the right model for your use case. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 13, 2024in Salesforce

29Aug

Key Insights on Navigating AI Compliance

Grammarly’s AI Regulatory Master Class: Key Insights on Navigating AI Compliance On August 27, 2024, Grammarly hosted an AI Regulatory Master Class webinar, featuring Scout Moran, Senior Product Counsel, and Alan Luk, Head of Governance, Risk, and Compliance (GRC). The event provided a comprehensive overview of the current and upcoming AI regulations affecting organizations’ AI strategies, along with guidance on evaluating AI solution providers, including those offering generative AI. While the webinar avoided deep legal analysis and did not serve as legal advice, Moran and Luk spotlighted key regulations emerging from both the U.S. and European Union (EU), highlighting the rapid response of regulatory bodies to AI’s growth. Overview of AI Regulations The AI regulatory landscape is changing quickly. A May 2024 report from law firm Davis & Gilbert noted that nearly 200 AI-related laws have been proposed across various U.S. states. Grammarly’s presentation emphasized the need for organizations to stay updated, as both U.S. and EU regulations are shaping the future of AI governance. The EU AI Act: A New Regulatory Framework The EU AI Act, which took effect on August 2, 2024, applies to AI system providers, importers, distributors, and others connected to the EU market, regardless of where they are based. As Moran pointed out, the Act is designed to ensure AI systems are deployed safely. Unsafe systems may be removed from the market, establishing a regulatory baseline that individual EU countries can strengthen with more stringent measures. However, the Act does not fully define “safety.” Legal experts Hadrien Pouget and Ranj Zuhdi noted that while safety requirements are crucial to the Act, they are currently broad, allowing room for further development of standards. The Act prohibits certain AI practices, such as manipulative systems, those exploiting personal vulnerabilities, and AI used to assess or predict criminal risk. AI systems are categorized into four risk levels: unacceptable, high-risk, limited risk, and minimal risk. High-risk systems—such as those in critical infrastructure or public services—face stricter regulation, while minimal-risk systems like spam filters have fewer requirements. Full enforcement of the Act will begin in 2025. U.S. AI Regulations Unlike the EU, the U.S. focuses more on national security than consumer safety in its AI regulation. The U.S. Executive Order on Safe, Secure, and Trustworthy AI addresses these concerns. At the state level, Moran highlighted trends such as requiring clear disclosure when interacting with AI and giving individuals the right to opt out of having their data used for AI model training. States like California and Utah are leading the way with specific laws (SB-1047 and SB-149, respectively) addressing accountability and disclosure in AI use. Key Considerations When Selecting AI Vendors Moran stressed the importance of thoroughly vetting AI vendors. Organizations should ensure vendors meet cybersecurity standards, such as SOC 2, and clearly define how their data will be used, particularly in training large language models (LLMs). “Eyes off” agreements, which prevent vendor employees from accessing customer data, should also be considered. Martha Buyer, a frequent contributor to No Jitter, emphasized verifying the originality of AI-generated content from providers like Grammarly or Microsoft Copilot. She urged caution in ensuring the ownership and authenticity of AI-assisted outputs. The Importance of Strong Third-Party Agreements Luk highlighted Grammarly’s commitment to data privacy, noting that the company neither sells customer data nor uses it to train models. Additionally, Grammarly enforces agreements preventing its third-party LLM providers from doing so. These contractual protections are crucial for safeguarding customer data. Organizations should also ensure third-party vendors adhere to strict guidelines, including securing customer data, encrypting it, and preventing unauthorized access. Vendors should maintain updated security certifications and manage risks like bias, which, while not entirely avoidable, must be actively addressed. Staying Ahead in a Changing Regulatory Environment Both Moran and Luk stressed the importance of ongoing monitoring. Organizations need to regularly reassess whether their vendors comply with their data-sharing policies and meet evolving regulatory standards. As AI technology and regulations continue to evolve, staying informed and agile will be critical for compliance and risk mitigation. In conclusion, organizations adopting AI-powered solutions must navigate a dynamic regulatory environment. As AI advances and regulations become more comprehensive, remaining vigilant and asking the right questions will be key to ensuring compliance and reducing risks. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

August 29, 2024in Generative AI

31Jul

AI Data Cloud and Integration

The enterprise has transitioned from merely speculating about artificial intelligence to actively implementing it. In doing so, companies must determine the optimal combination of ancillary technologies that, when strategically paired with AI, can drive relevant use cases and business outcomes. With AI Data Cloud and Integration, your data-driven decisions happen in real-time. Salesforce Inc. is leveraging a powerful trio — its Data Cloud, automation, and AI — to deliver what it considers transformative outcomes for organizations. “AI has such wonderful capability today from predictive to generative, [but] it’s not new to Salesforce,” said Param Kahlon, executive vice president and general manager at Salesforce. “Salesforce has been doing predictive AI for almost 10 years now. But what is great is that generative AI now gives the ability to process these large language models on large amounts of unstructured, semi-structured content to generate great content that can be used by salespeople to send relevant emails and marketing people to create personalized landing pages.” Kahlon spoke with theCUBE Research Senior Analyst George Gilbert during a recent “The Road to Intelligent Data Apps” podcast series. They discussed how Salesforce is revolutionizing business operations in the digital age by harnessing AI-driven insights, contextualizing data with the company’s Data Cloud, and enabling real-time actions. Gen AI and Data Cloud for Contextualization In today’s business environment, intelligence is the cornerstone of success. Salesforce’s AI platform empowers companies with predictive and generative AI capabilities, enabling them to make insightful decisions and craft personalized experiences for their customers. Businesses can now process vast amounts of unstructured data and generate compelling content. “For this AI to be meaningful and for companies to harness the full value of AI, you want to make sure that you’re grounding the data that’s being used to generate those predictions with some things that are relevant to the current business process, to the current transaction, to the current context of interaction you’re happening with the customer,” Kahlon said. Salesforce’s Data Cloud acts as the AI foundation, enriching existing data models with relevant contextual data tailored to the specific needs of each business and their interactions with customers. “When we talk to our large Salesforce customers, they all tell us that AI is really important for them,” Kahlon said. “That is something that they want to drive, but they’re also saying that the data for them is spread out across the enterprise. Some of them tell us that they have more than 900 different business systems in which data is stored, and they want the ability to bring that data together in a seamless way so it can be processed by AI through Data Cloud.” Automation and Integration for Real-Time Action The combination of AI and Data Cloud generates actionable insights, but these insights alone aren’t enough. Businesses need to act swiftly on these predictions, driving real-time actions to capitalize on opportunities. This is where integration and automation come into play, according to Kahlon. “[Customers are] essentially telling us that data is spread across the enterprise and they want the data in real time to be available to customers,” he said. “With MuleSoft and Salesforce integration capabilities, we’ve focused on the real-time nature of making sure that you can take real-time business transactions in the context of the process that is happening, and that’s what’s differentiated in our approach to making sure that we can collect the data in real time and make actions happen in real time.” Integration is the glue that brings together data from various sources, allowing AI to derive meaningful insights. Salesforce’s integration capabilities, powered by MuleSoft, focus on real-time data processing, ensuring that businesses can act on insights as they occur. This low-latency approach enables not only Salesforce applications but also other third-party applications to contribute to the data ecosystem, Kahlon explained. “We’ve got a very large North American airline that has built their entire customer experience, from booking an airline ticket to checking into your flight and ordering special meals for your flight, all of that on an API-based platform — and we’re able to process that scale of transactions,” he said. “As you get into AI, all of that becomes extremely relevant to drive that real-time throughput, and that’s where our customers are finding value in our technology.” When the customer experience is the driver, the experience is always stellar. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

July 31, 2024in Generative AI, Mulesoft

25Jul

Salesforce Data Cloud Pioneer

While many organizations are still building their data platforms, Salesforce Data Cloud Pioneer has made a significant leap forward. By seamlessly incorporating metadata integration, Salesforce has transformed the modern data stack into a comprehensive application platform known as the Einstein 1 Platform. Led by Muralidhar Krishnaprasad, executive vice president of engineering at Salesforce, the Einstein 1 Platform is built on the company’s metadata framework. This platform harmonizes metadata and integrates it with AI and automation, marking a new era of data utilization. The Einstein 1 Platform: Innovations and Capabilities Salesforce’s goal with the Einstein 1 Platform is to empower all business users—salespeople, service engineers, marketers, and analysts—to access, use, and act on all their data, regardless of its location, according to Krishnaprasad. The open, extensible platform not only unlocks trapped data but also equips organizations with generative AI functionality, enabling personalized experiences for employees and customers. “Analytics is very important to know how your business is doing, but you also want to make sure all that data and insights are actionable,” Krishnaprasad said. “Our goal is to blend AI, automation, and analytics together, with the metadata layer being the secret sauce.” Salesforce Data Cloud Pioneer In a conversation with George Gilbert, senior analyst at theCUBE Research, Krishnaprasad discussed the platform’s metadata integration, open-API technology, and key features. They explored how its extensibility and interoperability enhance usability across various data formats and sources. Metadata Integration: Accommodating Any IT Environment The Einstein 1 Platform is built on Trino, the federated open-source query engine, and Spark for data processing. It offers a rich set of connectors and an open, extensible environment, enabling organizations to share data between warehouses, lake houses, and other systems. “We use a hyper-engine for sub-second response times in Tableau and other data explorations,” Krishnaprasad explained. “This in-memory overlap engine ensures efficient data processing.” The platform supports various machine learning options and allows users to integrate their own large language models. Whether using Salesforce Einstein, Databricks, Vertex, SageMaker, or other solutions, users can operate without copying data. The platform includes three levels of extensibility, enabling organizations to standardize and extend their customer journey models. Users can start with basic reference models, customize them, and then generate insights, including AI-driven insights. Finally, they can introduce their own functions or triggers to act on these insights. The platform continuously performs unification, allowing users to create different unified graphs based on their needs. “We’re a multimodal system, considering your entire customer journey,” Krishnaprasad said. “We provide flexibility at all levels of the stack to create the right experience for your business.” The Triad of AI, Automation, and Analytics The platform’s foundation ingests, harmonizes, and unifies data, resulting in a standardized metadata model that offers a 360-degree view of customer interactions. This approach unlocks siloed data, much of which is in unstructured forms like conversations, documents, emails, audio, and video. “What we’ve done with this customer 360-degree model is to use unified data to generate insights and make these accessible across application surfaces, enabling reactions to these insights,” Krishnaprasad said. “This unlocks a comprehensive customer journey.” For instance, when a customer views an ad and visits the website, salespeople know what they’re interested in, service personnel understand their concerns, and analysts have the information needed for business insights. These capabilities enhance customer engagement. “Couple this with generative AI, and we enable a lot of self-service,” Krishnaprasad added. “We aim to provide accurate answers, elevating data to create a unified model and powering a unified experience across the entire customer journey.” Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

July 25, 2024in Einstein 1 Platform, Generative AI, Salesforce Data Cloud, Salesforce Einstein

22Jul

Einstein Code Generation and Amazon SageMaker

Salesforce and the Evolution of AI-Driven CRM Solutions Salesforce, Inc., headquartered in San Francisco, California, is a leading American cloud-based software company specializing in customer relationship management (CRM) software and applications. Their offerings include sales, customer service, marketing automation, e-commerce, analytics, and application development. Salesforce is at the forefront of integrating artificial general intelligence (AGI) into its services, enhancing its flagship SaaS CRM platform with predictive and generative AI capabilities and advanced automation features. Einstein Code Generation and Amazon SageMaker. Salesforce Einstein: Pioneering AI in Business Applications Salesforce Einstein represents a suite of AI technologies embedded within Salesforce’s Customer Success Platform, designed to enhance productivity and client engagement. With over 60 features available across different pricing tiers, Einstein’s capabilities are categorized into machine learning (ML), natural language processing (NLP), computer vision, and automatic speech recognition. These tools empower businesses to deliver personalized and predictive customer experiences across various functions, such as sales and customer service. Key components include out-of-the-box AI features like sales email generation in Sales Cloud and service replies in Service Cloud, along with tools like Copilot, Prompt, and Model Builder within Einstein 1 Studio for custom AI development. The Salesforce Einstein AI Platform Team: Enhancing AI Capabilities The Salesforce Einstein AI Platform team is responsible for the ongoing development and enhancement of Einstein’s AI applications. They focus on advancing large language models (LLMs) to support a wide range of business applications, aiming to provide cutting-edge NLP capabilities. By partnering with leading technology providers and leveraging open-source communities and cloud services like AWS, the team ensures Salesforce customers have access to the latest AI technologies. Optimizing LLM Performance with Amazon SageMaker In early 2023, the Einstein team sought a solution to host CodeGen, Salesforce’s in-house open-source LLM for code understanding and generation. CodeGen enables translation from natural language to programming languages like Python and is particularly tuned for the Apex programming language, integral to Salesforce’s CRM functionality. The team required a hosting solution that could handle a high volume of inference requests and multiple concurrent sessions while meeting strict throughput and latency requirements for their EinsteinGPT for Developers tool, which aids in code generation and review. After evaluating various hosting solutions, the team selected Amazon SageMaker for its robust GPU access, scalability, flexibility, and performance optimization features. SageMaker’s specialized deep learning containers (DLCs), including the Large Model Inference (LMI) containers, provided a comprehensive solution for efficient LLM hosting and deployment. Key features included advanced batching strategies, efficient request routing, and access to high-end GPUs, which significantly enhanced the model’s performance. Key Achievements and Learnings Einstein Code Generation and Amazon SageMaker The integration of SageMaker resulted in a dramatic improvement in the performance of the CodeGen model, boosting throughput by over 6,500% and reducing latency significantly. The use of SageMaker’s tools and resources enabled the team to optimize their models, streamline deployment, and effectively manage resource use, setting a benchmark for future projects. Conclusion and Future Directions Salesforce’s experience with SageMaker highlights the critical importance of leveraging advanced tools and strategies in AI model optimization. The successful collaboration underscores the need for continuous innovation and adaptation in AI technologies, ensuring that Salesforce remains at the cutting edge of CRM solutions. For those interested in deploying their LLMs on SageMaker, Salesforce’s experience serves as a valuable case study, demonstrating the platform’s capabilities in enhancing AI performance and scalability. To begin hosting your own LLMs on SageMaker, consider exploring their detailed guides and resources. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

July 22, 2024in Generative AI, Salesforce Einstein

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Large and Small Language Models

Key Insights on Navigating AI Compliance

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