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Trust Einstein Copilot for Tableau

Trust Einstein Copilot for Tableau

Are you prepared to utilize the capabilities of Einstein Copilot to expand your organization’s analytical advantages? This robust tool facilitates data exploration, insights generation, and visualization development at an unprecedented pace. However, before immersing yourself in its capabilities, it’s crucial to grasp how Einstein Copilot upholds Tableau and Salesforce’s core value: Trust. Let’s discover how the Einstein Trust Layer safeguards your data, ensures result accuracy, and facilitates auditing, addressing common questions and concerns raised by our customers.Trust Einstein Copilot for Tableau. What is Einstein Copilot for Tableau? Using generative AI and statistical analysis, Einstein Copilot for Tableau is able to understand the context of your data to create and suggest relevant business questions to help kickstart your analysis. A smart, conversational assistant for Tableau users, Einstein Copilot for Tableau automates data curation—the organization and integration of data collected from various sources—by generating calculations and metadata descriptions. Einstein Copilot for Tableau can fill data gaps and enhance analysis by creating synthetic datasets where real data is limited. Einstein Copilot helps you anticipate outcomes with predictive analytics that simulate diverse scenarios and uncover hidden correlations. Additionally, generative models can increase data privacy by producing non-traceable data for analysis.  Fulfilling the promise of generative AI, Einstein Copilot for Tableau presents an efficient, insightful, and ethical approach to data analytics. Think of it as an intelligent assistant integrated into the Tableau suite of products to make everyone successful in their analysis workflow—whether they’re an experienced data analyst or a data explorer. As your intelligent analytics AI assistant, Einstein Copilot for Tableau guides you through the process of creating data visualizations in Tableau by assisting you with recommended questions, conversational data exploration, guided calculation creation, and more. Understanding the Einstein Trust Layer The Einstein Trust Layer constitutes a secure AI architecture embedded within the Salesforce platform. Comprising agreements, security technology, and data privacy controls, it ensures the safety of your data while exploring generative AI solutions. Built upon the Einstein Trust Layer, Einstein Copilot for Tableau and other Tableau AI features inherit its security, governance, and Trust capabilities. The Einstein Trust Layer is a secure AI architecture, built into the Salesforce platform. It is a set of agreements, security technology, and data and privacy controls used to keep your company safe while you explore generative AI solutions. Tableau has been on the journey to help people see and understand their data for over two decades. Thanks to data analysts, this mission has been a success and will continue to be a success. Data analysts are the backbone of organizations that champion data culture, capture business requirements, prep data, and create data content for end users. Data Access and Privacy Who Accesses Your Data? A primary concern among our customers revolves around data access. Rest assured, the Einstein Trust Layer enforces strict policies to safeguard your organization’s data. Third-party LLM providers, including Open AI and Azure Open AI, adhere to a zero data retention policy. This means that data sent to LLMs isn’t stored; once processed, both the prompt and response are promptly forgotten. Additionally, each Einstein Copilot for Tableau customer receives their own Data Cloud instance, securely storing prompts and responses for auditing purposes. Data Residency and Access Control Einstein Copilot for Tableau respects permissions, row-level security, and data policies within Tableau Cloud, ensuring that only authorized personnel within your organization access specific data. Whether using Einstein Copilot or not, data access is restricted based on organizational roles and permissions. Data Handling and Processing Data Sent Outside of Tableau Cloud Site Einstein Copilot for Tableau operates within the confines of your Tableau site, scanning connected data sources to create a summary context. This summarized data is sent to third-party LLM providers for vectorization, enabling accurate interpretation of user queries. Importantly, the zero data retention policy ensures that summarized data is forgotten post-vectorization. Personally Identifiable Information (PII) Data To enhance data privacy, Einstein Copilot for Tableau employs data masking for PII data. This technique replaces sensitive information with placeholder text, ensuring privacy without sacrificing context. While our detection models strive for accuracy, continuous evaluation and refinement are paramount to maintain trust. Result Trustworthiness Ensuring Safe and Accurate Results Einstein Copilot for Tableau employs Toxicity Confidence Scoring to identify harmful inputs and responses. By combining rule-based filters and AI models, potentially harmful content is filtered and flagged for review. Furthermore, accuracy benchmarks ensure that generated results align closely with human-authored ones, bolstering trust in the platform. Future Trust Enhancements Trust remains an ongoing focus for our teams. Initiatives such as a BYO LLM solution and improved disambiguation capabilities are underway to further enhance trustworthiness. Continuous feedback, testing, and iteration drive our efforts to maintain your trust in Einstein Copilot for Tableau and the Einstein Trust Layer. Data analysis and data-driven decision-making have been part of the vocabulary in organizations over the years. And, while data analysis is one of the most in-demand tech skills sought by employers today, not everyone in an organization has “analyst” in their job title—myself included. Yet, so many of us use data daily to make informed decisions. The rise of generative AI presents a significant opportunity for us to bring transformative benefits to analytics. Businesses are eager to embrace generative AI because it can help save time, provide faster insights, and empower analysts to be even more productive with an AI assistant—freeing analysts to focus on delivering high-quality, data-driven insights. Is Tableau replacing Einstein analytics? Einstein Analytics has a new name. Say hello to Tableau CRM. Everything about how it works stays the same, just with that snazzy new name. When Tableau joined the Salesforce family, we brought together analytics capabilities of incredible depth and power. What is the difference between Einstein analytics and Tableau? If you’re only planning on analyzing Salesforce data, Einstein Analytics would probably make the most sense for you. However, if you need to analyze information that is coming from all over the place, Tableau will give your users more options. Tableau GPT infuses automation in every part of analytics – from preparation to communicating

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Slack for Manufacturing and Automotive

Slack for Manufacturing and Automotive

Slack for Manufacturing and Automotive Enhance productivity, reduce costs, and provide exceptional experiences with a consolidated perspective of your customer data. Streamline diverse systems, teams, and processes effortlessly through automation. Forge connections with external partners to integrate your entire ecosystem seamlessly. As manufacturing, automotive, and energy organizations transition to developing new powertrains and digital products for innovative service and revenue models, Slack offers an efficient platform for innovation. Pioneering Enterprise Security Setting the standard in enterprise security, Slack ensures data encryption in transit and at rest. It boasts comprehensive compliance and assurance programs, along with features such as audit logs, data loss prevention, and single sign-on. As the productivity platform for manufacturing, automotive, and energy, Slack ensures a secure environment. Utilizing Slack AI for Smarter Work Engage with a Slack sales representative or join the waitlist to experience the empowering capabilities of Slack AI throughout your organization. Leverage AI-powered search for swift answers, summarize conversations effortlessly, and rest assured with secure data handling by Slack AI. Explore Slack’s pivotal role in accelerating innovation across manufacturing, automotive, and energy sectors. Empowering Software Developers Discover how Slack empowers teams to introduce novel digital products and services, driving revenue and transforming customer experiences. For software developers, Slack accelerates the delivery of high-quality code, making it a preferred choice for the world’s leading producers of software, hardware, and services. Explore Slack’s webinar to uncover its potential for your team. Revolutionizing Fleet Management with Automile Challenges abound for businesses managing fleets, particularly in integrating solutions seamlessly with existing toolsets for increased productivity. Automile aims to disrupt the billion fleet management market by introducing a mobile-first API-centric solution. With REST-based JSON APIs and SDKs for PHP, Java, and C# .NET, Automile simplifies fleet management, offering web and mobile apps. Slack Integration with Automile Automile is set to release new features in March, including integrations such as Slack. By submitting the app to Slack’s App Directory, Automile aims to provide businesses with a streamlined fleet management experience within Slack. The upcoming Slack App supports Slash Commands, Interactive Messages, and Incoming Webhooks. Security First Approach Automile prioritizes security with the new Slack App, ensuring that authorized Slack team members have access. The app supports Slash Commands, enabling users to achieve specific tasks, such as checking out drivers and locating vehicles. The admin can control user access to these commands for added security. Fleet Management Commands Automile’s Slack App introduces Slash Commands for drivers and vehicles. The Driver command allows fleet managers to search for drivers, check their status, and interact with them directly from Slack. Similarly, the Vehicle command provides information on vehicle location, status, and enables task assignment. Driving Field Service Efficiency with Slack and Salesforce Service Cloud Witness how manufacturers harness the combined capabilities of Slack and Salesforce Service Cloud to empower field employees and enhance customer satisfaction. Slack’s Continued Impact Slack continues to thrive globally, supporting businesses of all sizes in achieving growth and skyrocketing productivity. Acquired by Salesforce in 2021, Slack remains an influential force in the business communication and collaboration landscape. Like1 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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An Eye on AI

Humans often cast uneasy glances over their shoulders as artificial intelligence (AI) rapidly advances, achieving feats once exclusive to human intellect. An Eye on AI should ease their troubled minds. AI-driven chatbots can now pass rigorous exams like the bar and medical licensing tests, generate tailored images and summaries from complex texts, and simulate human-like interactions. Yet, amidst these advancements, concerns loom large — fears of widespread job loss, existential threats to humanity, and the specter of machines surpassing human control to safeguard their own existence. Skeptics of these doomsday scenarios argue that today’s AI lacks true cognition. They assert that AI, including sophisticated chatbots, operates on predictive algorithms that generate responses based on patterns in data inputs rather than genuine understanding. Even as AI capabilities evolve, it remains tethered to processing inputs into outputs without cognitive reasoning akin to human thought processes. So, are we venturing into perilous territory or merely witnessing incremental advancements in technology? Perhaps both. While the prospect of creating a malevolent AI akin to HAL 9000 from “2001: A Space Odyssey” seems far-fetched, there is a prudent assumption that human ingenuity, prioritizing survival, would prevent engineering our own demise through AI. Yet, the existential question remains — are we sufficiently safeguarded against ourselves? Doubts about AI’s true cognitive abilities persist despite its impressive functionalities. While AI models like large language models (LLMs) operate on vast amounts of data to simulate human reasoning and context awareness, they fundamentally lack consciousness. AI’s creativity, exemplified by its ability to invent new ideas or solve complex problems, remains a simulated mimicry rather than authentic intelligence. Moreover, AI’s domain-specific capabilities are constrained by its training data and programming limitations, unlike human cognition which adapts dynamically to diverse and novel situations. AI excels in pattern recognition tasks, from diagnosing diseases to classifying images, yet it does so without comprehending the underlying concepts or contexts. For instance, in medical diagnostics or art authentication, AI can achieve remarkable accuracy in identifying patterns but lacks the interpretative skills and contextual understanding that humans possess. This limitation underscores the necessity for human oversight and critical judgment in areas where AI’s decisions impact significant outcomes. The evolution of AI, rooted in neural network technologies and deep learning paradigms, marks a profound shift in how we approach complex tasks traditionally performed by human experts. However, AI’s reliance on data patterns and algorithms highlights its inherent limitations in achieving genuine cognitive understanding or autonomous decision-making. In conclusion, while AI continues to transform industries and enhance productivity, its capabilities are rooted in computational algorithms rather than conscious reasoning. As we navigate the future of AI integration, maintaining a balance between leveraging its efficiencies and preserving human expertise and oversight remains paramount. Ultimately, the intersection of AI and human intelligence will define the boundaries of technological advancement and ethical responsibility in the years to come. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Salesforce Summer 24 Data Cloud Release

Salesforce Summer 24 Data Cloud Release

Data Cloud Ingest, harmonize, unify, and analyze streaming and batch data with Data Cloud. Then use that data to unlock meaningful and intelligent experiences across Customer 360 applications and beyond. Salesforce Summer 24 Data Cloud Release. Salesforce Summer 24 Data Cloud Release 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Salesforce Field Service Lightning

Salesforce Field Service Lightning

Many companies worldwide seek quality services associated with Salesforce Field Service Lightning (FSL) to differentiate between lacking customer experiences and excellent ones. Satisfied customers associate such services with high-quality ratings, gradually building trust with the company and recommending it to others. The ability of any business to generate successful recognition and experience with clients helps establish an invaluable competitive advantage. Salesforce Field Service Lightning We are here to assist you in mapping and quoting various FSL Salesforce services such as equipment installation, repair, general customer service management, and maintenance. Field Service technicians, also known as mobile technicians, play a crucial role in delivering these tasks. They receive notifications on mobile devices and quickly find users in need of speedy solutions to their problems. What is Salesforce Field Service? Salesforce Field Service (formerly known as Field Service Lightning) is designed for the automation and optimization of work offered by dispatchers and field service agents. It ensures that no employee sacrifices any functionality of the related services when working outside the company. This system is part of the FSL Salesforce Service Cloud and aims to create a seamless workflow and avoid mistakes with the help of service technicians. Integral Parts of Salesforce Field Service After implementing Salesforce Field Service Lightning, clients can immediately see the benefits reflected in the increased efficiency of developed services. Advantages of Salesforce Field Service Lightning Bottom Line We hope this comprehensive guide on Salesforce Field Service Lightning has provided valuable insights into its aspects and benefits. Our experienced executives offer valuable advice and risk-free solutions for managing projects involving field service. You can contact Tectonic 24/7 for error removal and maintaining Salesforce FSL service deployments. Tasks such as project management and exception diagnosis are easily handled with the Service Cloud platform. We offer a strong framework for different service models and prepare reports for various service territory designs, ensuring a seamless and efficient operation. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Technical Debt

Understanding and Managing Technical Debt in Salesforce

Salesforce is a powerful and dynamic CRM platform with a vast array of tools and features. Given its complexity, users must make critical decisions daily—whether creating custom objects, automating workflows, nurturing leads, or developing applications. Each choice impacts how effectively Salesforce is utilized, influencing both short-term success and long-term sustainability. However, users often opt for the quickest solution rather than the most robust one. While this may provide immediate results, it can lead to inefficiencies and challenges over time. This is where technical debt comes into play. What Is Technical Debt in Salesforce? Technical debt refers to the hidden cost an organization incurs when prioritizing speed over quality in software development and system configuration. It results from taking shortcuts that may seem convenient at first but ultimately require additional work—often in the form of rework, maintenance, or system inefficiencies. A Real-World Analogy Imagine you’re on a trek and encounter two paths leading to the same destination. The shorter route is steep and exhausting, while the longer path includes rest stops and is easier on your body. Although the shorter path may seem efficient, it leaves you drained. Similarly, in Salesforce, quick fixes—such as writing redundant code, skipping documentation, or excessive customization—may seem efficient initially but create long-term complications, leading to technical debt. Common Causes of Technical Debt in Salesforce Types of Technical Debt in Salesforce Identifying and Measuring Technical Debt To assess technical debt, consider both business-related and technical questions: Business-Related Questions Technical Questions How to Avoid Technical Debt in Salesforce Final Thoughts Technical debt is an inevitable challenge in any complex system, but with proactive planning and best practices, it can be minimized. The key is to prioritize sustainability over speed—choosing well-structured, scalable solutions rather than quick fixes that may lead to costly rework in the future. By maintaining best practices, regular system reviews, and strategic planning, organizations can optimize their Salesforce environment for efficiency, scalability, and long-term 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Salesforce Summer 24 Flow Release

Salesforce Summer 24 Flow Release

Salesforce Flow Salesforce Summer 24 Flow Release. Compose intelligent workflows with Flow Builder and Flow Orchestration. Integrate across any system with Flow Integration. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Salesforce Solutions for Defense

Salesforce Solutions for Defense

Defence Technology Solutions from Salesforce: Enabling Secure, Mission-Critical Operations Salesforce provides defense organizations with powerful technology solutions to deliver on their missions quickly and securely. By increasing IT flexibility, enhancing productivity, and safeguarding sensitive data in a secure, compliant cloud environment, Salesforce empowers defence organizations to achieve mission success with confidence. Salesforce Solutions for Defense. Modernizing Personnel Operations Salesforce streamlines workforce management and accelerates employee operations, providing a unified view of personnel and fast, digital workflows. Public sector organizations can transform hiring, recruiting, HR actions, IT requests, and employee services within a trusted operational hub. How It Works: Salesforce enhances productivity and digitizes government employee tasks throughout their lifecycle, from hire to retirement. Maximizing IT Agility Salesforce enables defence organizations to unlock data from legacy systems, cloud applications, and third-party platforms with an API-led approach, securely bridging on-premises and cloud environments. Rapid application development, consolidation, and system access become seamless with Salesforce’s low-code/no-code tools. How It Works: Salesforce compresses development cycles and increases flexibility, enabling defense organizations to deliver on mission objectives faster. Salesforce Solutions for Defense Delivering Successful Case Outcomes Salesforce supports the full case management lifecycle by integrating critical data points from multiple systems into a single, unified view. Defense organizations can empower caseworkers with purpose-built tools, ensuring successful case outcomes while reducing information silos and providing clients with the necessary support. How It Works: Salesforce creates a single source of truth for case management, enabling defense organizations to streamline workflows and improve service outcomes. By leveraging Salesforce’s defense technology solutions, organizations can modernize operations, maximize agility, and ensure successful case management outcomes, all while maintaining the highest levels of security and compliance. Salesforce Solutions for Defense from Tectonic. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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RAG Chunking Method

RAG Chunking Method

Enhancing Retrieval-Augmented Generation (RAG) Systems with Topic-Based Document Segmentation Dividing large documents into smaller, meaningful parts is crucial for the performance of Retrieval-Augmented Generation (RAG) systems. RAG Chunking Method. These systems benefit from frameworks that offer multiple document-splitting options. This Tectonic insight introduces an innovative approach that identifies topic changes using sentence embeddings, improving the subdivision process to create coherent topic-based sections. RAG Systems: An Overview A Retrieval-Augmented Generation (RAG) system combines retrieval-based and generation-based models to enhance output quality and relevance. It first retrieves relevant information from a large dataset based on an input query, then uses a transformer-based language model to generate a coherent and contextually appropriate response. This hybrid approach is particularly effective in complex or knowledge-intensive tasks. Standard Document Splitting Options Before diving into the new approach, let’s explore some standard document splitting methods using the LangChain framework, known for its robust support of various natural language processing (NLP) tasks. LangChain Framework: LangChain assists developers in applying large language models across NLP tasks, including document splitting. Here are key splitting methods available: Introducing a New Approach: Topic-Based Segmentation Segmenting large-scale documents into coherent topic-based sections poses significant challenges. Traditional methods often fail to detect subtle topic shifts accurately. This innovative approach, presented at the International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications (ACDSA 2024), addresses this issue using sentence embeddings. The Core Challenge Large documents often contain multiple topics. Conventional segmentation techniques struggle to identify precise topic transitions, leading to fragmented or overlapping sections. This method leverages Sentence-BERT (SBERT) to generate embeddings for individual sentences, which reflect changes in the vector space as topics shift. Approach Breakdown 1. Using Sentence Embeddings: 2. Calculating Gap Scores: 3. Smoothing: 4. Boundary Detection: 5. Clustering Segments: Algorithm Pseudocode Gap Score Calculation: pythonCopy code# Example pseudocode for gap score calculation def calculate_gap_scores(sentences, n): embeddings = [sbert.encode(sentence) for sentence in sentences] gap_scores = [] for i in range(len(sentences) – n): before = embeddings[i:i+n] after = embeddings[i+n:i+2*n] score = cosine_similarity(before, after) gap_scores.append(score) return gap_scores Gap Score Smoothing: pythonCopy code# Example pseudocode for smoothing gap scores def smooth_gap_scores(gap_scores, k): smoothed_scores = [] for i in range(len(gap_scores)): start = max(0, i – k) end = min(len(gap_scores), i + k + 1) smoothed_score = sum(gap_scores[start:end]) / (end – start) smoothed_scores.append(smoothed_score) return smoothed_scores Boundary Detection: pythonCopy code# Example pseudocode for boundary detection def detect_boundaries(smoothed_scores, c): boundaries = [] mean_score = sum(smoothed_scores) / len(smoothed_scores) std_dev = (sum((x – mean_score) ** 2 for x in smoothed_scores) / len(smoothed_scores)) ** 0.5 for i, score in enumerate(smoothed_scores): if score < mean_score – c * std_dev: boundaries.append(i) return boundaries Future Directions Potential areas for further research include: Conclusion This method combines traditional principles with advanced sentence embeddings, leveraging SBERT and sophisticated smoothing and clustering techniques. This approach offers a robust and efficient solution for accurate topic modeling in large documents, enhancing the performance of RAG systems by providing coherent and contextually relevant text sections. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Personalize Product Catalog Management Items with Custom Fields

Personalize Product Catalog Management Items with Custom Fields

Personalize Product Catalog Management Objects with Custom Fields You can now add custom fields to multiple Product Catalog Management objects and pass the custom fields as additional fields in the Product List API and Bulk Product Details API. These APIs return values of all the custom fields to the requesting run time systems. Personalize Product Catalog Management Items with Custom Fields. Where: This change applies to Lightning Experience in Enterprise, Unlimited, and Developer editions. Why: You can add custom fields to these objects: Product Catalog Management adds standard and custom fields to some standard Salesforce objects. These fields are available only in orgs where Product Catalog Management is enabled. This object is available in API version 60.0 and later. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Einstein Personalization and Copilots

Einstein Personalization and Copilots

Salesforce launched a suite of new generative AI products at Connections in Chicago, including new Einstein Copilots for marketers and merchants, and Einstein Personalization. Einstein Personalization and Copilots To gain insights into these products and Salesforce’s evolving architecture, Bobby Jania, CMO of Marketing Cloud was interviewed. Salesforce’s Evolving Architecture Salesforce has a knack for introducing new names for its platforms and products, sometimes causing confusion about whether something is entirely new or simply rebranded. Reporters sought clarification on the Einstein 1 platform and its relationship to Salesforce Data Cloud. “Data Cloud is built on the Einstein 1 platform,” Jania explained. “Einstein 1 encompasses the entire Salesforce platform, including products like Sales Cloud and Service Cloud, continuing the original multi-tenant cloud concept.” Data Cloud, developed natively on Einstein 1, was the first product built on Hyperforce, Salesforce’s new cloud infrastructure. “From the start, Data Cloud has been able to connect to and read anything within Sales Cloud, Service Cloud, etc. Additionally, it can now handle both structured and unstructured data.” This marks significant progress from a few years ago when Salesforce’s platform comprised various acquisitions (like ExactTarget) that didn’t seamlessly integrate. Previously, data had to be moved between products, often resulting in duplicates. Now, Data Cloud serves as the central repository, with applications like Tableau, Commerce Cloud, Service Cloud, and Marketing Cloud all accessing the same operational customer profile without duplicating data. Salesforce customers can also import their own datasets into Data Cloud. “We wanted a federated data model,” Jania said. “If you’re using Snowflake, for example, we virtually sit on your data lake, providing value by forming comprehensive operational customer profiles.” Understanding Einstein Copilot “Copilot means having an assistant within the tool you’re using, contextually aware of your tasks and assisting you at every step,” Jania said. For marketers, this could start with a campaign brief created with Copilot’s help, identifying an audience, and developing content. “Einstein Studio is exciting because customers can create actions for Copilot that we hadn’t even envisioned.” Contrary to previous reports, there is only one Copilot, Einstein Copilot, with various use cases like marketing, merchants, and shoppers. “We use these names for clarity, but there’s just one Copilot. You can build your own use cases in addition to the ones we provide.” Marketers will need time to adapt to Copilot. “Adoption takes time,” Jania acknowledged. “This Connections event offers extensive hands-on training to help people use Data Cloud and these tools, beyond just demonstrations.” What’s New with Einstein Personalization Einstein Personalization is a real-time decision engine designed to choose the next best action or offer for customers. “What’s new is that it now runs natively on Data Cloud,” Jania explained. While many decision engines require a separate dataset, Einstein Personalization evaluates a customer holistically and recommends actions directly within Service Cloud, Sales Cloud, or Marketing Cloud. Ensuring Trust Connections presentations emphasized that while public LLMs like ChatGPT can be applied to customer data, none of this data is retained by the LLMs. This isn’t just a matter of agreements; it involves the Einstein Trust Layer. “All data passing through an LLM runs through our gateway. Personally identifiable information, such as credit card numbers or email addresses, is stripped out. The LLMs do not store the output; Salesforce retains it for auditing. Any output that returns through our gateway is logged, checked for toxicity, and only then is PII reinserted into the response. These measures ensure data safety beyond mere handshakes,” Jania 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Did Google Dethrone ChatGPT

Did Google Dethrone ChatGPT?

Google’s Bard has emerged as a contender in the realm of large language models (LLMs), sparking speculation about its potential to outshine OpenAI’s ChatGPT. This insight explores the validity of this claim and examines the tests and factors that could determine the ultimate victor in this ongoing AI rivalry. Did Google Dethrone ChatGPT? Google’s Gemini 1.5 Pro is a generational leap in terms of Multimodal Large Language Models, or MLLMs, much like GPT-4 was to LLMs back in March 2023. Did Google Dethrone ChatGPT? While initial rumors of Bard’s “dethronement” of ChatGPT surfaced from a single LinkedIn post in February 2024, substantial evidence is required to substantiate such claims. Let’s determine the potential battleground: The Testing Grounds: There’s no singular, universally recognized benchmark for evaluating LLMs. Here are some areas where Google and OpenAI may showcase their AI prowess: Generative Text Quality: Can the LLM generate various creative text formats—such as poems, code, scripts, and emails—while maintaining coherence and factual accuracy? Question Answering: How effectively can the LLM respond to open-ended, challenging, or unconventional questions, drawing on its knowledge base? Following Instructions: Can the LLM adhere to complex instructions and perform tasks requiring multi-step reasoning? Bias Mitigation: Does the LLM demonstrate impartiality in its responses, or does it exhibit traces of prejudice or social stereotypes? Beyond the Tests: While test results offer insights into LLM capabilities, other factors influence their overall impact: Accessibility: How easily can the LLM be accessed by the public? Is there a user-friendly interface or developer API? Real-World Applications: How seamlessly can the LLM be integrated into practical applications like chatbots, virtual assistants, or educational tools? Continuous Learning: How adeptly does the LLM adapt and enhance its performance over time, incorporating new data and user feedback? The Current Landscape: Declaring a definitive winner is challenging. Bard and ChatGPT excel in different domains. Here’s a speculative analysis: Generative Text Quality: Bard may have a slight advantage, leveraging Google’s extensive dataset. Question Answering: ChatGPT might excel in responding to open-ended queries with creativity, while Bard may prioritize factual accuracy. Following Instructions & Bias Mitigation: Both LLMs are continually refining their capabilities in these areas. The Future of LLMs: The landscape of LLMs is dynamic, with Google and OpenAI poised to make significant advancements. Anticipated developments include: Focus on Explainability: Efforts to understand the reasoning behind LLM responses to foster transparency and trust. Bias Mitigation: Strategies to address bias in LLMs for fairer and more inclusive interactions. Specialized LLMs: Development of domain-specific LLMs tailored to fields like medicine or law. Is Google AI better than ChatGPT? Gemini offers a better user experience, with more imagery and website links. Gemini Advanced generates better AI images than ChatGPT Plus. Gemini responses were often set out in a more readable format than ChatGPT’s responses. Gemini was better at generating spreadsheet formulas than ChatGPT. How is Bard better than ChatGPT? Bard has real-time access to the internet through Google Search, allowing it to incorporate the latest information and news into its responses. Trained on a static dataset not updated since 2021, however, ChatGPT can only access external information through plugins, and this functionality is limited. Is Google nervous about ChatGPT? It’s that the technology represents everything Google was afraid artificial intelligence would become. If ChatGPT runs rampant, the search giant fears it could ruin AI adoption for everyone. Since going viral, ChatGPT has demonstrated how generative AI can be user-friendly, practical, and productive. The narrative of ChatGPT’s dethronement may be premature. Bard and ChatGPT are evolving entities, and the ultimate victor will be determined by their ability to navigate future challenges and opportunities. As these LLMs progress, users stand to benefit from access to increasingly sophisticated and beneficial AI tools. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, 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

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Digital Experience Enhancement Via Commerce and Content

Digital Experience Enhancement Via Commerce and Content

Businesses need robust solutions to enhance their digital experiences and streamline operations. Digital Experience Enhancement Via Commerce and Content can be addressed in multiple ways. When considering different systems, the approach to implementation and integration is crucial, especially with incumbent commerce platforms like Salesforce Commerce Cloud or Shopify Plus. The choice of integration method can significantly impact both customer engagement and operational efficiency. Traditional Monolithic Integration Approaches Traditional commerce systems typically fall into two categories: CMS/DXP-oriented and commerce-oriented platforms. Each has distinct characteristics but faces limitations inherent to a monolithic structure. CMS / DXP-oriented Platforms Platforms like Acquia Drupal, Adobe Experience Manager, and Sitecore, known for their strong digital experience capabilities, have evolved to include integrated and embedded commerce functionalities. Integrated Approach Platforms like Drupal Commerce for Acquia and Sitecore Experience Commerce exemplify an integrated approach where commerce capabilities are built directly into the DXP. This allows for a unified administrative and development experience, enabling business users to manage both content and commerce features seamlessly. While this approach offers benefits, it also presents challenges. The primary issue is adaptability. Commerce capabilities tightly coupled with the CMS can make customizing or scaling specific functionalities complex and restrictive. Additionally, without dedicated investment in the commerce platform as a standalone solution, it may lack many capabilities required for effective enterprise deployments. Performance issues can also arise as the system scales, with content management and commerce functionalities managed through the same monolithic architecture, leading to slower website performance and longer load times under heavy traffic conditions. Commerce Behind Content Some businesses use an embedded approach, integrating standalone or home-grown commerce capabilities, product information, and pricing data through batch or event-based synchronizations. This method allows the CMS’s presentation layer to dynamically display product information and transactions while maintaining core content management functionality. However, this approach also has downsides, impacting system efficiency and customer experience. Batch or event-based synchronizations can lead to data update delays, causing discrepancies between actual inventory and what’s displayed to customers. This can frustrate customers due to order issues like out-of-stock items appearing available. Additionally, the embedded method often requires complex integrations and middleware to sync data between the commerce platform and the CMS, increasing technical issues and complicating troubleshooting. This approach may also limit dynamic handling of commerce data within the CMS, restricting advanced features like real-time personalization and dynamic pricing. Maintaining such a system can require significant developer resources, leading to higher operational costs. Commerce-Oriented Platforms Commerce-centric platforms such as Salesforce Commerce Cloud and Shopify Plus focus on delivering comprehensive commerce solutions. These platforms have adapted to integrate with other technologies like headless CMS and search and product discovery tools to enable brands to deliver improved digital experiences. Content Behind Commerce Even when incorporating a headless CMS for easier content creation and management, commerce-centric platforms often feed this content back into their own presentation layer. This has been a popular approach for Salesforce Commerce Cloud (formerly Demandware) for over a decade. While this setup may improve operational efficiencies, it can limit overall performance and customer experience due to potential bottlenecks at the commerce platform’s presentation layer. Moreover, it does not provide the performance or customer experience benefits of a modern headless commerce frontend. Cartridges, Modules, and Plugins Using cartridges, modules, or plugins for functionalities like search and product discovery can enhance the onsite customer experience. However, these integrations can face limitations in supporting more complex capabilities, such as combining content from a headless CMS, product information from a PIM, and product data from the commerce platform, or managing merchandising that accounts for inventory levels across various distribution centers and store fulfillment locations. These limitations can restrict the ability to fully leverage integrated data to enhance customer interactions and operational insights. Digital Experience Enhancement Via Commerce and Content, as businesses seek to enhance their digital experiences and streamline operations, choosing the right integration approach for their commerce platform is critical. Balancing the benefits and challenges of traditional monolithic and commerce-oriented platforms will be key to optimizing customer engagement and operational efficiency. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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AI in Marketing

AI in Marketing

John Dutton recently posted in his blog about AI “representatives” who talk to you. It’s an interesting look into the “creep” factor potentially in artificial intelligence and certainly provides plenty of food for thought on robots and AI in marketing. Read it here. Summarized below. When the media or the internet shares a look at this wierd generated image talking, its easy to spot. When not flagged, it is getting a little harder to know for sure-is it real or is it Memorex. Unveiling AI in Ukraine Last week, Ukraine’s Ministry of Foreign Affairs introduced Victoriya Shi, a “digital representative” and AI-produced avatar. Shi delivers official statements in videos shared on the Ministry’s online social channels. According to Ukrainian Foreign Minister Dmytro Kuleba, Shi was created to “save time and resources” for diplomats. Given the ongoing conflict in Ukraine, this rationale seems reasonable. However, the introduction of such an AI avatar raises questions about the future and the potential for dystopian developments. A key concern is the ease of deepfaking an already artificial persona. This challenge has been addressed by the MFA through a smart yet simple solution: a QR code in the corner of each video that directs viewers to the official text version of the announcement on the Ministry’s website. It’s worth noting that the official statements themselves are not AI-generated, which could set a worrying precedent. While the Ukrainian version’s reception is unknown, the English version of Victoriya Shi struggles to escape the “uncanny valley” of artificial humans. Her sign-off, “I look forward to our fruitful cooperation,” has an eerie, robotic undertone. This unsettling impression might not be entirely negative. Navigating the Age of AI We are deeply entrenched in the Age of AI, where trust has become a scarce commodity. The concept of “fake news” emerged well before generative AI, gaining prominence in late 2016 with the rise of certain political figures. A search on Google Trends reveals the sudden spike in terms like “fake news” and “post-truth” during that period. With AI’s potential to create convincing deepfakes, the challenge of distinguishing real from fake is intensifying. A recent incident in Hong Kong saw an employee deceived by an AI-generated video, leading to a $25 million fraud. This highlights the need for secure credentialing, especially in large organizations and potential metaverse meetings. However, in-person meetings remain immune to such digital deceptions. AI’s Role in Authenticity Ironically, AI might help combat its own deceptions. OpenAI’s recent collaboration with the Coalition for Content Provenance and Authenticity (C2PA) aims to develop tools for identifying AI-generated content. As deepfakes become more sophisticated, the absence of C2PA authentication could become a red flag. If this leads to a heightened skepticism towards digital media, it might not be entirely negative. AI could bolster our defenses against scams, encouraging a healthy suspicion of the digital content we consume. The Balance of Authenticity and Truth The distinction between authenticity and truth is crucial. A government-created AI avatar can be fake in its artificiality but still deliver authentic, official statements. As generative AI advances, we must fine-tune our skepticism. Victoriya Shi’s name reflects Ukraine’s hope for “victory” and the integration of AI (“Shi” in Ukrainian). The war may ultimately hinge on intelligent tech use rather than sheer military might. Update and Reflections Following the newsletter’s release, it was revealed that WPP, the world’s largest ad agency network, nearly fell victim to a deepfake scam, with the CEO’s voice being replicated by AI. The Dystopia/Utopia Dichotomy The generative AI revolution has begun, and its trajectory could lead to either a utopian or dystopian future. My novel, “2084,” explores a world where life appears superficially perfect, masking underlying issues. Artistic AI Innovations One of my book’s main characters is a sculptor, a profession I initially believed immune to AI. However, Monumental Labs, founded in 2022, uses “sensors and AI” to produce sculptures at a fraction of traditional costs. This reality mirrors the AI-driven world imagined in “2084.” Genetic Modifications and Luxury Fresh Del Monte’s Rubyglow® pineapple, an ultra-premium, genetically modified fruit, exemplifies the future of designer foods. My novel envisions similar advancements with patented food items and drone-pollinated plants. The Challenger Mindset Adam Morgan, an expert in the challenger brand mindset, emphasizes the importance of maintaining a challenger attitude regardless of market position. Companies like Netflix exemplify this, adapting and thriving in a competitive landscape by retaining a challenger’s drive. The Right to Repair and Brand Identity The US Government Accountability Office highlights the “softwareification” of cars, making independent repairs difficult. Similarly, Apple’s restrictive policies on iPhone repairs underline the broader trend of manufacturers controlling repair markets. Cult of Brand Identity The Gray Area podcast discusses how modern consumers interact with brands, focusing on identity over product quality. This shift underscores the evolving landscape of commercial competition and consumer behavior. 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. 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