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AI-Powered Smarter Media
23Dec

AI Transforming Precision Medicine

How AI-Driven Data Curation is Transforming Precision Medicine Precision medicine—a healthcare approach that personalizes disease prevention and treatment based on insights into a patient’s genes, environment, and behavior—holds incredible promise. However, its success depends on high-quality, curated data from sources like electronic health records (EHRs). This reliance creates significant challenges for healthcare providers and researchers. Can artificial intelligence (AI) help address these hurdles? AI-enabled data curation is already making strides in advancing precision medicine, particularly in oncology. By analyzing vast datasets, including structured and unstructured information, AI is helping healthcare organizations accelerate research and improve patient outcomes. Data Curation Challenges in Precision Medicine Real-world data (RWD) is a key driver of precision medicine, but processing this data is fraught with challenges. According to Dr. C.K. Wang, Chief Medical Officer at COTA, Inc., EHRs provide unprecedented access to detailed patient information, enabling deeper insights into care patterns. However, much of this data resides in unstructured formats, such as clinicians’ notes, making it difficult to extract and analyze. “To transform this unstructured data into actionable insights, significant human expertise and resources are required,” Wang explained. While AI tools like COTA’s CAILIN, which uses advanced search capabilities, streamline this process, human involvement remains essential. Wang emphasized that even with the rapid advancements in AI, healthcare data curation requires expert oversight to ensure quality and reliability. “The adage ‘junk in, junk out’ applies here—without high-quality training data, AI cannot generate meaningful insights,” he noted. PHI and COTA: A Collaborative Approach to AI-Driven Curation To overcome these challenges, Precision Health Informatics (PHI), a subsidiary of Texas Oncology, partnered with COTA to enhance their data curation capabilities. The collaboration aims to integrate structured and unstructured data, including clinician notes and patient-reported outcomes, into a unified resource for precision medicine. PHI’s database, which represents 1.6 million patient journeys, provides a rich resource for hypothesis-driven studies and clinical trial enrichment. However, much of this data was siloed or unstructured, requiring advanced tools and expert intervention. Lori Brisbin, Chief Operating Officer at PHI, highlighted the importance of partnering with a data analytics leader. “COTA’s strong clinical knowledge in oncology allowed them to identify data gaps and recommend improvements,” she said. This partnership is yielding significant results, including a high data attrition rate of 87%—far surpassing the industry average of 50% for similar projects. The Role of AI in Cancer Care AI tools like CAILIN are helping PHI and COTA refine data curation processes by: Brisbin likened the role of AI to sorting images: “If you’re looking for German shepherds, AI will narrow the search but might include similar images, like wolves or huskies. Experts are still needed to validate and refine the results.” Building the Foundation for Better Outcomes The integration of high-quality RWD into analytics efforts is reshaping precision medicine. While clinical trial data offers valuable insights, it often lacks the variability seen in real-world scenarios. Adding RWD to these datasets helps expand the scope of research and ensure broader applicability. For instance, cancer care guidelines developed with RWD can account for diverse patient populations and treatment approaches. COTA’s work with PHI underscores the value of collaborative data curation, with AI streamlining processes and human experts ensuring accuracy. The Future of AI in Precision Medicine As healthcare organizations invest in data-driven innovation, AI will play an increasingly pivotal role in enabling precision medicine. However, challenges remain. Wang noted that gaps in EHR data, such as missing survival metrics, can undermine oncological outcomes research. Advances in interoperability and external data sources will be key to addressing these issues. “The foundation of our partnership is built on leveraging data insights to enhance care quality and improve operational efficiency,” Wang said. Through AI-powered tools and meaningful partnerships, precision medicine is poised to deliver transformative results, empowering providers to offer tailored treatments that improve patient outcomes at scale. 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

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December 23, 2024in AI Tools, Artificial Intelligence, Data0 Comments
New Service Cloud Tools
23Dec

Service Cloud for HR

Salesforce has expanded its Service Cloud capabilities to include a new HR-focused solution, Employee Service, designed to streamline employee support and enhance productivity. Employee Service introduces a dedicated HR service console paired with an employee portal. This portal acts as a centralized hub for staff to access HR resources, offering instant answers via Generative AI (GenAI), direct communication with HR specialists across multiple channels, and self-service options for tasks like requesting paid time off (PTO). For HR teams, the service console consolidates employee data, case details, and a company’s knowledge base into a unified workspace. It leverages AI-driven tools to resolve cases faster, automate routine tasks, and deliver seamless employee experiences. Salesforce’s Agentforce customers can integrate AI agents into Employee Service to further automate processes, saving time and reducing repetitive workloads. In a LinkedIn announcement, Kishan Chetan, EVP and GM for Service Cloud, highlighted the solution’s potential: “This new solution unifies employee data, case details, and a company’s corporate knowledge base all in one workspace that gives HR teams a 360-degree view of each employee and the ability to manage employee support cases with built-in AI and productivity tools. HR teams can efficiently resolve employee issues using Agentforce to quickly search, respond, summarize, and close cases, extending teams to get work done faster.” Salesforce’s broader goal is to eliminate the reliance on fragmented HR tools and reduce the need for employees to navigate disparate platforms like email, internal systems, and collaboration tools to complete HR-related tasks. By doing so, Salesforce aims to simplify HR processes, minimize manual effort, and enhance overall productivity. Early adopters of Employee Service are already reporting significant results. According to Sherin Sunny, Sr. Director of Product Management at Salesforce, customers have observed a 31% increase in employee productivity. This aligns with broader trends: Recognizing the need for a unified HR ecosystem, Salesforce includes a prebuilt MuleSoft integration with Workday and configurable connectors to other Human Capital Management (HCM) systems. These integrations establish a centralized HR data foundation, reducing inefficiencies caused by siloed tools. Looking ahead, Beth Schultz, VP of Research & Principal Analyst at Metrigy, emphasized the importance of integrating Employee Service with Slack, Salesforce’s collaboration platform: “We’ll be particularly watching how Salesforce’s multifaceted plans for bringing [Employee Service] into Slack play out as Slack evolves into a fully connected, collaborative workspace.” Slack itself is undergoing a transformation, with Salesforce Co-Founder Patrick Harris returning to revamp the platform as a core part of the Salesforce ecosystem. Meanwhile, Salesforce continues to expand Service Cloud’s offerings beyond Employee Service. Recent developments include a revamped CCaaS (Contact Center as a Service) integration program and a new product discovery tool. Still, Agentforce remains a key focus for Salesforce’s marketing efforts, showcasing its potential to redefine how businesses deploy autonomous AI agents across use cases like HR and beyond. 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

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December 23, 2024in Data, Generative AI, Mulesoft, Salesforce0 Comments
Python-Based Reasoning Engine
22Dec

Python-Based Reasoning Engine

Introducing a Python-Based Reasoning Engine for Deterministic AI In the age of large language models (LLMs), there’s a growing need for deterministic systems that enforce rules and constraints while reasoning about information. We’ve developed a Python-based reasoning and validation framework that bridges the gap between traditional rule-based logic and modern AI capabilities, inspired by frameworks like Pydantic. This approach is designed for developers and non-technical experts alike, making it easy to build complex rule engines that translate natural language instructions into enforceable code. Our fine-tuned model automates the creation of rules while ensuring human oversight for quality and conflict detection. The result? Faster implementation of rule engines, reduced developer overhead, and flexible extensibility across domains. The Framework at a Glance Our system consists of five core components: To analogize, this framework operates like a game of chess: Our framework supports two primary use cases: Key Features and Benefits Case Studies Validation Engine: Ensuring Compliance A mining company needed to validate employee qualifications based on age, region, and role. Example Data Structure: jsonCopy code{ “employees”: [ { “name”: “Sarah”, “age”: 25, “role”: “Manager”, “documents”: [“safe_handling_at_work”, “heavy_lifting”] }, { “name”: “John”, “age”: 17, “role”: “Laborer”, “documents”: [“heavy_lifting”] } ] } Rules: jsonCopy code{ “rules”: [ { “type”: “min_age”, “parameters”: { “min_age”: 18 } }, { “type”: “dozer_operator”, “parameters”: { “document_type”: “dozer_qualification” } } ] } Outcome:The system flagged violations, such as employees under 18 or missing required qualifications, ensuring compliance with organizational rules. Reasoning Engine: Solving the River Crossing Puzzle The classic river crossing puzzle demonstrates the engine’s reasoning capabilities. Problem Setup:A farmer must ferry a goat, a wolf, and a cabbage across a river, adhering to specific constraints (e.g., the goat cannot be left alone with the cabbage). Steps: Output:The engine generated a solution in 0.0003 seconds, showcasing its efficiency in navigating complex logic. Advanced Features: Dynamic Rule Expansion The system supports real-time rule adjustments. For instance, adding a “wolf cannot be left with a chicken” constraint introduces a conflict. By extending rules (e.g., allowing the farmer to carry two items), the engine dynamically resolves previously unsolvable scenarios. Sample Code Snippet: pythonCopy codeclass CarryingCapacityRule(Rule): def evaluate(self, state): items_moved = sum(1 for item in [‘wolf’, ‘goat’, ‘cabbage’, ‘chicken’] if getattr(state, item) == state.farmer) return items_moved <= 2 def get_description(self): return “Farmer can carry up to two items at a time” Result:The adjusted engine solved the puzzle in three moves, down from seven, while maintaining rule integrity. Collaborative UI for Rule Creation Our user interface empowers domain experts to define rules without writing code. Developers validate these rules, which are then seamlessly integrated into the system. Visual Workflow: 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

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December 22, 2024in Data0 Comments
Standards in Healthcare Cybersecurity
21Dec

Deploying Large Language Models in Healthcare

Study Identifies Cost-Effective Strategies for Deploying Large Language Models in Healthcare Efficient deployment of large language models (LLMs) at scale in healthcare can streamline clinical workflows and reduce costs by up to 17 times without compromising reliability, according to a study published in NPJ Digital Medicine by researchers at the Icahn School of Medicine at Mount Sinai. The research highlights the potential of LLMs to enhance clinical operations while addressing the financial and computational hurdles healthcare organizations face in scaling these technologies. To investigate solutions, the team evaluated 10 LLMs of varying sizes and capacities using real-world patient data. The models were tested on chained queries and increasingly complex clinical notes, with outputs assessed for accuracy, formatting quality, and adherence to clinical instructions. “Our study was driven by the need to identify practical ways to cut costs while maintaining performance, enabling health systems to confidently adopt LLMs at scale,” said Dr. Eyal Klang, director of the Generative AI Research Program at Icahn Mount Sinai. “We aimed to stress-test these models, evaluating their ability to manage multiple tasks simultaneously and identifying strategies to balance performance and affordability.” The team conducted over 300,000 experiments, finding that high-capacity models like Meta’s Llama-3-70B and GPT-4 Turbo 128k performed best, maintaining high accuracy and low failure rates. However, performance began to degrade as task volume and complexity increased, particularly beyond 50 tasks involving large prompts. The study further revealed that grouping tasks—such as identifying patients for preventive screenings, analyzing medication safety, and matching patients for clinical trials—enabled LLMs to handle up to 50 simultaneous tasks without significant accuracy loss. This strategy also led to dramatic cost savings, with API costs reduced by up to 17-fold, offering a pathway for health systems to save millions annually. “Understanding where these models reach their cognitive limits is critical for ensuring reliability and operational stability,” said Dr. Girish N. Nadkarni, co-senior author and director of The Charles Bronfman Institute of Personalized Medicine. “Our findings pave the way for the integration of generative AI in hospitals while accounting for real-world constraints.” Beyond cost efficiency, the study underscores the potential of LLMs to automate key tasks, conserve resources, and free up healthcare providers to focus more on patient care. “This research highlights how AI can transform healthcare operations. Grouping tasks not only cuts costs but also optimizes resources that can be redirected toward improving patient outcomes,” said Dr. David L. Reich, co-author and chief clinical officer of the Mount Sinai Health System. The research team plans to explore how LLMs perform in live clinical environments and assess emerging models to determine whether advancements in AI technology can expand their cognitive thresholds. 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 Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

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December 21, 2024in Data, Generative AI, Technology0 Comments
Integrate Digital Delivery and Human Connection
21Dec

Types of Salesforce Integration

Types of Salesforce Integration: A Comprehensive Guide As a leading CRM platform, Salesforce is often required to integrate with other systems to deliver a seamless experience and ensure efficient business operations. Whether it’s syncing data, automating workflows, or enabling real-time communication, Salesforce provides robust integration methods tailored to various needs. In this guide, we’ll explore the different types of Salesforce integrations, their practical applications, and how to choose the right approach for your business. Why Integrate Salesforce? Integrating Salesforce with other systems empowers businesses to: Types of Salesforce Integration 1. Data Integration Ensures data consistency between Salesforce and external systems, enabling seamless synchronization. 2. Process Integration Links workflows across systems, ensuring actions in one system trigger automated processes in another. 3. User Interface (UI) Integration Combines multiple applications into a single interface for a unified user experience. 4. Application Integration Connects Salesforce with external apps for real-time data exchange and functional synchronization. 5. Real-Time Integration Facilitates instant synchronization of data and events between Salesforce and external systems. 6. Batch Integration Processes large data volumes in chunks, typically during off-peak hours. 7. Hybrid Integration Combines multiple integration types, such as real-time and batch, to handle complex requirements. Tools for Salesforce Integration Native Salesforce Tools: Third-Party Tools: Best Practices for Salesforce Integration Conclusion Salesforce integration is essential for streamlining operations and unlocking business potential. With options like data, process, and real-time integration, Salesforce offers the flexibility to meet diverse needs. By adopting the right integration approach and adhering to best practices, businesses can create a unified, efficient ecosystem, enhancing operations and improving customer experience. Whether integrating with ERP systems, marketing tools, or support platforms, Salesforce provides the tools to make integration seamless and impactful. 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

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December 21, 2024in Data, Enterprise, Marketing Automation, Mulesoft, Salesforce, Salesforce Flow0 Comments

Transitioning to Granular Case Categorization in Salesforce

Making Data Work: Transitioning to Granular Case Categorization in Salesforce As businesses grow and their reporting needs evolve, the demand for more granular data categorization becomes essential to maintaining clarity and driving decision-making. Salesforce’s Case Object, which traditionally uses Type and Sub-type fields to classify cases, may no longer suffice for organizations with increasingly complex reporting standards. To address this, a new approach is being implemented: introducing New Type, New Sub-type, and New Sub-sub-Type fields. These fields enable deeper categorization and allow organizations to align their data architecture with modern reporting demands. However, adopting these new fields presents challenges, especially for legacy systems that rely on the older fields. Here’s how organizations can navigate this transition seamlessly using Apex and custom metadata. The Challenge: Transitioning to New Fields The move toward granular categorization brings clear benefits, but it also introduces complexities: To ensure a smooth transition, organizations need a solution that automates field mapping while maintaining data consistency across both legacy and updated systems. The Solution: Apex Mapping with Custom Metadata By leveraging Apex and custom metadata, organizations can create an automated system to map old field values (Type and Sub-type) to the new fields (New Type, New Sub-type, and New Sub-sub-Type). Here’s how the mapping process works: The Benefits of Apex Mapping The Technical Process Real-World Impact Final Thoughts In today’s data-driven world, evolving reporting standards demand more precise data categorization. Transitioning from Type and Sub-type fields to New Type, New Sub-type, and New Sub-sub-Type fields ensures your organization can meet modern reporting needs. By implementing Apex mapping with custom metadata, you can automate the transition, maintain legacy compatibility, and unlock the full potential of granular reporting. While the shift requires planning and effort, the long-term benefits of improved accuracy, efficiency, and decision-making make it an invaluable step for any organization. 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

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December 20, 2024in Data0 Comments
Meta Joins the Race to Reinvent Search with AI
18Dec

Meta Joins the Race to Reinvent Search with AI

Meta Joins the Race to Reinvent Search with AI Meta, the parent company of Facebook, Instagram, and WhatsApp, is stepping into the evolving AI-driven search landscape. As vendors increasingly embrace generative AI to transform search experiences, Meta aims to challenge Google’s dominance in this space. The company is reportedly developing an AI-powered search engine designed to provide conversational, AI-generated summaries of recent events and news. These summaries would be delivered via Meta’s AI chatbot, supported by a multiyear partnership with Reuters for real-time news insights, according to The Information. AI Search: A Growing Opportunity The push comes as generative AI reshapes search technology across the industry. Google, the long-standing leader, has integrated AI features such as AI Overviews into its search platform, offering users summarized search results, product comparisons, and more. This feature, now available in over 100 countries as of October 2024, signals a shift in traditional search strategies. Similarly, OpenAI, the creator of ChatGPT, has been exploring its own AI search model, SearchGPT, and forging partnerships with media organizations like the Associated Press and Hearst. However, OpenAI faces legal challenges, such as a lawsuit from The New York Times over alleged copyright infringement. Meta’s entry into AI-powered search aligns with a broader trend among tech giants. “It makes sense for Meta to explore this,” said Mark Beccue, an analyst with TechTarget’s Enterprise Strategy Group. He noted that Meta’s approach seems more targeted at consumer engagement than enterprise solutions, particularly appealing to younger audiences who are shifting away from traditional search behaviors. Shifting User Preferences Generational changes in search habits are creating opportunities for new players in the market. Younger users, particularly Gen Z and Gen Alpha, are increasingly turning to platforms like TikTok for lifestyle advice and Amazon for product recommendations, bypassing traditional search engines like Google. “Recent studies show younger generations are no longer using ‘Google’ as a verb,” said Lisa Martin, an analyst with the Futurum Group. “This opens the playing field for competitors like Meta and OpenAI.” Forrester Research corroborates this trend, noting a diversification in search behaviors. “ChatGPT’s popularity has accelerated this shift,” said Nikhil Lai, a Forrester analyst. He added that these changes could challenge Google’s search ad market, with its dominance potentially waning in the years ahead. Meta’s AI Search Potential Meta’s foray into AI search offers an opportunity to enhance user experiences and deepen engagement. Rather than pushing news content into users’ feeds—an approach that has drawn criticism—AI-driven search could empower users to decide what content they see and when they see it. “If implemented thoughtfully, it could transform the user experience and give users more control,” said Martin. This approach could also boost engagement by keeping users within Meta’s ecosystem. The Race for Revenue and Trust While AI-powered search is expected to increase engagement, monetization strategies remain uncertain. Google has yet to monetize its AI Overviews, and OpenAI’s plans for SearchGPT remain unclear. Other vendors, like Perplexity AI, are experimenting with models such as sponsored questions instead of traditional results. Trust remains a critical factor in the evolving search landscape. “Google is still seen as more trustworthy,” Lai noted, with users often returning to Google to verify AI-generated information. Despite the competition, the conversational AI search market lacks a definitive leader. “Google dominated traditional search, but the race for conversational search is far more open-ended,” Lai concluded. Meta’s entry into this competitive space underscores the ongoing evolution of search technology, setting the stage for a reshaped digital landscape driven by AI innovation. 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

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December 18, 2024in Enterprise, Generative AI, Google, Laws, Salesforce, Technology0 Comments
Google Prepares AI-Powered Jarvis Agent
18Dec

Google Prepares AI-Powered Jarvis Agent

Google Prepares AI-Powered Jarvis Agent for Automated Browser Tasks in Chrome Google is reportedly gearing up to launch “Project Jarvis,” an AI-powered browser agent designed to automate tasks directly within the Chrome ecosystem. According to The Information, the tool is expected to roll out in December to select users and will leverage Google’s advanced Gemini 2.0 AI model. Jarvis aims to simplify repetitive online tasks, such as organizing information or booking reservations, offering a seamless and efficient digital assistant embedded within Chrome. This initiative reflects Google’s broader vision to enhance user experiences by automating web-based routines, making its browser a central hub for task automation. Anthropic Expands Desktop Automation with Claude 3.5 Sonnet Anthropic, a key player in the AI landscape, has advanced its Claude 3.5 model with a new “Computer Use” feature, enabling direct interaction with a user’s desktop. This update allows Claude to perform tasks such as typing, clicking, and managing multiple applications, making it a powerful tool for automating workflows like data entry, document management, and customer service. Available through APIs and platforms like Amazon Bedrock and Google Cloud’s Vertex AI, Claude’s new capabilities position it as a versatile solution for businesses seeking desktop-level automation, contrasting Google Jarvis’s browser-specific approach. By interpreting screen elements, Claude’s “Computer Use” mode supports broader applications beyond web tasks, offering businesses an edge in efficiency and scalability. How Google Jarvis Stands Out Unlike Anthropic’s desktop-oriented Claude Sonnet, Google Jarvis focuses on automating tasks within Chrome. Jarvis analyzes screenshots of web pages, interprets user commands, and executes actions like clicks or data entry. While still in development, Jarvis’s design suggests a future where mundane web-based tasks are seamlessly handled by AI. Powered by Google’s Gemini 2.0 language model, Jarvis is tailored for users who prioritize web-specific functions, creating a user-friendly assistant that requires no external software. This aligns with Google’s strategy to deepen integration within its ecosystem, making Chrome a more intuitive and productive environment. Microsoft’s Copilot Agents Lead Business Automation Microsoft, meanwhile, continues to enhance its Copilot AI agents, particularly within Dynamics 365. These specialized agents are designed to automate industry-specific workflows, from lead qualification in sales to financial data reconciliation. Unlike Google Jarvis or Anthropic Claude, Microsoft’s Copilot agents target enterprise users, embedding automation within business applications like Teams, Outlook, and SharePoint. With tools like Copilot Studio, organizations can customize workflows to meet specific needs, offering a level of flexibility that resonates with enterprise clients. Early adopters, including Vodafone and Cognizant, have reported significant productivity gains through these integrations. Microsoft’s efforts position Copilot as a robust partner for day-to-day operations, transforming tasks like analysis, project coordination, and document management into automated, efficient processes. Competing Visions for AI Agents As Google, Anthropic, and Microsoft refine their AI strategies, they’re carving out distinct niches in the AI agent landscape: These approaches highlight the diverse applications of AI agents, from enhancing individual user experiences to transforming business operations. 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

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December 18, 2024in Data, Enterprise, Google, Salesforce0 Comments
Artificial Intelligence (AI) is significantly transforming threat detection by enabling faster, more accurate identification of potential security breaches through its ability to analyze vast amounts of data in real-time, detect anomalies and patterns that might indicate a threat, even when those threats are new or previously unknown, thus providing a proactive approach to cybersecurity compared to traditional rule-based systems.
16Dec

AI is Transforming Threat Detection

Artificial Intelligence (AI) is significantly transforming threat detection by enabling faster, more accurate identification of potential security breaches through its ability to analyze vast amounts of data in real-time, detect anomalies and patterns that might indicate a threat, even when those threats are new or previously unknown, thus providing a proactive approach to cybersecurity compared to traditional rule-based systems.

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December 16, 2024in AI Tools, Data, Enterprise, Generative AI, Salesforce, Technology0 Comments
Salesforce prompt builder
16Dec

Salesforce Prompt Builder

Salesforce Prompt Builder: Field Generation Prompt Template What is a Prompt? A prompt is a set of detailed instructions designed to guide a Large Language Model (LLM) in generating relevant and high-quality output. Just like chefs fine-tune their recipes through testing and adjustments, prompt design involves iterating on instructions to ensure that the LLM delivers accurate, actionable results. Effective prompt design involves “grounding” your prompts with specific data, such as business context, product details, and customer information. By tailoring prompts to your particular needs, you help the LLM provide responses that align with your business goals. Like a well-crafted recipe, an effective prompt consists of both ingredients and instructions that work together to produce optimal results. A great prompt offers clear directions to the LLM, ensuring it generates output that meets your expectations. But what does an ideal prompt template look like? Here’s a breakdown: What is a Field Generation Prompt Template? The Field Generation Prompt Template is a tool that integrates AI-powered workflows directly into fields within Lightning record pages. This template allows users to populate fields with summaries or descriptions generated by an LLM, streamlining interactions and enhancing productivity during customer conversations. Let’s explore how to set up a Field Generation Prompt Template by using an example: generating a summary of case comments to help customer service agents efficiently review a case. Steps to Create a Field Generation Prompt Template 1. Create a New Rich Text Field on the Case Object 2. Enable Einstein Setup 3. Create a Prompt Template with the Field Generation Template Type 4. Configure the Prompt Template Workspace Optional: You can also use Flow or Apex to incorporate additional merge fields. 5. Preview the LLM’s Response Example Prompt: Scenario:You are a customer service representative at a company called ENForce.com, and you need a quick summary of a case’s comments. Record Merge Fields: Instructions: vbnetCopy codeFollow these instructions precisely. Do not add information not provided. – Refer to the “contact” as “client” in the summary. – Use clear, concise, and straightforward language in the active voice with a friendly, informal, and informative tone. – Include an introductory sentence and closing sentence, along with several bullet points. – Use a variety of emojis as bullet points to make the list more engaging. – Limit the summary to no more than seven sentences. – Do not include any reference to missing values or incomplete data. 6. Add the “Case Summary” Field to the Lightning Record Page 7. Generate the Summary By following these steps, you can leverage Salesforce’s Prompt Builder to enhance case management processes and improve the efficiency of customer service interactions through AI-assisted summaries. 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

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December 16, 2024in Data, Hyperforce, Salesforce, Salesforce Copilots, Salesforce Einstein0 Comments
MOIRAI-MoE
15Dec

MOIRAI-MoE

MOIRAI-MoE represents a groundbreaking advancement in time series forecasting by introducing a flexible, data-driven approach that addresses the limitations of traditional models. Its sparse mixture of experts architecture achieves token-level specialization, offering significant performance improvements and computational efficiency. By dynamically adapting to the unique characteristics of time series data, MOIRAI-MoE sets a new standard for foundation models, paving the way for future innovations and expanding the potential of zero-shot forecasting across diverse industries.

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December 15, 2024in Data, Salesforce, Technology0 Comments

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