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Can Tech Companies Use Generative AI for Good?
16Apr

AI and the Future of IT Careers

AI and the Future of IT Careers: Jobs That Remain Secure As AI technology advances, concerns about job security in the IT sector continue to grow. AI excels at handling repetitive, high-speed tasks and has made significant strides in software development and error prediction. However, while AI offers exciting possibilities, the demand for human expertise remains strong—particularly in roles that require interpersonal skills, strategic thinking, and decision-making. So, which IT jobs are most secure from AI displacement? To answer this question, industry experts shared their insights: Their forecasts highlight the IT roles most resistant to AI replacement. In all cases, professionals should enhance their AI knowledge to stay competitive in an evolving landscape. Top AI-Resistant IT Roles 1. Business Analyst Role Overview:Business analysts act as a bridge between IT and business teams, identifying technology opportunities and facilitating collaboration to optimize solutions. Why AI Won’t Replace It:While AI can process vast amounts of data quickly, it lacks emotional intelligence, relationship-building skills, and the ability to interpret nuanced human communication. Business analysts leverage these soft skills to understand software needs and drive successful implementations. How to Stay Competitive:Develop strong data analysis, business intelligence (BI), communication, and presentation skills to enhance your value in this role. 2. Cybersecurity Engineer Role Overview:Cybersecurity engineers protect organizations from evolving security threats, including AI-driven cyberattacks. Why AI Won’t Replace It:As AI tools become more sophisticated, cybercriminals will exploit them to develop advanced attack strategies. Human expertise is essential to adapt defenses, investigate threats, and implement security measures AI alone cannot handle. How to Stay Competitive:Continuously update your cybersecurity knowledge, obtain relevant certifications, and develop a strong understanding of business security needs. 3. End-User Support Professional Role Overview:These professionals assist employees with technical issues and provide hands-on training to ensure smooth software adoption. Why AI Won’t Replace It:Technology adoption is becoming increasingly complex, requiring personalized support that AI cannot yet replicate. Human interaction remains crucial for troubleshooting and user training. How to Stay Competitive:Pursue IT certifications, strengthen customer service skills, and gain experience in enterprise software environments. 4. Data Analyst Role Overview:Data analysts interpret business and product data, generate insights, and predict trends to guide strategic decisions. Why AI Won’t Replace It:AI can analyze data, but human oversight is needed to ensure accuracy, recognize context, and derive meaningful insights. Companies will continue to rely on professionals who can interpret and act on data effectively. How to Stay Competitive:Specialize in leading BI platforms, gain hands-on experience with data visualization tools, and develop strong analytical thinking skills. 5. Data Governance Professional Role Overview:These professionals set policies for data usage, access, and security within an organization. Why AI Won’t Replace It:As AI handles increasing amounts of data, the need for governance professionals grows to ensure ethical and compliant data management. How to Stay Competitive:Obtain a degree in computer science or business administration and seek training in data privacy, security, and governance frameworks. 6. Data Privacy Professional Role Overview:Data privacy professionals ensure compliance with data protection regulations and safeguard personal information. Why AI Won’t Replace It:With AI collecting vast amounts of personal data, organizations require human experts to manage legal compliance and maintain trust. How to Stay Competitive:Develop expertise in privacy laws, cybersecurity, and regulatory compliance through certifications and training programs. 7. IAM Engineer (Identity and Access Management) Role Overview:IAM engineers develop and implement systems that regulate user access to sensitive data. Why AI Won’t Replace It:The growing complexity of digital identities and security protocols requires human oversight to manage, audit, and secure access rights. How to Stay Competitive:Pursue a computer science degree, gain experience in authentication frameworks, and build expertise in programming and operating systems. 8. IT Director Role Overview:IT directors oversee technology strategies, manage teams, and align IT initiatives with business goals. Why AI Won’t Replace It:Leadership, motivation, and strategic decision-making are human-driven capabilities that AI cannot replicate. How to Stay Competitive:Develop strong leadership, business acumen, and team management skills to effectively align IT with organizational success. 9. IT Product Manager Role Overview:Product managers oversee tech adoption, service management, and organizational change strategies. Why AI Won’t Replace It:Effective product management requires a human touch, particularly in change management and stakeholder communication. How to Stay Competitive:Pursue project management training and certifications while gaining experience in software development and enterprise technology. Staying AI-Proof: Learning AI Expert Insights on Future IT Careers Final Thoughts As AI continues to reshape the IT landscape, the key to job security lies in adaptability. Professionals who develop AI-related skills and focus on roles that require human judgment, creativity, and leadership will remain indispensable in the evolving workforce. 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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April 16, 2025in AI Tools, Data, Enterprise, Generative AI, Laws, Technology0 Comments
time series artificial intelligence
07Apr

Revolutionizing Time Series AI

Revolutionizing Time Series AI: Salesforce’s Synthetic Data Breakthrough for Foundation Models Revolutionizing Time Series AI. Time series analysis is hindered by critical challenges in data availability, quality, and diversity—key factors in building powerful foundation models. Real-world datasets often suffer from regulatory constraints, inherent biases, inconsistent quality, and a lack of paired textual annotations, making it difficult to develop robust Time Series Foundation Models (TSFMs) and Time Series Large Language Models (TSLLMs). These limitations stifle progress in forecasting, classification, anomaly detection, reasoning, and captioning, restricting AI’s full potential. To tackle these obstacles, Salesforce AI Research has pioneered an innovative approach: leveraging synthetic data to enhance TSFMs and TSLLMs. Their groundbreaking study, “Empowering Time Series Analysis with Synthetic Data,” introduces a strategic framework for using synthetic data to refine model training, evaluation, and fine-tuning—while mitigating biases, expanding dataset diversity, and enriching contextual understanding. This approach is particularly transformative in regulated sectors like healthcare and finance, where real-world data sharing is heavily restricted. The Science Behind Synthetic Data Generation Salesforce’s methodology employs advanced synthetic data generation techniques tailored to replicate real-world time series dynamics, including trends, seasonality, and noise patterns. Key innovations include: These methods enable controlled yet highly varied data generation, capturing a broad spectrum of time series behaviors essential for robust model training. Proven Benefits: How Synthetic Data Supercharges Model Performance Salesforce’s research reveals significant performance gains from synthetic data across multiple stages of AI development: ✅ Pretraining Boost – Models like ForecastPFN, Mamba4Cast, and TimesFM showed marked improvements when pretrained on synthetic data. ForecastPFN, for instance, excelled in zero-shot forecasting after full synthetic pretraining. ✅ Optimal Data Blending – Chronos found peak performance by mixing 10% synthetic data with real-world datasets, beyond which excessive synthetic data could reduce diversity and effectiveness. ✅ Enhanced Evaluation – Synthetic data allowed precise assessment of model capabilities, uncovering hidden biases and gaps. For example, Moment used synthetic sinusoidal waves to analyze embedding sensitivity and trend detection accuracy. Future Directions: Overcoming Limitations While synthetic data offers immense promise, Salesforce identifies key areas for improvement: 🔹 Systematic Integration – Developing structured frameworks to strategically fill gaps in real-world datasets.🔹 Beyond Statistical Methods – Exploring diffusion models and other generative AI techniques for richer, more realistic synthetic data.🔹 Fine-Tuning Potential – Leveraging synthetic data adaptively to address domain-specific weaknesses during fine-tuning. The Path Forward Salesforce AI Research demonstrates that synthetic data is a game-changer for time series analysis, enabling stronger generalization, reduced bias, and superior performance across AI tasks. While challenges like realism and alignment remain, the future is bright—advancements in generative AI, human-in-the-loop refinement, and systematic gap-filling will further propel the reliability and applicability of time series models. By embracing synthetic data, Salesforce is laying the foundation for the next generation of AI-driven time series innovation—ushering in a new era of accuracy, adaptability, and intelligence. 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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April 7, 2025in Data, Generative AI, Salesforce0 Comments
Is the Future Agentic for ERP?
02Apr

Enterprise Tech Buyers Face a Flood of Agentic AI Options

Enterprise tech buyers feeling overwhelmed by the surge of autonomous AI platforms aren’t alone—soon, they may need AI agents just to evaluate the growing array of options. At last week’s Adobe Summit, the company unveiled its own AI agents, deeply integrated with the Adobe Experience Platform. Adobe now joins a crowded field of major players—including AWS, Microsoft, Salesforce, Oracle, OpenAI, Qualtrics, and Deloitte—all offering agentic AI solutions. Adobe CEO Shantanu Narayen emphasized in his keynote that the company’s approach to AI is about enhancing human creativity, not replacing it. “AI has the power to assist and amplify human ingenuity to enhance productivity,” he said. One early adopter, Coca-Cola, has leveraged Adobe’s agentic AI for Project Vision, ensuring brand consistency across 200+ international markets—adapting packaging designs for different sizes, shapes, and languages while still allowing local designers creative flexibility. “We needed an AI system that doesn’t just replicate designs but truly understands what makes Coca-Cola feel like Coca-Cola,” said Rapha Abreu, Global VP of Design at Coca-Cola. “This isn’t about replacing designers—it’s about empowering them.” Navigating the Agentic AI Maze With so many platforms emerging, buyers face a critical challenge: Which agents fit their tech stack, and which platform delivers the best results? Even experts are still figuring it out. Lou Reinemann, an IDC analyst, noted that companies will need different AI agents depending on their size, industry, and product maturity. “Early on, customer experience can be a differentiator. As brands grow, AI must reinforce their core identity.” Ross Monaghan, Adobe Principal at consultancy Perficient, observed that vendors are refining AI use cases—Salesforce focuses on CRM data, while Adobe leans into marketing applications. For now, these agents operate within their own ecosystems, though cross-platform communication may evolve. Data Strategy: The Key to AI Success According to Liz Miller, analyst at Constellation Research, most enterprises will end up using multiple AI platforms—making a unified data schema essential. “The real challenge is ensuring all AI agents pull from a single, curated data source,” she said. CDP tools like Salesforce’s Data Cloud will be important resources for a unified data schema. Jamie Dimon, CEO of JPMorgan Chase, stressed in a conversation with Narayen that business leaders—not just IT—must drive AI adoption. The bank uses AI for customer prospecting, fraud detection, ad buying, and document automation, with a dedicated team prioritizing use cases. “AI should be part of your company’s DNA,” Dimon said. “You don’t need to know how it works—just what it can do for your business.” The Bottom Line Agentic AI is transforming enterprise operations, but buyers must navigate a fragmented landscape. The winners will be those who align AI with business goals, maintain clean data pipelines, and choose platforms that enhance—not replace—human expertise. 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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April 2, 2025in Agentic AI, Data, Enterprise, Salesforce0 Comments
AI Captivates the World
17Mar

AI vs Human Intelligence

Artificial Intelligence vs. Human Intelligence: Key Differences Explained Artificial intelligence (AI) often mimics human-like capabilities, but there are fundamental differences between natural human intelligence and artificial systems. While AI has made remarkable strides in replicating certain aspects of human cognition, it operates in ways that are distinct from how humans think, learn, and solve problems. Below, we explore three key areas where AI and human intelligence diverge. Defining Intelligence Human IntelligenceHuman intelligence is often described using terms like smartness, understanding, brainpower, reasoning, sharpness, and wisdom. These concepts reflect the complexity of human cognition, which has been debated for thousands of years. At its core, human intelligence is a biopsychological capacity to acquire, apply, and adapt knowledge and skills. It encompasses not only logical reasoning but also emotional understanding, creativity, and social interaction. Artificial IntelligenceAI refers to machines designed to perform tasks traditionally associated with human intelligence, such as learning, problem-solving, and decision-making. Over the past few decades, AI has advanced rapidly, particularly in areas like machine learning and generative AI. However, AI lacks the depth and breadth of human intelligence, operating instead through algorithms and data processing. Human Intelligence: What Humans Do Better Humans excel in areas that require empathy, judgment, intuition, and creativity. These qualities are deeply rooted in our evolution as social beings. For example: These capabilities make human intelligence uniquely suited for tasks that involve emotional connection, ethical decision-making, and creative thinking. Artificial Intelligence: What AI Does Better AI outperforms humans in several areas, particularly those involving data processing, pattern recognition, and speed: However, AI’s strengths are limited to the data it is trained on and the algorithms it uses, lacking the adaptability and contextual understanding of human intelligence. 3 Key Differences Between AI and Human Intelligence AI and Human Intelligence: Working Together The future lies in human-AI collaboration, where the strengths of both are leveraged to address complex challenges. For example: While some may find the idea of integrating AI into decision-making unsettling, the scale of global challenges—from climate change to healthcare—demands the combined power of human and artificial intelligence. By working together, humans and AI can amplify each other’s strengths while mitigating weaknesses. Conclusion AI and human intelligence are fundamentally different, each excelling in areas where the other falls short. Human intelligence is unparalleled in creativity, empathy, and ethical reasoning, while AI dominates in data processing, pattern recognition, and speed. The key to unlocking the full potential of AI lies in human-AI collaboration, where the unique strengths of both are harnessed to solve the world’s most pressing problems. As we move forward, this partnership will likely become not just beneficial but essential. 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 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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March 17, 2025in Artificial Intelligence, Data, Generative AI0 Comments
Generative AI in Marketing
28Feb

Generative AI in Marketing

Generative Artificial Intelligence (GenAI) continues to reshape industries, providing product managers (PMs) across domains with opportunities to embrace AI-focused innovation and enhance their technical expertise. Over the past few years, GenAI has gained immense popularity. AI-enabled products have proliferated across industries like a rapidly expanding field of dandelions, fueled by abundant venture capital investment. From a product management perspective, AI offers numerous ways to improve productivity and deepen strategic domain knowledge. However, the fundamentals of product management remain paramount. This discussion underscores why foundational PM practices continue to be indispensable, even in the evolving landscape of GenAI, and how these core skills can elevate PMs navigating this dynamic field. Why PM Fundamentals Matter, AI or Not Three core reasons highlight the enduring importance of PM fundamentals and actionable methods for excelling in the rapidly expanding GenAI space. 1. Product Development is Inherently Complex While novice PMs might assume product development is straightforward, the reality reveals a web of interconnected and dynamic elements. These may include team dependencies, sales and marketing coordination, internal tooling managed by global teams, data telemetry updates, and countless other tasks influencing outcomes. A skilled product manager identifies and orchestrates these moving pieces, ensuring product growth and delivery. This ability is often more impactful than deep technical AI expertise (though having both is advantageous). The complexity of modern product development is further amplified by the rapid pace of technological change. Incorporating AI tools such as GitHub Copilot can accelerate workflows but demands a strong product culture to ensure smooth integration. PMs must focus on fundamentals like understanding user needs, defining clear problems, and delivering value to avoid chasing fleeting AI trends instead of solving customer problems. While AI can automate certain tasks, it is limited by costs, specificity, and nuance. A PM with strong foundational knowledge can effectively manage these limitations and identify areas for automation or improvement, such as: 2. Interpersonal Skills Are Irreplaceable As AI product development grows more complex, interpersonal skills become increasingly critical. PMs work with diverse teams, including developers, designers, data scientists, marketing professionals, and executives. While AI can assist in specific tasks, strong human connections are essential for success. Key interpersonal abilities for PMs include: Stakeholder management remains a cornerstone of effective product management. PMs must build trust and tailor their communication to various audiences—a skill AI cannot replicate. 3. Understanding Vertical Use Cases is Essential Vertical use cases focus on niche, specific tasks within a broader context. In the GenAI ecosystem, this specificity is exemplified by AI agents designed for narrow applications. For instance, Microsoft Copilot includes a summarization agent that excels at analyzing Word documents. The vertical AI market has experienced explosive growth, valued at .1 billion in 2024 and projected to reach .1 billion by 2030. PMs are crucial in identifying and validating these vertical use cases. For example, the team at Planview developed the AI Assistant “Planview Copilot” by hypothesizing specific use cases and iteratively validating them through customer feedback and data analysis. This approach required continuous application of fundamental PM practices, including discovery, prioritization, and feedback internalization. PMs must be adept at discovering vertical use cases and crafting strategies to deliver meaningful solutions. Key steps include: Conclusion Foundational product management practices remain critical, even as AI transforms industries. These core skills ensure that PMs can navigate the challenges of GenAI, enabling organizations to accelerate customer value in work efficiency, time savings, and quality of life. By maintaining strong fundamentals, PMs can lead their teams to thrive in an AI-driven future. AI Agents on Madison Avenue: The New Frontier in Advertising AI agents, hailed as the next big advancement in artificial intelligence, are making their presence felt in the world of advertising. Startups like Adaly and Anthrologic are introducing personalized AI tools designed to boost productivity for advertisers, offering automation for tasks that are often time-consuming and tedious. Retail brands such as Anthropologie are already adopting this technology to streamline their operations. How AI Agents WorkIn simple terms, AI agents operate like advanced AI chatbots. They can handle tasks such as generating reports, optimizing media budgets, or analyzing data. According to Tyler Pietz, CEO and founder of Anthrologic, “They can basically do anything that a human can do on a computer.” Big players like Salesforce, Microsoft, Anthropic, Google, and Perplexity are also championing AI agents. Perplexity’s CEO, Aravind Srinivas, recently suggested that businesses will soon compete for the attention of AI agents rather than human customers. “Brands need to get comfortable doing this,” he remarked to The Economic Times. AI Agents Tailored for Advertisers Both Adaly and Anthrologic have developed AI software specifically trained for advertising tasks. Built on large language models like ChatGPT, these platforms respond to voice and text prompts. Advertisers can train these AI systems on internal data to automate tasks like identifying data discrepancies or analyzing economic impacts on regional ad budgets. Pietz noted that an AI agent can be set up in about a month and take on grunt work like scouring spreadsheets for specific figures. “Marketers still log into 15 different platforms daily,” said Kyle Csik, co-founder of Adaly. “When brands in-house talent, they often hire people to manage systems rather than think strategically. AI agents can take on repetitive tasks, leaving room for higher-level work.” Both Pietz and Csik bring agency experience to their ventures, having crossed paths at MediaMonks. Industry Response: Collaboration, Not Replacement The targets for these tools differ: Adaly focuses on independent agencies and brands, while Anthrologic is honing in on larger brands. Meanwhile, major holding companies like Omnicom and Dentsu are building their own AI agents. Omnicom, on the verge of merging with IPG, has developed internal AI solutions, while Dentsu has partnered with Microsoft to create tools like Dentsu DALL-E and Dentsu-GPT. Havas is also developing its own AI agent, according to Chief Activation Officer Mike Bregman. Bregman believes AI tools won’t immediately threaten agency jobs. “Agencies have a lot of specialization that machines can’t replace today,” he said. “They can streamline processes, but

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February 28, 2025in AI Tools, Artificial Intelligence, Data, Generative AI, Google, Laws, Salesforce, Technology
collaboration between humans and AI
05Feb

The Synergy of AI and Human Expertise in Modern Customer Service

The Balanced Approach to Customer Support In today’s demanding service landscape, businesses face a critical challenge: meeting rising customer expectations while maintaining operational efficiency. Research reveals that 77% of customers demand immediate interaction when contacting companies, while 65% expect organizations to adapt dynamically to their evolving needs. The solution lies not in choosing between artificial intelligence and human representatives, but in strategically combining their complementary strengths. The AI Advantage in Customer Service Artificial intelligence brings transformative capabilities to customer support operations: 1. Operational Efficiency 2. Intelligent Insights 3. Proactive Engagement Platforms like Salesforce’s Agentforce demonstrate AI’s potential, combining around-the-clock availability with continuous learning capabilities that adapt to changing customer needs while operating within established business parameters. The Irreplaceable Human Element While AI excels at efficiency, human representatives provide essential qualities that technology cannot replicate: 1. Emotional Intelligence 2. Complex Problem-Solving 3. Relationship Building The Power of Collaborative Service The most effective customer service strategies integrate AI and human capabilities through: 1. Intelligent Assistance ToolsPlatforms like Service Assistant demonstrate this synergy by: 2. Optimized Workflows 3. Continuous Improvement Implementing an Integrated Service Strategy Organizations can develop this balanced approach through: 1. Strategic Technology Deployment 2. Workforce Development 3. Performance Measurement The Future of Customer Service The most successful service organizations will be those that: By harmonizing artificial intelligence with human expertise, businesses can deliver the responsive, personalized service that modern customers demand while maintaining the authentic connections that build lasting loyalty. This balanced approach represents not just the present of customer service, but its future. 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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February 5, 2025in Artificial Intelligence, Data, Salesforce, Technology

Service Cloud or Sales Cloud for Service

4 Reasons to Use Salesforce Service Cloud Over Sales Cloud’s Standard Case Functionality When businesses aim to elevate their customer support operations, Salesforce is often their platform of choice. While Sales Cloud and Service Cloud both help manage customer interactions, their core purposes differ. Sales Cloud focuses on managing the sales pipeline, whereas Service Cloud is specifically designed to optimize customer service and support processes. Here are four compelling reasons to choose Service Cloud for your customer support needs. 1. Advanced Case Management Features Service Cloud offers robust tools to manage customer cases with efficiency, far surpassing the basic case functionality available in Sales Cloud. Key Service Cloud Features: While Sales Cloud does support basic case management, it lacks these advanced features. Attempting to replicate them in Sales Cloud often requires extensive customization and development. 2. Omni-Channel Support for Seamless Customer Communication Modern customer service spans multiple channels, including chat, email, phone, and social media. Service Cloud provides powerful omni-channel capabilities to unify communication across all these touchpoints—something Sales Cloud does not offer. Key Service Cloud Features: Sales Cloud’s functionality centers on sales processes, leaving it without native support for omni-channel routing or social media integrations for customer support. 3. Knowledge Base for Self-Service and Agent Efficiency Service Cloud enables organizations to build and maintain a knowledge base, empowering both customers and agents with quick access to solutions. Key Service Cloud Features: Sales Cloud does not include tools for creating a knowledge base, self-service portals, or case deflection, as it is designed primarily for sales teams. 4. Entitlements and Service Contracts for Enhanced Customer Support Service Cloud provides specialized tools for managing entitlements and service contracts, ensuring customers receive the level of support they’re entitled to. Key Service Cloud Features: Sales Cloud does not offer dedicated features for managing entitlements or service contracts, limiting its utility for businesses focused on structured customer support. Why Service Cloud is the Better Choice for Customer Support While Sales Cloud is a powerful tool for managing sales pipelines, it falls short in addressing the complex needs of modern customer support. Service Cloud provides: If your priority is delivering exceptional customer support and enhancing customer satisfaction, Service Cloud is the clear choice. With its comprehensive features, your support team will be empowered to work more efficiently, resolve issues faster, and provide outstanding service across all channels. Invest in Service Cloud to transform your support operations and create seamless, satisfying experiences for your customers. 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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January 6, 2025in Salesforce, Salesforce Service Cloud
agentforce testing center
08Dec

Agentforce Testing Center

Salesforce has launched the Agentforce Testing Center, a groundbreaking tool for testing, deploying, and managing AI agents at scale. This innovative platform uses synthetically generated data to ensure AI agents perform accurate actions and deliver precise responses while providing comprehensive monitoring of usage and feedback. A New Era of AI Testing The Agentforce Testing Center operates within Salesforce’s enterprise-grade platform, integrated seamlessly with Data Cloud. It introduces no-code tools that allow organizations to test AI agents securely in sandbox environments, ensuring minimal risk to live production systems. By enabling robust testing and optimization, this tool helps businesses confidently deploy AI agents tailored to their needs. According to Adam Evans, EVP and GM for the Salesforce AI Platform, the Agentforce Testing Center is a critical tool for modern enterprises: “Agentforce is helping businesses create a limitless workforce. CIOs need tools for testing and monitoring agentic systems. This offering marks the next evolution of lifecycle management, enabling businesses to deploy trusted AI agents in a secure, repeatable way.” Key Capabilities of the Agentforce Testing Center Industry Implications Keith Kirkpatrick, Research Director at The Futurum Group, emphasized the importance of this offering: “To build trust in AI, businesses need robust tools to pressure test autonomous agents. Salesforce’s Testing Center delivers this capability, ensuring AI agents can reason, retrieve data, and adapt effectively.” Enhancing AI Agent Strategies The Agentforce Testing Center’s integration with Salesforce tools like DevOps Center and Change Sets further simplifies deployment workflows. Features like User Acceptance Testing (UAT) and real-time alerts in the Digital Wallet give teams deeper control and visibility into AI performance, ensuring business readiness before live deployment. As Salesforce continues to invest in AI innovation, including recent acquisitions to enhance Data Cloud security, the Agentforce Testing Center positions the CRM leader as a pioneer in responsible and scalable AI deployment. This offering exemplifies how businesses can create a “limitless workforce” while maintaining trust, transparency, and efficiency in AI 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 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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December 8, 2024in Agentforce Platform, Data, Enterprise, Salesforce
Is Your LLM Agent Enterprise-Ready?
26Nov

Is Your LLM Agent Enterprise-Ready?

Customer Relationship Management (CRM) systems are the backbone of modern business operations, orchestrating customer interactions, data management, and process automation. As businesses embrace advanced AI, the potential for transformative growth is clear—automating workflows, personalizing customer experiences, and enhancing operational efficiency. However, deploying large language model (LLM) agents in CRM systems demands rigorous, real-world evaluations to ensure they meet the complexity and dynamic needs of professional environments.

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November 26, 2024in Data, Enterprise, Salesforce, Salesforce Service Cloud
Salesforce adds Testing Center to Agentforce for AI agents
21Nov

Salesforce adds Testing Center to Agentforce for AI agents

Salesforce Unveils Agentforce Testing Center to Streamline AI Agent Lifecycle Management Salesforce has introduced the Agentforce Testing Center, a suite of tools designed to help enterprises test, deploy, and monitor autonomous AI agents in a secure and controlled environment. These innovations aim to support businesses adopting agentic AI, a transformative approach that enables intelligent systems to reason, act, and execute tasks on behalf of employees and customers. Agentforce Testing Center: A New Paradigm for AI Agent Deployment The Agentforce Testing Center offers several key capabilities to help businesses confidently deploy AI agents without risking disruptions to live production systems: Supporting a Limitless Workforce Adam Evans, EVP and GM for Salesforce AI Platform, emphasized the importance of these tools in accelerating the adoption of AI agents: “Agentforce is helping businesses create a limitless workforce. To deliver this value fast, CIOs need new tools for testing and monitoring agentic systems. Salesforce is meeting the moment with Agentforce Testing Center, enabling companies to roll out trusted AI agents with no-code tools for testing, deploying, and monitoring in a secure, repeatable way.” From Testing to Deployment Once testing is complete, enterprises can seamlessly deploy their AI agents to production using Salesforce’s proprietary tools such as Change Sets, DevOps Center, and the Salesforce CLI. Additionally, the Digital Wallet feature offers transparent usage monitoring, allowing teams to track consumption and optimize resources throughout the AI development lifecycle. Customer and Analyst Perspectives Shree Reddy, CIO of PenFed, praised the potential of Agentforce and Data Cloud Sandboxes: “By enabling rigorous pre-deployment testing, we can deliver faster, more accurate support and recommendations to our members, aligning with our commitment to financial well-being.” Keith Kirkpatrick, Research Director at The Futurum Group, highlighted the broader implications: “Salesforce is instilling confidence in AI adoption by testing hundreds of variations of agent interactions in parallel. These enhancements make it easier for businesses to pressure-test autonomous systems and ensure reliability.” Availability With these tools, Salesforce solidifies its leadership in the agentic AI space, empowering enterprises to adopt AI systems with confidence and transform their operations 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 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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November 21, 2024in Agentforce Platform, Data, Enterprise, Salesforce
RAGate
14Nov

RAGate

RAGate: Revolutionizing Conversational AI with Adaptive Retrieval-Augmented Generation Building Conversational AI systems is challenging.It’s not just feasible; it’s complex, resource-intensive, and time-consuming. The difficulty lies in creating systems that can not only understand and generate human-like responses but also adapt effectively to conversational nuances, ensuring meaningful engagement with users. Retrieval-Augmented Generation (RAG) has already transformed Conversational AI by combining the internal knowledge of large language models (LLMs) with external knowledge sources. By leveraging RAG with business data, organizations empower their customers to ask natural language questions and receive insightful, data-driven answers. The challenge?Not every query requires external knowledge. Over-reliance on external sources can disrupt conversational flow, much like consulting a book for every question during a conversation—even when internal knowledge is sufficient. Worse, if no external knowledge is available, the system may respond with “I don’t know,” despite having relevant internal knowledge to answer. The solution?RAGate — an adaptive mechanism that dynamically determines when to use external knowledge and when to rely on internal insights. Developed by Xi Wang, Procheta Sen, Ruizhe Li, and Emine Yilmaz and introduced in their July 2024 paper on Adaptive Retrieval-Augmented Generation for Conversational Systems, RAGate addresses this balance with precision. What Is Conversational AI? At its core, conversation involves exchanging thoughts, emotions, and information, guided by tone, context, and subtle cues. Humans excel at this due to emotional intelligence, socialization, and cultural exposure. Conversational AI aims to replicate these human-like interactions by leveraging technology to generate natural, contextually appropriate, and engaging responses. These systems adapt fluidly to user inputs, making the interaction dynamic—like conversing with a human. Internal vs. External Knowledge in AI Systems To understand RAGate’s value, we need to differentiate between two key concepts: Limitations of Traditional RAG Systems RAG integrates LLMs’ natural language capabilities with external knowledge retrieval, often guided by “guardrails” to ensure responsible, domain-specific responses. However, strict reliance on external knowledge can lead to: How RAGate Enhances Conversational AI RAGate, or Retrieval-Augmented Generation Gate, adapts dynamically to determine when external knowledge retrieval is necessary. It enhances response quality by intelligently balancing internal and external knowledge, ensuring conversational relevance and efficiency. The mechanism: Traditional RAG vs. RAGate: An Example Scenario: A healthcare chatbot offers advice based on general wellness principles and up-to-date medical research. This adaptive approach improves response accuracy, reduces latency, and enhances the overall conversational experience. RAGate Variants RAGate offers three implementation methods, each tailored to optimize performance: Variant Approach Key Feature RAGate-Prompt Uses natural language prompts to decide when external augmentation is needed. Lightweight and simple to implement. RAGate-PEFT Employs parameter-efficient fine-tuning (e.g., QLoRA) for better decision-making. Fine-tunes the model with minimal resource requirements. RAGate-MHA Leverages multi-head attention to interactively assess context and retrieve external knowledge. Optimized for complex conversational scenarios. RAGate Varients How to Implement RAGate Key Takeaways RAGate represents a breakthrough in Conversational AI, delivering adaptive, contextually relevant, and efficient responses by balancing internal and external knowledge. Its potential spans industries like healthcare, education, finance, and customer support, enhancing decision-making and user engagement. By intelligently combining retrieval-augmented generation with nuanced adaptability, RAGate is set to redefine the way businesses and individuals interact with AI. 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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November 14, 2024in Data, Technology
Will AI Hinder Digital Transformation in Healthcare?
06Nov

Poisoning Your Data

Protecting Your IP from AI Training: Poisoning Your Data As more valuable intellectual property (IP) becomes accessible online, concerns over AI vendors scraping content for training models without permission are rising. If you’re worried about AI theft and want to safeguard your assets, it’s time to consider “poisoning” your content—making it difficult or even impossible for AI systems to use it effectively. Key Principle: AI “Sees” Differently Than Humans AI processes data in ways humans don’t. While people view content based on context, AI “sees” data in raw, specific formats that can be manipulated. By subtly altering your content, you can protect it without affecting human users. Image Poisoning: Misleading AI Models For images, you can “poison” them to confuse AI models without impacting human perception. A great example of this is Nightshade, a tool designed to distort images so that they remain recognizable to humans but useless to AI models. This technique ensures your artwork or images can’t be replicated, and applying it across your visual content protects your unique style. For example, if you’re concerned about your images being stolen or reused by generative AI systems, you can embed misleading text into the image itself, which is invisible to human users but interpreted by AI as nonsensical data. This ensures that an AI model trained on your images will be unable to replicate them correctly. Text Poisoning: Adding Complexity for Crawlers Text poisoning requires more finesse, depending on the sophistication of the AI’s web crawler. Simple methods include: Invisible Text One easy method is to hide text within your page using CSS. This invisible content can be placed in sidebars, between paragraphs, or anywhere within your text: cssCopy code.content { color: black; /* Same as the background */ opacity: 0.0; /* Invisible */ display: none; /* Hidden in the DOM */ } By embedding this “poisonous” content directly in the text, AI crawlers might have difficulty distinguishing it from real content. If done correctly, AI models will ingest the irrelevant data as part of your content. JavaScript-Generated Content Another technique is to use JavaScript to dynamically alter the content, making it visible only after the page loads or based on specific conditions. This can frustrate AI crawlers that only read content after the DOM is fully loaded, as they may miss the hidden data. htmlCopy code<script> // Dynamically load content based on URL parameters or other factors </script> This method ensures that AI gets a different version of the page than human users. Honeypots for AI Crawlers Honeypots are pages designed specifically for AI crawlers, containing irrelevant or distorted data. These pages don’t affect human users but can confuse AI models by feeding them inaccurate information. For example, if your website sells cheese, you can create pages that only AI crawlers can access, full of bogus details about your cheese, thus poisoning the AI model with incorrect information. By adding these “honeypot” pages, you can mislead AI models that scrape your data, preventing them from using your IP effectively. Competitive Advantage Through Data Poisoning Data poisoning can also work to your benefit. By feeding AI models biased information about your products or services, you can shape how these models interpret your brand. For example, you could subtly insert favorable competitive comparisons into your content that only AI models can read, helping to position your products in a way that biases future AI-driven decisions. For instance, you might embed positive descriptions of your brand or products in invisible text. AI models would ingest these biases, making it more likely that they favor your brand when generating results. Using Proxies for Data Poisoning Instead of modifying your CMS, consider using a proxy server to inject poisoned data into your content dynamically. This approach allows you to identify and respond to crawlers more easily, adding a layer of protection without needing to overhaul your existing systems. A proxy can insert “poisoned” content based on the type of AI crawler requesting it, ensuring that the AI gets the distorted data without modifying your main website’s user experience. Preparing for AI in a Competitive World With the increasing use of AI for training and decision-making, businesses must think proactively about protecting their IP. In an era where AI vendors may consider all publicly available data fair game, implementing data poisoning should become a standard practice for companies concerned about protecting their content and ensuring it’s represented correctly in AI models. Businesses that take these steps will be better positioned to negotiate with AI vendors if they request data for training and will have a competitive edge if AI systems are used by consumers or businesses to make decisions about their products or services. 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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November 6, 2024in Data, Generative AI
AI and Disability
28Oct

AI and Disability

Dr. Johnathan Flowers of American University recently sparked a conversation on Bluesky regarding a statement from the organizers of NaNoWriMo, which endorsed the use of generative AI technologies, such as LLM chatbots, in this year’s event. Dr. Flowers expressed concern about the implication that AI assistance was necessary for accessibility, arguing that it could undermine the creativity and agency of individuals with disabilities. He believes that art often serves as a unique space where barriers imposed by disability can be transcended without relying on external help or engaging in forced intimacy. For Dr. Flowers, suggesting the need for AI support may inadvertently diminish the perceived capabilities of disabled and marginalized artists. Since the announcement, NaNoWriMo organizers have revised their stance in response to criticism, though much of the social media discussion has become unproductive. In earlier discussions, the author has explored the implications of generative AI in art, focusing on the human connection that art typically fosters, which AI-generated content may not fully replicate. However, they now wish to address the role of AI as a tool for accessibility. Not being personally affected by physical disability, the author approaches this topic from a social scientific perspective. They acknowledge that the views expressed are personal and not representative of any particular community or organization. Defining AI In a recent presentation, the author offered a new definition of AI, drawing from contemporary regulatory and policy discussions: AI: The application of specific forms of machine learning to perform tasks that would otherwise require human labor. This definition is intentionally broad, encompassing not just generative AI but also other machine learning applications aimed at automating tasks. AI as an Accessibility Tool AI has potential to enhance autonomy and independence for individuals with disabilities, paralleling technological advancements seen in fields like the Paris Paralympics. However, the author is keen to explore what unique benefits AI offers and what risks might arise. Benefits Risks AI and Disability The author acknowledges that this overview touches only on some key issues related to AI and disability. It is crucial for those working in machine learning to be aware of these dynamics, striving to balance benefits with potential risks and ensuring equitable access to technological advancements. 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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October 28, 2024in AI Tools, Data, Generative AI
What is Explainable AI
22Oct

What is Explainable AI

Building a trusted AI system starts with ensuring transparency in how decisions are made. Explainable AI is vital not only for addressing trust issues within organizations but also for navigating regulatory challenges. According to research from Forrester, many business leaders express concerns over AI, particularly generative AI, which surged in popularity following the 2022 release of ChatGPT by OpenAI. “AI faces a trust issue,” explained Forrester analyst Brandon Purcell, underscoring the need for explainability to foster accountability. He highlighted that explainability helps stakeholders understand how AI systems generate their outputs. “Explainability builds trust,” Purcell stated at the Forrester Technology and Innovation Summit in Austin, Texas. “When employees trust AI systems, they’re more inclined to use them.” Implementing explainable AI does more than encourage usage within an organization—it also helps mitigate regulatory risks, according to Purcell. Explainability is crucial for compliance, especially under regulations like the EU AI Act. Forrester analyst Alla Valente emphasized the importance of integrating accountability, trust, and security into AI efforts. “Don’t wait for regulators to set standards—ensure you’re already meeting them,” she advised at the summit. Purcell noted that explainable AI varies depending on whether the AI model is predictive, generative, or agentic. Building an Explainable AI System AI explainability encompasses several key elements, including reproducibility, observability, transparency, interpretability, and traceability. For predictive models, transparency and interpretability are paramount. Transparency involves using “glass-box modeling,” where users can see how the model analyzed the data and arrived at its predictions. This approach is likely to be a regulatory requirement, especially for high-risk applications. Interpretability is another important technique, useful for lower-risk cases such as fraud detection or explaining loan decisions. Techniques like partial dependence plots show how specific inputs affect predictive model outcomes. “With predictive AI, explainability focuses on the model itself,” Purcell noted. “It’s one area where you can open the hood and examine how it works.” In contrast, generative AI models are often more opaque, making explainability harder. Businesses can address this by documenting the entire system, a process known as traceability. For those using models from vendors like Google or OpenAI, tools like transparency indexes and model cards—which detail the model’s use case, limitations, and performance—are valuable resources. Lastly, for agentic AI systems, which autonomously pursue goals, reproducibility is key. Businesses must ensure that the model’s outputs can be consistently replicated with similar inputs before deployment. These systems, like self-driving cars, will require extensive testing in controlled environments before being trusted in the real world. “Agentic systems will need to rack up millions of virtual miles before we let them loose,” Purcell concluded. 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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October 22, 2024in Data, Generative AI, Google, Technology
Recent advancements in AI
18Oct

Recent advancements in AI

Recent advancements in AI have been propelled by large language models (LLMs) containing billions to trillions of parameters. Parameters—variables used to train and fine-tune machine learning models—have played a key role in the development of generative AI. As the number of parameters grows, models like ChatGPT can generate human-like content that was unimaginable just a few years ago. Parameters are sometimes referred to as “features” or “feature counts.” While it’s tempting to equate the power of AI models with their parameter count, similar to how we think of horsepower in cars, more parameters aren’t always better. An increase in parameters can lead to additional computational overhead and even problems like overfitting. There are various ways to increase the number of parameters in AI models, but not all approaches yield the same improvements. For example, Google’s Switch Transformers scaled to trillions of parameters, but some of their smaller models outperformed them in certain use cases. Thus, other metrics should be considered when evaluating AI models. The exact relationship between parameter count and intelligence is still debated. John Blankenbaker, principal data scientist at SSA & Company, notes that larger models tend to replicate their training data more accurately, but the belief that more parameters inherently lead to greater intelligence is often wishful thinking. He points out that while these models may sound knowledgeable, they don’t actually possess true understanding. One challenge is the misunderstanding of what a parameter is. It’s not a word, feature, or unit of data but rather a component within the model‘s computation. Each parameter adjusts how the model processes inputs, much like turning a knob in a complex machine. In contrast to parameters in simpler models like linear regression, which have a clear interpretation, parameters in LLMs are opaque and offer no insight on their own. Christine Livingston, managing director at Protiviti, explains that parameters act as weights that allow flexibility in the model. However, more parameters can lead to overfitting, where the model performs well on training data but struggles with new information. Adnan Masood, chief AI architect at UST, highlights that parameters influence precision, accuracy, and data management needs. However, due to the size of LLMs, it’s impractical to focus on individual parameters. Instead, developers assess models based on their intended purpose, performance metrics, and ethical considerations. Understanding the data sources and pre-processing steps becomes critical in evaluating the model’s transparency. It’s important to differentiate between parameters, tokens, and words. A parameter is not a word; rather, it’s a value learned during training. Tokens are fragments of words, and LLMs are trained on these tokens, which are transformed into embeddings used by the model. The number of parameters influences a model’s complexity and capacity to learn. More parameters often lead to better performance, but they also increase computational demands. Larger models can be harder to train and operate, leading to slower response times and higher costs. In some cases, smaller models are preferred for domain-specific tasks because they generalize better and are easier to fine-tune. Transformer-based models like GPT-4 dwarf previous generations in parameter count. However, for edge-based applications where resources are limited, smaller models are preferred as they are more adaptable and efficient. Fine-tuning large models for specific domains remains a challenge, often requiring extensive oversight to avoid problems like overfitting. There is also growing recognition that parameter count alone is not the best way to measure a model’s performance. Alternatives like Stanford’s HELM and benchmarks such as GLUE and SuperGLUE assess models across multiple factors, including fairness, efficiency, and bias. Three trends are shaping how we think about parameters. First, AI developers are improving model performance without necessarily increasing parameters. A study of 231 models between 2012 and 2023 found that the computational power required for LLMs has halved every eight months, outpacing Moore’s Law. Second, new neural network approaches like Kolmogorov-Arnold Networks (KANs) show promise, achieving comparable results to traditional models with far fewer parameters. Lastly, agentic AI frameworks like Salesforce’s Agentforce offer a new architecture where domain-specific AI agents can outperform larger general-purpose models. As AI continues to evolve, it’s clear that while parameter count is an important consideration, it’s just one of many factors in evaluating a model’s overall capabilities. To stay on the cutting edge of artificial intelligence, contact Tectonic today. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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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October 18, 2024in Data, Generative AI, Google, Salesforce
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