AI Hallucinations Archives - gettectonic.com
agentforce testing center
06Aug

Agentforce Testing Center

A New Framework for Reliable AI Agent Testing Testing traditional software is well understood, but AI agents introduce unique challenges. Their responses can vary based on interactions, memory, tool access, and sometimes inherent randomness. This unpredictability makes agent testing difficult—especially when repeatability, safety, and clarity are critical. Enter the Agentforce Testing Center. Agentforce Testing Center (ATC), part of Salesforce’s open-source Agentforce ecosystem, provides a structured framework to simulate, test, and monitor AI agent behavior before deployment. It supports real-world scenarios, tool mocking, memory control, guardrails, and test coverage—bringing testing discipline to dynamic agent environments. This insight explores how ATC works, its key differences from traditional testing, and how to set it up for Agentforce-based agents. We’ll cover test architecture, mock tools, memory injection, coverage tracking, and real-world use cases in SaaS, fintech, and HR. Why AI Agents Need a New Testing Paradigm? AI agents powered by LLMs don’t follow fixed instructions—they reason, adapt, and interact with tools and memory. Traditional testing frameworks assume: ✅ Deterministic inputs/outputs✅ Predefined state machines✅ Synchronous, linear flows But agentic systems are: ❌ Probabilistic (LLM outputs vary)❌ Stateful (memory affects decisions)❌ Non-deterministic (tasks may take different paths) Without proper testing, hallucinations, tool misuse, or logic loops can slip into production. Agentforce Testing Center bridges this gap by simulating realistic, repeatable agent behavior. What Is Agentforce Testing Center? ATC is a testing framework for Agentforce-based AI agents, offering: How ATC Works: Architecture & Testing Flow ATC wraps the Agentforce agent loop in a controlled testing environment: Step-by-Step Setup 1. Install Agentforce + ATC bash Copy Download pip install agentforce atc *(Requires Python 3.8+)* 2. Define a Test Scenario python Copy Download from atc import TestScenario scenario = TestScenario( name=”Customer Support Ticket”, goal=”Resolve a refund request”, memory_seed={“prior_chat”: “User asked about refund policy”} ) 3. Mock Tools python Copy Download scenario.mock_tool( name=”payment_api”, mock_response={“status”: “refund_approved”} ) 4. Add Assertions python Copy Download scenario.add_assertion( condition=lambda output: “refund” in output.lower(), error_message=”Agent failed to process refund” ) 5. Run & Analyze python Copy Download results = scenario.run() print(results.report()) Sample Output: text Copy Download ✅ Test Passed: Refund processed correctly 🛑 Tool Misuse: Called CRM API without permission ⚠️ Coverage Gap: Missing fallback logic Advanced Testing Patterns 1. Loop Detection Prevent agents from repeating actions indefinitely: python Copy Download scenario.add_guardrail(max_steps=10) 2. Regression Testing for LLM Upgrades Compare outputs between model versions: python Copy Download scenario.compare_versions( current_model=”gpt-4″, previous_model=”gpt-3.5″ ) 3. Multi-Agent Testing Validate workflows with multiple agents (e.g., research → writer → reviewer): python Copy Download scenario.test_agent_flow( agents=[researcher, writer, reviewer], expected_output=”Accurate, well-structured report” ) Best Practices for Agent Testing Real-World Use Cases Industry Agent Use Case Test Scenario SaaS Sales Copilot Generate follow-up email for healthcare lead Fintech Fraud Detection Bot Flag suspicious wire transfer HR Tech Resume Screener Rank top candidates with Python skills The Future of Agent Testing As AI agents move from prototypes to production, reliable testing is critical. Agentforce Testing Center provides: ✔ Controlled simulations (memory, tools, scenarios)✔ Actionable insights (coverage, guardrails, regressions)✔ CI/CD integration (automate safety checks) Start testing early—unchecked agents quickly become technical debt. Ready to build trustworthy AI agents?Agentforce Testing Center ensures they behave as expected—before they reach users. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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August 6, 2025in Agentforce Platform, Atlas Reasoning Engine, Salesforce Data Cloud
Data Governance for the AI Enterprise
17Jun

A Strategic Approach to Governing Enterprise AI Systems

The Imperative of AI Governance in Modern Enterprises Effective data governance is widely acknowledged as a critical component of deploying enterprise AI applications. However, translating governance principles into actionable strategies remains a complex challenge. This article presents a structured approach to AI governance, offering foundational principles that organizations can adapt to their needs. While not exhaustive, this framework provides a starting point for managing AI systems responsibly. Defining Data Governance in the AI Era At its core, data governance encompasses the policies and processes that dictate how organizations manage data—ensuring proper storage, access, and usage. Two key roles facilitate governance: Traditional data systems operate within deterministic governance frameworks, where structured schemas and well-defined hierarchies enable clear rule enforcement. However, AI introduces non-deterministic challenges—unstructured data, probabilistic decision-making, and evolving models—requiring a more adaptive governance approach. Core Principles for Effective AI Governance To navigate these complexities, organizations should adopt the following best practices: Multi-Agent Architectures: A Governance Enabler Modern AI applications should embrace agent-based architectures, where multiple AI models collaborate to accomplish tasks. This approach draws from decades of distributed systems and microservices best practices, ensuring scalability and maintainability. Key developments facilitating this shift include: By treating AI agents as modular components, organizations can apply service-oriented governance principles, improving oversight and adaptability. Deterministic vs. Non-Deterministic Governance Models Traditional (Deterministic) Governance AI (Non-Deterministic) Governance Interestingly, human governance has long managed non-deterministic actors (people), offering valuable lessons for AI oversight. Legal systems, for instance, incorporate checks and balances—acknowledging human fallibility while maintaining societal stability. Mitigating AI Hallucinations Through Specialization Large language models (LLMs) are prone to hallucinations—generating plausible but incorrect responses. Mitigation strategies include: This mirrors real-world expertise—just as a medical specialist provides domain-specific advice, AI agents should operate within bounded competencies. Adversarial Validation for AI Governance Inspired by Generative Adversarial Networks (GANs), AI governance can employ: This adversarial dynamic improves quality over time, much like auditing processes in human systems. Knowledge Management: The Backbone of AI Governance Enterprise knowledge is often fragmented, residing in: To govern this effectively, organizations should: Ethics, Safety, and Responsible AI Deployment AI ethics remains a nuanced challenge due to: Best practices include: Conclusion: Toward Responsible and Scalable AI Governance AI governance demands a multi-layered approach, blending:✔ Technical safeguards (specialized agents, adversarial validation).✔ Process rigor (knowledge certification, human oversight).✔ Ethical foresight (bias mitigation, risk-aware automation). By learning from both software engineering and human governance paradigms, enterprises can build AI systems that are effective, accountable, and aligned with organizational values. The path forward requires continuous refinement, but with strategic governance, AI can drive innovation while minimizing unintended consequences. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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June 17, 2025in Data, Enterprise, Google
deepseek deep dive
05May

Is Using DeepSeek a Security Risk?

Despite its efficiency and customizability, DeepSeek—a Chinese AI chatbot—raises serious concerns about data privacy, censorship, and security vulnerabilities for business users. DeepSeek’s Rapid Rise and Security Concerns Two years after ChatGPT’s launch, China introduced a major rival: DeepSeek. Within days of its release in January 2025, DeepSeek became the most downloaded freeware app on Apple’s iOS App Store and Google Play in the U.S. However, its fast adoption has sparked significant privacy and security concerns, especially for businesses that may input sensitive data. What Is DeepSeek? Developed by a Chinese AI startup of the same name, DeepSeek is an open-source generative AI chatbot that competes with ChatGPT, Google’s Gemini, and Anthropic’s Claude. Unlike its rivals, DeepSeek is entirely free to use via a web application, requiring only an email or phone number for registration. DeepSeek stands out for its efficiency—achieving results comparable to ChatGPT with significantly fewer GPUs. While it excels in technical and mathematical domains, ChatGPT offers broader, context-aware responses across a wider range of topics. DeepSeek Web App vs. Local Hosting Users can access DeepSeek through its web and mobile apps or by hosting models locally via GitHub. When using DeepSeek’s web app, interactions occur on servers controlled by DeepSeek, subjecting users to its data-sharing policies. In contrast, local hosting allows organizations to maintain control over their data but shifts security responsibilities to the user. Key Security Risks of DeepSeek 1. Data Privacy & Government Oversight DeepSeek operates under China’s strict data laws, which require companies to cooperate with government authorities. Its privacy policy explicitly states that user data—including account details, input history, device metadata, and location data—is stored on servers in China, making it accessible to the Chinese government. This centralization raises concerns for businesses, as sensitive corporate data entered into DeepSeek may be subject to state oversight without notification. Some Western governments, such as Italy, have already banned DeepSeek over privacy concerns, while U.S. lawmakers are considering similar restrictions. 2. Open-Source Risks: Disinformation & Security Exploits DeepSeek’s open-source model offers flexibility but also creates risks: Unlike OpenAI and Google, which enforce strict content safety measures, DeepSeek’s open-source nature makes it more vulnerable to manipulation. 3. Centralized Data Storage Risks Most Western AI providers use decentralized, region-specific data centers to comply with privacy laws like GDPR. DeepSeek, however, stores all user data in China, raising concerns for international businesses handling sensitive information. 4. AI Hallucinations & Misinformation DeepSeek has been found to be more prone to AI hallucinations—where the model generates false or misleading information. This can lead to: 5. Encryption & Security Weaknesses Security audits of DeepSeek’s iOS app revealed several vulnerabilities: How DeepSeek Compares to ChatGPT & Gemini Security Factor DeepSeek ChatGPT Gemini Data Storage China-only Global, region-specific Global, region-specific Government Oversight Subject to China’s data laws U.S./EU privacy laws apply U.S./EU privacy laws apply Guardrail Protection Easier to bypass Strong, regularly updated Strong, regularly updated Transparency Limited public disclosures Security white papers, third-party audits Security white papers, third-party audits Should Businesses Use DeepSeek? Businesses handling sensitive information should exercise extreme caution when considering DeepSeek. Its centralized data storage in China, potential government oversight, and open-source vulnerabilities make it a higher-risk alternative compared to Western AI tools like ChatGPT and Gemini. Organizations concerned about data security should either avoid using DeepSeek’s web application or only interact with the model through local hosting to maintain control over their data. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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May 5, 2025in AI Tools, Data, Generative AI, Google, Laws
Agentforce to the Team
29Mar

Redefining AI-Driven Customer Service

Salesforce’s Agentforce: Redefining AI-Driven Customer Service Salesforce has made major strides in AI-powered customer service with Agentforce, its agentic AI platform. The CRM leader now resolves 85% of customer queries without human intervention—an achievement driven by three key factors: Speaking at the Agentforce World Tour, Salesforce Co-Founder & CTO Parker Harris emphasized the platform’s role in handling vast volumes of customer interactions. The remaining 15% of queries are escalated to human agents for higher-value interactions, ensuring complex issues receive the necessary expertise. “We’re all shocked by the power of these LLMs. AI has truly hit a tipping point over the past two years,” Harris said. Currently, Agentforce manages 30,000 weekly conversations for Salesforce, proving its growing impact. Yet, the journey to adoption wasn’t without its challenges. From Caution to Acceleration: Agentforce’s Evolution Initially, Salesforce approached the Agentforce rollout with caution, concerned about AI hallucinations and accuracy. However, the company ultimately embraced a learn-by-doing approach. “So, we went for it!” Harris recalled. “We put it out there and improved it every hour. Every interaction helped us refine it.” This iterative process led to significant advancements, with Agentforce now seamlessly handling a high volume of inquiries. Expanding Beyond Customer Support Agentforce’s impact extends beyond customer service—it’s also revolutionizing sales operations at Salesforce. The platform acts as a virtual sales coach for 25,000 sales representatives, offering real-time guidance without the social pressures of a human supervisor. “Salespeople aren’t embarrassed to ask an AI coach questions, which makes them more effective,” Harris noted. This AI-driven coaching has enhanced sales efficiency and confidence, allowing teams to perform at a higher level. Real-World Impact and Competitive Edge Salesforce isn’t just promoting Agentforce—it’s using it to prove its value. Harris shared success stories, including reMarkable, which automated 35% of its customer service inquiries, reducing workload by 7,350 queries per month. Salesforce CEO Marc Benioff highlighted this competitive edge during the launch of Agentforce 2.0, pointing out that while many companies talk about AI adoption, few truly implement it at scale. “When you visit their websites, you still find a lot of forms and FAQs—but not a lot of AI agents,” Benioff said. He specifically called out Microsoft, stating: “If you look for Co-Pilot on their website, or how they’re automating support, it’s the same as it was two years ago.” Microsoft pushed back on Benioff’s critique, sparking a war of words between the tech giants. What’s Next for Salesforce? Beyond AI-driven service and sales, Salesforce is making bold moves in IT Service Management (ITSM), positioning itself against competitors like ServiceNow. During a recent Motley Fool podcast, Benioff hinted at Salesforce’s ITSM ambitions, stating: “We’re building new apps, like ITSM.” At the TrailheadDX event, Salesforce teased this new product, signaling its expansion into enterprise IT management—a move that could shake up the ITSM landscape. With AI agents redefining work across industries, Salesforce’s aggressive push into automation and ITSM underscores its vision for the future of enterprise AI. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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March 29, 2025in Agentforce Platform, Agentic AI, Data, Enterprise, Salesforce
Agentforce Redefines Generative AI
17Feb

The Rise of Agentic AI: Balancing Innovation and Trust

Agentic AI is transforming industries, and Salesforce’s Agentforce is proving to be a catalyst for both economic growth and workforce empowerment. For companies like Wiley, Agentforce has increased case resolutions by 40%, surpassing the performance of its previous chatbot and allowing employees to focus on more complex cases. However, a new Salesforce white paper emphasizes that simply deploying AI agents isn’t enough to drive productivity and build trust—they must operate within well-defined frameworks that ensure responsible AI adoption. “AI has the potential to enhance trust, efficiency, and effectiveness in our institutions,” said Eric Loeb, EVP of Global Government Affairs at Salesforce. “Salesforce research shows 90% of constituents are open to using AI agents for government services, drawn by benefits like 24/7 access, faster response times, and streamlined processes.” Key Considerations for Policymakers in the Age of AI Agents To strike a balance between risk and opportunity, the Salesforce white paper outlines critical areas policymakers must address: 🔹 Human-AI Collaboration – Employees must develop new skills to configure, manage, and oversee AI agents, ensuring they can be easily programmed and adapted for various tasks. 🔹 Reliability & Guardrails – AI agents must be engineered with fail-safes that enable clear handoffs to human workers and mechanisms to detect and correct AI hallucinations. 🔹 Cross-Domain Fluency – AI must be designed to interpret and act on data from diverse sources, making seamless enterprise-wide integrations essential. 🔹 Transparency & Explainability – Users must know when they’re interacting with AI, and regulators need visibility into how decisions are made to ensure compliance and accountability. 🔹 Data Governance & Privacy – AI agents often require access to sensitive information. Strong privacy and security safeguards are crucial to maintaining trust. 🔹 Security & AI Safety – AI systems must be resilient against adversarial attacks that attempt to manipulate or deceive them into producing inaccurate outputs. 🔹 Ethical AI Use – Companies should establish clear ethical guidelines to govern AI behavior, ensuring responsible deployment and human-AI collaboration. 🔹 Agent-to-Agent Interactions – Standardized protocols and security measures must be in place to ensure controlled, predictable AI behavior and auditability of decisions. Building an Agent-Ready Ecosystem While AI agents represent the next wave of enterprise innovation, policy frameworks must evolve to foster responsible adoption. Policymakers must look beyond AI development and equip the workforce with the skills needed to work alongside these digital assistants. “It’s no longer a question of whether AI agents should be part of the workforce—but how to optimize human and digital labor to achieve the best outcomes,” said Loeb. “Governments must implement policies that ensure AI agents are deployed responsibly, creating more meaningful and productive work environments.” Next Steps Salesforce’s white paper provides a roadmap for policymakers navigating the agentic AI revolution. By focusing on risk-based approaches, transparency, and robust safety measures, businesses and governments alike can unlock the full potential of AI agents—while ensuring trust, accountability, and innovation. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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February 17, 2025in Agentic AI, Data, Enterprise, Salesforce
AI evolves with tools like Agentforce and Atlas
03Feb

Salesforce Atlas

Salesforce Atlas: The Brainpower Behind AI-Driven Transformation A New Era of AI for Enterprise AI is reshaping industries at an unprecedented pace, and agentic AI—AI that can think, plan, and act autonomously—is at the forefront of this revolution. Salesforce is leading the charge with Agentforce, a low-code platform that allows businesses to build, refine, and deploy autonomous AI agents across multiple business functions. At the core of this innovation is Salesforce Atlas, the reasoning engine that empowers Agentforce to tackle complex decision-making tasks just like a human. But Atlas goes further—it continuously learns, adapts, and evolves, setting a new standard for AI-driven enterprises. Let’s explore how Atlas works, its capabilities, and why it’s a game-changer for businesses. Salesforce Atlas: The Reasoning Engine Powering Agentforce Atlas is the intelligent decision-making engine that powers Agentforce’s AI agents. Rather than simply following predefined rules, Atlas evaluates data, refines its approach, and continuously learns from outcomes. When an AI agent encounters a decision point, Atlas asks: ➡️ Do I have enough data to ensure accuracy?✔ If yes, it proceeds with a decision.❌ If no, it seeks additional data or escalates the issue. This iterative learning process ensures that AI agents become more reliable, context-aware, and autonomous over time. Salesforce CEO Marc Benioff teased the potential of Atlas, revealing that: 📊 “We are seeing 90-95% resolution on all service and sales issues with the new Atlas.” That’s a staggering success rate, demonstrating how AI-driven reasoning can transform enterprise efficiency and customer engagement. How Salesforce Atlas Works: The “Flywheel” Process Atlas operates using a structured flywheel process that enables self-improvement and adaptability. Here’s how it works: 1️⃣ Data Retrieval – Atlas pulls structured and unstructured data from the Salesforce Data Cloud.2️⃣ Evaluation – It analyzes the data, generates a plan of action, and assesses whether the plan will drive the desired outcome.3️⃣ Refinement – If the plan isn’t strong enough, Atlas loops back, refines its approach, and iterates until it’s confident in its decision. This cycle repeats continuously, ensuring AI agents deliver accurate, data-driven outcomes that align with business goals. Once a task is completed, Atlas learns from the results, refining its approach to become even smarter over time. The Core Capabilities of Salesforce Atlas Atlas stands out because of its advanced reasoning, adaptive learning, and built-in safeguards—all designed to deliver trustworthy, autonomous AI experiences. 1. Advanced Reasoning & Decision-Making Atlas doesn’t just execute tasks; it thinks critically, determining the best way to approach each challenge. Unlike traditional AI models that follow rigid scripts, Atlas: 🔍 Analyzes real-time data to determine the most effective course of action.📊 Refines its decisions dynamically based on live feedback.🌍 Adapts to changing circumstances to optimize outcomes. At Dreamforce 2024, Marc Benioff demonstrated Atlas’s power by showcasing how it could optimize theme park experiences in real time, analyzing: 🎢 Ride availability👥 Guest preferences🚶 Park flow patterns This real-time decision-making showcases the game-changing potential of agentic AI. 2. Advanced Data Retrieval Atlas leverages Retrieval-Augmented Generation (RAG) to pull highly relevant, verified data from multiple sources. This ensures: ✔ More accurate responses✔ Minimized AI hallucinations✔ Reliable, data-driven insights For example, Saks Fifth Avenue uses Atlas to personalize shopping recommendations for millions of customers—tailoring experiences with precision. 3. Built-in Guardrails for Security & Compliance Salesforce recognizes the importance of AI governance, and Atlas includes robust safeguards to ensure responsible AI usage. 🔐 Ethical AI protocols – Ensures compliance with evolving regulations.🚨 Escalation capabilities – AI knows when to seek human intervention for complex issues.🌍 Hyperforce security – Provides enterprise-grade privacy and security standards. These protections ensure Atlas operates securely, responsibly, and at scale across global enterprises. 4. Reinforcement Learning & Continuous Improvement Atlas doesn’t just process data—it learns from outcomes. 🔄 Refines decisions based on real-world results📈 Optimizes performance over time⚡ Becomes increasingly efficient and tailored to business needs Whether it’s increasing sales conversions, resolving service issues, or optimizing workflows, Atlas ensures AI agents grow smarter with every interaction. Why Salesforce Atlas is a Game-Changer Salesforce Atlas isn’t just another AI tool—it’s the brain behind Salesforce’s next-generation AI ecosystem. With Atlas, businesses can: ✅ Automate complex tasks with AI-driven decision-making.✅ Deliver hyper-personalized customer experiences with confidence.✅ Scale AI-powered workflows across sales, service, and operations.✅ Ensure compliance and trust with built-in governance measures.✅ Adapt AI capabilities to meet evolving business needs. Marc Benioff envisions Atlas as the core of a future where AI and humans collaborate to drive innovation and efficiency. By combining advanced reasoning, dynamic adaptability, and enterprise security, Atlas empowers organizations to work smarter, faster, and more effectively—unlocking the full potential of agentic AI. The future of AI-driven enterprise has arrived. With Salesforce Atlas, businesses can build AI agents that don’t just follow instructions—they think, learn, and evolve. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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February 3, 2025in Agentforce Platform, Agentic AI, Data, Enterprise, Hyperforce, Salesforce, Salesforce Data Cloud
computer hackers in a genai desert
19Jan

How Hackers Exploit GenAI

Hackers are increasingly leveraging generative AI (GenAI) to execute sophisticated cyberattacks, with real-world incidents highlighting its growing role in cybercrime. In early 2024, fraudsters used a deepfake of a multinational firm’s CFO to trick a finance employee into transferring $25 million—a stark example of how GenAI is reshaping cyber threats. Experts warn this is just the beginning. Here’s how cybercriminals are using GenAI to their advantage: 1. Crafting Advanced Phishing & Social Engineering Attacks GenAI-powered tools like ChatGPT enable hackers to generate professional-grade phishing emails that closely mimic corporate communications. These emails, now nearly flawless in grammar and formatting, are far more convincing to targets. Additionally, GenAI can: 2. Writing & Enhancing Malicious Code Just as developers use GenAI to accelerate coding, cybercriminals use it to: This automation fuels a rise in zero-day attacks, where vulnerabilities are exploited before developers can patch them. 3. Identifying Vulnerabilities at Scale GenAI accelerates the discovery of security weaknesses by: With GenAI, cybercriminals can scale and refine their tactics faster than ever. 4. Automating Target Research & Attack Planning Hackers use GenAI to: While mainstream AI tools have built-in safeguards, threat actors find ways to bypass them, using alternative AI models or dark web resources. 5. Lowering the Barrier to Cybercrime GenAI democratizes cyberattacks by: This increased accessibility means more people—beyond seasoned cybercriminals—can launch effective cyberattacks. The Hidden Risk: AI-Powered Coding in Enterprises The security risk of GenAI isn’t limited to adversarial use. Businesses adopting AI-powered coding tools may unintentionally introduce vulnerabilities into their systems. Joseph Nwankpa, director of cybersecurity initiatives at Miami University’s Farmer School of Business, warns: The Takeaway While GenAI offers groundbreaking advancements, it also amplifies cyber threats. Organizations must remain vigilant—investing in AI security measures, strengthening human oversight, and educating employees to counter AI-powered attacks. The race between AI-driven innovation and cybercrime is just getting started. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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January 19, 2025in AI Tools, Enterprise, Generative AI, Laws
Statement Accuracy Prediction based on Language Model Activations
14Jan

Statement Accuracy Prediction based on Language Model Activations

When users first began interacting with ChatGPT, they noticed an intriguing behavior: the model would often reverse its stance when told it was wrong. This raised concerns about the reliability of its outputs. How can users trust a system that appears to contradict itself? Recent research has revealed that large language models (LLMs) not only generate inaccurate information (often referred to as “hallucinations”) but are also aware of their inaccuracies. Despite this awareness, these models proceed to present their responses confidently. Unveiling LLM Awareness of Hallucinations Researchers discovered this phenomenon by analyzing the internal mechanisms of LLMs. Whenever an LLM generates a response, it transforms the input query into a numerical representation and performs a series of computations before producing the output. At intermediate stages, these numerical representations are called “activations.” These activations contain significantly more information than what is reflected in the final output. By scrutinizing these activations, researchers can identify whether the LLM “knows” its response is inaccurate. A technique called SAPLMA (Statement Accuracy Prediction based on Language Model Activations) has been developed to explore this capability. SAPLMA examines the internal activations of LLMs to predict whether their outputs are truthful or not. Why Do Hallucinations Occur? LLMs function as next-word prediction models. Each word is selected based on its likelihood given the preceding words. For example, starting with “I ate,” the model might predict the next words as follows: The issue arises when earlier predictions constrain subsequent outputs. Once the model commits to a word, it cannot go back to revise its earlier choice. For instance: In another case: This mechanism reveals how the constraints of next-word prediction can lead to hallucinations, even when the model “knows” it is generating an incorrect response. Detecting Inaccuracies with SAPLMA To investigate whether an LLM recognizes its own inaccuracies, researchers developed the SAPLMA method. Here’s how it works: The classifier itself is a simple neural network with three dense layers, culminating in a binary output that predicts the truthfulness of the statement. Results and Insights The SAPLMA method achieved an accuracy of 60–80%, depending on the topic. While this is a promising result, it is not perfect and has notable limitations. For example: However, if LLMs can learn to detect inaccuracies during the generation process, they could potentially refine their outputs in real time, reducing hallucinations and improving reliability. The Future of Error Mitigation in LLMs The SAPLMA method represents a step forward in understanding and mitigating LLM errors. Accurate classification of inaccuracies could pave the way for models that can self-correct and produce more reliable outputs. While the current limitations are significant, ongoing research into these methods could lead to substantial improvements in LLM performance. By combining techniques like SAPLMA with advancements in LLM architecture, researchers aim to build models that are not only aware of their errors but capable of addressing them dynamically, enhancing both the accuracy and trustworthiness of AI systems. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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January 14, 2025in Data, Salesforce
Apple's Privacy Changes: A Call for Email Marketing Innovation
09Jan

Liar Liar Apple on Fire

Apple Developing Update After AI System Generates Inaccurate News Summaries Apple is working on a software update to address inaccuracies generated by its Apple Intelligence system after multiple instances of false news summaries were reported. The BBC first alerted Apple in mid-December to significant errors in the system, including a fabricated summary that falsely attributed a statement to BBC News. The summary suggested Luigi Mangione, accused of killing United Healthcare CEO Brian Thompson, had shot himself, a claim entirely unsubstantiated. Other publishers, such as ProPublica, also raised concerns about Apple Intelligence producing misleading summaries. While Apple did not respond immediately to the BBC’s December report, it issued a statement after pressure mounted from groups like the National Union of Journalists and Reporters Without Borders, both of which called for the removal of Apple Intelligence. Apple assured stakeholders it is working to refine the technology. A Widespread AI Issue: Hallucinations Apple joins the ranks of other AI vendors struggling with generative AI hallucinations—instances where AI produces false or misleading information. In October 2024, Perplexity AI faced a lawsuit from Dow Jones & Co. and the New York Post over fabricated news content attributed to their publications. Similarly, Google had to improve its AI summaries after providing users with inaccurate information. On January 16, Apple temporarily disabled AI-generated summaries for news apps on iPhone, iPad, and Mac devices. The Core Problem: AI Hallucination Chirag Shah, a professor of Information Science at the University of Washington, emphasized that hallucination is inherent to the way large language models (LLMs) function. “The nature of AI models is to generate, synthesize, and summarize, which makes them prone to mistakes,” Shah explained. “This isn’t something you can debug easily—it’s intrinsic to how LLMs operate.” While Apple plans to introduce an update that clearly labels summaries as AI-generated, Shah believes this measure falls short. “Most people don’t understand how these headlines or summaries are created. The responsible approach is to pause the technology until it’s better understood and mitigation strategies are in place,” he said. Legal and Brand Implications for Apple The hallucinated summaries pose significant reputational and legal risks for Apple, according to Michael Bennett, an AI adviser at Northeastern University. Before launching Apple Intelligence, the company was perceived as lagging in the AI race. The release of this system was intended to position Apple as a leader. Instead, the inaccuracies have damaged its credibility. “This type of hallucinated summarization is both an embarrassment and a serious legal liability,” Bennett said. “These errors could form the basis for defamation claims, as Apple Intelligence misattributes false information to reputable news sources.” Bennett criticized Apple’s seemingly minimal response. “It’s surprising how casual Apple’s reaction has been. This is a major issue for their brand and could expose them to significant legal consequences,” he added. Opportunity for Publishers The incident highlights the need for publishers to protect their interests when partnering with AI vendors like Apple and Google. Publishers should demand stronger safeguards to prevent false attributions and negotiate new contractual clauses to minimize brand risk. “This is an opportunity for publishers to lead the charge, pushing AI companies to refine their models or stop attributing false summaries to news sources,” Bennett said. He suggested legal action as a potential recourse if vendors fail to address these issues. Potential Regulatory Action The Federal Trade Commission (FTC) may also scrutinize the issue, as consumers paying for products like iPhones with AI capabilities could argue they are not receiving the promised service. However, Bennett believes Apple will likely act to resolve the problem before regulatory involvement becomes necessary. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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January 9, 2025in Generative AI, Google, Laws, Technology
AI in Programming
23Oct

AI in Programming

Since the launch of ChatGPT in 2022, developers have been split into two camps: those who ban AI in coding and those who embrace it. Many seasoned programmers not only avoid AI-generated code but also prohibit their teams from using it. Their reasoning is simple: “AI-generated code is unreliable.” Even if one doesn’t agree with this anti-AI stance, they’ve likely faced challenges, hurdles, or frustrations when using AI for programming. The key is finding the right strategies to use AI to your advantage. Many are still using outdated AI strategies from two years ago, likened to cutting down a tree with kitchen knives. Two Major Issues with AI for Developers The Wrong Way to Use AI… …can be broken down into two parts: When ChatGPT first launched, the typical way to work with AI was to visit the website and chat with GPT-3.5 in a browser. The process was straightforward: copy code from the IDE, paste it into ChatGPT with a basic prompt like “add comments,” get the revised code, check for errors, and paste it back into the IDE. Many developers, especially beginners and students, are still using this same method. However, the AI landscape has changed significantly over the last two years, and many have not adjusted their approach to fully leverage AI’s potential. Another common pitfall is how developers use AI. They ask the LLM to generate code, test it, and go back and forth to fix any issues. Often, they fall into an endless loop of AI hallucinations when trying to get the LLM to understand what’s wrong. This can be frustrating and unproductive. Four Tools to Boost Programming Productivity with AI 1. Cursor: AI-First IDE Cursor is an AI-first IDE built on VScode but enhanced with AI features. It allows developers to integrate a chatbot API and use AI as an assistant. Some of Cursor’s standout features include: Cursor integrates seamlessly with VScode, making it easy for existing users to transition. It supports various models, including GPT-4, Claude 3.5 Sonnet, and its built-in free cursor-small model. The combination of Cursor and Sonnet 3.5 has been particularly praised for producing reliable coding results. This tool is a significant improvement over copy-pasting code between ChatGPT and an IDE. 2. Micro Agent: Code + Test Case Micro Agent takes a different approach to AI-generated code by focusing on test cases. Instead of generating large chunks of code, it begins by creating test cases based on the prompt, then writes code that passes those tests. This method results in more grounded and reliable output, especially for functions that are tricky but not overly complex. 3. SWE-agent: AI for GitHub Issues Developed by Princeton Language and Intelligence, SWE-agent specializes in resolving real-world GitHub repository issues and submitting pull requests. It’s a powerful tool for managing large repositories, as it reviews codebases, identifies issues, and makes necessary changes. SWE-agent is open-source and has gained considerable popularity on GitHub. 4. AI Commits: git commit -m AI Commits generates meaningful commit messages based on your git diff. This simple tool eliminates the need for vague or repetitive commit messages like “minor changes.” It’s easy to install and uses GPT-3.5 for efficient, AI-generated commit messages. The Path Forward To stay productive and achieve goals in the rapidly evolving AI landscape, developers need the right tools. The limitations of AI, such as hallucinations, can’t be eliminated, but using the appropriate tools can help mitigate them. Simple, manual interactions like generating code or comments through ChatGPT can be frustrating. By adopting the right strategies and tools, developers can avoid these pitfalls and confidently enhance their coding practices. AI is evolving fast, and keeping up with its changes is crucial. The right tools can make all the difference in your programming workflow. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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October 23, 2024in Generative AI, Salesforce
AI Risk Management
22Oct

AI Risk Management

Organizations must acknowledge the risks associated with implementing AI systems to use the technology ethically and minimize liability. Throughout history, companies have had to manage the risks associated with adopting new technologies, and AI is no exception. Some AI risks are similar to those encountered when deploying any new technology or tool, such as poor strategic alignment with business goals, a lack of necessary skills to support initiatives, and failure to secure buy-in across the organization. For these challenges, executives should rely on best practices that have guided the successful adoption of other technologies. In the case of AI, this includes: However, AI introduces unique risks that must be addressed head-on. Here are 15 areas of concern that can arise as organizations implement and use AI technologies in the enterprise: Managing AI Risks While AI risks cannot be eliminated, they can be managed. Organizations must first recognize and understand these risks and then implement policies to minimize their negative impact. These policies should ensure the use of high-quality data, require testing and validation to eliminate biases, and mandate ongoing monitoring to identify and address unexpected consequences. Furthermore, ethical considerations should be embedded in AI systems, with frameworks in place to ensure AI produces transparent, fair, and unbiased results. Human oversight is essential to confirm these systems meet established standards. For successful risk management, the involvement of the board and the C-suite is crucial. As noted, “This is not just an IT problem, so all executives need to get involved in this.” Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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October 22, 2024in AI Tools, Data, Enterprise, Generative AI, Laws, Technology
New Technology Risks
27Sep

New Technology Risks

Organizations have always needed to manage the risks that come with adopting new technologies, and implementing artificial intelligence (AI) is no different. Many of the risks associated with AI are similar to those encountered with any new technology: poor alignment with business goals, insufficient skills to support the initiatives, and a lack of organizational buy-in. To address these challenges, executives should rely on best practices that have guided the successful adoption of other technologies, according to management consultants and AI experts. When it comes to AI, this includes: However, AI presents unique risks that executives must recognize and address proactively. Below are 15 areas of risk that organizations may encounter as they implement and use AI technologies: Managing AI Risks While the risks associated with AI cannot be entirely eliminated, they can be managed. Organizations must first recognize and understand these risks and then implement policies to mitigate them. This includes ensuring high-quality data for AI training, testing for biases, and continuous monitoring of AI systems to catch unintended consequences. Ethical frameworks are also crucial to ensure AI systems produce fair, transparent, and unbiased results. Involving the board and C-suite in AI governance is essential, as managing AI risk is not just an IT issue but a broader organizational challenge. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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September 27, 2024in AI Tools, Artificial Intelligence, Data, Enterprise, Generative AI, Laws, Salesforce, Technology
Reflection 70B
17Sep

Reflection 70B

Reflection 70B: HyperWrite’s Breakthrough AI That Thinks About Its Own Thinking In the rapid advancement of AI, we’ve seen models that can write, code, and even create art. But now, an AI has arrived that does something truly revolutionary—reflect on its own thinking. Enter Reflection 70B, HyperWrite’s latest large language model (LLM), not just pushing the boundaries of AI, but redefining them. Tackling AI Hallucinations: A Critical Issue AI hallucinations—the generation of false or misleading information—are like digital conspiracy theories. They sound plausible until you pause to scrutinize them. And unlike people, AI doesn’t get embarrassed when it’s wrong; it confidently continues, which is more than just frustrating—it’s potentially dangerous. As AI plays an increasing role in everything from content creation to medical diagnoses, having models that produce reliable, fact-based outputs is vital. Reflection 70B: An AI That Checks Its Own Work HyperWrite’s Reflection 70B is built to directly address this issue. It does something uniquely human: it reflects on its thought process. This model is designed to check its own work, functioning like an AI with a conscience, minus the existential crisis. Reflection-Tuning: The Game-Changing Technology At the core of Reflection 70B is a new technique called Reflection-Tuning. This is a major shift in how AI processes information. Here’s how it works: This entire process happens in real-time before the model delivers its final answer, ensuring a higher degree of accuracy. Why Reflection 70B is a Game-Changer You may wonder what sets this AI model apart. Here’s why it matters: Real-World Applications: How Reflection 70B Can Improve Lives Reflection 70B’s accuracy and self-correction abilities can have a transformative impact in several fields: Looking Forward: What’s Next for Reflection 70B? HyperWrite is already working on Reflection 405B, an even more advanced model that promises to further elevate AI accuracy and reliability. They’re not just building a better AI—they’re redefining how AI works. Conclusion: The AI That Reflects Reflection 70B marks a major leap in AI by introducing self-reflection and correction capabilities. This model isn’t just smarter; it’s more trustworthy. As AI continues to permeate our daily lives, this kind of reliability is no longer optional—it’s essential. HyperWrite’s Reflection 70B gives us a glimpse into a future where AI isn’t just intelligent but wise—an AI that understands the information it generates and ensures it’s accurate. This is the kind of AI we’ve been waiting for, and it’s a future worth getting excited about. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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September 17, 2024in Salesforce
2024 AI Glossary
02Sep

2024 AI Glossary

Artificial intelligence (AI) has moved from an emerging technology to a mainstream business imperative, making it essential for leaders across industries to understand and communicate its concepts. To help you unlock the full potential of AI in your organization, this 2024 AI Glossary outlines key terms and phrases that are critical for discussing and implementing AI solutions. Tectonic 2024 AI Glossary Active LearningA blend of supervised and unsupervised learning, active learning allows AI models to identify patterns, determine the next step in learning, and only seek human intervention when necessary. This makes it an efficient approach to developing specialized AI models with greater speed and precision, which is ideal for businesses aiming for reliability and efficiency in AI adoption. AI AlignmentThis subfield focuses on aligning the objectives of AI systems with the goals of their designers or users. It ensures that AI achieves intended outcomes while also integrating ethical standards and values when making decisions. AI HallucinationsThese occur when an AI system generates incorrect or misleading outputs. Hallucinations often stem from biased or insufficient training data or incorrect model assumptions. AI-Powered AutomationAlso known as “intelligent automation,” this refers to the integration of AI with rules-based automation tools like robotic process automation (RPA). By incorporating AI technologies such as machine learning (ML), natural language processing (NLP), and computer vision (CV), AI-powered automation expands the scope of tasks that can be automated, enhancing productivity and customer experience. AI Usage AuditingAn AI usage audit is a comprehensive review that ensures your AI program meets its goals, complies with legal requirements, and adheres to organizational standards. This process helps confirm the ethical and accurate performance of AI systems. Artificial General Intelligence (AGI)AGI refers to a theoretical AI system that matches human cognitive abilities and adaptability. While it remains a future concept, experts predict it may take decades or even centuries to develop true AGI. Artificial Intelligence (AI)AI encompasses computer systems that can perform complex tasks traditionally requiring human intelligence, such as reasoning, decision-making, and problem-solving. BiasBias in AI refers to skewed outcomes that unfairly disadvantage certain ideas, objectives, or groups of people. This often results from insufficient or unrepresentative training data. Confidence ScoreA confidence score is a probability measure indicating how certain an AI model is that it has performed its assigned task correctly. Conversational AIA type of AI designed to simulate human conversation using techniques like NLP and generative AI. It can be further enhanced with capabilities like image recognition. Cost ControlThis is the process of monitoring project progress in real-time, tracking resource usage, analyzing performance metrics, and addressing potential budget issues before they escalate, ensuring projects stay on track. Data Annotation (Data Labeling)The process of labeling data with specific features to help AI models learn and recognize patterns during training. Deep LearningA subset of machine learning that uses multi-layered neural networks to simulate complex human decision-making processes. Enterprise AIAI technology designed specifically to meet organizational needs, including governance, compliance, and security requirements. Foundational ModelsThese models learn from large datasets and can be fine-tuned for specific tasks. Their adaptability makes them cost-effective, reducing the need for separate models for each task. Generative AIA type of AI capable of creating new content such as text, images, audio, and synthetic data. It learns from vast datasets and generates new outputs that resemble but do not replicate the original data. Generative AI Feature GovernanceA set of principles and policies ensuring the responsible use of generative AI technologies throughout an organization, aligning with company values and societal norms. Human in the Loop (HITL)A feedback process where human intervention ensures the accuracy and ethical standards of AI outputs, essential for improving AI training and decision-making. Intelligent Document Processing (IDP)IDP extracts data from a variety of document types using AI techniques like NLP and CV to automate and analyze document-based tasks. Large Language Model (LLM)An AI technology trained on massive datasets to understand and generate text. LLMs are key in language understanding and generation and utilize transformer models for processing sequential data. Machine Learning (ML)A branch of AI that allows systems to learn from data and improve accuracy over time through algorithms. Model AccuracyA measure of how often an AI model performs tasks correctly, typically evaluated using metrics such as the F1 score, which combines precision and recall. Natural Language Processing (NLP)An AI technique that enables machines to understand, interpret, and generate human language through a combination of linguistic and statistical models. Retrieval Augmented Generation (RAG)This technique enhances the reliability of generative AI by incorporating external data to improve the accuracy of generated content. Supervised LearningA machine learning approach that uses labeled datasets to train AI models to make accurate predictions. Unsupervised LearningA type of machine learning that analyzes and groups unlabeled data without human input, often used to discover hidden patterns. By understanding these terms, you can better navigate the AI implementation world and apply its transformative power to drive innovation and efficiency across your organization. Tectonic 2024 AI Glossary Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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September 2, 2024in Generative AI
AI Trust and Optimism
23Jul

AI Trust and Optimism

Building Trust in AI: A Complex Yet Essential Task The Importance of Trust in AI Trust in artificial intelligence (AI) is ultimately what will make or break the technology. AI Trust and Optimism. Amid the hype and excitement of the past 18 months, it’s widely recognized that human beings need to have faith in this new wave of automation. This trust ensures that AI systems do not overstep boundaries or undermine personal freedoms. However, building this trust is a complicated task, thankfully receiving increasing attention from responsible thought leaders in the field. The Challenge of Responsible AI Development There is a growing concern that in the AI arms race, some individuals and companies prioritize making their technology as advanced as possible without considering long-term human-centric issues or the present-day realities. This concern was highlighted when OpenAI CEO Sam Altman presented AI hallucinations as a feature, not a bug, at last year’s Dreamforce, shortly after Salesforce CEO Marc Benioff emphasized the vital nature of trust. Insights from Salesforce’s Global Study Salesforce recently released the results of a global study involving 6,000 knowledge workers from various companies. The study reveals that while respondents trust AI to manage 43% of their work tasks, they still prefer human intervention in areas such as training, onboarding, and data handling. A notable finding is the difference in trust levels between leaders and rank-and-file workers. Leaders trust AI to handle over half (51%) of their work, while other workers trust it with 40%. Furthermore, 63% of respondents believe human involvement is key to building their trust in AI, though a subset is already comfortable offloading certain tasks to autonomous AI. Specifically: The study predicts that within three years, 41% of global workers will trust AI to operate autonomously, a significant increase from the 10% who feel comfortable with this today. Ethical Considerations in AI Paula Goldman, Salesforce’s Chief Ethical and Humane Use Officer, is responsible for establishing guidelines and best practices for technology adoption. Her interpretation of the study findings indicates that while workers are excited about a future with autonomous AI and are beginning to transition to it, trust gaps still need to be bridged. Goldman notes that workers are currently comfortable with AI handling tasks like writing code, uncovering data insights, and building communications. However, they are less comfortable delegating tasks such as inclusivity, onboarding, training employees, and data security to AI. Salesforce advocates for a “human at the helm” approach to AI. Goldman explains that human oversight builds trust in AI, but the way this oversight is designed must evolve to keep pace with AI’s rapid development. The traditional “human in the loop” model, where humans review every AI-generated output, is no longer feasible even with today’s sophisticated AI systems. Goldman emphasizes the need for more sophisticated controls that allow humans to focus on high-risk, high-judgment decisions while delegating other tasks. These controls should provide a macro view of AI performance and the ability to inspect it, which is crucial. Education and Training Goldman also highlights the importance of educating those steering AI systems. Trust and adoption of technology require that people are enabled to use it successfully. This includes comprehensive knowledge and training to make the most of AI capabilities. Optimism Amidst Skepticism Despite widespread fears about AI, Goldman finds a considerable amount of optimism and curiosity among workers. The study reflects a recognition of AI’s transformative potential and its rapid improvement. However, it is essential to distinguish between genuine optimism and hype-driven enthusiasm. Salesforce’s Stance on AI and Trust Salesforce has taken a strong stance on trust in relation to AI, emphasizing the non-silver bullet nature of this technology. The company acknowledges the balance between enthusiasm and pragmatism that many executives experience. While there is optimism about trusting autonomous AI within three years, this prediction needs to be substantiated with real-world evidence. Some organizations are already leading in generative AI adoption, while many others express interest in exploring its potential in the future. Conclusion Overall, this study contributes significantly to the ongoing debate about AI’s future. The concept of “human at the helm” is compelling and highlights the importance of ethical considerations in the AI-enabled future. Goldman’s role in presenting this research underscores Salesforce’s commitment to responsible AI development. For more insights, check out her blog on the subject. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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July 23, 2024in Einstein 1 Platform, Einstein Insights, Generative AI, Salesforce Einstein

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