Large Language Model

13Aug

Qwary Salesforce Integration

Qwary Enhances Customer Insights with New Salesforce Integration HERNDON, Va., Aug. 13, 2024 /PRNewswire/ — While surveys have long been a staple for gathering customer feedback, data entry often poses a challenge in obtaining comprehensive insights. Qwary’s new Salesforce integration aims to resolve this issue by enabling seamless data transfer and synchronization between the two platforms. This integration allows teams to consolidate customer information into a single hub, providing real-time visibility and enhancing strategic planning and collaboration. Key features include creating email campaigns, importing contacts, mapping survey results, and automating event-based workflows. What Is Qwary’s Salesforce Integration? Qwary’s Salesforce integration is designed to streamline the analysis of Salesforce survey data, offering a more efficient way to understand customer interactions with your brand. By integrating survey feedback with CRM data, this tool helps you quickly adapt your products and services to meet evolving customer needs. It tracks customer journeys, collects feedback, and reveals pain points, enabling you to deliver tailored solutions. Benefits of Using Qwary’s Salesforce Integration Qwary’s integration offers several notable benefits: Automate Feedback Collection The integration automates the feedback collection process by triggering surveys at strategic points in the customer lifecycle. This allows your team to act swiftly to foster engagement and generate leads. Gain Actionable Insights Seamlessly integrating with Salesforce CRM, Qwary scores, analyzes, and enriches customer data, helping your team identify emerging trends and seize opportunities for personalization and customer development. Synchronize Data Automatically With Qwary’s integration, your contact data is consolidated into a single, reliable source of truth. Whether you’re using Salesforce or Qwary, automated data synchronization ensures consistency and provides real-time updates. Collaborate Effectively The integration promotes effective teamwork by sharing data between Salesforce and Qwary, enabling your team to solve problems collaboratively and refine strategies to boost customer retention. Key Capabilities Qwary’s Salesforce integration excels in managing customer feedback, automating workflows, and consolidating contact data: Salesforce Workflow Automation The integration simplifies scheduling and automating survey triggers, eliminating manual processes. Surveys can be initiated via email or following significant events, with responses seamlessly mapped into Salesforce. This creates a comprehensive view of customer behavior, helping your team act on insights, strengthen connections, and enhance satisfaction. Contact Data Importation Qwary facilitates quick access to Salesforce contacts, providing a holistic view of your customer base. The integration streamlines contact data importation and updates, eliminating manual data entry and speeding up data management. Potential Business Impacts By combining automation, synchronization, and data consolidation with a user-friendly interface, Qwary’s Salesforce integration enhances your sales team’s ability to collect and leverage customer feedback. Immediate access to comprehensive consumer insights allows your business to respond promptly to customer needs, improving satisfaction and loyalty. Real-time data aggregation helps your company adapt quickly and refine offerings to exceed customer expectations. Stay Ahead with Qwary’s Salesforce Integration Qwary continuously updates its solutions to meet the evolving needs of businesses focused on customer engagement. Leveraging automation, synchronization, and advanced analytics through an accessible platform, Qwary’s Salesforce integration empowers your team to enhance offerings and connect with customers efficiently. By optimizing the use of survey data and Salesforce feedback, Qwary keeps your business at the forefront of market trends, enabling you to consistently delight 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 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

August 13, 2024in Generative AI, Marketing Automation, Salesforce Data Cloud, Salesforce Einstein, Salesforce Implementation Services, Salesforce Managed Services, Salesforce Marketing Automation, Salesforce Optimization, Salesforce Personalization

11Aug

AI Adoption Rates

Businesses Eager to Embrace AI, Yet Concerned About Trust, Data, and Ethics in AI Adoption Rates As AI adoption rates are projected to surge, only 10% of people currently have full trust in AI for making informed decisions. According to Salesforce’s latest research, nearly half of customer service teams, over 40% of salespeople, and a third of marketers have fully integrated AI to enhance their work. However, 77% of business leaders express concerns about trusted data and ethics that could potentially stall their AI initiatives. The “Trends in AI for CRM” report highlights that companies fear missing out on the benefits of generative AI if the data supporting large language models (LLMs) is not based on their own reliable customer records. Additionally, respondents are worried about the lack of clear company policies governing the ethical use of AI and the complex landscape of LLM vendors, with 80% of companies currently using multiple models. Data Trust Issues Stymie AI Progress Despite expectations for a dramatic increase in AI adoption, only 10% of individuals fully trust AI to make informed decisions. The report reveals that 59% of organizations lack unified data strategies essential for ensuring AI reliability and accuracy. While 80% of employees using AI at work report increased productivity—a key driver for rapid AI adoption—only 21% of surveyed workers said their company has established clear policies on approved AI tools and use cases. Many employees, undeterred by the absence of formal policies, continue to use unapproved (55%) or explicitly banned (40%) tools. Furthermore, 69% of respondents noted that their employers have not provided training on AI usage. Critical Focus Areas: Trust, Data Security, and Transparency The report also underscores that 74% of the general public is concerned about the unethical use of AI. Companies that emphasize end-user control are better positioned to build customer trust in their AI strategies, with 56% of survey respondents expressing openness to AI under these conditions. Key factors for deepening trust in AI include increased visibility into AI use, human validation of outputs, and enhanced user control. “This is a pivotal moment as business leaders across various industries look to AI to drive growth, efficiency, and customer loyalty,” said Clara Shih, CEO of Salesforce AI. “Success with AI requires more than just deploying LLMs. It demands trusted data, user access control, vector search capabilities, audit trails, citations, data masking, low-code builders, and seamless UI integration to truly succeed,” Shih added. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

August 11, 2024in Generative AI

11Aug

APIs and Software Development

The Role of APIs in Modern Software Development APIs (Application Programming Interfaces) are central to modern software development, enabling teams to integrate external features into their products, including advanced third-party AI systems. For instance, you can use an API to allow users to generate 3D models from prompts on MatchboxXR. The Rise of AI-Powered Applications Many startups focus exclusively on AI, but often they are essentially wrappers around existing technologies like ChatGPT. These applications provide specialized user interfaces for interacting with OpenAI’s GPT models rather than developing new AI from scratch. Some branding might make it seem like they’re creating groundbreaking technology, when in reality, they’re leveraging pre-built AI solutions. Solopreneur-Driven Wrappers Large Language Models (LLMs) enable individuals and small teams to create lightweight apps and websites with AI features quickly. A quick search on Reddit reveals numerous small-scale startups offering: Such features can often be built using ChatGPT or Gemini within minutes for free. Well-Funded Ventures Larger operations invest heavily in polished platforms but may allocate significant budgets to marketing and design. This raises questions about whether these ventures are also just sophisticated wrappers. Examples include: While these products offer interesting functionalities, they often rely on APIs to interact with LLMs, which brings its own set of challenges. The Impact of AI-First, API-Second Approaches Design Considerations Looking Ahead Developer Experience: As AI technologies like LLMs become mainstream, focusing on developer experience (DevEx) will be crucial. Good DevEx involves well-structured schemas, flexible functions, up-to-date documentation, and ample testing data. Future Trends: The future of AI will likely involve more integrations. Imagine: AI is powerful, but the real innovation lies in integrating hardware, data, and interactions effectively. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

August 11, 2024in Salesforce

10Aug

Generative AI Tools

One of the most significant use cases for generative AI in business is customer service and support. Most of us have likely experienced the frustration of dealing with traditional automated systems. However, today’s advanced AI, powered by large language models and natural language chatbots, is rapidly improving these interactions. While many still prefer human agents for complex or sensitive issues, AI is proving highly capable of handling routine inquiries efficiently. Here’s an overview of some of the top AI-powered tools for automating customer service. Although the human element will always be essential in customer experience, these tools free up human agents from repetitive tasks, allowing them to focus on more complex challenges requiring empathy and creativity. Cognigy Cognigy is an AI platform designed to automate customer service voice and chat channels. It goes beyond simply reading FAQ responses by delivering personalized, context-sensitive answers in multiple languages. Cognigy’s AI Copilot feature enhances human contact center workers by offering real-time AI assistance during interactions, making both fully automated and human-augmented support possible. IBM WatsonX Assistant IBM’s WatsonX Assistant helps businesses create AI-powered personal assistants to streamline tasks, including customer support. With its drag-and-drop configuration, companies can set up seamless self-service experiences. The platform uses retrieval-augmented generation (RAG) to ensure that responses are accurate and up-to-date, continuously improving as it learns from customer interactions. Salesforce Einstein Service Cloud Einstein Service Cloud, part of the Salesforce platform, automates routine and complex customer service tasks. Its AI-powered Agentforce bots manage “low-touch” interactions, while “high-touch” cases are overseen by human agents supported by AI. Fully customizable, Einstein ensures that responses align with your brand’s tone and voice, all while leveraging enterprise data securely. Zendesk AI Zendesk, a leader in customer support, integrates generative AI to boost its service offerings. By using machine learning and natural language processing, Zendesk understands customer sentiment and intent, generates personalized responses, and automatically routes inquiries to the most suitable agent—be it human or machine. It also provides human agents with real-time guidance on resolving issues efficiently. Ada Ada is a conversational AI platform built for large-scale customer service automation. Its no-code interface allows businesses to create custom bots, reducing the cost of handling inquiries by up to 78% per ticket. By integrating domain-specific data, Ada helps improve both support efficiency and customer experience across omnichannel support environments. More AI Tools for Customer Service There are numerous other AI tools designed to enhance automated customer support: While AI tools are transforming customer service, the key lies in using them to complement human agents, allowing for a balance of efficiency and personalized care. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

August 10, 2024in AI Tools, Data, Enterprise, Generative AI, Salesforce, Salesforce Einstein

10Aug

Small Language Models

Large language models (LLMs) like OpenAI’s GPT-4 have gained acclaim for their versatility across various tasks, but they come with significant resource demands. In response, the AI industry is shifting focus towards smaller, task-specific models designed to be more efficient. Microsoft, alongside other tech giants, is investing in these smaller models. Science often involves breaking complex systems down into their simplest forms to understand their behavior. This reductionist approach is now being applied to AI, with the goal of creating smaller models tailored for specific functions. Sébastien Bubeck, Microsoft’s VP of generative AI, highlights this trend: “You have this miraculous object, but what exactly was needed for this miracle to happen; what are the basic ingredients that are necessary?” In recent years, the proliferation of LLMs like ChatGPT, Gemini, and Claude has been remarkable. However, smaller language models (SLMs) are gaining traction as a more resource-efficient alternative. Despite their smaller size, SLMs promise substantial benefits to businesses. Microsoft introduced Phi-1 in June last year, a smaller model aimed at aiding Python coding. This was followed by Phi-2 and Phi-3, which, though larger than Phi-1, are still much smaller than leading LLMs. For comparison, Phi-3-medium has 14 billion parameters, while GPT-4 is estimated to have 1.76 trillion parameters—about 125 times more. Microsoft touts the Phi-3 models as “the most capable and cost-effective small language models available.” Microsoft’s shift towards SLMs reflects a belief that the dominance of a few large models will give way to a more diverse ecosystem of smaller, specialized models. For instance, an SLM designed specifically for analyzing consumer behavior might be more effective for targeted advertising than a broad, general-purpose model trained on the entire internet. SLMs excel in their focused training on specific domains. “The whole fine-tuning process … is highly specialized for specific use-cases,” explains Silvio Savarese, Chief Scientist at Salesforce, another company advancing SLMs. To illustrate, using a specialized screwdriver for a home repair project is more practical than a multifunction tool that’s more expensive and less focused. This trend towards SLMs reflects a broader shift in the AI industry from hype to practical application. As Brian Yamada of VLM notes, “As we move into the operationalization phase of this AI era, small will be the new big.” Smaller, specialized models or combinations of models will address specific needs, saving time and resources. Some voices express concern over the dominance of a few large models, with figures like Jack Dorsey advocating for a diverse marketplace of algorithms. Philippe Krakowski of IPG also worries that relying on the same models might stifle creativity. SLMs offer the advantage of lower costs, both in development and operation. Microsoft’s Bubeck emphasizes that SLMs are “several orders of magnitude cheaper” than larger models. Typically, SLMs operate with around three to four billion parameters, making them feasible for deployment on devices like smartphones. However, smaller models come with trade-offs. Fewer parameters mean reduced capabilities. “You have to find the right balance between the intelligence that you need versus the cost,” Bubeck acknowledges. Salesforce’s Savarese views SLMs as a step towards a new form of AI, characterized by “agents” capable of performing specific tasks and executing plans autonomously. This vision of AI agents goes beyond today’s chatbots, which can generate travel itineraries but not take action on your behalf. Salesforce recently introduced a 1 billion-parameter SLM that reportedly outperforms some LLMs on targeted tasks. Salesforce CEO Mark Benioff celebrated this advancement, proclaiming, “On-device agentic AI is here!” 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 Guide to Creating a Working Sales Plan Creating a sales plan is a pivotal step in reaching your revenue objectives. To ensure its longevity and adaptability to Read more CRM Cloud Salesforce What is a CRM Cloud Salesforce? Salesforce Service Cloud is a customer relationship management (CRM) platform for Salesforce clients to Read more

August 10, 2024in Generative AI

09Aug

Demandbase One for Sales iFrame

Understanding the Demandbase One for Sales iFrame in Salesforce The Demandbase One for Sales iFrame (formerly known as Sales Intelligence) allows sales teams to access deep, actionable insights directly within Salesforce. This feature provides account-level and people-level details, including engagement data, technographics, intent signals, and even relevant news, social media posts, and email communications. By offering this level of visibility, sales professionals can make informed decisions and take the most effective next steps on accounts. Key Points: Overview of the Demandbase One for Sales iFrame The iFrame is divided into several key sections: Account, People, Engagement, and Insights tabs. Each of these provides critical information to help you better understand and engage with the companies and people you’re researching. Account Tab People Tab Engagement Tab Final Notes: The Demandbase One for Sales iFrame is a powerful tool that provides a complete view of account activity, helping sales teams make informed decisions and drive results. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

August 9, 2024in Data

08Aug

AI Infrastructure Flaws

Wiz Researchers Warn of Security Flaws in AI Infrastructure Providers AI infrastructure providers like Hugging Face and Replicate are vulnerable to emerging attacks and need to strengthen their defenses to protect sensitive user data, according to Wiz researchers. AI Infrastructure Flaws come from security being an afterthought. During Black Hat USA 2024 on Wednesday, Wiz security experts Hillai Ben-Sasson and Sagi Tzadik presented findings from a year-long study on the security of three major AI infrastructure providers: Hugging Face, Replicate, and SAP AI Core. Their research aimed to assess the security of these platforms and the risks associated with storing valuable data on them, given the increasing targeting of AI platforms by cybercriminals and nation-state actors. Hugging Face, a machine learning platform that allows users to create models and store datasets, was recently targeted in an attack. In June, the platform detected suspicious activity on its Spaces platform, prompting a key and token reset. The researchers demonstrated how they compromised these platforms by uploading malicious models and using container escape techniques to break out of their assigned environments, moving laterally across the service. In an April blog post, Wiz detailed how they compromised Hugging Face, gaining cross-tenant access to other customers’ data and training models. Similar vulnerabilities were later identified in Replicate and SAP AI Core, and these attack techniques were showcased during Wednesday’s session. Prior to Black Hat, Ben-Sasson, Tzadik, and Ami Luttwak, Wiz’s CTO and co-founder, discussed their research. They revealed that in all three cases, they successfully breached Hugging Face, Replicate, and SAP AI Core, accessing millions of confidential AI artifacts, including models, datasets, and proprietary code—intellectual property worth millions of dollars. Luttwak highlighted that many AI service providers rely on containers as barriers between different customers, but warned that these containers can often be bypassed due to misconfigurations. “Containerization is not a secure enough barrier for tenant isolation,” Luttwak stated. After discovering these vulnerabilities, the researchers responsibly disclosed the issues to each service provider. Ben-Sasson praised Hugging Face, Replicate, and SAP for their collaborative and professional responses, and Wiz worked closely with their security teams to resolve the problems. Despite these fixes, Wiz researchers recommended that organizations update their threat models to account for potential data compromises. They also urged AI service providers to enhance their isolation and sandboxing standards to prevent lateral movement by attackers within their platforms. The Risks of Rapid AI Adoption The session also addressed the broader challenges associated with the rapid adoption of AI. The researchers emphasized that security is often an afterthought in the rush to implement AI technologies. “AI security is also infrastructure security,” Luttwak explained, noting that the novelty and complexity of AI often leave security teams ill-prepared to manage the associated risks. Many organizations testing AI models are using unfamiliar tools, often open-source, without fully understanding the security implications. Luttwak warned that these tools are frequently not built with security in mind, putting companies at risk. He stressed the importance of performing thorough security validation on AI models and tools, especially given that even major AI service providers have vulnerabilities. In a related Black Hat session, Chris Wysopal, CTO and co-founder of Veracode, discussed how developers increasingly use large language models for coding but often prioritize functionality over security, leading to concerns like data poisoning and the replication of existing vulnerabilities. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

August 8, 2024in Salesforce

07Aug

SearchGPT and Knowledge Cutoff

Tackling the Knowledge Cutoff Challenge in Generative AI In the realm of generative AI, a significant hurdle has been the issue of knowledge cutoff—where a large language model (LLM) only has information up until a specific date. This was an early concern with OpenAI’s ChatGPT. For example, the GPT-4o model that currently powers ChatGPT has a knowledge cutoff in October 2023. The older GPT-4 model, on the other hand, had a cutoff in September 2021. Traditional search engines like Google, however, don’t face this limitation. Google continuously crawls the internet to keep its index up to date with the latest information. To address the knowledge cutoff issue in LLMs, multiple vendors, including OpenAI, are exploring search capabilities powered by generative AI (GenAI). Introducing SearchGPT: OpenAI’s GenAI Search Engine SearchGPT is OpenAI’s GenAI search engine, first announced on July 26, 2024. It aims to combine the strengths of a traditional search engine with the capabilities of GPT LLMs, eliminating the knowledge cutoff by drawing real-time data from the web. SearchGPT is currently a prototype, available to a limited group of test users, including individuals and publishers. OpenAI has invited publishers to ensure their content is accurately represented in search results. The service is positioned as a temporary offering to test and evaluate its performance. Once this evaluation phase is complete, OpenAI plans to integrate SearchGPT’s functionality directly into the ChatGPT interface. As of August 2024, OpenAI has not announced when SearchGPT will be generally available or integrated into the main ChatGPT experience. Key Features of SearchGPT SearchGPT offers several features designed to enhance the capabilities of ChatGPT: OpenAI’s Challenge to Google Search Google has long dominated the search engine landscape, a position that OpenAI aims to challenge with SearchGPT. Answers, Not Links Traditional search engines like Google act primarily as indexes, pointing users to other sources of information rather than directly providing answers. Google has introduced AI Overviews (formerly Search Generative Experience or SGE) to offer AI-generated summaries, but it still relies heavily on linking to third-party websites. SearchGPT aims to change this by providing direct answers to user queries, summarizing the source material instead of merely pointing to it. Contextual Continuity In contrast to Google’s point-in-time search queries, where each query is independent, SearchGPT strives to maintain context across multiple queries, offering a more seamless and coherent search experience. Search Accuracy Google Search often depends on keyword matching, which can require users to sift through several pages to find relevant information. SearchGPT aims to combine real-time data with an LLM to deliver more contextually accurate and relevant information. Ad-Free Experience SearchGPT offers an ad-free interface, providing a cleaner and more user-friendly experience compared to Google, which includes ads in its search results. AI-Powered Search Engine Comparison Here’s a comparison of the AI-powered search engines available today: Search Engine Platform Integration Publisher Collaboration Ads Cost SearchGPT (OpenAI) Standalone prototype Strong emphasis Ad-free Free (prototype stage) Google SGE Built on Google’s infrastructure SEO practices, content partnerships Includes ads Free Microsoft Bing AI/Copilot Built on Microsoft’s infrastructure SEO practices, content partnerships Includes ads Free Perplexity AI Standalone Basic source attribution Ad-free Free; $20/month for premium You.com AI assistant with various modes Basic source attribution Ad-free Free; premium tiers available Brave Search Independent search index Basic source attribution Ad-free Free Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

August 7, 2024in Generative AI

07Aug

Box Acquires Alphamoon

Box Inc. has acquired Alphamoon to enhance its intelligent document processing (IDP) capabilities and its enterprise knowledge management AI platform. Now that Box acquires Alphamoon, it will imr improves IDP. Box Acquires Alphamoon IDP goes beyond traditional optical character recognition (OCR) by applying AI to scanned paper documents and unstructured PDFs. While AI technologies like natural language processing (NLP), workflow automation, and document structure recognition have been around for some time, Alphamoon introduces generative AI (GenAI) into the mix, providing advanced capabilities. According to Rand Wacker, Vice President of AI Product Strategy at Box, the integration of GenAI helps not only with summarizing and extracting content from documents but also with recognizing document structures and categorizing them. GenAI works alongside existing OCR and NLP tools, making the digital conversion of paper documents more accurate. Box Acquires Alphamoon – Not LLM Although Box hasn’t acquired a large language model (LLM) outright, it has gained a toolkit that will enhance its Box AI platform. Box AI already uses retrieval-augmented generation to combine a user’s content with external LLMs, ensuring data security while training Box AI to better recognize and categorize documents. Alphamoon’s technology will further refine this process, enabling administrators to create tools more efficiently within the Box ecosystem. “For example, if Alphamoon’s OCR misreads or misextracts something, the system can adjust that specific part and feed it back into the LLM,” Wacker explained. “This approach is powered by an LLM, but it’s specifically trained to understand the documents it encounters, rather than relying on generic content from the internet.” Previewing an upcoming report from Deep Analysis, founder Alan Pelz-Sharpe shared that a survey of 500 enterprises across various industries, including financial services, manufacturing, healthcare, and government, revealed that 53% of enterprise documents still exist on paper. This highlights the need for Box users to have more precise tools to digitize contracts, letters, invoices, faxes, and other paper-based documents. Alphamoon’s generative AI-driven IDP solution allows for human oversight to ensure that attributes are correctly imported from the original documents. Pelz-Sharpe noted that IDP is challenging, but AI has made significant advancements, especially in handling imperfections like crumpled paper, coffee stains, and handwriting. He added that this acquisition addresses a critical gap for Box, which previously relied on partners for these capabilities. Box Buys Alphamoon – Integration Box plans to integrate Alphamoon’s tools into its platform later this year, with deeper integrations expected next year. These will include no-code app-building capabilities related to another acquisition, Crooze, as well as Box Relay’s forms and document generation tools. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

August 7, 2024in Generative AI

05Aug

AIs Role in Scams

How Generative AI is Supporting the Creation of Lures & Scams A Guide for Value Added Resellers Copyright © 2024 Gen Digital Inc. All rights reserved. Avast is part of Gen™. A long, long time ago, I worked for an antivirus company who has since been acquired by Avast. Knowing many of the people involved in this area of artificial intelligence, I pay attention when they publish a white paper. AI in scams is something we all should be concerned about. I am excited to share it in our Tectonic Insights. Executive Summary The capabilities and global usage of both large language models (LLMs) and generative AI are rapidly increasing. While these tools offer significant benefits to the general public and businesses, they also pose potential risks for misuse by malicious actors, including the misuse of tools like OpenAI’s ChatGPT and other GPTs. This document explores how the ChatGPT brand is exploited for lures, scams, and other social engineering threats. Generative AI is expected to play a crucial role in the cyber threat world challenges, particularly in creating highly believable, multilingual texts for phishing and scams. These advancements provide more opportunities for sophisticated social engineering by even less sophisticated scammers than ever before. Conversely, we believe generative AI will not drastically change the landscape of malware generation in the near term. Despite numerous proofs of concept, the complexity of generative AI methods still makes traditional, simpler methods more practical for malware creation. In short, the good may not outweigh the bad – just yet. Recognizing the value of generative AI for legitimate purposes is important. AI-based security and assistant tools with various levels of maturity and specialization are already emerging in the market. As these tools evolve and become more widely available, substantial improvements in their capabilities are anticipated. AI-Generated Lures and Scams AI-generated lures and scams are increasingly prevalent. Cybercriminals use AI to create lures and conduct phishing attempts and scams through various texts—emails, social media content, e-shop reviews, SMS scams, and more. AI improves the credibility of social scams by producing trustworthy, authentic texts, eliminating traditional phishing red flags like broken language and awkward addressing. These advanced threats have exploited societal issues and initiatives, including cryptocurrencies, Covid-19, and the war in Ukraine. The popularity of ChatGPT among hackers stems more from its widespread recognition than its AI capabilities, making it a prime target for investigation by attackers. How is Generative AI Supporting the Creation of Lures and Scams? Generative AI, particularly ChatGPT, enhances the language used in scams, enabling cybercriminals to create more advanced texts than they could otherwise. AI can correct grammatical errors, provide multilingual content, and generate multiple text variations to improve believability. For sophisticated phishing attacks, attackers must integrate the AI-generated text into credible templates. They can purchase functional, well-designed phishing kits or use web archiving tools to replicate legitimate websites, altering URLs to phish victims. Currently, attackers need to manually build some aspects of their attempts. ChatGPT is not yet an “out-of-the-box” solution for advanced malware creation. However, the emergence of multi-type models, combining outputs like images, audio, and video, will enhance the capabilities of generative AI for creating believable phishing and scam campaigns. Malvertising Malvertising, or “malicious advertising,” involves disseminating malware through online ads. Cybercriminals exploit the widespread reach and interactive nature of digital ads to distribute harmful content. Instances have been observed where ChatGPT’s name is used in malicious vectors on platforms like Facebook, leading users to fraudulent investment portals. Users who provide personal information become vulnerable to identity theft, financial fraud, account takeovers, and further scams. The collected data is often sold on the dark web, contributing to the broader cybercrime ecosystem. Recognizing and mitigating these deceptive tactics is crucial. YouTube Scams YouTube, one of the world’s most popular platforms, is not immune to cybercrime. Fake videos featuring prominent figures are used to trick users into harmful actions. This strategy, known as the “Appeal to Authority,” exploits trust and credibility to phish personal details or coerce victims into sending money. For example, videos featuring Elon Musk discussing OpenAI have been modified to scam victims. A QR code displayed in the video redirects users to a scam page, often a cryptocurrency scam or phishing attempt. As AI models like Midjourney and DALL-E mature, the use of fake images, videos, and audio is expected to increase, enhancing the credibility of these scams. Typosquatting Typosquatting involves minor changes in URLs to redirect users to different websites, potentially leading to phishing attacks or the installation of malicious applications. An example is an Android app named “Open Chat GBT: AI Chat Bot,” where a subtle URL alteration can deceive users into downloading harmful software. Browser Extensions The popularity of ChatGPT has led to the emergence of numerous browser extensions. While many are legitimate, others are malicious, designed to lure victims. Attackers create extensions with names resembling ChatGPT to deceive users into downloading harmful software, such as adware or spyware. These extensions can also subscribe users to services that periodically charge fees, known as fleeceware. For instance, a malicious extension mimicking “ChatGPT for Google” was reported by Guardio. This extension stole Facebook sessions and cookies but was removed from the Chrome Web Store after being reported. Installers and Cracks Malicious installers often mimic legitimate tools, tricking users into installing malware. These installers promise to install ChatGPT but instead deploy malware like NodeStealer, which steals passwords and browser cookies. Cracked or unofficial software versions pose similar risks, hiding malware that can steal personal information or take control of computers. This particular method of installing malware has been around for decades. However the usage of ChatGPT and other free to download tools has given it a resurrection. Fake Updates Fake updates are a common tactic where users are prompted to update their browser to access content. Campaigns like SocGholish use ChatGPT-related articles to lure users into downloading remote access trojans (RATs), giving attackers control over infected devices. These pages are often hosted on vulnerable WordPress sites or sites with

August 5, 2024in Generative AI

02Aug

Role of Small Language Models

The Role of Small Language Models (SLMs) in AI While much attention is often given to the capabilities of Large Language Models (LLMs), Small Language Models (SLMs) play a vital role in the AI landscape. Role of Small Language Models. Large vs. Small Language Models LLMs, like GPT-4, excel at managing complex tasks and providing sophisticated responses. However, their substantial computational and energy requirements can make them impractical for smaller organizations and devices with limited processing power. In contrast, SLMs offer a more feasible solution. Designed to be lightweight and resource-efficient, SLMs are ideal for applications operating in constrained computational environments. Their reduced resource demands make them easier and quicker to deploy, while also simplifying maintenance. What are Small Language Models? Small Language Models (SLMs) are neural networks engineered to generate natural language text. The term “small” refers not only to the model’s physical size but also to its parameter count, neural architecture, and the volume of data used during training. Parameters are numeric values that guide a model’s interpretation of inputs and output generation. Models with fewer parameters are inherently simpler, requiring less training data and computational power. Generally, models with fewer than 100 million parameters are classified as small, though some experts consider models with as few as 1 million to 10 million parameters to be small in comparison to today’s large models, which can have hundreds of billions of parameters. How Small Language Models Work SLMs achieve efficiency and effectiveness with a reduced parameter count, typically ranging from tens to hundreds of millions, as opposed to the billions seen in larger models. This design choice enhances computational efficiency and task-specific performance while maintaining strong language comprehension and generation capabilities. Techniques such as model compression, knowledge distillation, and transfer learning are critical for optimizing SLMs. These methods enable SLMs to encapsulate the broad understanding capabilities of larger models into a more concentrated, domain-specific toolset, facilitating precise and effective applications while preserving high performance. Advantages of Small Language Models Applications of Small Language Models Role of Small Language Models is lengthy. SLMs have seen increased adoption due to their ability to produce contextually coherent responses across various applications: Small Language Models vs. Large Language Models Feature LLMs SLMs Training Dataset Broad, diverse internet data Focused, domain-specific data Parameter Count Billions Tens to hundreds of millions Computational Demand High Low Cost Expensive Cost-effective Customization Limited, general-purpose High, tailored to specific needs Latency Higher Lower Security Risk of data exposure through APIs Lower risk, often not open source Maintenance Complex Easier Deployment Requires substantial infrastructure Suitable for limited hardware environments Application Broad, including complex tasks Specific, domain-focused tasks Accuracy in Specific Domains Potentially less accurate due to general training High accuracy with domain-specific training Real-time Application Less ideal due to latency Ideal due to low latency Bias and Errors Higher risk of biases and factual errors Reduced risk due to focused training Development Cycles Slower Faster Conclusion The role of Small Language Models (SLMs) is increasingly significant as they offer a practical and efficient alternative to larger models. By focusing on specific needs and operating within constrained environments, SLMs provide targeted precision, cost savings, improved security, and quick responsiveness. As industries continue to integrate AI solutions, the tailored capabilities of SLMs are set to drive innovation and efficiency across various domains. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

August 2, 2024in Salesforce

02Aug

Tectonic Guide to RAG

Guide to RAG (Retrieval-Augmented Generation) Retrieval-Augmented Generation (RAG) has become increasingly popular, and while it’s not yet as common as seeing it on a toaster oven manual, it is expected to grow in use. Despite its rising popularity, comprehensive guides that address all its nuances—such as relevance assessment and hallucination prevention—are still scarce. Drawing from practical experience, this insight offers an in-depth overview of RAG. Why is RAG Important? Large Language Models (LLMs) like ChatGPT can be employed for a wide range of tasks, from crafting horoscopes to more business-centric applications. However, there’s a notable challenge: most LLMs, including ChatGPT, do not inherently understand the specific rules, documents, or processes that companies rely on. There are two ways to address this gap: How RAG Works RAG consists of two primary components: While the system is straightforward, the effectiveness of the output heavily depends on the quality of the documents retrieved and how well the Retriever performs. Corporate documents are often unstructured, conflicting, or context-dependent, making the process challenging. Search Optimization in RAG To enhance RAG’s performance, optimization techniques are used across various stages of information retrieval and processing: Python and LangChain Implementation Example Below is a simple implementation of RAG using Python and LangChain: pythonCopy codeimport os import wget from langchain.vectorstores import Qdrant from langchain.embeddings import OpenAIEmbeddings from langchain import OpenAI from langchain_community.document_loaders import BSHTMLLoader from langchain.chains import RetrievalQA # Download ‘War and Peace’ by Tolstoy wget.download(“http://az.lib.ru/t/tolstoj_lew_nikolaewich/text_0073.shtml”) # Load text from html loader = BSHTMLLoader(“text_0073.shtml”, open_encoding=’ISO-8859-1′) war_and_peace = loader.load() # Initialize Vector Database embeddings = OpenAIEmbeddings() doc_store = Qdrant.from_documents( war_and_peace, embeddings, location=”:memory:”, collection_name=”docs”, ) llm = OpenAI() # Ask questions while True: question = input(‘Your question: ‘) qa = RetrievalQA.from_chain_type( llm=llm, chain_type=”stuff”, retriever=doc_store.as_retriever(), return_source_documents=False, ) result = qa(question) print(f”Answer: {result}”) Considerations for Effective RAG Ranking Techniques in RAG Dynamic Learning with RELP An advanced technique within RAG is Retrieval-Augmented Language Model-based Prediction (RELP). In this method, information retrieved from vector storage is used to generate example answers, which the LLM can then use to dynamically learn and respond. This allows for adaptive learning without the need for expensive retraining. Guide to RAG RAG offers a powerful alternative to retraining large language models, allowing businesses to leverage their proprietary knowledge for practical applications. While setting up and optimizing RAG systems involves navigating various complexities, including document structure, query processing, and ranking, the results are highly effective for most business use cases. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

August 2, 2024in Data

01Aug

Snowpark Container Services

Snowflake announced on Thursday the general availability of Snowpark Container Services, enabling customers to securely deploy and manage models and applications, including generative AI, within Snowflake’s environment. Initially launched in preview in June 2023, Snowpark Container Services is now a fully managed service available in all AWS commercial regions and in public preview in all Azure commercial regions. Containers are a software method used to isolate applications for secure deployment. Snowflake’s new feature allows customers to use containers to manage and deploy any type of model, optimally for generative AI applications, by securely integrating large language models (LLMs) and other generative AI tools with their data, explained Jeff Hollan, Snowflake’s head of applications and developer platform. Mike Leone, an analyst at TechTarget’s Enterprise Strategy Group, noted that Snowpark Container Services’ launch builds on Snowflake’s recent efforts to provide customers with an environment for developing generative AI models and applications. Sridhar Ramaswamy became Snowflake’s CEO in February, succeeding Frank Slootman, who led the company through a record-setting IPO. Under Ramaswamy, Snowflake has aggressively added generative AI capabilities, including launching its own LLM, integrating with Mistral AI, and providing tools for creating AI chatbots. “There has definitely been a concerted effort to enhance Snowflake’s capabilities and presence in the AI and GenAI markets,” Leone said. “Offerings like Snowpark help AI stakeholders like data scientists and developers use the languages they prefer.” As a result, Snowpark Container Services is a significant new feature for Snowflake customers. “It’s a big deal for the Snowflake ecosystem,” Leone said. “By enabling easy deployment and management of containers within the Snowflake platform, it helps customers handle complex workloads and maintain consistency across development and production stages.” Despite the secure environment provided by Snowflake Container Services, it was revealed in May that the login credentials of potentially 160 customers had been stolen and used to access their data. However, Snowflake has stated there is no evidence that the breach resulted from a vulnerability or misconfiguration of the Snowflake platform. Prominent customers affected include AT&T and Ticketmaster, and Snowflake’s investigation is ongoing. New Capabilities Generative AI can transform business by enabling employees to easily work with data to inform decisions and making trained experts more efficient. Generative AI, combined with an enterprise’s proprietary data, allows users to interact with data using natural language, reducing the need for coding and data literacy training. Non-technical workers can query and analyze data, freeing data engineers and scientists from routine tasks. Many data management and analytics vendors are focusing on developing generative AI-powered features. Enterprises are building models and applications trained on their proprietary data to inform business decisions. Among data platform vendors, AWS, Databricks, Google, IBM, Microsoft, and Oracle are providing environments for generative AI tool development. Snowflake, under Slootman, was less aggressive in this area but is now committed to generative AI development, though it still has ground to cover compared to its competitors. “Snowflake has gone as far as creating their own LLM,” Leone said. “But they still have a way to go to catch up to some of their top competitors.” Matt Aslett, an analyst at ISG’s Ventana Research, echoed that Snowflake is catching up to its rivals. The vendor initially focused on traditional data warehouse capabilities but made a significant step forward with the late 2023 launch of Cortex, a platform for developing AI models and applications. Cortex includes access to various LLMs and vector search capabilities, marking substantial progress. The general availability of Snowpark Container Services furthers Snowflake’s effort to foster generative AI development. The feature provides users with on-demand GPUs and CPUs to run any code next to their data. This enables the deployment and management of any type of model or application without moving data out of Snowflake’s platform. “It’s optimized for next-generation data and AI applications by pushing that logic to the data,” Hollan said. “This means customers can now easily and securely deploy everything from source code to homegrown models in Snowflake.” Beyond security, Snowpark Container Services simplifies model management and deployment while reducing associated costs. Snowflake provides a fully integrated managed service, eliminating the need for piecing together various services from different vendors. The service includes a budget control feature to reduce operational costs and provide cost certainty. Snowpark Container Services includes diverse storage options, observability tools like Snowflake Trail, and streamlined DevOps capabilities. It supports deploying LLMs with local volumes, memory, Snowflake stages, and configurable block storage. Integrations with observability specialists like Datadog, Grafana, and Monte Carlo are also included. Aslett noted that the 2020 launch of the Snowpark development environment enabled users to use their preferred coding languages with their data. Snowpark Container Services takes this further by allowing the use of third-party software, including generative AI models and data science libraries. “This potentially reduces complexity and infrastructure resource requirements,” Aslett said. Snowflake spent over a year moving Snowpark Container Services from private preview to general availability, focusing on governance, networking, usability, storage, observability, development operations, scalability, and performance. One customer, Landing AI, used Snowpark Container Services during its preview phases to develop LandingLens, an application for training and deploying computer vision models. “[With Snowflake], we are increasing access to AI for more companies and use cases, especially given the rapid growth of unstructured data in our increasingly digital world,” Landing AI COO Dan Maloney said in a statement Thursday. Future Plans With Snowpark Container Services now available on AWS, Snowflake plans to extend the feature to all cloud platforms. The vendor’s roadmap includes further improvements to Snowpark Container Services with more enterprise-grade tools. “Our team is investing in making it easy for companies ranging from startups to enterprises to build, deliver, distribute, and monetize next-generation AI products across their ecosystems,” Hollan said. Aslett said that making Snowpark Container Services available on Azure and Google Cloud is the logical next step. He noted that the managed service’s release is significant but needs broader availability beyond AWS regions. “The next step will be to bring Snowpark Container Services to general

August 1, 2024in Snowflake

31Jul

AI Data Cloud and Integration

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

July 31, 2024in Generative AI, Mulesoft

29Jul

Securing AI for Efficiency and Building Customer Trust

As businesses increasingly adopt AI to enhance automation, decision-making, customer support, and growth, they face crucial security and privacy considerations. The Salesforce Platform, with its integrated Einstein Trust Layer, enables organizations to leverage AI securely by ensuring robust data protection, privacy compliance, transparent AI functionality, strict access controls, and detailed audit trails. Why Secure AI Workflows Matter AI technology empowers systems to mimic human-like behaviors, such as learning and problem-solving, through advanced algorithms and large datasets that leverage machine learning. As the volume of data grows, securing sensitive information used in AI systems becomes more challenging. A recent Salesforce study found that 68% of Analytics and IT teams expect data volumes to increase over the next 12 months, underscoring the need for secure AI implementations. AI for Business: Predictive and Generative Models In business, AI depends on trusted data to provide actionable recommendations. Two primary types of AI models support various business functions: Addressing Key LLM Risks Salesforce’s Einstein Trust Layer addresses common risks associated with large language models (LLMs) and offers guidance for secure Generative AI deployment. This includes ensuring data security, managing access, and maintaining transparency and accountability in AI-driven decisions. Leveraging AI to Boost Efficiency Businesses gain a competitive edge with AI by improving efficiency and customer experience through: Four Strategies for Secure AI Implementation To ensure data protection in AI workflows, businesses should consider: The Einstein Trust Layer: Protecting AI-Driven Data The Einstein Trust Layer in Salesforce safeguards generative AI data by providing: Salesforce’s Einstein Trust Layer addresses the security and privacy challenges of adopting AI in business, offering reliable data security, privacy protection, transparent AI operations, and robust access controls. Through this secure approach, businesses can maximize AI benefits while safeguarding customer trust and meeting compliance requirements. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

July 29, 2024in Data, Generative AI, Laws, Salesforce, Technology

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Small Language Models

AI Infrastructure Flaws

SearchGPT and Knowledge Cutoff

Box Acquires Alphamoon

AIs Role in Scams

Tectonic Guide to RAG

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