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Salesforce Document Generation

Generative AI

Artificial Intelligence in Focus Generative Artificial Intelligence is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data. What is the difference between generative AI and general AI? Traditional AI focuses on analyzing historical data and making future numeric predictions, while generative AI allows computers to produce brand-new outputs that are often indistinguishable from human-generated content. Recently, there has been a surge in discussions about artificial intelligence (AI), and the spotlight on this technology seems more intense than ever. Despite AI not being a novel concept, as many businesses and institutions have incorporated it in various capacities over the years, the heightened interest can be attributed to a specific AI-powered chatbot called ChatGPT. ChatGPT stands out by being able to respond to plain-language questions or requests in a manner that closely resembles human-written responses. Its public release allowed people to engage in conversations with a computer, creating a surprising, eerie, and evocative experience that captured widespread attention. This ability of an AI to engage in natural, human-like conversations represents a notable departure from previous AI capabilities. The Artificial Intelligence Fundamentals badge on the Salesforce Trailhead delves into the various specific tasks that AI models are trained to execute, highlighting the remarkable potential of generative AI, particularly in its ability to create diverse forms of text, images, and sounds, leading to transformative impacts both in and outside the workplace. Let’s explore the tasks that generative AI models are trained to perform, the underlying technology, and how businesses are specializing within the generative AI ecosystem. It also delves into concerns that businesses may harbor regarding generative Artificial Intelligence. Exploring the Capabilities of Language Models While generative AI may appear as a recent phenomenon, researchers have been developing and training generative AI models for decades. Some notable instances made headlines, such as Nvidia unveiling an AI model in 2018 capable of generating photorealistic images of human faces. These instances marked the gradual entry of generative AI into public awareness. While some researchers focused on AI’s capabilities generating specific types of images, others concentrated on language-related AI. This involved training AI models to perform various tasks related to interpreting text, a field known as natural language processing (NLP). Large language models (LLMs), trained on extensive datasets of real-world text, emerged as a key component of NLP, capturing intricate language rules that humans take years to learn. Summarization, translation, error correction, question answering, guided image generation, and text-to-speech are among the impressive tasks accomplished by LLMs. They provide a tool that significantly enhances language-related tasks in real-world scenarios. Predictive Nature of Generative AI Despite the remarkable predictions generated by generative AI in the form of text, images, and sounds, it’s crucial to clarify that these outputs represent a form of prediction rather than a manifestation of “thinking” by the computer. Generative Artificial Intelligence doesn’t possess opinions, intentions, or desires; it excels at predicting sequences of words based on patterns learned during training. Understanding this predictive nature is key. The AI’s ability to predict responses aligns with expectations rather than reflecting any inherent understanding or preference. Recognizing the predictive character of generative AI underscores its role as a powerful tool, bridging gaps in language-related tasks for both professional and recreational purposes. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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AI Large Language Models

What Exactly Constitutes a Large Language Model? Picture having an exceptionally intelligent digital assistant that extensively combs through text, encompassing books, articles, websites, and various written content up to the year 2021. Yet, unlike a library that houses entire books, this digital assistant processes patterns from the textual data it undergoes. This digital assistant, akin to a large language model (LLM), represents an advanced computer model tailored to comprehend and generate text with humanlike qualities. Its training involves exposure to vast amounts of text data, allowing it to discern patterns, language structures, and relationships between words and sentences. How Do These Large Language Models Operate? Fundamentally, large language models, exemplified by GPT-3, undertake predictions on a token-by-token basis, sequentially building a coherent sequence. Given a request, they strive to predict the subsequent token, utilizing their acquired knowledge of patterns during training. These models showcase remarkable pattern recognition, generating contextually relevant content across diverse topics. The “large” aspect of these models refers to their extensive size and complexity, necessitating substantial computational resources like powerful servers equipped with multiple processors and ample memory. This capability enables the model to manage and process vast datasets, enhancing its proficiency in comprehending and generating high-quality text. While the sizes of LLMs may vary, they typically house billions of parameters—variables learned during the training process, embodying the knowledge extracted from the data. The greater the number of parameters, the more adept the model becomes at capturing intricate patterns. For instance, GPT-3 boasts around 175 billion parameters, marking a significant advancement in language processing capabilities, while GPT-4 is purported to exceed 1 trillion parameters. While these numerical feats are impressive, the challenges associated with these mammoth models include resource-intensive training, environmental implications, potential biases, and more. Large language models serve as virtual assistants with profound knowledge, aiding in a spectrum of language-related tasks. They contribute to writing, offer information, provide creative suggestions, and engage in conversations, aiming to make human-technology interactions more natural. However, users should be cognizant of their limitations and regard them as tools rather than infallible sources of truth. What Constitutes the Training of Large Language Models? Training a large language model is analogous to instructing a robot in comprehending and utilizing human language. The process involves: Fine-Tuning: A Closer Look Fine-tuning involves further training a pre-trained model on a more specific and compact dataset than the original. It is akin to training a robot proficient in various cuisines to specialize in Italian dishes using a dedicated cookbook. The significance of fine-tuning lies in: Versioning and Progression Large language models evolve through versions, with changes in size, training data, or parameters. Each iteration aims to address weaknesses, handle a broader task spectrum, or minimize biases and errors. The progression is simplified as follows: In essence, large language model versions emulate successive editions of a book series, each release striving for refinement, expansiveness, and captivating capabilities. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Salesforce CRM for AI driven transformation

Salesforce Artificial Intelligence

Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust AI technologies directly into users’ workspaces. The Einstein Platform offers administrators and developers a comprehensive suite of platform services, empowering them to create smarter applications and tailor AI solutions for their enterprises. What is the designated name for Salesforce’s AI? Salesforce Einstein represents an integrated array of CRM AI technologies designed to facilitate personalized and predictive experiences, enhancing the professionalism and attractiveness of businesses. Since its introduction in 2016, it has consistently been a leading force in AI technology within the CRM realm. Is Salesforce Einstein a current feature? “Einstein is now every customer’s data scientist, simplifying the utilization of best-in-class AI capabilities within the context of their business.” Is Salesforce Einstein genuinely AI? Salesforce Einstein for Service functions as a generative AI tool, contributing to the enhancement of customer service and field service operations. Its capabilities extend to improving customer satisfaction, cost reduction, increased productivity, and informed decision-making. Salesforce Artificial Intelligence AI is just the starting point; real-time access to customer data, robust analytics, and business-wide automation are essential for AI effectiveness. Einstein serves as a comprehensive solution for businesses to initiate AI implementation with a trusted architecture that prioritizes data security. Einstein is constructed on an open platform, allowing the safe utilization of any large language model (LLM), whether developed by Salesforce Research or external sources. It offers flexibility in working with various models within a leading ecosystem of LLM platforms. Salesforce’s commitment to AI is evident through substantial investments in researching diverse AI areas, including Conversational AI, Natural Language Processing (NLP), Multimodal Data Intelligence and Generation, Time Series Intelligence, Software Intelligence, Fundamentals of Machine Learning, Science, Economics, and Environment. These endeavors aim to advance technology, improve productivity, and contribute to fields such as science, economics, and environmental sustainability. Content updated April 2023. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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