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Salesforce AI Research Introduces LaTRO
14Nov

Salesforce AI Research Introduces LaTRO

Salesforce AI Research Introduces LaTRO: A Breakthrough in Enhancing Reasoning for Large Language Models Large Language Models (LLMs) have revolutionized tasks such as answering questions, generating content, and assisting with workflows. However, they often struggle with advanced reasoning tasks like solving complex math problems, logical deduction, and structured data analysis. Salesforce AI Research has addressed this challenge by introducing LaTent Reasoning Optimization (LaTRO), a groundbreaking framework that enables LLMs to self-improve their reasoning capabilities during training. The Need for Advanced Reasoning in LLMs Reasoning—especially sequential, multi-step reasoning—is essential for tasks that require logical progression and problem-solving. While current models excel at simpler queries, they often fall short in tackling more complex tasks due to a reliance on external feedback mechanisms or runtime optimizations. Enhancing reasoning abilities is therefore critical to unlocking the full potential of LLMs across diverse applications, from advanced mathematics to real-time data analysis. Existing techniques like Chain-of-Thought (CoT) prompting guide models to break problems into smaller steps, while methods such as Tree-of-Thought and Program-of-Thought explore multiple reasoning pathways. Although these techniques improve runtime performance, they don’t fundamentally enhance reasoning during the model’s training phase, limiting the scope of improvement. Salesforce AI Research Introduces LaTRO: A Self-Rewarding Framework LaTRO shifts the paradigm by transforming reasoning into a training-level optimization problem. It introduces a self-rewarding mechanism that allows models to evaluate and refine their reasoning pathways without relying on external feedback or supervised fine-tuning. This intrinsic approach fosters continual improvement and empowers models to solve complex tasks more effectively. How LaTRO Works LaTRO’s methodology centers on sampling reasoning paths from a latent distribution and optimizing these paths using variational techniques. Here’s how it works: This self-rewarding cycle ensures that the model continuously refines its reasoning capabilities during training. Unlike traditional methods, LaTRO’s framework operates autonomously, without the need for external reward models or costly supervised feedback loops. Key Benefits of LaTRO Performance Highlights LaTRO’s effectiveness has been validated across various datasets and models: Applications and Implications LaTRO’s ability to foster logical coherence and structured reasoning has far-reaching applications in fields requiring robust problem-solving: By enabling LLMs to autonomously refine their reasoning processes, LaTRO brings AI closer to achieving human-like cognitive abilities. The Future of AI with LaTRO LaTRO sets a new benchmark in AI research by demonstrating that reasoning can be optimized during training, not just at runtime. This advancement by Salesforce AI Research highlights the potential for self-evolving AI models that can independently improve their problem-solving capabilities. Salesforce AI Research Introduces LaTRO As the field of AI progresses, frameworks like LaTRO pave the way for more autonomous, intelligent systems capable of navigating complex reasoning tasks across industries. LaTRO represents a significant leap forward, moving AI closer to achieving true autonomous reasoning. 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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November 14, 2024in Data0 Comments
Where LLMs Fall Short
09Nov

Where LLMs Fall Short

Large Language Models (LLMs) have transformed natural language processing, showcasing exceptional abilities in text generation, translation, and various language tasks. Models like GPT-4, BERT, and T5 are based on transformer architectures, which enable them to predict the next word in a sequence by training on vast text datasets. How LLMs Function LLMs process input text through multiple layers of attention mechanisms, capturing complex relationships between words and phrases. Here’s an overview of the process: Tokenization and Embedding Initially, the input text is broken down into smaller units, typically words or subwords, through tokenization. Each token is then converted into a numerical representation known as an embedding. For instance, the sentence “The cat sat on the mat” could be tokenized into [“The”, “cat”, “sat”, “on”, “the”, “mat”], each assigned a unique vector. Multi-Layer Processing The embedded tokens are passed through multiple transformer layers, each containing self-attention mechanisms and feed-forward neural networks. Contextual Understanding As the input progresses through layers, the model develops a deeper understanding of the text, capturing both local and global context. This enables the model to comprehend relationships such as: Training and Pattern Recognition During training, LLMs are exposed to vast datasets, learning patterns related to grammar, syntax, and semantics: Generating Responses When generating text, the LLM predicts the next word or token based on its learned patterns. This process is iterative, where each generated token influences the next. For example, if prompted with “The Eiffel Tower is located in,” the model would likely generate “Paris,” given its learned associations between these terms. Limitations in Reasoning and Planning Despite their capabilities, LLMs face challenges in areas like reasoning and planning. Research by Subbarao Kambhampati highlights several limitations: Lack of Causal Understanding LLMs struggle with causal reasoning, which is crucial for understanding how events and actions relate in the real world. Difficulty with Multi-Step Planning LLMs often struggle to break down tasks into a logical sequence of actions. Blocksworld Problem Kambhampati’s research on the Blocksworld problem, which involves stacking and unstacking blocks, shows that LLMs like GPT-3 struggle with even simple planning tasks. When tested on 600 Blocksworld instances, GPT-3 solved only 12.5% of them using natural language prompts. Even after fine-tuning, the model solved only 20% of the instances, highlighting the model’s reliance on pattern recognition rather than true understanding of the planning task. Performance on GPT-4 Temporal and Counterfactual Reasoning LLMs also struggle with temporal reasoning (e.g., understanding the sequence of events) and counterfactual reasoning (e.g., constructing hypothetical scenarios). Token and Numerical Errors LLMs also exhibit errors in numerical reasoning due to inconsistencies in tokenization and their lack of true numerical understanding. Tokenization and Numerical Representation Numbers are often tokenized inconsistently. For example, “380” might be one token, while “381” might split into two tokens (“38” and “1”), leading to confusion in numerical interpretation. Decimal Comparison Errors LLMs can struggle with decimal comparisons. For example, comparing 9.9 and 9.11 may result in incorrect conclusions due to how the model processes these numbers as strings rather than numerically. Examples of Numerical Errors Hallucinations and Biases Hallucinations LLMs are prone to generating false or nonsensical content, known as hallucinations. This can happen when the model produces irrelevant or fabricated information. Biases LLMs can perpetuate biases present in their training data, which can lead to the generation of biased or stereotypical content. Inconsistencies and Context Drift LLMs often struggle to maintain consistency over long sequences of text or tasks. As the input grows, the model may prioritize more recent information, leading to contradictions or neglect of earlier context. This is particularly problematic in multi-turn conversations or tasks requiring persistence. Conclusion While LLMs have advanced the field of natural language processing, they still face significant challenges in reasoning, planning, and maintaining contextual accuracy. These limitations highlight the need for further research and development of hybrid AI systems that integrate LLMs with other techniques to improve reasoning, consistency, and overall performance. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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November 9, 2024in Data0 Comments
Cohere-Powered Slack Agents
21Oct

Cohere-Powered Slack Agents

Salesforce AI and Cohere-Powered Slack Agents: Seamless CRM Data Interaction and Enhanced Productivity Slack agents, powered by Salesforce AI and integrated with Cohere, enable seamless interaction with CRM data within the Slack platform. These agents allow teams to use natural language to surface data insights and take action, simplifying workflows. With Slack’s AI Workflow Builder and support for third-party AI agents, including Cohere, productivity is further enhanced through automated processes and customizable AI assistants. By leveraging these technologies, Slack agents provide users with direct access to CRM data and AI-powered insights, improving efficiency and collaboration. Key Features of Slack Agents: Salesforce AI and Cohere Productivity Enhancements with Slack Agents: Salesforce AI and Cohere AI Agent Capabilities in Slack: Salesforce and Cohere Data Security and Compliance for Slack Agents FAQ What are Slack agents, and how do they integrate with Salesforce AI and Cohere?Slack agents are AI-powered assistants that enable teams to interact with CRM data directly within Slack. Salesforce AI agents allow natural language data interactions, while Cohere’s integration enhances productivity with customizable AI assistants and automated workflows. How do Salesforce AI agents in Slack improve team productivity?Salesforce AI agents enable users to interact with both CRM and conversational data, update records, and analyze opportunities using natural language. This integration improves workflow efficiency, leading to a reported 47% productivity boost. What features does the Cohere integration with Slack AI offer?Cohere integration offers customizable AI assistants that can help generate workflows, summarize channel content, and provide intelligent responses to user queries within Slack. How do Slack agents handle data security and compliance?Slack agents leverage cloud-native DLP solutions, automatically detecting sensitive data across different file types and setting up automated remediation processes for enhanced security and compliance. Can Slack agents work with AI providers beyond Salesforce and Cohere?Yes, Slack supports AI agents from various providers. In addition to Salesforce AI and Cohere, integrations include Adobe Express, Anthropic, Perplexity, IBM, and Amazon Q Business, offering users a wide array of AI-powered 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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October 21, 2024in Data, Salesforce Slack
Natural Language Processing Explained
06Oct

Natural Language Processing Explained

What is Natural Language Processing (NLP)? Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that enables computers to interpret, analyze, and generate human language. By leveraging machine learning, computational linguistics, and deep learning, NLP helps machines understand written and spoken words, making communication between humans and computers more seamless. I apologize folks. I am feeling like the unicorn who missed the Ark. Tectonic has been providing you with tons of great material on artificial intelligence, but we left out a basic building block. Without further ado, Natural Language Processing Explained. Like a lot of components of AI, we often are using it without knowing we are using it. NLP is widely used in everyday applications such as: How Does NLP Work? Natural Language Processing combines several techniques, including computational linguistics, machine learning, and deep learning. It works by breaking down language into smaller components, analyzing these components, and then drawing conclusions based on patterns. If you have ever read a first grader’s reading primer it is the same thing. Learn a little three letter word. Recognize the meaning of the word. Understand it in the greater context of the sentence. Key NLP preprocessing steps include: Why Is NLP Important? NLP plays a vital role in automating and improving human-computer interactions by enabling systems to interpret, process, and respond to vast amounts of textual and spoken data. By automating tasks like sentiment analysis, content classification, and question answering, NLP boosts efficiency and accuracy across industries. For example: Key Use Cases of NLP in Business NLP Tasks NLP enables machines to handle various language tasks, including: Approaches to NLP Future of NLP NLP is becoming more integral in daily life as technology improves. From customer service chatbots to medical record summarization, NLP continues to evolve, but challenges remain, including improving coherence and reducing biases in machine-generated text. Essentially, NLP transforms the way machines and humans interact, making technology more intuitive and accessible across a range of industries. By Tectonic Solutions Architect – Shannan Hearne 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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October 6, 2024in Artificial Intelligence, Data, Google, Technology
A Company in Transition
26Sep

A Company in Transition

OpenAI Restructures: Increased Flexibility, But Raises Concerns OpenAI’s decision to restructure into a for-profit entity offers more freedom for the company and its investors but raises questions about its commitment to ethical AI development. Founded in 2015 as a nonprofit, OpenAI transitioned to a hybrid model in 2019 with the creation of a for-profit subsidiary. Now, its restructuring, widely reported this week, signals a shift where the nonprofit arm will no longer influence the day-to-day operations of the for-profit side. CEO Sam Altman is set to receive equity in the newly restructured company, which will operate as a benefit corporation (B Corp), similar to competitors like Anthropic and Sama. A Company in Transition This move comes on the heels of a turbulent year. OpenAI’s board initially voted to remove Altman over concerns about transparency, but later rehired him after significant backlash and the resignation of several board members. The company has seen a number of high-profile departures since, including co-founder Ilya Sutskever, who left in May to start Safe Superintelligence (SSI), an AI safety-focused venture that recently secured $1 billion in funding. This week, CTO Mira Murati, along with key research leaders Bob McGrew and Barret Zoph, also announced their departures. OpenAI’s restructuring also coincides with an anticipated multi-billion-dollar investment round involving major players such as Nvidia, Apple, and Microsoft, potentially pushing the company’s valuation to as high as $150 billion. Complex But Expected Move According to Michael Bennett, AI policy advisor at Northeastern University, the restructuring isn’t surprising given OpenAI’s rapid growth and increasingly complex structure. “Considering OpenAI’s valuation, it’s understandable that the company would simplify its governance to better align with investor priorities,” said Bennett. The transition to a benefit corporation signals a shift towards prioritizing shareholder interests, but it also raises concerns about whether OpenAI will maintain its ethical obligations. “By moving away from its nonprofit roots, OpenAI may scale back its commitment to ethical AI,” Bennett noted. Ethical and Safety Concerns OpenAI has faced scrutiny over its rapid deployment of generative AI models, including its release of ChatGPT in November 2022. Critics, including Elon Musk, have accused the company of failing to be transparent about the data and methods it uses to train its models. Musk, a co-founder of OpenAI, even filed a lawsuit alleging breach of contract. Concerns persist that the restructuring could lead to less ethical oversight, particularly in preventing issues like biased outputs, hallucinations, and broader societal harm from AI. Despite the potential risks, Bennett acknowledged that the company would have greater operational freedom. “They will likely move faster and with greater focus on what benefits their shareholders,” he said. This could come at the expense of the ethical commitments OpenAI previously emphasized when it was a nonprofit. Governance and Regulation Some industry voices, however, argue that OpenAI’s structure shouldn’t dictate its commitment to ethical AI. Veera Siivonen, co-founder and chief commercial officer of AI governance vendor Saidot, emphasized the role of regulation in ensuring responsible AI development. “Major players like Anthropic, Cohere, and tech giants such as Google and Meta are all for-profit entities,” Siivonen said. “It’s unfair to expect OpenAI to operate under a nonprofit model when others in the industry aren’t bound by the same restrictions.” Siivonen also pointed to OpenAI’s participation in global AI governance initiatives. The company recently signed the European Union AI Pact, a voluntary agreement to adhere to the principles of the EU’s AI Act, signaling its commitment to safety and ethics. Challenges for Enterprises The restructuring raises potential concerns for enterprises relying on OpenAI’s technology, said Dion Hinchcliffe, an analyst with Futurum Group. OpenAI may be able to innovate faster under its new structure, but the reduced influence of nonprofit oversight could make some companies question the vendor’s long-term commitment to safety. Hinchcliffe noted that the departure of key staff could signal a shift away from prioritizing AI safety, potentially prompting enterprises to reconsider their trust in OpenAI. New Developments Amid Restructuring Despite the ongoing changes, OpenAI continues to roll out new technologies. The company recently introduced a new moderation model, “omni-moderation-latest,” built on GPT-4o. This model, available through the Moderation API, enables developers to flag harmful content in both text and image outputs. A Company in Transition As OpenAI navigates its restructuring, balancing rapid innovation with maintaining ethical standards will be crucial to sustaining enterprise trust and market leadership. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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September 26, 2024in Data, Enterprise, Generative AI, Google, Laws, Technology
guide to RAG
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

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August 2, 2024in Data
tectonic logo
14Nov

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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November 14, 2022in Data, Salesforce, Salesforce Einstein, Technology

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