Generative AI - gettectonic.com - Page 15
DPD Salesforce AI Enhancements
20Jun

DPD Salesforce AI Enhancements

DPD’s AI Integration: Enhancing Customer and Employee Experience DPD has ambitious plans to integrate AI throughout its Salesforce platform, aiming to automate tasks and significantly enhance the experiences of both customers and employees. DPD Salesforce AI Enhancements. Adam Hooper, Head of Central Platforms at DPD, explains that with over 400 million parcels delivered annually, maintaining robust customer relationships is crucial. To this end, DPD leverages a range of Salesforce technologies, including Service Cloud, Sales Cloud, Marketing Cloud, and Mulesoft. AI-Powered Customer Service In Salesforce’s latest update on DPD: Financial and Operational Efficiency Targeted Marketing Spreadsheets to Salesforce At the Salesforce World Tour event in London, Ben Pyne, Salesforce Platform Manager at DPD, elaborated on their current usage and future AI plans. Pyne’s team acts as internal consultants to optimize organizational workflows. As he explains: “My role is essentially to get people off spreadsheets and onto Salesforce!” He noted that about 40 departments and teams within DPD use Salesforce, far beyond the typical Sales and CRM applications. Custom applications within Salesforce personalize and enhance user experiences by focusing on relevant information. Using tools like Prompt Builder, Pyne’s team recently developed a project management app within Salesforce, streamlining tasks like writing acceptance criteria and user stories. Pyne emphasized: “I want our guys to focus on designing and building, less on the admin.” AI Use Cases When considering AI and generative AI, DPD sees significant potential to reduce operational tasks. Pyne highlighted case summarization as an obvious application, given the millions of customer service cases created each year. Rolling Out Generative AI DPD adopts a cautious approach to rolling out new technologies like generative AI. Pyne explained: “It’s starting small, finding the right teams to be able to do it. But fundamentally, starting somewhere and making slow progressions into it to ensure we don’t scare everybody away.” Ensuring Security and Trust Security and trust are paramount for DPD. Pyne noted their robust IT security team scrutinizes every implementation. Fortunately, Salesforce’s security measures, such as data anonymization and preventing LLMs (Large Language Models) from learning from their data, provide peace of mind. Pyne concluded: “We can focus on what we’re good at and not worry about the rest because Salesforce has thought of everything for us.” 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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June 20, 2024in Generative AI, Mulesoft, Salesforce, Salesforce Einstein
Generative AI Benchmark from Salesforce
18Jun

Generative AI Benchmark from Salesforce

Salesforce Introduces Generative AI Benchmark Tool for CRM Generative AI Benchmark from Salesforce evaluation tool designed to help businesses select the most suitable large language models (LLMs) for their CRM needs. Key Benefits of the Generative AI Benchmark for CRM: Tailored for CRM Applications: Silvio Savarese, EVP and Chief Scientist of Salesforce Research, highlights the importance of aligning generative AI processes with CRM goals. The benchmark helps businesses assess various LLMs using real-world CRM data, covering use cases such as sales and service scenarios. Human-Centered Evaluation Approach: The benchmark, developed by Salesforce’s Frontier AI applied research group and core product teams, leverages human professionals and real CRM data. This approach ensures a thorough evaluation across four key areas: Notable Insights: Savarese points out that larger models are not always the optimal choice. Smaller, more cost-effective models can offer satisfactory performance. He also mentions that this benchmark is just the beginning, with plans to expand metrics, use cases, and data annotations. Future evaluations will include the performance of fine-tuned models on CRM data, promising further differentiation and improvement. Salesforce’s generative AI benchmark tool offers a comprehensive and practical framework for businesses to choose the best LLMs for their CRM needs, ensuring a balance of accuracy, cost, speed, and trust and safety. 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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June 18, 2024in Generative AI
Hollywood's Fear of AI
17Jun

Hollywood’s Fear of AI

For the past 18 months, there has been a focus on why Generative AI (GenAI) poses a disruptive threat to Hollywood. Hollywood’s Fear of AI is entirely reasonable. This insight, most recently detailed in an investor presentation, highlights how GenAI will democratize high production-value video creation and exacerbate the low-end disruption of Hollywood already underway by creator content. The exploration now turns to another angle: why it will be challenging for major studios to capitalize on these tools, particularly AI video generators like Sora, Veo, and Runway Gen-3. Hollywood’s Fear of AI. Key Points: Early Adoption Outside Major Studios: The most promising early use cases for AI video generators will be outside major Hollywood studios. These include empowering independent creators and producers, as well as applications in advertising, music videos, and educational or corporate videos. AI won’t replace Hollywood but poses a bigger risk of disrupting it, requiring a disruptive innovation and incumbents who can’t respond effectively. Detailed Analysis: Sora’s Impact: AI has been part of the TV and film production process for a long time. Many studios use AI for concept art, localization services, and post-production work. However, AI video generators like Sora represent a new way to create video, potentially replacing much of the principal photography. The announcement of Sora was a watershed moment, showcasing radical improvements in temporal coherence, character consistency, and video length. Hollywood’s Fear of AI Legal Quagmire: Hollywood’s extensive legal framework complicates the adoption of GenAI. Studios need clear legal resolutions before extensively using GenAI in principal photography. The primary concerns are: Technical Limitations: AI video generators still fall short of professional standards. Issues include limited training sets, lack of understanding of physics, history, and geography, and insufficient control over video production elements like camera angles, lighting, and film stock. Near-Term Use Cases: Conclusion: While AI video generators won’t replace traditional Hollywood production soon, they have the potential to significantly disrupt the industry. The early adoption of these tools will likely occur outside the major studios, empowering independent creators and other sectors like advertising and education. The evolution of AI video generators continues to pose both challenges and opportunities for Hollywood. 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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June 17, 2024in Generative AI
The AI Tech Stack
15Jun

The AI Tech Stack

The AI tech stack integrates various tools, libraries, and solutions to enable applications with generative AI capabilities, such as image and text generation. Key components of the AI stack include programming languages, model providers, large language model (LLM) frameworks, vector databases, operational databases, monitoring and evaluation tools, and deployment solutions. The infrastructure of the AI tech stack leverages both parametric knowledge from foundational models and non-parametric knowledge from information sources (PDFs, databases, search engines) to perform generative AI functions like text and image generation, text summarization, and LLM-powered chat interfaces. This insight explores each component of the AI tech stack, aiming to provide a comprehensive understanding of the current state of generative AI applications, including the tools and technologies used to develop them. For those with a technical background, this Tectonic insight includes familiar tools and introduces new players within the AI ecosystem. For those without a technical background, the article serves as a guide to understanding the AI stack, emphasizing its importance across business roles and departments. Content Overview: The AI layer is a vital part of the modern application tech stack, integrating across various layers to enhance software applications. This layer brings intelligence to the stack through capabilities such as data visualization, generative AI (image and text generation), predictive analysis (behavior and trends), and process automation and optimization. In the backend layer, responsible for orchestrating user requests, the AI layer introduces techniques like semantic routing, which allocates user requests based on the meaning and intent of the tasks, leveraging LLMs. As the AI layer becomes more effective, it also reduces the roles and responsibilities of the application and data layers, potentially blurring the boundaries between them. Nevertheless, the AI stack is becoming a cornerstone of software development. Components of the AI Stack in the Generative AI Landscape: The emphasis on model providers over specific models highlights the importance of a provider’s ability to update models, offer support, and engage with the community, making switching models as simple as changing a name in the code. Programming Languages Programming languages are crucial in shaping the AI stack, influencing the selection of other components and ultimately determining the development of AI applications. The choice of a programming language is especially important for modern AI applications, where security, latency, and maintainability are critical factors. Among the options for AI application development, Python, JavaScript, and TypeScript (a superset of JavaScript) are the primary choices. Python holds a significant market share among data scientists, machine learning, and AI engineers, largely due to its extensive library support, including TensorFlow, PyTorch, and Scikit-learn. Python’s readable syntax and flexibility, accommodating both simple scripts and full applications with an object-oriented approach, contribute to its popularity. According to the February 2024 update on the PYPL (PopularitY of Programming Language) Index, Python leads with a 28.11% share, reflecting a growth trend of +0.6% over the previous year. The PYPL Index tracks the popularity of programming languages based on the frequency of tutorial searches on Google, indicating broader usage and interest. JavaScript, with an 8.57% share in the PYPL Index, has traditionally dominated web application development and is now increasingly used in the AI domain. This is facilitated by libraries and frameworks that integrate AI functionalities into web environments, enabling web developers to leverage AI infrastructure directly. Notable AI frameworks like LlamaIndex and LangChain offer implementations in both Python and JavaScript/TypeScript. The GitHub 2023 State of Open Source report highlights JavaScript’s dominance among developers, with Python showing a 22.5% year-over-year increase in usage on the platform. This growth reflects Python’s versatility in applications ranging from web development to data-driven systems and machine learning. The programming language component of the AI stack is more established and predictable compared to other components, with Python and JavaScript/TypeScript solidifying their positions among software engineers, web developers, data scientists, and AI engineers. Model Providers Model providers are a pivotal component of the AI stack, offering access to powerful large language models (LLMs) and other AI models. These providers, ranging from small organizations to large corporations, make available various types of models, including embedding models, fine-tuned models, and foundational models, for integration into generative AI applications. The AI landscape features a wide array of models that support capabilities like predictive analytics, image generation, and text completion. These models can be broadly classified into closed-source and open-source categories. Closed-Source Models have privatized internal configurations, architectures, and algorithms, which are not shared with the model consumers. Key information about the training processes and data is withheld, limiting the extent to which users can modify the model’s behavior. Access to these models is typically provided through an API or a web interface. Examples of closed-source models include: Open-Source Models offer a different approach, with varying levels of openness regarding their internal architecture, training data, weights, and parameters. These models foster collaboration and transparency in the AI community. The levels of open-source models include: Open-source models democratize access to advanced AI technology, reducing the entry barriers for developers to experiment with niche use cases. Examples of open-source models include: Open-Source vs. Closed-Source Large Language Models (LLMs) AI engineers and machine learning practitioners often face the critical decision of whether to use open- or closed-source LLMs in their AI stacks. This choice significantly impacts the development process, scalability, ethical considerations, and the application’s utility and commercial flexibility. Key Considerations for Selecting LLMs and Their Providers Resource Availability The decision between open- and closed-source models often hinges on the availability of compute resources and team expertise. Closed-source model providers simplify the development, training, and management of LLMs, albeit at the cost of potentially using consumer data for training or relinquishing control of private data access. Utilizing closed-source models allows teams to focus on other aspects of the stack, such as user interface design and data integrity. In contrast, open-source models offer more control and privacy but require significant resources to fine-tune, maintain, and deploy. Project Requirements The scale and technical requirements of a project are crucial in deciding whether to use open-

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June 15, 2024in Generative AI
Unity Catalog Open Sourced by Databricks
14Jun

Unity Catalog Open Sourced by Databricks

Databricks Announces Open Sourcing of Unity Catalog for Data and AI Governance Databricks, the Data and AI company, has announced the open-sourcing of Unity Catalog, the industry’s only unified solution for data and artificial intelligence (AI) governance across clouds, data formats, and data platforms. This initiative underscores Databricks’ commitment to open ecosystems, providing customers with the flexibility and control they need without vendor lock-in. The announcement marks a new era for open catalog standards for data and AI, with support from major partners such as Amazon Web Services (AWS), Google Cloud, Microsoft, NVIDIA, Salesforce, and others. Unity Catalog Open Sourced by Databricks. Key Features of Unity Catalog OSS Interoperability: Unity Catalog OSS offers a universal interface supporting any data format and compute engine. It can read tables with Delta Lake, Apache Iceberg™, and Apache Hudi™ clients via Delta Lake UniForm, and supports the Iceberg REST Catalog and Hive Metastore (HMS) interface standards. It is interoperable with all major cloud platforms, compute engines, and data and AI platforms. Unified Governance: Unity Catalog OSS enables unified governance across tabular data, non-tabular data, and AI assets such as ML models and generative AI tools, simplifying management, discovery, and development at scale. Openness: With open APIs and an Apache 2.0 licensed open source server, Unity Catalog OSS maximizes flexibility and customer choice by enabling broad interoperability across various engines, tools, and platforms. Industry and Partner Support Unity Catalog OSS is the industry’s only universal catalog for data and AI. Since its introduction in 2021, Unity Catalog has helped over 10,000 organizations break down silos created by multiple single-purpose solutions. Customer Testimonials: Supporting Cloud Partners: Supporting Data and AI Partners: The Future of Data and AI Governance With the open-sourcing of Unity Catalog, Databricks continues to lead in data and AI governance, fostering an ecosystem of interoperable tools, universal support for data and AI assets, and built-in security. Unity Catalog OSS will be available at the Data + AI Summit, furthering Databricks’ mission to empower organizations with the tools needed for modern data and AI applications. 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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June 14, 2024in Generative AI
AI Yes Gen AI No
12Jun

AI Yes Gen AI No

The race in generative AI may conclude sooner than anticipated, despite it barely starting for most. Consider the role of generative AI as a supportive assistant, aiding users across dashboards, messaging platforms, and datasets by summarizing documents, chats, and answering queries in natural language. AI Yes Gen AI No. At PegaWorld, significant attention was drawn to Pega’s Knowledge Buddy, an assistant integrated with LLM, specifically OpenAI on Azure, tailored to organization-specific data. CTO Don Schuerman emphasized the practicality of Pega’s solution: “Knowledge Buddy solves many enterprise problems, but it’s not the only RAG-based product out there. Everyone’s got one.” Indeed, major companies each boast their AI assistants: Adobe with AI Assistant, Salesforce with Einstein Copilot, Microsoft with Copilot, HubSpot with various AI assistants, Oracle’s Digital Assistant, and SAP’s Joule. Possessing an AI assistant is now a necessity, not a differentiator, as it has become standard across competitors. Generative AI tools like text and image generators have garnered public interest due to their accessibility. For instance, tools like Google Gemini enable anyone to create, blurring the lines between creator roles. The prevalence of generative AI across over 14,000 martech products is notable, exemplified by MarTechBot, which leverages AI to answer queries and generate images based on MarTech’s vast archive. While text and image generation capabilities rapidly advance, offering these tools is becoming a norm rather than a novelty. Soon, lacking these capabilities will be akin to a supermarket not selling eggs. Does this signify the end of the AI arms race? While generative AI will continue to evolve, it is becoming ubiquitous as a fundamental requirement. However, it’s crucial to distinguish the generative AI arms race from the broader AI landscape. Non-generative AI, such as predictive analytics and classification AI used in digital asset management systems, plays a critical role. This statistical AI analyzes data at scale to derive insights, recommend products, or guide customers through complex journeys. Pega exemplifies this with its AI-driven decisioning and workflow automation, predicting optimal actions for specific challenges, which is distinct from the generative AI focus seen in competitors like Salesforce, Adobe, and Oracle. Looking forward, while generative AI will permeate everyday applications, the true transformative AI for enterprises might lie in refined predictive AI or fully autonomous AI capable of unsupervised business decision-making. In conclusion, while text and image generation will become commonplace, their revolutionary impact may wane compared to the potential of other AI applications poised to redefine enterprise 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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June 12, 2024in Generative AI
BERT and GPT
11Jun

BERT and GPT

Breakthroughs in Language Models: From Word2Vec to Transformers Language models have rapidly evolved since 2018, driven by advancements in neural network architectures for text representation. This journey began with Word2Vec and N-Grams in 2013, followed by the emergence of Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks in 2014. The pivotal moment came with the introduction of the Attention Mechanism, which paved the way for large pre-trained models and transformers. BERT and GPT. From Word Embedding to Transformers The story of language models begins with word embedding. What is Word Embedding? Word embedding is a technique in natural language processing (NLP) where words are represented as vectors in a continuous vector space. These vectors capture semantic meanings, allowing words with similar meanings to have similar representations. For instance, in a word embedding model, “king” and “queen” would have vectors close to each other, reflecting their related meanings. Similarly, “car” and “truck” would be near each other, as would “cat” and “dog.” However, “car” and “dog” would not have close vectors due to their different meanings. A notable example of word embedding is Word2Vec. Word2Vec: Neural Network Model Using N-Grams Introduced by Mahajan, Patil, and Sankar in 2013, Word2Vec is a neural network model that uses n-grams by training on context windows of words. It has two main approaches: Both methods help capture semantic relationships, providing meaningful word embeddings that facilitate various NLP tasks like sentiment analysis and machine translation. Recurrent Neural Networks (RNNs) RNNs are designed for sequential data, processing inputs sequentially and maintaining a hidden state that captures information about previous inputs. This makes them suitable for tasks like time series prediction and natural language processing. The concept of RNNs can be traced back to 1925 with the Ising model, used to simulate magnetic interactions analogous to RNNs’ state transitions for sequence learning. Long Short-Term Memory (LSTM) Networks LSTMs, introduced by Hochreiter and Schmidhuber in 1997, are a specialized type of RNN designed to overcome the limitations of standard RNNs, particularly the vanishing gradient problem. They use gates (input, output, and forget gates) to regulate information flow, enabling them to maintain long-term dependencies and remember important information over long sequences. Comparing Word2Vec, RNNs, and LSTMs The Attention Mechanism and Its Impact The attention mechanism, introduced in the paper “Attention Is All You Need” by Vaswani et al., is a key component in transformers and large pre-trained language models. It allows models to focus on specific parts of the input sequence when generating output, assigning different weights to different words or tokens, and enabling the model to prioritize important information and handle long-range dependencies effectively. Transformers: Revolutionizing Language Models Transformers use self-attention mechanisms to process input sequences in parallel, capturing contextual relationships between all tokens in a sequence simultaneously. This improves handling of long-term dependencies and reduces training time. The self-attention mechanism identifies the relevance of each token to every other token within the input sequence, enhancing the model’s ability to understand context. Large Pre-Trained Language Models: BERT and GPT Both BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) are based on the transformer architecture. BERT Introduced by Google in 2018, BERT pre-trains deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. This enables BERT to create state-of-the-art models for tasks like question answering and language inference without substantial task-specific architecture modifications. GPT Developed by OpenAI, GPT models are known for generating human-like text. They are pre-trained on large corpora of text and fine-tuned for specific tasks. GPT is majorly generative and unidirectional, focusing on creating new text content like poems, code, scripts, and more. Major Differences Between BERT and GPT In conclusion, while both BERT and GPT are based on the transformer architecture and are pre-trained on large corpora of text, they serve different purposes and excel in different tasks. The advancements from Word2Vec to transformers highlight the rapid evolution of language models, enabling increasingly sophisticated NLP applications. 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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June 11, 2024in Generative AI, Google Analytics
Salesforce Driving Change
11Jun

Salesforce Driving Change

Sony Honda Mobility Leverages Salesforce Technology to Enhance Customer Relationships Sony Honda Mobility, a mobility tech startup, is utilizing Salesforce solutions—including Automotive Cloud, Data Cloud, Tableau, and MuleSoft—to deepen its customer relationships through enhanced data and insights. Sony Honda Mobility Inc. is a Japanese joint venture automotive company established by Sony Group Corporation and Honda Motor Company in 2022 to produce battery electric vehicles. The company will market its vehicles under the Afeela brand. Why It Matters: A significant 81% of business leaders report challenges with data fragmentation and silos. This disconnected, disjointed information often leads to immaterial and generic customer interactions, which is problematic as 80% of customers expect superior experiences given the extensive data companies collect. Key Developments: Sony Honda Mobility plans to launch its first electric vehicle in the United States in 2025, followed by Japan in 2026. To scale globally and stand out in the market, the company is empowering its service team with real-time data and insights on customer interactions with its products and services. How Is Salesforce Driving Change Data Cloud: A unified data platform that consolidates application, workflow, and data lake records, making it easier to train AI models, gain business insights, and improve customer relationships. Sony Honda Mobility is leveraging Data Cloud to integrate disparate data seamlessly across applications. MuleSoft API Integration: This integration connects data from Sony Honda Mobility’s vehicle and customer platform to Salesforce, ensuring a cohesive data flow. Automotive Cloud: Inside Automotive Cloud, Sony Honda Mobility now maintains a comprehensive profile of each customer and their vehicle, based on data from Data Cloud. This integration allows for personalized service and experiences at every touchpoint. The platform also streamlines contact center management, query handling, internal FAQ creation, web actions, and ongoing service support for incident and customer information management. Tableau: With Tableau’s visualizations of vehicle operation conditions and service usage status, Sony Honda Mobility can make quicker, more informed decisions about each customer’s needs. Customer Perspective “We aim to elevate our customer service and experiences to new heights. We are confident we can achieve this with Salesforce’s global expertise and proven track record in CRM, trusted AI, and data analytics,” said Yasuhide Mizuno, Chairman and CEO of Sony Honda Mobility. Salesforce Perspective “We are thrilled that Sony Honda Mobility has chosen the scalability, reliability, and expertise of Salesforce technology. By integrating their customer data on a single and trusted platform, we are excited to support Sony Honda Mobility as they scale excellent service to customers worldwide,” said Shinichi Koide, Chairman, President, and Chief Executive Officer of Salesforce Japan. The Result By harnessing Salesforce’s advanced technology, Sony Honda Mobility is poised to revolutionize its customer service and experience, ensuring a more connected, efficient, and personalized interaction for its customers globally. 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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June 11, 2024in Einstein 1 Platform, Generative AI, Mulesoft, Salesforce, Salesforce Data Cloud, Salesforce Implementation Services, Salesforce Manufacturing Automotive Energy, Salesforce Tableau
Adopt a Large Language Model
11Jun

Adopt a Large Language Model

In 2023, Algo Communications, a Canadian company, faced a significant challenge. With rapid growth on the horizon, the company struggled to train customer service representatives (CSRs) quickly enough to keep pace. To address this, Algo turned to an innovative solution: generative AI. They needed to Adopt a Large Language Model. Algo adopted a large language model (LLM) to accelerate the onboarding of new CSRs. However, to ensure CSRs could accurately and fluently respond to complex customer queries, Algo needed more than a generic, off-the-shelf LLM. These models, typically trained on public internet data, lack the specific business context required for accurate answers. This led Algo to use retrieval-augmented generation, or RAG. Many people have already used generative AI models like OpenAI’s ChatGPT or Google’s Gemini (formerly Bard) for tasks like writing emails or crafting social media posts. However, achieving the best results can be challenging without mastering the art of crafting precise prompts. An AI model is only as effective as the data it’s trained on. For optimal performance, it needs accurate, contextual information rather than generic data. Off-the-shelf LLMs often lack up-to-date, reliable access to your specific data and customer relationships. RAG addresses this by embedding the most current and relevant proprietary data directly into LLM prompts. RAG isn’t limited to structured data like spreadsheets or relational databases. It can retrieve all types of data, including unstructured data such as emails, PDFs, chat logs, and social media posts, enhancing the AI’s output quality. How RAG Works RAG enables companies to retrieve and utilize data from various internal sources for improved AI results. By using your own trusted data, RAG reduces or eliminates hallucinations and incorrect outputs, ensuring responses are relevant and accurate. This process involves a specialized database called a vector database, which stores data in a numerical format suitable for AI and retrieves it when prompted. “RAG can’t do its job without the vector database doing its job,” said Ryan Schellack, Director of AI Product Marketing at Salesforce. “The two go hand in hand. Supporting retrieval-augmented generation means supporting a vector store and a machine-learning search mechanism designed for that data.” RAG, combined with a vector database, significantly enhances LLM outputs. However, users still need to understand the basics of crafting clear prompts. Faster Responses to Complex Questions In December 2023, Algo Communications began testing RAG with a few CSRs using a small sample of about 10% of its product base. They incorporated vast amounts of unstructured data, including chat logs and two years of email history, into their vector database. After about two months, CSRs became comfortable with the tool, leading to a wider rollout. In just two months, Algo’s customer service team improved case resolution times by 67%, allowing them to handle new inquiries more efficiently. “Exploring RAG helped us understand we could integrate much more data,” said Ryan Zoehner, Vice President of Commercial Operations at Algo Communications. “It enabled us to provide detailed, technically savvy responses, enhancing customer confidence.” RAG now touches 60% of Algo’s products and continues to expand. The company is continually adding new chat logs and conversations to the database, further enriching the AI’s contextual understanding. This approach has halved onboarding time, supporting Algo’s rapid growth. “RAG is making us more efficient,” Zoehner said. “It enhances job satisfaction and speeds up onboarding. Unlike other LLM efforts, RAG lets us maintain our brand identity and company ethos.” RAG has also allowed Algo’s CSRs to focus more on personalizing customer interactions. “It allows our team to ensure responses resonate well,” Zoehner said. “This human touch aligns with our brand and ensures quality across all interactions.” Write Better Prompts – Adopt a Large Language Model If you want to learn how to craft effective generative AI prompts or use Salesforce’s Prompt Builder, check out Trailhead, Salesforce’s free online learning platform. Start learning Trail: Get Started with Prompts and Prompt Builder 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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June 11, 2024in Generative AI, Salesforce Einstein
Salesforce Customers Take On AI Hallucinations
09Jun

Salesforce Customers Take On AI Hallucinations

Earlier this month, CRM specialists Salesforce hosted the latest edition of its World Tour Essentials event in Johannesburg. This event provided Salesforce with an opportunity to engage more personally with businesses in the region and showcase the AI-powered solutions it is developing, including the Einstein 1 platform. Now Salesforce Customers Take On AI Hallucinations. Einstein 1 is designed for AI-focused enterprises, leveraging existing CRM applications from Salesforce, along with data cloud and AI-powered tools. This platform aims to address key business challenges, one of which is the issue of generative AI hallucinations—where AI generates false information due to data gaps. A notable example of this issue was seen with Google’s Gemini, which produced bizarre and potentially harmful suggestions, like advising users to put epoxy glue on pizza. This occurred because the AI lacked sufficient data to generate accurate responses. While some companies continue to use the internet to train their platforms to avoid such hallucinations, businesses, particularly in the CRM field, cannot afford these inaccuracies. Salesforce has introduced a tool called Einstein 1 Studio to combat this problem. This tool allows business engineers and developers to create prompts and refine the overall experience of conversational platforms like Slack AI. During a media roundtable at the World Tour Essentials Johannesburg event, Linda Saunders, Salesforce’s Director of Solutions Engineering Africa, explained how Einstein 1 Studio helps mitigate AI hallucinations. “If you ask Einstein an ungrounded prompt like, ‘Please summarize the case for me,’ it may not know which case you’re referring to. By pulling metadata elements into the prompt and using certain word triggers, we can provide a much richer and more accurate AI response,” Saunders highlighted. She added that once a setup is built, it can be activated across multiple use cases, creating a consistent and efficient deployment process. Saunders also emphasized the importance of the trust layer within Einstein 1, which includes data grounding, audit trails, data masking, and mechanisms to prevent hallucinations. “The trust layer is integral to Einstein 1. Whether you build it or use the out-of-the-box capabilities, the trust layer ensures grounded data, audit trails, and other critical features,” Saunders explained. She also pointed out that Einstein 1’s building tools can address localization and tailor experiences to specific markets, like South Africa. “South African customers have unique needs compared to those in the US. This tool allows for prompt customization to better suit local business requirements,” Saunders noted. The configuration engine on top of Copilot functionality allows businesses to refine prompt engineering, ensuring that AI interactions are more tailored and effective. As AI integration becomes more widespread in business operations, addressing issues like AI hallucinations is crucial. According to Salesforce, Einstein 1 is designed with these considerations in mind, ensuring a reliable and accurate AI experience. 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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June 9, 2024in Einstein 1 Platform, Generative AI
Can Tech Companies Use Generative AI for Good?
09Jun

Can Tech Companies Use Generative AI for Good?

Responsible AI Innovation: Benefits and Strategies Can Tech Companies Use Generative AI for Good?. As generative AI reshapes our world, the conversation has largely focused on its risks and downsides. However, Suzanne DiBianca, EVP and Chief Impact Officer at Salesforce, highlights the often-overlooked benefits that AI can bring to society and the fight against climate change. Optimizing AI for Sustainability Salesforce is driven by a mission to reduce emissions, accelerate climate solutions, and advance inclusivity. According to DiBianca, with proper guardrails and ethical development, AI can significantly aid in achieving these goals. Key Strategies for Sustainable AI Development: Best Practices for Reducing Carbon Emissions in Software Development Technologists must follow best practices such as identifying and shutting down underused systems, moving storage to more energy-efficient options, and developing energy-efficient software code. Tracking Climate Impact Improving the tracking of climate impact involves simplifying reporting processes and investing in emissions-tracking solutions. DiBianca advises starting with a clear climate action plan, conducting energy usage audits, prioritizing renewable energy, and collaborating with suppliers to collect emissions data. Accelerating Climate Innovation Through AI Investments DiBianca emphasizes that reducing global emissions and limiting warming to 1.5°C requires urgent innovation and investment. Salesforce invests in companies using AI to address climate challenges, such as PanoAI, which deploys AI to detect and respond to wildfires. Driving Sustainability and Equity with AI To ensure AI benefits all, it is essential to integrate sustainability into the business core. Salesforce’s Sustainable AI Policy Principles guide the company’s efforts in minimizing environmental impact and promoting climate innovation. Upholding Responsibilities As a technology leader, Salesforce is committed to shaping AI’s impact on the world responsibly. DiBianca stresses the importance of a dual commitment to the planet and global communities, fostering a legacy of responsible AI innovation for future generations. 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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June 9, 2024in Generative AI
SMPL
09Jun

SMPL

SMPL: A Virtual “Robot” for Embodied AI Embodied AI isn’t just for physical robots; it’s equally vital for virtual humans. Surprisingly, there’s a significant overlap between training robots to move and teaching avatars to behave like real people. Embodiment connects an AI agent’s “brain” to a “body” that navigates and interacts with the world, whether real or virtual, grounding AI in a dynamic 3D environment. Skinned Multi Person Linear model is a realistic 3D model of the human body that is based on skinning and blend shapes and is learned from thousands of 3D body scans. Virtual Humans and SMPL The most common “body” for virtual humans is Skinned Multi Person Linear Model, a parametric 3D model encapsulating human shape and movement. SMPL represents body shape, pose, facial expressions, hand gestures, soft-tissue deformations, and more in about 100 numbers. This post explores why Skinned Multi Person Linear can be thought of as a “robot.” Virtual Humans as Robots The goal is to create virtual humans that behave like real ones, embodying AI that perceives, understands, plans, and executes actions to change its environment. In a recent talk at Stanford, I described virtual humans as “3D human foundation agents,” akin to robots. Replace the SMPL body with a humanoid robot and the virtual world with the real world, and the challenges are quite similar. Key Differences Between Virtual and Physical Robots However, virtual humans must move convincingly like real humans, which isn’t always necessary for physical robots. Another difference is physics; while real-world robots can’t ignore physics, virtual worlds can selectively model real-world physics, making training “SMPL robots” easier. Plus, SMPL never breaks down! SMPL as a Universal Humanoid SMPL serves as a “universal language” of behavior. At Meshcapade, we often call it a “secret decoder ring.” Various data forms like images, video, IMUs, 3D scans, or text can be encoded into SMPL format. This data can then be decoded back into the same formats or retargeted to new humanoid characters, such as game avatars using the Meshcapade UEFN plugin for Unreal or even physical robots. AMASS: A Warehouse of Human Behavior A first paper at Meshcapade was AMASS, the world’s largest collection of 3D human movement data in a unified format (SMPL-X). Modern AI requires large-scale data to learn human behavior, and most deep learning methods modeling human motion rely on AMASS for training data. Researchers mine AMASS to train diffusion models to generate human movement. Adding text labels (see BABEL) enables conditioning generative models of motion on text. With speech and gesture data (see EMAGE), full-body avatars can be driven purely by speech. AMASS continues to grow, aiming to catalog all human behaviors. Learning from Humans At Perceiving Systems and Meshcapade, we use data like AMASS to train virtual humans and robots. For example, OmiH2O uses AMASS to retarget SMPL to a humanoid robot, and reinforcement learning methods mimic human behavior using AMASS data. Methods like WHAM can estimate SMPL from video in 3D world coordinates, crucial for robotic applications. This allows robots to learn from video demonstrations encoded into SMPL format, using an encoder for input and a decoder for output retargeting. SMPL as the “Latent Space” In machine learning, encoder-decoder architectures encode data into a latent space, which is typically compact. SMPL, though not truly latent because its parameters are interpretable, serves as a compact representation of humans. It factors body shape from pose, modeling correlations with “pose corrective” blend shapes and using principal component analysis for data compression. Summary Embodiment is crucial for both physical robots and virtual humans. Viewing virtual humans as robots can benefit robotics. We consider SMPL a virtual robot, collecting human behavior data at scale, learning from it, and retargeting this behavior to other virtual or physical embodiments. SMPL acts as a “universal language” for human movement, translating data into and out of various forms of embodiment. 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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June 9, 2024in Generative AI
Cautionary AI Tale
09Jun

A Cautionary AI Tale

Oliver Lovstrom, an AI student, wrote an interesting perspective on artificial intelligence, a cautionary AI tale, if you will. The Theory and Fairy Tale My first introduction to artificial intelligence was during high school when I began exploring its theories and captivating aspects. In 2018, as self-driving cars were gaining traction, I decided to create a simple autonomous vehicle for my final project. This project filled me with excitement and hope, spurring my continued interest and learning in AI. However, I had no idea that within a few years, AI would become significantly more advanced and accessible, reaching the masses through affordable robots. For instance, who could have imagined that just two years later, we would have access to incredible AI models like ChatGPT and Gemini, developed by tech giants? The Dark Side of Accessibility My concerns grew as I observed the surge in global cybersecurity issues driven by advanced language model-powered bots. Nowadays, it’s rare to go a day without hearing about some form of cybercrime somewhere in the world. A Brief Intro to AI for Beginners To understand the risks associated with AI, we must first comprehend what AI is and its inspiration: the human brain. In biology, I learned that the human brain consists of neurons, which have two main functions: Neurons communicate with sensory organs or other neurons, determining the signals they send through learning. Throughout our lives, we learn to associate different external stimuli (inputs) with sensory outputs, like emotions. Imagine returning to your childhood home. Walking in, you are immediately overwhelmed by nostalgia. This is a learned response, where the sensory input (the scene) passes through a network of billions of neurons, triggering an emotional output. Similarly, I began learning about artificial neural networks, which mimic this behavior in computers. Artificial Neural Networks Just as biological neurons communicate within our brains, artificial neural networks try to replicate this in computers. Each dot in the graph above represents an artificial neuron, all connected and communicating with one another. Sensory inputs, like a scene, enter the network, and the resulting output, such as an emotion, emerges from the network’s processing. A unique feature of these networks is their ability to learn. Initially, an untrained neural network might produce random outputs for a given input. However, with training, these networks learn to associate specific inputs with particular outputs, mirroring the learning process of the human brain. This capability can be leveraged to handle tedious tasks, but there are deeper implications to explore. The Wishing Well As AI technology advances, it begins to resemble a wishing well from a fairy tale—a tool that could fulfill any desire, for better or worse. In 2022, the release of ChatGPT and various generative AI tools astonished many. For the first time, people had free access to a system capable of generating coherent and contextually appropriate responses to almost any prompt. And this is just the beginning. Multimodal AI and the Next Step I explored multimodal AI, which allows the processing of data in different formats, such as text, images, audio, and possibly even physical actions. This development supports the “wishing well” hypothesis, but also revealed a darker side of AI. The Villains While a wishing well in fairy tales is associated with good intentions and moral outcomes, the reality of AI is more complex. The morality of AI usage depends on the people who wield it, and the potential for harm by a single bad actor is immense. The Big Actors and Bad Apples The control of AI technology is likely to be held by powerful entities, whether governments or private corporations. Speculating on their use of this technology can be unsettling. While we might hope AI acts as a deterrent, similar to nuclear weapons, AI’s invisibility and potential for silent harm make it particularly dangerous. We are already witnessing malicious uses of AI, from fake kidnappings to deepfakes, impacting everyone from ordinary people to politicians. As AI becomes more accessible, the risk of bad actors exploiting it grows. Even if AI maintains peace on a global scale, the issue of individuals causing harm remains—a few bad apples can spoil the bunch. Unexpected Actions and the Future AI systems today can perform unexpected actions, often through jailbreaking—manipulating models to give unintended information. While currently, the consequences might seem minor, they could escalate significantly in the future. AI does not follow predetermined rules but chooses the “best” path to achieve a goal, often learned independently from human oversight. This unpredictability, especially in multimodal models, is alarming. Consider an AI tasked with making pancakes. It might need money for ingredients and, determined by its learning, might resort to creating deepfakes for blackmail. This scenario, though seemingly absurd, highlights potential dangers as AI evolves with the growth of IoT, quantum computing, and big data, leading to superintelligent, self-managing systems. As AI surpasses human intelligence, more issues will emerge, potentially leading to a loss of control. Dr. Yildiz, an AI expert, highlighted these concerns in a story titled “Artificial Intelligence Does Not Concern Me, but Artificial Super-Intelligence Frightens Me.” Hope and Optimism Despite the fears surrounding AI, I remain hopeful. We are still in the early stages of this technology, providing ample time to course-correct. This can be achieved through recognizing the risks, fostering ethical AI systems, and raising a morally conscious new generation. Although I emphasized potential dangers, my intent is not to incite fear. Like previous industrial and digital revolutions, AI has the potential to greatly enhance our lives. I stay optimistic and continue my studies to contribute positively to the field. The takeaway from my story is that by using AI ethically and collaboratively, we can harness its power for positive change and a better future for everyone. This article by Oliver Lovstrom originally was published by Medium, 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

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June 9, 2024in Generative AI, Salesforce Einstein
AI for Marketing and Retail
09Jun

AI for Marketing and Retail

Salesforce Expands AI Capabilities for Marketing and Retail with Einstein Copilot Salesforce is making waves in the marketing and retail sectors by enhancing its Einstein Copilot feature, bringing advanced AI tools even closer to its users. AI for Marketing and Retail. For those unfamiliar with Salesforce, it offers Einstein 1, a comprehensive platform integrating AI and machine learning across its various applications. Einstein Copilot, a part of this platform, functions as an AI-powered assistant designed to provide real-time assistance, automate tasks, and deliver contextual insights to boost productivity. New Additions: Einstein Copilot for Marketers and Merchants Salesforce has recently announced two exciting additions: Einstein Copilot for Marketers and Einstein Copilot for Merchants. Einstein Copilot for Marketers: Einstein Copilot for Merchants: Availability Einstein Copilot for Marketers is set to be generally available this summer, while Einstein Copilot for Merchants will enter the beta stage in the autumn. Leadership Insight “With the Einstein 1 Platform, we’re giving organizations the power to unify all of their data on one trusted platform,” said Ariel Kelman, President and CMO of Salesforce. “This is the key to getting results from generative AI that are actually useful in driving your business forward.” About Salesforce Founded in 1999, Salesforce is a cloud-based software company renowned as one of the best CRM software providers. With over 72,000 employees globally and an annual revenue of $26.49 billion, Salesforce continues to lead in innovation and customer satisfaction. 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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June 9, 2024in Einstein 1 Platform, Generative AI

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