Testing Archives - gettectonic.com - Page 7
Mamba-2
19Jun

Mamba-2

Introducing Mamba-2: A New Era in State Space Model Architecture Researchers Tri Dao and Albert Gu have unveiled Mamba-2, the next iteration of their widely popular Mamba-1 model on GitHub. This new model promises significant improvements and innovations in the realm of state space models, particularly for information-dense data like language models. What is Mamba-2? M2 is a state space model architecture designed to outperform older models, including the widely used transformers. It shows remarkable promise in handling data-intensive tasks with greater efficiency and speed. Key Features of Mamba-2 Core Innovation: Structured State Space Duality (SSD) Performance Improvements Architectural Changes Performance Metrics In rigorous testing, M2 demonstrated superior scaling and faster training times compared to M1. Pretrained models, with sizes ranging from 130 million to 2.8 billion parameters, have been trained on extensive datasets like Pile and SlimPajama. Performance remains consistent across various tasks, with only minor variations due to evaluation noise. Specifications Getting Started with Mamba-2 To start using M2, install it via the command !pip install mamba-ssm and integrate it with PyTorch. Pretrained models are available on Hugging Face, facilitating easy deployment for various tasks. Conclusion Mamba-2 marks a significant advancement in state space model architecture, offering enhanced performance and efficiency over its predecessor and other models like transformers. Whether you’re engaged in language modeling or other data-intensive projects, M2 provides a powerful and efficient solution. 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 19, 2024in Salesforce
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
AI Tools for Automation
07Jun

AI Tools for Automation

Revolutionizing QA Testing: Top 10 AI Tools for Automation Artificial intelligence (AI) is transforming many aspects of our daily lives and professional environments, and its impact on Quality Assurance (QA) testing is particularly groundbreaking. While AI applications in areas like photo-to-anime converters gain attention, its role in automating QA testing processes is truly revolutionary. In this article, we’ll explore the top 10 AI tools that are changing the game in QA automation. AI Tools for Automation. Why Use AI in QA Testing? AI is a game-changer in QA testing, streamlining processes that were once manual and time-consuming. It enhances efficiency by optimizing test scenarios, predicting defects, and automating test creation and execution. Although manual testing remains important, AI tools are becoming crucial for achieving more accurate and efficient QA processes. Top 10 AI Testing Tools Here’s a curated list of the top 10 AI tools for test automation. Choosing the Right AI Tool To select the best AI testing tool for your needs, follow these steps: Conclusion AI is transforming QA testing by reducing preparation time, improving accuracy, and enhancing software quality. By leveraging the AI tools outlined above, you can optimize your testing processes and achieve superior results. For expert QA assistance and a detailed product testing estimate, contact our professional team. 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 7, 2024in Salesforce
RAG Chunking Method
01Jun

RAG Chunking Method

Enhancing Retrieval-Augmented Generation (RAG) Systems with Topic-Based Document Segmentation Dividing large documents into smaller, meaningful parts is crucial for the performance of Retrieval-Augmented Generation (RAG) systems. RAG Chunking Method. These systems benefit from frameworks that offer multiple document-splitting options. This Tectonic insight introduces an innovative approach that identifies topic changes using sentence embeddings, improving the subdivision process to create coherent topic-based sections. RAG Systems: An Overview A Retrieval-Augmented Generation (RAG) system combines retrieval-based and generation-based models to enhance output quality and relevance. It first retrieves relevant information from a large dataset based on an input query, then uses a transformer-based language model to generate a coherent and contextually appropriate response. This hybrid approach is particularly effective in complex or knowledge-intensive tasks. Standard Document Splitting Options Before diving into the new approach, let’s explore some standard document splitting methods using the LangChain framework, known for its robust support of various natural language processing (NLP) tasks. LangChain Framework: LangChain assists developers in applying large language models across NLP tasks, including document splitting. Here are key splitting methods available: Introducing a New Approach: Topic-Based Segmentation Segmenting large-scale documents into coherent topic-based sections poses significant challenges. Traditional methods often fail to detect subtle topic shifts accurately. This innovative approach, presented at the International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications (ACDSA 2024), addresses this issue using sentence embeddings. The Core Challenge Large documents often contain multiple topics. Conventional segmentation techniques struggle to identify precise topic transitions, leading to fragmented or overlapping sections. This method leverages Sentence-BERT (SBERT) to generate embeddings for individual sentences, which reflect changes in the vector space as topics shift. Approach Breakdown 1. Using Sentence Embeddings: 2. Calculating Gap Scores: 3. Smoothing: 4. Boundary Detection: 5. Clustering Segments: Algorithm Pseudocode Gap Score Calculation: pythonCopy code# Example pseudocode for gap score calculation def calculate_gap_scores(sentences, n): embeddings = [sbert.encode(sentence) for sentence in sentences] gap_scores = [] for i in range(len(sentences) – n): before = embeddings[i:i+n] after = embeddings[i+n:i+2*n] score = cosine_similarity(before, after) gap_scores.append(score) return gap_scores Gap Score Smoothing: pythonCopy code# Example pseudocode for smoothing gap scores def smooth_gap_scores(gap_scores, k): smoothed_scores = [] for i in range(len(gap_scores)): start = max(0, i – k) end = min(len(gap_scores), i + k + 1) smoothed_score = sum(gap_scores[start:end]) / (end – start) smoothed_scores.append(smoothed_score) return smoothed_scores Boundary Detection: pythonCopy code# Example pseudocode for boundary detection def detect_boundaries(smoothed_scores, c): boundaries = [] mean_score = sum(smoothed_scores) / len(smoothed_scores) std_dev = (sum((x – mean_score) ** 2 for x in smoothed_scores) / len(smoothed_scores)) ** 0.5 for i, score in enumerate(smoothed_scores): if score < mean_score – c * std_dev: boundaries.append(i) return boundaries Future Directions Potential areas for further research include: Conclusion This method combines traditional principles with advanced sentence embeddings, leveraging SBERT and sophisticated smoothing and clustering techniques. This approach offers a robust and efficient solution for accurate topic modeling in large documents, enhancing the performance of RAG systems by providing coherent and contextually relevant text sections. 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 1, 2024in Salesforce Einstein
Salesforce Unlimited+ Edition Explained
30May

Salesforce Unlimited+ Edition Explained

Salesforce Unlimited Plus (UE+) is designed as an advanced offering that incorporates several specialized features tailored for different industries, making it particularly suitable for larger organizations and enterprises that require robust, integrated solutions for complex business processes and customer relationship management. Salesforce Unlimited+ Edition Explained. Target Audience UE+ is targeted toward large enterprises that need extensive CRM functionalities combined with AI and data analytics capabilities. This solution is ideal for organizations that: • Manage complex customer relationships across multiple channels. • Require deep integration of data and processes across departments. • Are looking to leverage advanced AI capabilities for predictive insights and automation. • Need industry-specific solutions that can be customized for unique business requirements. The integration of various Salesforce clouds (e.g., Sales Cloud, Service Cloud, Data Cloud) with enhanced features like AI and specific industry capabilities makes UE+ a comprehensive solution for organizations aiming to streamline their operations and gain a competitive edge through advanced technology adoption. Here are the five Salesforce editions for every purpose: ·Starter/Essentials: Ideal for small businesses, offering basic contact, lead, and opportunity management. ·Professional: Tailored for mid-sized companies with enhanced sales forecasting and automation capabilities. ·Enterprise: Geared towards larger organizations, providing advanced customization, reporting, and integration options. ·Unlimited: Offers comprehensive functionality, customizability, 24/7 support, and access to premium features like generative AI. ·Unlimited Plus: Most robust solution for businesses of all sizes, featuring additional functionalities and enhanced capabilities. Key Considerations: ·Business Size: Consider the number of users and overall business scale when choosing an edition. ·Features Needed: Identify the specific features crucial for your sales, service, or marketing processes. ·Scalability: Choose an edition that accommodates your projected growth and future needs. ·Budget: Evaluate the cost of each edition against its offered features and value proposition. Sales Cloud Unlimited Edition+ Features: Account and Contact Management: Complete visibility of customer profiles including activity history and communications. Opportunity Management: Tracking and details of every sales deal at each stage. Pipeline Inspection: A comprehensive tool that allows sales managers to monitor pipeline changes, offering AI-driven insights and recommendations to optimize sales strategies. Einstein AI Capabilities: Includes tools like Einstein Conversation Insights which transcribe and analyze sales calls, highlighting key parts for review and deeper analysis. Customizable Reports and Dashboards: Enhanced capabilities for building real-time reports and visualizations to track sales metrics and forecasts. Advanced Integration Features: Integration with external data and systems through various APIs including REST and SOAP. Automation and Customization: Extensive options for workflow automation and personalization of user interfaces and customer interactions using the Flow Builder and Lightning App Builder. Developer Tools: Access to tools like Developer Sandbox for safe testing and app development environments. Service Cloud Unlimited+ Features: Einstein Bots: AI-powered chatbots to handle customer inquiries automatically, available 24/7 across various communication channels. Enhanced Messaging: Integration with popular messaging platforms like WhatsApp, SMS, and Apple Messages to facilitate seamless customer interactions. Feedback Management: Tools to gather and analyze customer feedback directly within the CRM. Self-Service Capabilities: Including customizable help centers and service catalogs that allow customers to find information and resolve issues independently. Field Service Tools: Comprehensive management of field operations including work order and asset management. Real-Time Analytics: Advanced reporting features for creating in-depth analytics to monitor and improve customer service processes. Additional features include Data Cloud, Generative AI, Service Cloud Voice, Digital Engagement, Feedback Management, Self-Service, and Slack. Salesforce Unlimited+ for Industries UE+ for Industries: UE+ for Industries includes Unlimited+ for Sales and Service together with industry-specific data models and capabilities to help customers drive faster time to value within their sectors: •Financial Services Cloud UE+ for Sales and Financial Services Cloud UE+ for Service helps banks, asset management, and insurance agencies connect all of their customer data on one platform and embed AI to deliver personalized financial engagement, at scale. •An insurance carrier can use Financial Services Cloud UE+ to connect engagement data like emails, webinars, and educational content with third-party conference attendance, social media follows, and business performance data to understand what is motivating agents, helping drive more personalized relationships and grow revenue with Data Cloud and Einstein AI. •Health Cloud UE+ for Service helps healthcare, pharmaceutical, and other medical organizations improve response times at their contact centers and offer digital healthcare services with built-in intelligence, real-time collaboration, and a 360-degree view of every patient, provider, and partner. •A hospital can use the bundle to quickly create a personalized, AI-powered support center to triage and speed up time to care with self-service tools like scheduling and connecting patients and members with care teams on their preferred channels. •Manufacturing Cloud UE+ for Sales brings together tools for manufacturing organizations to build their data foundation, embed AI capabilities across the sales cycle, and maximize productivity, empowering them to scale their commercial operations and grow revenues. •A manufacturer can now look across the entire book of business to see how companies are performing against negotiated sales agreements and then use AI-generated summaries to determine where to prioritize their time and resources. By Tectonic’s AArchitecture Team 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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May 30, 2024in Generative AI, Salesforce
Did Google Dethrone ChatGPT
30May

Did Google Dethrone ChatGPT?

Google’s Bard has emerged as a contender in the realm of large language models (LLMs), sparking speculation about its potential to outshine OpenAI’s ChatGPT. This insight explores the validity of this claim and examines the tests and factors that could determine the ultimate victor in this ongoing AI rivalry. Did Google Dethrone ChatGPT? Google’s Gemini 1.5 Pro is a generational leap in terms of Multimodal Large Language Models, or MLLMs, much like GPT-4 was to LLMs back in March 2023. Did Google Dethrone ChatGPT? While initial rumors of Bard’s “dethronement” of ChatGPT surfaced from a single LinkedIn post in February 2024, substantial evidence is required to substantiate such claims. Let’s determine the potential battleground: The Testing Grounds: There’s no singular, universally recognized benchmark for evaluating LLMs. Here are some areas where Google and OpenAI may showcase their AI prowess: Generative Text Quality: Can the LLM generate various creative text formats—such as poems, code, scripts, and emails—while maintaining coherence and factual accuracy? Question Answering: How effectively can the LLM respond to open-ended, challenging, or unconventional questions, drawing on its knowledge base? Following Instructions: Can the LLM adhere to complex instructions and perform tasks requiring multi-step reasoning? Bias Mitigation: Does the LLM demonstrate impartiality in its responses, or does it exhibit traces of prejudice or social stereotypes? Beyond the Tests: While test results offer insights into LLM capabilities, other factors influence their overall impact: Accessibility: How easily can the LLM be accessed by the public? Is there a user-friendly interface or developer API? Real-World Applications: How seamlessly can the LLM be integrated into practical applications like chatbots, virtual assistants, or educational tools? Continuous Learning: How adeptly does the LLM adapt and enhance its performance over time, incorporating new data and user feedback? The Current Landscape: Declaring a definitive winner is challenging. Bard and ChatGPT excel in different domains. Here’s a speculative analysis: Generative Text Quality: Bard may have a slight advantage, leveraging Google’s extensive dataset. Question Answering: ChatGPT might excel in responding to open-ended queries with creativity, while Bard may prioritize factual accuracy. Following Instructions & Bias Mitigation: Both LLMs are continually refining their capabilities in these areas. The Future of LLMs: The landscape of LLMs is dynamic, with Google and OpenAI poised to make significant advancements. Anticipated developments include: Focus on Explainability: Efforts to understand the reasoning behind LLM responses to foster transparency and trust. Bias Mitigation: Strategies to address bias in LLMs for fairer and more inclusive interactions. Specialized LLMs: Development of domain-specific LLMs tailored to fields like medicine or law. Is Google AI better than ChatGPT? Gemini offers a better user experience, with more imagery and website links. Gemini Advanced generates better AI images than ChatGPT Plus. Gemini responses were often set out in a more readable format than ChatGPT’s responses. Gemini was better at generating spreadsheet formulas than ChatGPT. How is Bard better than ChatGPT? Bard has real-time access to the internet through Google Search, allowing it to incorporate the latest information and news into its responses. Trained on a static dataset not updated since 2021, however, ChatGPT can only access external information through plugins, and this functionality is limited. Is Google nervous about ChatGPT? It’s that the technology represents everything Google was afraid artificial intelligence would become. If ChatGPT runs rampant, the search giant fears it could ruin AI adoption for everyone. Since going viral, ChatGPT has demonstrated how generative AI can be user-friendly, practical, and productive. The narrative of ChatGPT’s dethronement may be premature. Bard and ChatGPT are evolving entities, and the ultimate victor will be determined by their ability to navigate future challenges and opportunities. As these LLMs progress, users stand to benefit from access to increasingly sophisticated and beneficial AI tools. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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May 30, 2024in Generative AI, Salesforce Einstein
Gen AI and Test Automation
19May

Gen AI and Test Automation

Generative AI has brought transformative advancements across industries, and test automation is no exception. By generating code, test scenarios, and even entire suites, Generative AI enables Software Development Engineers in Test (SDETs) to boost efficiency, expand test coverage, and improve reliability. 1. Enhanced Test Case Generation One of the biggest hurdles in test automation is generating diverse, comprehensive test cases. Traditional methods often miss edge cases or diverse scenarios. Generative AI, however, can analyze existing data and automatically generate extensive test cases, including potential edge cases that may not be apparent to human testers. Example: An SDET can use Generative AI to create test cases for a web application by feeding it requirements and user data. This enables the AI to produce hundreds of test cases, capturing diverse user behaviors and interactions that manual testers may overlook. pythonCopy codeimport openai openai.api_key = ‘YOUR_API_KEY’ def generate_test_cases(application_description): response = openai.Completion.create( engine=”text-davinci-003″, prompt=f”Generate comprehensive test cases for the following application: {application_description}”, max_tokens=500 ) return response.choices[0].text app_description = “An e-commerce platform for browsing products, adding to cart, and checking out.” test_cases = generate_test_cases(app_description) print(test_cases) Sample Output: 2. Intelligent Test Script Creation Writing test scripts manually can be labor-intensive and error-prone. Generative AI can simplify this by generating test scripts based on an application’s flow, ensuring consistency and precision. Example: If an SDET needs to automate tests for a mobile app, they can use Generative AI to generate scripts for various scenarios, significantly reducing manual work. pythonCopy codeimport hypothetical_ai_test_tool ui_description = “”” Login Page: – Username field – Password field – Login button Home Page: – Search bar – Product listings – Add to cart buttons “”” test_scripts = hypothetical_ai_test_tool.generate_selenium_scripts(ui_description) Sample Output for test_login.py: pythonCopy codefrom selenium import webdriver from selenium.webdriver.common.keys import Keys def test_login(): driver = webdriver.Chrome() driver.get(“http://example.com/login”) username_field = driver.find_element_by_name(“username”) password_field = driver.find_element_by_name(“password”) login_button = driver.find_element_by_name(“login”) username_field.send_keys(“testuser”) password_field.send_keys(“password”) login_button.click() assert “Home” in driver.title driver.quit() 3. Automated Maintenance of Test Suites As applications evolve, maintaining test suites is critical. Generative AI can monitor app changes and update test cases automatically, keeping test suites accurate and relevant. Example: In a CI/CD pipeline, an SDET can deploy Generative AI to track code changes and update affected test scripts. This minimizes downtime and ensures tests stay aligned with application updates. pythonCopy codeimport hypothetical_ai_maintenance_tool def maintain_test_suite(): changes = hypothetical_ai_maintenance_tool.analyze_code_changes() updated_scripts = hypothetical_ai_maintenance_tool.update_test_scripts(changes) for script_name, script_content in updated_scripts.items(): with open(script_name, ‘w’) as file: file.write(script_content) maintain_test_suite() Sample Output:“Updating test_login.py with new login flow changes… Test scripts updated successfully.” 4. Natural Language Processing for Test Case Design Generative AI with NLP can interpret human language, enabling SDETs to create test cases from plain-language descriptions, enhancing collaboration across technical and non-technical teams. Example: An SDET can use an NLP-powered tool to translate a feature description from a product manager into test cases. This speeds up the process and ensures that test cases reflect intended functionality. pythonCopy codeimport openai openai.api_key = ‘YOUR_API_KEY’ def create_test_cases(description): response = openai.Completion.create( engine=”text-davinci-003″, prompt=f”Create test cases based on this feature description: {description}”, max_tokens=500 ) return response.choices[0].text feature_description = “Allow users to reset passwords via email to regain account access.” test_cases = create_test_cases(feature_description) print(test_cases) Sample Output: 5. Predictive Analytics for Test Prioritization Generative AI can analyze historical data to prioritize high-risk areas, allowing SDETs to focus testing on critical functionalities. Example: An SDET can use predictive analytics to identify areas with frequent bugs, allocating resources more effectively and ensuring robust testing of high-risk components. pythonCopy codeimport hypothetical_ai_predictive_tool def prioritize_tests(): risk_areas = hypothetical_ai_predictive_tool.predict_risk_areas() prioritized_tests = hypothetical_ai_predictive_tool.prioritize_test_cases(risk_areas) return prioritized_tests prioritized_test_cases = prioritize_tests() print(“Prioritized Test Cases:”) for test in prioritized_test_cases: print(test) Sample Output: Gen AI and Test Automation Generative AI has the potential to revolutionize test automation, offering SDETs tools to enhance efficiency, coverage, and reliability. By embracing Generative AI for tasks like test case generation, script creation, suite maintenance, NLP-based design, and predictive prioritization, SDETs can reduce manual effort and focus on strategic tasks, accelerating testing processes and ensuring robust, reliable software systems. 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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May 19, 2024in Data, Generative AI
Gen AI Role in Healthcare
11May

Gen AI Role in Healthcare

Generative AI’s Growing Role in Healthcare: Potential and Challenges The rapid advancements in large language models (LLMs) have introduced generative AI tools into nearly every business sector, including healthcare. As defined by the Government Accountability Office, generative AI is “a technology that can create content, including text, images, audio, or video, when prompted by a user.” These systems learn patterns and relationships from vast datasets, enabling them to generate new content that resembles but is not identical to the original training data. This capability is powered by machine learning algorithms and statistical models. In healthcare, generative AI is being utilized for various applications, including clinical documentation, patient communication, and clinical text summarization. Streamlining Clinical Documentation Excessive documentation is a leading cause of clinician burnout, as highlighted by a 2022 athenahealth survey conducted by the Harris Poll. Generative AI shows promise in easing these documentation burdens, potentially improving clinician satisfaction and reducing burnout. A 2024 study published in NEJM Catalyst explored the use of ambient AI scribes within The Permanente Medical Group (TPMG). This technology employs smartphone microphones and generative AI to transcribe patient encounters in real-time, providing clinicians with draft documentation for review. In October 2023, TPMG deployed this ambient AI technology across various settings, benefiting 10,000 physicians and staff. Physicians who used the ambient AI scribe reported positive outcomes, including more personal and meaningful patient interactions and reduced after-hours electronic health record (EHR) documentation. Early patient feedback was also favorable, with improved provider interactions noted. Additionally, ambient AI produced high-quality clinical documentation for clinician review. However, a 2023 study in the Journal of the American Medical Informatics Association (JAMIA) cautioned that ambient AI might struggle with non-lexical conversational sounds (NLCSes), such as “mm-hm” or “uh-uh,” which can convey clinically relevant information. The study found that while the ambient AI tools had a word error rate of about 12% for all words, the error rate for NLCSes was significantly higher, reaching up to 98.7% for those conveying critical information. Misinterpretation of these sounds could lead to inaccuracies in clinical documentation and potential patient safety issues. Enhancing Patient Communication With the digital transformation in healthcare, patient portal messages have surged. A 2021 study in JAMIA reported a 157% increase in patient portal inbox messages since 2020. In response, some healthcare organizations are exploring the use of generative AI to draft replies to these messages. A 2024 study published in JAMA Network Open evaluated the adoption of AI-generated draft replies to patient messages at an academic medical center. After five weeks, clinicians used the AI-generated drafts 20% of the time, a notable rate considering the LLMs were not fine-tuned for patient communication. Clinicians reported reduced task load and emotional exhaustion, suggesting that AI-generated replies could help alleviate burnout. However, the study found no significant changes in reply time, read time, or write time between the pre-pilot and pilot periods. Despite this, clinicians expressed optimism about time savings, indicating that the cognitive ease of editing drafts rather than writing from scratch might not be fully captured by time metrics. Summarizing Clinical Data Summarizing information within patient records is a time-consuming task for clinicians, and errors in this process can negatively impact clinical decision support. Generative AI has shown potential in this area, with a 2023 study finding that LLM-generated summaries could outperform human expert summaries in terms of conciseness, completeness, and correctness. However, using generative AI for clinical data summarization presents risks. A viewpoint in JAMA argued that LLMs performing summarization tasks might not fall under FDA medical device oversight, as they provide language-based outputs rather than disease predictions or numerical estimates. Without statutory changes, the FDA’s authority to regulate these LLMs remains unclear. The authors also noted that differences in summary length, organization, and tone could influence clinician interpretations and subsequent decision-making. Furthermore, LLMs might exhibit biases, such as sycophancy, where responses are tailored to user expectations. To address these concerns, the authors called for comprehensive standards for LLM-generated summaries, including testing for biases and errors, as well as clinical trials to quantify potential harms and benefits. The Path Forward Generative AI holds significant promise for transforming healthcare and reducing clinician burnout, but realizing this potential requires comprehensive standards and regulatory clarity. A 2024 study published in npj Digital Medicine emphasized the need for defined leadership, adoption incentives, and ongoing regulation to deliver on the promise of generative AI in healthcare. Leadership should focus on establishing guidelines for LLM performance and identifying optimal clinical settings for AI tool trials. The study suggested that a subcommittee within the FDA, comprising physicians, healthcare administrators, developers, and investors, could effectively lead this effort. Additionally, widespread deployment of generative AI will likely require payer incentives, as most providers view these tools as capital expenses. With the right leadership, incentives, and regulatory framework, generative AI can be effectively implemented across the healthcare continuum to streamline clinical workflows and improve patient care. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more 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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May 11, 2024in Einstein 1 Platform, Generative AI, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud, Salesforce Implementation Services, Salesforce Optimization
Advances in AI Models
11May

Advances in AI Models

Advances in AI Models Let’s take a moment to appreciate the transformative impact large language models (LLMs) have had on the world. Before the rise of LLMs, researchers spent years training AI to generate images, but these models had significant limitations. Advances in AI Models. One promising neural network architecture was the generative adversarial network (GAN). In a GAN, two networks play a cat-and-mouse game: one tries to create realistic images while the other tries to distinguish between generated and real images. Over time, the image-creating network improves at tricking the other. While GANs can generate convincing images, they typically excel at creating images of a single subject type. For example, a GAN that creates excellent images of cats might struggle with images of mice. GANs can also experience “mode collapse,” where the network generates the same image repeatedly because it always tricks the discriminator. An AI that produces only one image repeatedly isn’t very useful. What’s truly useful is an AI model capable of generating diverse images, whether it’s a cat, a mouse, or a cat in a mouse costume. Such models exist and are known as diffusion models, named for the underlying math that resembles diffusion processes like a drop of dye spreading in water. These models are trained to connect images and text, leveraging vast amounts of captioned images on the internet. With enough samples, a model can extract the essence of “cat,” “mouse,” and “costume,” embedding these elements into generated images using diffusion principles. The results are often stunning. Some of the most well-known diffusion models include DALL-E, Imagen, Stable Diffusion, and Midjourney. Each model differs in training data, embedding language details, and user interaction, leading to varied results. As research and development progress, these tools continue to evolve rapidly. Uses of Generative AI for Imagery Generative AI can do far more than create cute cat cartoons. By fine-tuning generative AI models and combining them with other algorithms, artists and innovators can create, manipulate, and animate imagery in various ways. Here are some examples: Text-to-Image Generative AI allows for incredible artistic variety using text-to-image techniques. For instance, you can generate a hand-drawn cat or opt for a hyperrealistic or mosaic style. If you can imagine it, diffusion models can interpret your intention successfully. Text-to-3D Model Creating 3D models traditionally requires technical skill, but generative AI tools like DreamFusion can generate 3D models along with detailed descriptions of coloring, lighting, and material properties, meeting the growing demand in commerce, manufacturing, and entertainment. Image-to-Image Images can be powerful prompts for generative AI models. Here are some use cases: Animation Creating a series of consistent images for animation is challenging due to inherent randomness in generated images. However, researchers have developed methods to reduce variations, enabling smoother animations. All the use cases for still images can be adapted for animation. For example, style transfer can turn a video of a skateboarder into an anime-style animation. AI models trained on speech patterns can animate the lips of a generated 3D character. Embracing Generative AI Generative AI offers enormous possibilities for creating stunning imagery. As you explore these capabilities, it’s essential to use them responsibly. In the next unit, you’ll learn how to leverage generative AI’s potential in an ethical and effective manner. 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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May 11, 2024in Generative AI
Copado AI Testing for Salesforce
29Apr

Copado AI Testing for Salesforce

The DevOps company Copado has announced a new AI assistant for Salesforce test creation called Test Copilot. Copado already integrates with Salesforce. Copado AI Testing for Salesforce This follows the company’s recent announcement of Copado Explorer, which is an automated testing solution designed for Salesforce users, as well as the launch of its AI assistant CopadoGPT, which Test Copilot is built on. Users provide a text prompt of what needs to be tested and Test Copilot creates a test that fits those requirements. It can convert existing tests, Selenium tests, or Copado Explorer results into a new test, create tests from scratch, or turn recorded user sessions into test scripts.  Copado AI Testing for Salesforce brings AI-powered test automation for every cloud under the sun. “Copado is in the business of giving people their time back,” said Esko Hannula, senior vice president of product management at Copado. “By eliminating repeated tasks and using AI to automate the test creation process, Copado is helping release teams work faster than ever before while improving release quality. With our AI-powered testing solutions, Copado customers are not only accelerating software testing, but simplifying it.” Why do thousands of Salesforce teams use Copado? Because we make it easy to build, test and deploy the applications that power your business. 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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April 29, 2024in Generative AI, Salesforce
Train On Your Own Data
29Apr

Train On Your Own Data

General-purpose large language models (LLMs) offer businesses the convenience of immediate use without requiring any special setup or customization. However, to maximize the potential of LLMs in business environments, organizations can achieve significant benefits by customizing these models through training on their own data. Custom LLMs excel at handling organization-specific tasks that generic LLMs—such as OpenAI’s ChatGPT or Google’s Gemini—may not manage as effectively. By training an LLM on data unique to the enterprise, businesses can fine-tune the model to produce responses that are highly relevant to specific products, workflows, and customer interactions. To determine whether to customize an LLM with organization-specific data, businesses should first explore the various types of LLMs and understand the advantages of fine-tuning a model on custom data sets. Following this, they can proceed with the necessary steps: identifying data sources, cleaning and formatting the data, adjusting model parameters, retraining the model, and testing it in production. Generic vs. Customized LLMs LLMs can be broadly categorized into two types: Training an LLM on custom data doesn’t imply starting from scratch; instead, it often involves fine-tuning a pre-trained generic model with additional training on the organization’s data. This approach allows the model to retain the broad knowledge it acquired during initial training while enhancing its capabilities in areas specific to the business. Benefits of Customizing an LLM The primary reason for retraining or fine-tuning an LLM is to achieve superior performance on business-specific tasks compared to using a generic model. For example, a company that wants to deploy a chatbot for customer support needs an LLM that understands its products in detail. Even if a generic LLM has some familiarity with the product from public data sources, it may lack the depth of knowledge that the company’s internal documentation provides. Without this comprehensive context, a generic LLM might struggle to generate accurate responses when interacting with customers about specific products. Generic models are optimized for broad usability, which means they may not be tailored for the specialized conversations required in business scenarios. Organizations can overcome these limitations by retraining or fine-tuning an LLM with data related to their products and services. During this process, AI teams can also adjust parameters, such as model weights, to influence the type of output the model generates, making it more relevant to the organization’s needs. Steps to Customize an LLM with Organization-Specific Data To customize an LLM with your organization’s data, follow these steps: By following these steps, organizations can transform a generic LLM into a powerful, customized tool tailored to their unique business needs, enhancing efficiency, customer satisfaction, and overall operational effectiveness. 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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April 29, 2024in Salesforce
Who Calls AI Ethical
18Apr

Who Calls AI Ethical

Background – Who Calls AI Ethical On March 13, 2024, the European Union (EU) enacted the EU AI Act, a move that some argue has hindered its position in the global AI race. This legislation aims to ‘unify’ the development and implementation of AI within the EU, but it is seen as more restrictive than progressive. Rather than fostering innovation, the act focuses on governance, which may not be sufficient for maintaining a competitive edge. The EU AI Act embodies the EU’s stance on Ethical AI, a concept that has been met with skepticism. Critics argue that Ethical AI is often misinterpreted and, at worst, a monetizable construct. In contrast, Responsible AI, which emphasizes ensuring products perform as intended without causing harm, is seen as a more practical approach. This involves methodologies such as red-teaming and penetration testing to stress-test products. This critique of Ethical AI forms the basis of this insight,and Eric Sandosham article here. The EU AI Act To understand the implications of the EU AI Act, it is essential to summarize its key components and address the broader issues with the concept of Ethical AI. The EU defines AI as “a machine-based system designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment. It infers from the input it receives to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.” Based on this definition, the EU AI Act can be summarized into several key points: Fear of AI The EU AI Act appears to be driven by concerns about AI being weaponized or becoming uncontrollable. Questions arise about whether the act aims to prevent job disruptions or protect against potential risks. However, AI is essentially automating and enhancing tasks that humans already perform, such as social scoring, predictive policing, and background checks. AI’s implementation is more consistent, reliable, and faster than human efforts. Existing regulations already cover vehicular safety, healthcare safety, and infrastructure safety, raising the question of why AI-specific regulations are necessary. AI solutions automate decision-making, but the parameters and outcomes are still human-designed. The fear of AI becoming uncontrollable lacks evidence, and the path to artificial general intelligence (AGI) remains distant. Ethical AI as a Red Herring In AI research and development, the terms Ethical AI and Responsible AI are often used interchangeably, but they are distinct. Ethics involve systematized rules of right and wrong, often with legal implications. Morality is informed by cultural and religious beliefs, while responsibility is about accountability and obligation. These constructs are continuously evolving, and so must the ethics and rights related to technology and AI. Promoting AI development and broad adoption can naturally improve governance through market forces, transparency, and competition. Profit-driven organizations are incentivized to enhance AI’s positive utility. The focus should be on defining responsible use of AI, especially for non-profit and government agencies. Towards Responsible AI Responsible AI emphasizes accountability and obligation. It involves defining safeguards against misuse rather than prohibiting use cases out of fear. This aligns with responsible product development, where existing legal frameworks ensure products work as intended and minimize misuse risks. AI can improve processes such as recruitment by reducing errors compared to human solutions. AI’s role is to make distinctions based on data attributes, striving for accuracy. The concern is erroneous discrimination, which can be mitigated through rigorous testing for bias as part of product quality assurance. Conclusion The EU AI Act is unlikely to become a global standard. It may slow AI research, development, and implementation within the EU, hindering AI adoption in the region and causing long-term harm. Humanity has an obligation to push the boundaries of AI innovation. As a species facing eventual extinction from various potential threats, AI could represent a means of survival and advancement beyond our biological limitations. 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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April 18, 2024in Salesforce
What is Salesforce Einstein 1
09Apr

What is Salesforce Einstein 1?

The bold new future of enterprise AI requires a new type of platform. What is Salesforce Einstein 1? One platform that can handle terabytes of disconnected data, gives you the freedom to choose your AI models, and connects directly into the flow of work, all while maintaining customer trust. The Einstein 1 Platform unifies your data, AI, CRM, development, and security into a single, comprehensive platform. It empowers IT, admins, and developers with an extensible AI platform, facilitating fast development of generative apps and automation. What is Salesforce Einstein 1 Einstein 1 has a mixture of artificial intelligence tools on the platform, and it kind of mirrors the way the core Salesforce platform is built, standardized and custom. We have out of the box AI features such as sales email generation in Sales Cloud, and service replies in Service Cloud. What is Einstein One? Einstein One seamlessly integrates our robust data platform with Data Cloud, offering a comprehensive AI and data-driven solution within our CRM service. As the leading comprehensive AI for CRM, Salesforce Einstein transforms the Customer Success Platform by incorporating a suite of integrated AI technologies, making it more intelligent and accessible to innovators worldwide. Introducing the Einstein One Platform The future of enterprise AI demands a groundbreaking platform capable of managing terabytes of diverse data, providing the flexibility to select AI models, and seamlessly integrating into daily workflows while maintaining customer trust. The Einstein One Platform consolidates data, AI, CRM, development, and security into a singular, comprehensive platform. It empowers IT, administrators, and developers with an extensible AI platform, facilitating rapid development of generative apps and automation. Salesforce has made significant investments in enhancing the Einstein One platform by introducing two prompt engineering features – a testing center and prompt engineering suggestions, demonstrating the company’s commitment to advancing AI capabilities. Data-driven and AI-driven customer experiences are vital in constructing Data Cloud. Salesforce’s predictive AI, Einstein, has been operational since 2016, conducting over a trillion predictions per week. The company continuously evolves its capabilities, introducing generative AI products like Einstein GPT, discussed at Dreamforce [2023]. ‘Customer 360′ now represents Salesforce customers’ ability to transform into customer-centric entities, delivering integrated and cohesive experiences across sales, marketing, service, and commerce. Leveraging Data Cloud, structured and unstructured engagement data comes alive, offering a personalized perspective of customer interactions. Einstein is accessible through various Salesforce products, including Sales Cloud, Service Cloud, Marketing Cloud, Salesforce Platform, Analytics Cloud, and Community Cloud. What is the Einstein One platform? Salesforce’s Einstein One is a transformative platform that securely integrates data, connects multiple Salesforce products, and empowers customer-centric businesses to create AI-driven apps, revolutionizing CRM interactions. What applications are connected with the Einstein One platform? Einstein Trust Layer: Ensuring the security of generative AI applications within Salesforce, this application acts as a safeguard for data, preventing unauthorized access. Data Cloud: Unifying structured and unstructured data, Data Cloud completes the customer 360 profile, integrating seamlessly with the Einstein One platform and making data actionable. Einstein Copilot: A built-in conversational assistant, Einstein Copilot enables users to ask questions in Natural Language and receive intelligent answers, actions, and more. Copilot Studio: An essential component of Einstein One, Copilot Studio manages Einstein One and Einstein Copilot, allowing businesses to control user access and use generative AI in their workspace. Prompt Builder, Skill Builder, Model Builder: Tools within Copilot Studio, these components empower businesses to create trusted AI prompts, add skills to user access, and choose AI models. What is Data Cloud for Einstein One? Data Cloud, combined with the Einstein Trust Layer, provides secure access to a unified data repository, forming a reliable resource for complete customer profiles and supporting AI assistants. Data Cloud facilitates valuable visualizations through reports, dashboards, and tools like Tableau, offering insights to enhance understanding of customers and business operations. What will Einstein One do for businesses? Einstein One aims to: Increase productivity Reduce operational costs Enhance the delivery of exceptional customer experiences The debut of Einstein One marks a significant stride for Salesforce and businesses, offering a secure path to boost productivity through AI technologies, ensuring continuous access to cutting-edge technologies for businesses. Where can generative AI impact businesses today? Generative AI has the potential to impact businesses by enhancing repetitive processes, making them faster. Exploring text-based processes, such as repetitive emails or form filling, is a great starting point for businesses looking to enhance capabilities. What can generative AI do for sales, service, field service teams? Generative AI can positively impact teams by automating processes, offering intelligent insights, and improving overall efficiency. Specific use cases for generative AI in sales, service, and field service teams can be explored through Salesforce’s 101 video series. What does Einstein One Tableau do? Einstein One Data Cloud, formerly known as Genie, serves as a metadata management tool connecting data from various sources. It makes data usable for AI and analysis, integrating with Einstein One, Tableau, and Salesforce’s CRM applications. Is Salesforce Einstein the same as Tableau? Formerly known as Einstein Analytics, Tableau CRM is Salesforce’s premium analytic platform, offering a powerful way to explore data, especially for current Salesforce users. Salesforce has renamed Einstein Analytics to Tableau CRM, reflecting its commitment to empowering users to make better decisions faster. In essence, the Einstein One platform combines Salesforce Data Cloud, Einstein Copilot, and the Einstein Trust Layer, providing a comprehensive solution for secure and connected data, facilitating generative AI on a comprehensive platform. Salesforce’s Einstein One Platform & the Data-Driven Revolution This innovation has the potential to revolutionize the way businesses interact with their customers. In this insight, we delve deeper into this transformative development, offering valuable insights, considerations for implementation, and the indispensable expertise of Tectonic as a data-oriented technology partner. Salesforce Data Cloud and Einstein AI are now seamlessly integrated into the Einstein One Platform. This integration empowers companies to effortlessly connect their diverse data sources, enabling the creation of AI-driven applications with minimal coding effort. Moreover, it forges a cohesive view of your data across

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April 9, 2024in Artificial Intelligence, Data, Einstein 1 Platform, Einstein Insights, Enterprise, Generative AI, Google, Mulesoft, Salesforce, Salesforce Data Cloud, Salesforce Einstein, Salesforce Sales Cloud, Technology
Legacy Salesforce URL Redirection Retires
31Mar

Legacy Salesforce URL Redirection Retires

Legacy Salesforce URL Redirection Retires – Salesforce Announcement Security Notification At Salesforce, we understand that the confidentiality, integrity, and availability of your data is vital to your business, and we take the protection of your data very seriously. We’re writing to remind you that redirections for some of your Salesforce orgs’ URLs stop in the Winter ’25 release, starting in August 2024 for sandboxes and in September 2024 for production orgs. We highly encourage you to test for this change and update all references to your previous URLs before those redirections stop. Security Notification Why the Change? To comply with the latest browser and security standards, Salesforce recently required that customers deploy enhanced domains. This feature updated the hostname formats for the domains that serve Salesforce orgs. To reduce the impact of this change, your previous hostnames are redirected by default. However, to meet Salesforce security standards, some of the previous domains will be retired in 2024. The corresponding redirections stop in the Winter ’25 release. Why the Change? Actions Required Review the affected hostnames and how you can disable the redirections for testing. For details, see Prepare for the End of Redirections for Non-Enhanced Domains in Salesforce Help. Additional Resources Salesforce Enhanced Domains FAQSalesforce Help: Prepare for the End of Redirections for Non-Enhanced DomainsSalesforce Help: Log My Domain Hostname RedirectionsSalesforce Help: Enhanced Domains TimelineTo ask questions about enhanced domains, visit our monitored My Domain and Enhanced Domains group in the Trailblazer Community.We appreciate your trust in us as we continue to make your success and security our top priorities. Additional Resources Legacy Salesforce URL Redirection Retires Prepare for the End of Redirections for Non-Enhanced Domains After enhanced domains are deployed, your previous non-enhanced hostnames are redirected. Those redirections stop in Winter ’25, starting in August 2024 for sandboxes.. Review the affected hostnames and how you can disable the redirections to test before Salesforce disables them. REQUIRED EDITIONS Available in: both Salesforce Classic (not available in all orgs) and Lightning Experience Available in: Group, Essentials, Professional, Enterprise, Performance, Unlimited, and Developer Editions After enhanced domains are deployed, Salesforce redirects two categories of non-enhanced hostnames. To see if redirections are in place for a previous My Domain, check the Redirections section of the My Domain Setup page. NOTE My Domain URL redirections help prevent disruption, but they’re not intended as a permanent solution. Not all services work well with redirections, and a redirection adds a step to the process of loading the final web page. When you deploy enhanced domains, we highly recommend that you disable redirections during testing and update all references to your old URLs. Track the Source of Redirections You can search the metadata for your org to determine where the impacted URLs are used in Salesforce, but discovering all the other places that your URLs are used can be more complex. For example, site URLs and content URLs can be used on your website, social media pages, marketing materials, and templates such as email signatures and automated responses. To help you identify these locations outside of Salesforce, the Hostname Redirects log includes the referrer and origin sent with each request that Salesforce redirects. For more information, see Log My Domain Hostname Redirections. Notify Users During Redirections To help your users update outdated links and bookmarks, display a brief message during the redirection that provides the current URL. See Manage My Domain Redirections. Previous *.force.com Site Hostnames When you enable and deploy enhanced domains, the hostname formats change for your Experience Cloud sites and Salesforce Sites. To minimize potential disruption, the *.force.com hostnames that Salesforce hosted for your sites are redirected to your current site hostnames. Those redirections remain in place until Winter ’25 or until you disable these redirections. To disable these redirections for testing purposes, use an option in the Redirections section of the My Domain page and test. For more information, see Disable Redirections for Your Previous Force.com Site URLs. For the replacement format for each of these hostnames, see My Domain URL Format Changes When You Enable Enhanced Domains. To better understand the purpose of each hostname type and whether it applies to you, see My Domain Hostnames. Here are the *.force.com site hostnames for a production org that are temporarily redirected after you enable and deploy enhanced domains. Here are the .*force.com site hostnames for a sandbox that are temporarily redirected after you enable and deploy enhanced domains. Other Non-Enhanced My Domain Hostnames When you enable and deploy enhanced domains, your other previous non-enhanced My Domain hostnames are redirected. Those redirections remain in place until Winter ’25 or until you disable these redirections. For the type and the replacement format for each of these hostnames, see My Domain URL Format Changes When You Enable Enhanced Domains. To better understand the purpose of each hostname type and whether it applies to you, see My Domain Hostnames. NOTE If you deploy another My Domain change after you enable and deploy enhanced domains, these hostnames are no longer redirected. For more information, see Understand Redirections for Previous My Domain Hostnames. To disable the redirections of these non-enhanced My Domain hostnames for testing purposes, you can temporarily disable your previous My Domain. For more information, see Disable or Remove Your Previous My Domain. Here are the non-enhanced hostnames for a production org that are temporarily redirected after you enable and deploy enhanced domains. Here are the non-enhanced hostnames for a sandbox that are temporarily redirected after you enable and deploy enhanced domains. 1 If your installed package is unmanaged, the package name is c. Instanced URLs Without My Domain If your org was created before October 2020, you didn’t get a My Domain by default. In that case, your users accessed Salesforce with these hostnames that contained your instance name but not your My Domain name. These hostnames are redirected if the Instanced URL setting in the Redirections section on the My Domain Setup page is set to Redirect to the same page within the domain

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March 31, 2024in Hyperforce, Salesforce, Salesforce Experience Cloud

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