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Fully Formatted Facts
20Oct

Fully Formatted Facts

A recent discovery by programmer and inventor Michael Calvin Wood is addressing a persistent challenge in AI: hallucinations. These false or misleading outputs, long considered an inherent flaw in large language models (LLMs), have posed a significant issue for developers. However, Wood’s breakthrough is challenging this assumption, offering a solution that could transform how AI-powered applications are built and used. The Importance of Wood’s Discovery for Developers Wood’s findings have substantial implications for developers working with AI. By eliminating hallucinations, developers can ensure that AI-generated content is accurate and reliable, particularly in applications where precision is critical. Understanding the Root Cause of Hallucinations Contrary to popular belief, hallucinations are not primarily caused by insufficient training data or biased algorithms. Wood’s research reveals that the issue stems from how LLMs process and generate information based on “noun-phrase routes.” LLMs organize information around noun phrases, and when they encounter semantically similar phrases, they may conflate or misinterpret them, leading to incorrect outputs. How LLMs Organize Information For example: The Noun-Phrase Dominance Model Wood’s research led to the development of the Noun-Phrase Dominance Model, which posits that neural networks in LLMs self-organize around noun phrases. This model is key to understanding and eliminating hallucinations by addressing how AI processes noun-phrase conflicts. Fully-Formatted Facts (FFF): A Solution Wood’s solution involves transforming input data into Fully-Formatted Facts (FFF)—statements that are literally true, devoid of noun-phrase conflicts, and structured as simple, complete sentences. Presenting information in this format has led to significant improvements in AI accuracy, particularly in question-answering tasks. How FFF Processing Works While Wood has not provided a step-by-step guide for FFF processing, he hints that the process began with named-entity recognition using the Python SpaCy library and evolved into using an LLM to reduce ambiguity while retaining the original writing style. His company’s REST API offers a wrapper around GPT-4o and GPT-4o-mini models, transforming input text to remove ambiguity before processing it. Current Methods vs. Wood’s Approach Current approaches, like Retrieval Augmented Generation (RAG), attempt to reduce hallucinations by adding more context. However, these methods often introduce additional noun-phrase conflicts. For instance, even with RAG, ChatGPT-3.5 Turbo experienced a 23% hallucination rate when answering questions about Wikipedia articles. In contrast, Wood’s method focuses on eliminating noun-phrase conflicts entirely. Results: RAG FF (Retrieval Augmented Generation with Formatted Facts) Wood’s method has shown remarkable results, eliminating hallucinations in GPT-4 and GPT-3.5 Turbo during question-answering tasks using third-party datasets. Real-World Example: Translation Error Elimination Consider a simple translation example: This transformation eliminates hallucinations by removing the potential noun-phrase conflict. Implications for the Future of AI The Noun-Phrase Dominance Model and the use of Fully-Formatted Facts have far-reaching implications: Roadmap for Future Development Wood and his team plan to expand their approach by: Conclusion: A New Era of Reliable AI Wood’s discovery represents a significant leap forward in the pursuit of reliable AI. By aligning input data with how LLMs process information, he has unlocked the potential for accurate, trustworthy AI systems. As this technology continues to evolve, it could have profound implications for industries ranging from healthcare to legal services, where AI could become a consistent and reliable tool. While there is still work to be done in expanding this method across all AI tasks, the foundation has been laid for a revolution in AI accuracy. Future developments will likely focus on refining and expanding these capabilities, enabling AI to serve as a trusted resource across a range of applications. Experience RAGFix For those looking to explore this technology, RAGFix offers an implementation of these groundbreaking concepts. Visit their official website to access demos, explore REST API integration options, and stay updated on the latest advancements in hallucination-free AI: Visit RAGFix.ai Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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October 20, 2024in Salesforce
AI evolves with tools like Agentforce and Atlas
02Oct

AI Evolves With Agentforce and Atlas

Not long ago, financial services companies were still struggling with the challenge of customer data trapped in silos. Though it feels like a distant issue, this problem remains for many large organizations unable to integrate different divisions that deal separately with the same customers. Salesforce AI evolves with tools like Agentforce and Atlas. The solution is a concept known as a “single source of truth.” This theme took center stage at Dreamforce 2024 in San Francisco, hosted by Salesforce (NYSE). The event showcased Salesforce’s latest AI innovations, including Agentforce, which is set to revolutionize customer engagement through its advanced AI capabilities. Agentforce, which becomes generally available on October 25, enables businesses to deploy autonomous AI agents to manage a wide variety of tasks. These agents differ from earlier Salesforce-based AI tools by leveraging Atlas, a cutting-edge reasoning engine that allows the bots to think like human beings. Unlike generative AI models, which might write an email based on prompts, Agentforce’s AI agents can answer complex, high-order questions such as, “What should I do with all my customers?” The agents break down these queries into actionable steps—whether that’s sending emails, making phone calls, or texting customers—thanks to the deep capabilities of Atlas. Atlas is at the heart of what makes these AI agents so powerful. It combines multiple large language models (LLMs), large action models (LAMs), and retrieval-augmented generation (RAG) modules, along with REST APIs and connectors to various datasets. This robust system processes user queries through multiple layers, checking for validity and then expanding the query into manageable chunks for processing. Once a query passes through the chit-chat detector—which filters out non-relevant inputs—it enters the evaluation phase, where the AI determines if it has enough data to provide a meaningful answer. If not, the system loops back to the user for more information in a process Salesforce calls the agentic loop. The fewer loops required, the more efficient the AI becomes, making the experience seamless for users. Phil Mui, Senior Vice President of Salesforce AI Research, explained that the AI agents created via Agentforce are powered by the Atlas reasoning engine, which makes use of several key tools like a re-ranker, a refiner, and a response synthesizer. These tools ensure that the AI retrieves, ranks, and synthesizes relevant information to generate high-quality, natural language responses for the user. But Salesforce’s AI agents don’t stop at automation—they also emphasize trust. Before responses reach users, they go through additional checks for toxicity detection, bias prevention, and personally identifiable information (PII) masking. This ensures that the output is both accurate and safe. The potential of Agentforce is massive. According to Wedbush, Salesforce’s AI strategy could generate over $4 billion annually by 2025. Wedbush analysts recently increased their price target for Salesforce stock to $325, reflecting the strong customer reception of Agentforce’s AI ecosystem. While some analysts, such as Yiannis Zourmpanos from Seeking Alpha, have expressed caution due to Salesforce’s high valuation and slower revenue growth, the company’s continued focus on AI and multi-cloud solutions places it in a strong position for the future. Robin Fisher, Salesforce’s head of growth markets for Europe, the Middle East, and Africa, highlighted two major takeaways from Dreamforce for African businesses: the Data Cloud and AI. Data Cloud provides a 360-degree view of the customer, consolidating data into a single source of truth without requiring full data migration. Meanwhile, Agentforce’s autonomous AI agents will drive operational efficiency across industries, especially in markets like Africa. Zuko Mdwaba, Salesforce’s managing director for South Africa, added that the company’s decade-long AI journey is culminating in its most advanced AI offerings yet. This new wave of AI, he said, is transforming not just customer engagement but also internal operations, empowering employees to focus on more strategic tasks while AI handles repetitive ones. The future is clear: as AI evolves with tools like Agentforce and Atlas, businesses across sectors, from banking to retail, are poised to harness the transformative power of autonomous technology and data-driven insights, finally breaking free from the silos of the past. 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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October 2, 2024in Agentforce Platform, AI Tools, Data, Generative AI, Salesforce, Salesforce Implementation Services, Technology
Exploring Large Action Models
05Sep

Exploring Large Action Models

Exploring Large Action Models (LAMs) for Automated Workflow Processes While large language models (LLMs) are effective in generating text and media, Large Action Models (LAMs) push beyond simple generation—they perform complex tasks autonomously. Imagine an AI that not only generates content but also takes direct actions in workflows, such as managing customer relationship management (CRM) tasks, sending emails, or making real-time decisions. LAMs are engineered to execute tasks across various environments by seamlessly integrating with tools, data, and systems. They adapt to user commands, making them ideal for applications in industries like marketing, customer service, and beyond. Key Capabilities of LAMs A standout feature of LAMs is their ability to perform function-calling tasks, such as selecting the appropriate APIs to meet user requirements. Salesforce’s xLAM models are designed to optimize these tasks, achieving high performance with lower resource demands—ideal for both mobile applications and high-performance environments. The fc series models are specifically tuned for function-calling, enabling fast, precise, and structured responses by selecting the best APIs based on input queries. Practical Examples Using Salesforce LAMs In this article, we’ll explore: Implementation: Setting Up the Model and API Start by installing the necessary libraries: pythonCopy code! pip install transformers==4.41.0 datasets==2.19.1 tokenizers==0.19.1 flask==2.2.5 Next, load the xLAM model and tokenizer: pythonCopy codeimport json import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_name = “Salesforce/xLAM-7b-fc-r” model = AutoModelForCausalLM.from_pretrained(model_name, device_map=”auto”, torch_dtype=”auto”, trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained(model_name) Now, define instructions and available functions. Task Instructions: The model will use function calls where applicable, based on user questions and available tools. Format Example: jsonCopy code{ “tool_calls”: [ {“name”: “func_name1”, “arguments”: {“argument1”: “value1”, “argument2”: “value2”}} ] } Define available APIs: pythonCopy codeget_weather_api = { “name”: “get_weather”, “description”: “Retrieve weather details”, “parameters”: {“location”: “string”, “unit”: “string”} } search_api = { “name”: “search”, “description”: “Search for online information”, “parameters”: {“query”: “string”} } Creating Flask APIs for Business Logic We can use Flask to create APIs to replicate business processes. pythonCopy codefrom flask import Flask, request, jsonify app = Flask(__name__) @app.route(“/customer”, methods=[‘GET’]) def get_customer(): customer_id = request.args.get(‘customer_id’) # Return dummy customer data return jsonify({“customer_id”: customer_id, “status”: “active”}) @app.route(“/send_email”, methods=[‘GET’]) def send_email(): email = request.args.get(’email’) # Return dummy response for email send status return jsonify({“status”: “sent”}) Testing the LAM Model and Flask APIs Define queries to test LAM’s function-calling capabilities: pythonCopy codequery = “What’s the weather like in New York in fahrenheit?” print(custom_func_def(query)) # Expected: {“tool_calls”: [{“name”: “get_weather”, “arguments”: {“location”: “New York”, “unit”: “fahrenheit”}}]} Function-Calling Models in Action Using base_call_api, LAMs can determine the correct API to call and manage workflow processes autonomously. pythonCopy codedef base_call_api(query): “””Calls APIs based on LAM recommendations.””” base_url = “http://localhost:5000/” json_response = json.loads(custom_func_def(query)) api_url = json_response[“tool_calls”][0][“name”] params = json_response[“tool_calls”][0][“arguments”] response = requests.get(base_url + api_url, params=params) return response.json() With LAMs, businesses can automate and streamline tasks in complex workflows, maximizing efficiency and empowering teams to focus on strategic initiatives. 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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September 5, 2024in Data, Salesforce, Technology, Third Party Apps
Python Alongside Salesforce
17Sep

Python Alongside Salesforce

Salesforce can integrate with Python, though the platform primarily relies on its proprietary languages and frameworks for core development. Python, however, plays a crucial role in enhancing Salesforce’s capabilities through integrations, automation, data analysis, and extending functionalities via external applications. Here’s an overview of how Python works within the Salesforce ecosystem: 1. Salesforce’s Core Development Stack Before exploring Python’s use, it’s important to understand the key development tools within Salesforce: These tools are the foundation for Salesforce development. However, Python complements Salesforce by enabling integrations and automation that go beyond these native tools. 2. Python in Salesforce Integrations Python shines when integrating Salesforce with other systems, automating workflows, and extending functionality. Here’s how: a. API Interactions Salesforce’s REST and SOAP APIs allow external systems to communicate with Salesforce data. Python, with its powerful libraries, is excellent for interfacing with these APIs. Key Libraries: Example: Extracting Data via API: pythonCopy codefrom simple_salesforce import Salesforce # Connect to Salesforce sf = Salesforce(username=’your_username’, password=’your_password’, security_token=’your_token’) # Query Salesforce data accounts = sf.query(“SELECT Id, Name FROM Account LIMIT 10”) for account in accounts[‘records’]: print(account[‘Name’]) b. Data Processing and Analysis Python’s data manipulation libraries like Pandas and NumPy make it ideal for processing Salesforce data. Example: Data Cleaning and Analysis: pythonCopy codeimport pandas as pd from simple_salesforce import Salesforce # Connect to Salesforce sf = Salesforce(username=’your_username’, password=’your_password’, security_token=’your_token’) # Fetch data query = “SELECT Id, Name, AnnualRevenue FROM Account” accounts = sf.query_all(query) df = pd.DataFrame(accounts[‘records’]).drop(columns=[‘attributes’]) # Process data df[‘AnnualRevenue’] = df[‘AnnualRevenue’].fillna(0) high_revenue_accounts = df[df[‘AnnualRevenue’] > 1000000] print(high_revenue_accounts) 3. Automation and Scripting Python can automate Salesforce-related tasks, improving productivity and reducing manual effort. This can involve automating data updates, generating reports, or scheduling backups. Example: Automating Data Backup: pythonCopy codeimport schedule import time from simple_salesforce import Salesforce def backup_salesforce_data(): sf = Salesforce(username=’your_username’, password=’your_password’, security_token=’your_token’) query = “SELECT Id, Name, CreatedDate FROM Contact” contacts = sf.query_all(query) df = pd.DataFrame(contacts[‘records’]).drop(columns=[‘attributes’]) df.to_csv(‘contacts_backup.csv’, index=False) print(“Salesforce data backed up successfully.”) # Schedule the backup schedule.every().day.at(“00:00”).do(backup_salesforce_data) while True: schedule.run_pending() time.sleep(1) 4. Building External Applications Using platforms like Heroku, developers can build external applications in Python that integrate with Salesforce, extending its functionality for custom portals or advanced analytics. Example: Web App Integrating with Salesforce: pythonCopy codefrom flask import Flask, request, jsonify from simple_salesforce import Salesforce app = Flask(__name__) @app.route(‘/get_accounts’, methods=[‘GET’]) def get_accounts(): sf = Salesforce(username=’your_username’, password=’your_password’, security_token=’your_token’) accounts = sf.query(“SELECT Id, Name FROM Account LIMIT 10”) return jsonify(accounts[‘records’]) if __name__ == ‘__main__’: app.run(debug=True) 5. Data Integration and ETL Python is commonly used in ETL (Extract, Transform, Load) processes that involve Salesforce data. Tools like Apache Airflow allow you to create complex data pipelines for integrating Salesforce data with external databases. Example: ETL Pipeline with Airflow: pythonCopy codefrom airflow import DAG from airflow.operators.python_operator import PythonOperator from simple_salesforce import Salesforce import pandas as pd from datetime import datetime def extract_salesforce_data(): sf = Salesforce(username=’your_username’, password=’your_password’, security_token=’your_token’) query = “SELECT Id, Name, CreatedDate FROM Opportunity” opportunities = sf.query_all(query) df = pd.DataFrame(opportunities[‘records’]).drop(columns=[‘attributes’]) df.to_csv(‘/path/to/data/opportunities.csv’, index=False) default_args = { ‘owner’: ‘airflow’, ‘start_date’: datetime(2023, 1, 1), ‘retries’: 1, } dag = DAG(‘salesforce_etl’, default_args=default_args, schedule_interval=’@daily’) extract_task = PythonOperator( task_id=’extract_salesforce_data’, python_callable=extract_salesforce_data, dag=dag, ) extract_task 6. Machine Learning and Predictive Analytics Python’s machine learning libraries, such as Scikit-learn and TensorFlow, enable predictive analytics on Salesforce data. This helps in building models for sales forecasting, lead scoring, and customer behavior analysis. Example: Predicting Lead Conversion: pythonCopy codeimport pandas as pd from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from simple_salesforce import Salesforce # Fetch Salesforce data sf = Salesforce(username=’your_username’, password=’your_password’, security_token=’your_token’) query = “SELECT Id, LeadSource, AnnualRevenue, NumberOfEmployees, Converted FROM Lead” leads = sf.query_all(query) df = pd.DataFrame(leads[‘records’]).drop(columns=[‘attributes’]) # Preprocess and split data df = pd.get_dummies(df, columns=[‘LeadSource’]) X = df.drop(‘Converted’, axis=1) y = df[‘Converted’] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Train model model = RandomForestClassifier(n_estimators=100, random_state=42) model.fit(X_train, y_train) # Evaluate accuracy accuracy = model.score(X_test, y_test) print(f”Model Accuracy: {accuracy * 100:.2f}%”) 7. Best Practices for Using Python with Salesforce To maximize the efficiency and security of Python with Salesforce: 8. Recommended Learning Resources By leveraging Python alongside Salesforce, organizations can automate tasks, integrate systems, and enhance their data analytics, all while boosting productivity. Content updated August 2024. 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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September 17, 2022in Data

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