A naukri.com initiative
ML News
Medium
5h
331
Image Credit: Medium
WildeWeb: Safety Improvement PoC using Continual Pretraining
- A PoC to find out if soft skills datasets can improve model safety was created using the fine-web edu project
- A combination of empathy, critical thinking, and intercultural fluency are the soft skills that need to be focused on to enable models to consider consequences of unsafe responses
- The PoC was carried out on about 1M documents from FineWeb-Edu using torchtune to train a larger model- Llama-3.1-70B along with Llama 3.3 70B
- A dataset containing 38.3k rows with scores and justification was created, which led to about 61M tokens of data
- The resulting model was tested for safety benchmarking, a process that involves generating completions to partially written sentences with the help of LLMs. In this case, SALAD-Bench was used instead of Perspective API
- The dataset consisted of questions that asked for advice on illegal, unsafe, or unethical actions. The safety classification score improved slightly from 0.57 to 0.609 with the use of Wildeweb-CPT model
- The results showed a small jump in social awareness, which is good to see as it follows intuition; there was a small but noticeable drop in college_mathematics formal_logic
- The author intends to improve the scoring prompt using human annotations, filter promotional texts, and run the full dataset on smaller models to see their impact
- The findings show that using soft skills datasets can improve model safety and can be accessed by anyone interested in making language models safe for use.
Read Full Article
19 Likes
Medium
7h
319
Image Credit: Medium
Tuning scikit-learn hyperparamters using Optuna
- Optuna is an automatic hyperparameter optimisation framework that can be used with scikit-learn and other machine learning and deep learning frameworks.
- Hyperparameter tuning is an important and time-consuming part of the modeling task, and Optuna provides a more efficient way to converge to the best set of hyperparameters.
- The example demonstrates the usage of Optuna with scikit-learn on the MNIST dataset to train a classifier.
- Optuna offers visualizations to analyze the tuning process, such as graphs of trials and objective values, hyperparameter importance, and slice plots.
Read Full Article
19 Likes
Medium
2h
195
Image Credit: Medium
$200 AI Hack: CAG Beats RAG Instantly
- Cache-Augmented Generation (CAG) is a revolutionary approach that optimizes data retrieval and generation tasks.
- CAG implements an intelligent caching mechanism to optimize computational resources.
- It creates an internal, dynamic memory system that learns and adapts, unlike Retrieval-Augmented Generation (RAG) which relies on external knowledge bases.
- Implementing CAG requires considerations of memory management, cache invalidation, and response diversity.
Read Full Article
11 Likes
Medium
3h
149
The Evolution of LLM AI: Redefining Human-AI Collaboration
- LLMs have revolutionized creativity, enabling AI to participate in activities like poetry, art, and screenplay writing.
- LLMs are now capable of hyper-personalization, tailoring experiences to individual needs and emotional contexts.
- The rise of decentralized LLMs empowers individuals and communities to train and deploy AI for their specific requirements.
- Ethical concerns surrounding LLMs can be addressed through self-regulating models and transparency dashboards.
Read Full Article
9 Likes
Marktechpost
4h
172
Plurai Introduces IntellAgent: An Open-Source Multi-Agent Framework to Evaluate Complex Conversational AI System
- Evaluating conversational AI systems powered by large language models (LLMs) presents a critical challenge in artificial intelligence.
- Existing evaluation methods struggle to assess the capabilities of these systems in handling multi-turn dialogues, integrating domain-specific tools, and adhering to complex policy constraints.
- To address these limitations, Plurai researchers have introduced IntellAgent, an open-source, multi-agent framework designed to automate the creation of diverse, policy-driven scenarios.
- IntellAgent combines graph-based policy modeling, synthetic event generation, and interactive simulations to evaluate conversational AI agents holistically.
Read Full Article
10 Likes
Marktechpost
4h
71
Advancing Protein Science with Large Language Models: From Sequence Understanding to Drug Discovery
- Protein Language Models (pLMs) integrated with Large Language Models (LLMs) have advanced computational protein science.
- pLMs have improved protein structure prediction and can predict structures for orphan proteins without relying on multiple sequence alignments.
- pLMs have been utilized in antibody design, enzyme engineering, and drug discovery, offering more controlled and cost-effective alternatives to traditional methods.
- In drug discovery, pLMs help predict drug-target interactions, accelerating the screening of potential drug candidates.
Read Full Article
4 Likes
Amazon
4h
64
Image Credit: Amazon
Streamline custom environment provisioning for Amazon SageMaker Studio: An automated CI/CD pipeline approach
- Amazon SageMaker Studio's automated continuous integration and delivery (CI/CD) pipeline solution helps deploy custom Docker images to SageMaker Studio domains.
- The solution promoted consistency of analytical environment standards across data science teams across enterprises.
- The pipeline is automated by AWS CodePipeline and automates creation and attachment of Docker images.
- The pipeline automates code base check-out, Docker image creation based on configuration, push to Amazon Elastic Container Registry.
- If no high-security vulnerabilities are found, the deployment proceeds to manual approval stage before deployment.
- The default automation helps create SageMaker domain and attach custom images to the domain.
- The solution is geared towards platform teams and ML engineers responsible for managing and standardizing custom environments across organizations.
- Individual data scientists seeking self-service experience are advised to the native Docker support in SageMaker Studio.
- The authors also explain how to add more custom images using Dockerfile specifications.
- After a successful deployment, the custom image is attached to domain configuration.
Read Full Article
3 Likes
Medium
4h
140
Image Credit: Medium
Simplicity Over Black Boxes
- Machine learning models often referred to as 'black boxes' are difficult to interpret and apply.
- A different approach called Human Knowledge Models (HKM) focuses on finding concise and interpretable rules.
- HKM creates simple rules using basic Boolean operators and thresholds, making them easy to use in various domains.
- While HKMs have limitations, they can play a critical role in applied fields like healthcare, providing practical and interpretable solutions.
Read Full Article
8 Likes
Amazon
5h
124
Image Credit: Amazon
Enhance your customer’s omnichannel experience with Amazon Bedrock and Amazon Lex
- Amazon has developed new technologies to enhance customer experience across multiple channels, such as natural language understanding and automatic speech recognition.
- Amazon Lex bots can be used by businesses to integrate AI capabilities into their call centers, reducing handling times and streamlining tasks.
- Generative AI expands the potential to improve customer experience, but security and compliance concerns put businesses off using them with customers.
- The integration of Amazon Lex and Amazon Bedrock enables the use of large language models (LLMs) to enhance omnichannel customer experiences safely.
- By using Amazon Lex for initial touchpoints and Amazon Bedrock as a secondary validation layer, LLMs can provide better intent classification, slot resolution assistance, and background noise mitigation.
- Businesses can integrate this AI-driven experience with contact center solutions like Amazon Connect to deliver intelligent customer experiences across channels seamlessly.
- To deploy this solution, users need an AWS account and access to FMs on Amazon Bedrock.
- The workflow involves messages sent to the Amazon Lex omnichannel, with the Lambda function being invoked to handle certain phases of conversation.
- Amazon Bedrock returns the intent or slot identified, or responds to Amazon Lex that it is unable to classify the utterance as related to intent or slot.
- This solution requires specific models (FMs) to be selected from an AWS CloudFormation template through Amazon Bedrock to identify the intent, identify the slot or determine if the transcribed messages contain background noise.
Read Full Article
7 Likes
Medium
5h
241
Image Credit: Medium
Testing ReAct Agents with Pytest/Vitest and LangChain: A Comprehensive Guide
- Large language models (LLMs) have made the construction of intelligent systems easier in recent years.
- The ReAct agent is developed to execute complex tasks using reasoning and action.
- Testing ReAct agents can be done using frameworks such as Vitest and Pytest, along with LangChain, LangSmith, and agent testing.
- This article focuses on setting up the testing environment for ReAct agents that use external APIs, with a specific example of testing a stock-related ReAct agent.
Read Full Article
14 Likes
Medium
5h
138
Image Credit: Medium
The Curious Case of Missing Data — How Do We Handle It?
- Missing data can cause serious issues in data analysis and machine learning models.
- Examples of missing data include customers not providing income or employment details, missing transaction history, incomplete survey data, and undisclosed medical history with missing lab results.
- Common techniques to handle missing data include removing rows or columns, replacing missing values with specific values like mean or median, estimating missing values based on surrounding data points, and using machine learning models to predict missing values based on other features.
- Each technique has its own advantages and disadvantages, and the choice depends on the nature of the data and the context of the analysis.
Read Full Article
8 Likes
Global Fintech Series
8h
137
Image Credit: Global Fintech Series
Trumid Appoints Industry Leader Ryan Gwin as Head of Data Solutions
- Trumid, a financial technology company, has appointed Ryan Gwin as Head of Data Solutions.
- Gwin will focus on designing and distributing customized data sets and insights to clients, emphasizing Trumid's commitment to data-driven workflow and trade automation solutions.
- Trumid's Data & Intelligence and Automation teams include experts in AI, machine learning, pricing, analytics, and software engineering.
- Trumid has already developed data-led workflow solutions, such as Trumid FVMP and Trumid AutoPilot, and aims to further optimize platform intelligence in the credit asset class.
Read Full Article
8 Likes
Global Fintech Series
10h
121
Image Credit: Global Fintech Series
Why Forward-Thinking CFOs Are Banking on AI
- Chief Financial Officers (CFOs) are embracing artificial intelligence (AI) for various reasons like driving innovation, enhancing operational efficiency, and fostering growth within organizations.
- AI's ability to analyze vast amounts of data, recognize patterns, and predict outcomes makes it a transformative tool that can reshape finance leaders' roles and deliver value.
- CFOs, keen to capitalize on AI's capabilities, are exploring how it can streamline operations, enhance decision-making, and drive growth across their companies.
- AI is not just another tool in the tech toolbox but a powerful enabler that can propel organizations towards greater productivity, accuracy, and efficiency.
- The role of the CFO has shifted dramatically in recent years from just focused on financial reporting and budgeting to take a more holistic approach to decision-making- becoming the "Chief Future Officer."
- By allowing CFOs to process vast amounts of business data quickly and accurately, AI helps them uncover insights that were once difficult or time-consuming to find.
- With AI at their side, CFOs can act proactively rather than reactively, positioning their organizations to thrive in an unpredictable world.
- For CFOs striving to stay ahead in an ever-changing business landscape, the ability to understand and harness generative AI is a must.
- CFOs have a unique opportunity to lead innovation within the finance function by embracing AI to develop programs that not only streamline operations but also deliver significant value across the entire organization.
- By adopting AI, CFOs can develop programs that not only streamline operations but also deliver significant value across the entire organization. Embracing these technologies positions CFOs to lead their companies toward sustainable growth and success.
Read Full Article
7 Likes
Medium
10h
250
Image Credit: Medium
Making AI Transparent: A Simple Guide to Explainable AI
- Explainable AI (XAI) aims to make AI systems more transparent and understandable.
- XAI opens up the 'black box' of AI and provides insights into how it reaches decisions.
- By explaining its reasoning, XAI helps build trust and enables better-informed decision-making.
- XAI is crucial as AI becomes more integrated into various aspects of our lives.
Read Full Article
15 Likes
Medium
11h
64
Image Credit: Medium
Cramér’s V: How to Quantify Strength Between Categories
- Cramér’s V is a statistic that quantifies the association strength between categorical variables.
- Understanding the relationship between categorical variables is crucial in data science and machine learning.
- Cramér’s V can be used to guide decision-making in personalized marketing and product design.
- This guide provides a deep dive into Cramér’s V, including its definition, computation, and real-world applications.
Read Full Article
3 Likes
For uninterrupted reading, download the app