A naukri.com initiative
Deep Learning News
Medium
1w
385
Image Credit: Medium
Future-Proof Your Business with AI Agents
- AI agents are transforming businesses by automating tasks, enhancing customer service, and driving data-powered decisions.
- Implementing AI agents can bring cost challenges, but the benefits in efficiency and customer engagement are worth it.
- AI agents are no longer limited to big tech companies and are becoming essential for businesses of all sizes.
- Entrepreneurs are seeing the potential of AI agents to revolutionize their operations and drive innovation.
Read Full Article
23 Likes
Medium
1w
88
What is Guardrail in LLM
- Guardrail layers are crucial in the responsible use of Language Models (LLMs), such as OpenAI's GPT-3, to ensure safety, ethical compliance, and context control.
- The guardrail layer prevents the generation of harmful or offensive content, filters out sensitive topics or toxic language, and ensures compliance with ethical guidelines and legal requirements.
- It also implements custom business rules, protects against dangerous recommendations, and detects and mitigates hallucinations or false information provided by the LLMs.
- Guardrail layers can be implemented through input and output filtering, rule-based constraints, external API integrations, human-in-the-loop systems, and reinforcement learning from human feedback.
Read Full Article
5 Likes
Eweek
1w
335
Image Credit: Eweek
Top 10 AI Software Tools of 2025: Unleashing Business Innovation
- The top AI software applications offer distinct capabilities from increasing productivity to elevating customer experiences. Here are our picks for the top AI software for 2025 including Grammarly, Tidio, ChatGPT, Fireflies.ai, Microsoft Copilot, Synthesia, Salesforce Einstein, Claude, Jasper, and IBM watsonx.
- Grammarly, a writing assistant, offers suggestions to tailor content, tone, length, and formality to match brand. It detects AI in content as part of its AI features. Monthly access costs $12 per user.
- Tidio's AI-powered customer communication platform offers live chat and chatbot functionalities allowing businesses to automate interactions and improve response times on multiple channels. Monthly subscription starts at $29 for 100 handled conversations.
- ChatGPT engages in human-like conversations, assists in coding, generates images, and answers questions. Monthly pricing starts at $20 per user for its basic plan.
- Fireflies.ai allows automatic summarization and analysis of voice and video conversations. The platform has advanced Conversation Intelligence capabilities that include speaker talk time tracking, sentiment analysis, and customizable topic trackers. Paid plans start at $18 per seat, per month.
- Microsoft Copilot generates content and automates tasks within Microsoft environments. The platform generates images through its Designer integration. Its monthly subscription starts at $20 per user.
- Synthesia produces professional-quality videos with AI-generated avatars and voiceovers. The platform has a one-click translation feature and monthly subscription costs $29 per user.
- Salesforce Einstein is an advanced AI tool integrated into the Salesforce platform that can automate tasks and generate insights using predictive analytics. The subscription costs $500 per user, per month.
- Claude is an AI tool with a focus on ethical outcomes as much on high performance. Its emphasis on ethical AI development makes it reliable for priority in AI use. Monthly access costs $20 per user.
- Jasper, an AI tool for digital marketing teams, enables marketing teams to quickly respond to trends and audiences and keep marketing materials engaging. Its monthly plans start at $49 per seat, per month.
- IBM watsonx is an enterprise AI and data platform for extracting valuable insights from large datasets. Monthly subscription to this tool starts at $1,050.
Read Full Article
20 Likes
Eweek
1w
208
Image Credit: Eweek
Large Language Model: A Guide To The Question ‘What Is An LLM”
- Large language models (LLMs) are artificial intelligence systems trained on vast amounts of data that can understand and generate human language.
- LLMs use deep learning technology and natural language processing (NLP) to perform an array of tasks, including text classification, sentiment analysis, code creation, and query response.
- Advancements in AI and generative AI are pushing the boundaries of what was once considered far-fetched in the computing sector.
- LLMs bridge the gap between human understanding and machine learning to offer a better content output.
- LLMs continue to improve their ability to provide logical and trustworthy responses across many complex knowledge sectors.
- Pre-trained language representation models (LRM) can be fine-tuned for specific tasks like text classification and language generation.
- LLMs are highly beneficial for problem-solving and helping businesses with communication-related tasks.
- LLMs can generate natural-sounding translations across multiple languages, enable code and text generation, and perform tasks with minimal training examples or without any training at all.
- Large language models have challenges and limitations that may affect their efficacy and real-world usefulness.
- Duke University's specialized course teaches students about developing, managing, and optimizing LLMs across multiple platforms, including Azure, AWS, and Databricks.
Read Full Article
12 Likes
Medium
1w
29
Image Credit: Medium
Achieving Reliable Model Performance with cross_val_score
- Machine learning models are not always stable and we have to evaluate the stability of the machine learning model.
- Cross-Validation is a technique used to assess how well our Machine learning models perform on unseen data.
- Cross-Validation is a resampling technique with the fundamental idea of splitting the dataset into 2 parts- training data and test data.
- One of the major advantages of the Hold Out method is that it is computationally inexpensive compared to other cross-validation techniques.
- The Leave One Out Cross-Validation method gives unbiased estimates but has an extremely high variance because the model is run 'n' times to test every observation.
- In K-Fold Cross-Validation, the mean of errors from all the iterations is calculated as the CV test error estimate.
- Stratified K-Fold CV will create K-Folds by preserving the percentage of sample for each class.
- Cross-validation is a powerful technique to ensure that your machine learning model generalizes well to unseen data.
- The sklearn.model_selection.cross_val_score function further simplifies this process, making it easier to implement cross-validation and obtain consistent evaluation metrics for your models.
- K-fold Cross Validation is typically performed using k=5 or k=10 as these values have been empirically shown to yield test error estimates that neither have high bias nor high variance.
Read Full Article
1 Like
Medium
1w
165
Free Inference Platforms for LLMs: A Guide to Leading Websites
- Google’s AI Studio provides access to its latest AI models for developers.
- Meta AI hosts and deploys all Meta’s AI models on a centralized platform.
- Groq’s platform offers unparalleled performance in tokenization per second.
- Hugging Face offers free inference capabilities and access to open-source LLMs.
Read Full Article
9 Likes
Medium
1w
268
The Exception Handler (Catch Block)
- The exception handler (catch block) is a construction in a programming language that immediately follows the try block. It is used to handle exceptions that occur within the try block.
- When an exception occurs, control transfers to the exception handler (catch block) following the try block.
- The exception handler can print an error message, continue processing at that point, transfer control elsewhere, or terminate the program.
- The try-throw-catch arrangement simplifies coding as it automatically handles exceptions, allowing for cleaner code and eliminating the need to check return values for each statement.
Read Full Article
16 Likes
Medium
1w
38
Image Credit: Medium
The Future of Artificial Intelligence: What Does ChatGPT Predict for 2030?
- Artificial intelligence (AI) has emerged as a cornerstone of technological innovation, driving transformative changes across industries such as healthcare, transportation, education, and manufacturing.
- The 2020s have been pivotal in the history of AI, fueled by significant breakthroughs in computational power, data availability, and algorithmic sophistication.
- Breakthroughs in Language Models: The introduction of large-scale language models such as GPT-3 and GPT-4 showcased AI’s near-human capabilities in understanding and generating text.
- Advances in Computer Vision and Speech Recognition: AI’s journey also included remarkable strides in computer vision and speech recognition.
- AI Democratization: During this period, AI became more accessible to developers and organizations through open-source frameworks like TensorFlow, PyTorch, and Hugging Face.
- The convergence of big data and AI has been instrumental in the rapid evolution of intelligent systems.
- Data as the Driving Force: Big data acts as the lifeblood of AI, providing the raw material necessary for training complex models.
- Advances in Data Storage and Processing: The rise of cloud computing and distributed databases has enabled organizations to store and process massive datasets efficiently.
- Autonomous systems are anticipated to dominate transportation, logistics, and urban mobility, revolutionizing the way goods and people move.
- Natural Language Processing (NLP) technologies are set to achieve unprecedented capabilities, bridging communication gaps and enabling context-aware interactions.
Read Full Article
2 Likes
Towards Data Science
1w
353
Image Credit: Towards Data Science
Activation Functions in Neural Networks: How to Choose the Right One
- The activation function is a central component in every neural network.
- It determines how strongly a neuron in the network is activated and thus decides which structures are learned from the data.
- This article explores the properties of activation functions and compares commonly used functions.
- Tips are provided on how to choose the right activation function for a network architecture and specific use case.
Read Full Article
20 Likes
Medium
1w
384
Into the dark, my heart calls out.
- The author reflects on their struggle of sharing their writing and seeking validation.
- They find guidance in honoring their emotions and sharing what feels true to themselves.
- The author expresses their love for people, storytelling, and their journey of self-discovery.
- They acknowledge their past pain and abandonment, but also express gratitude for true connections and love.
Read Full Article
23 Likes
Medium
1w
141
Image Credit: Medium
The Future Is Here: 7 AI Technologies You Should Know About
- Artificial Intelligence is powering major innovations that are changing the world.
- Generative AI is evolving to personalize experiences beyond text and images.
- Multimodal AI combines vision, text, and audio processing for comprehensive understanding.
- AI-powered systems can diagnose medical conditions and assist in drug discovery.
Read Full Article
8 Likes
Medium
1w
162
Image Credit: Medium
Gemini 2.0: Ushering in a New Era of Multimodal AI
- Gemini 2.0 is a next-generation AI model that embraces a multimodal future.
- It can understand and generate content across various modalities and provide more immersive user experiences.
- Gemini 2.0 exhibits a deeper understanding of the world by integrating with tools like Google Search and Google Maps.
- Its agentic capabilities enable it to become more autonomous, purposeful, and anticipate user needs.
Read Full Article
9 Likes
Medium
1w
89
Here’s how Anthropic is approaching Agent safety…..
- Anthropic is focusing on agent safety and their approach to tackle it in the coming years.
- They emphasize the importance of measurement, model performance, and compute poured into model training over time.
- They introduce RSP (Responsible Scaling Policy) modeled on bio safety levels to precommit to safety rules and be publicly accountable.
- They highlight the need for defensive layers and mention their Long term benefit trust policy, prioritizing safety and public welfare.
Read Full Article
5 Likes
Medium
1w
29
Image Credit: Medium
From Simple Reflexes to General Intelligence: The Role of AI Agents
- AI agents are intelligent software programs comprising of adaptable problem solvers who can perceive their environment, reason about it and take an action to achieve specific goals.
- AI agents can operate independently, make decisions and adapt to changes without constant human intervention.
- AI agents can be categorized into goal-based agents, learning agents, model-based agents, and simple reflex agents.
- Simple reflex agents are basic types of AI agents that employ a reactive strategy, i.e., they select actions based on the current percept, ignoring percept history.
- The article analyzes the Python code through the lens of established AI agent frameworks, identifying characteristics and limitations while providing a concrete foundation for understanding more sophisticated AI agents and how these principles contribute to the development of AGI.
- The Python code is a valuable stepping stone to understanding more complex agents while shedding light on perception, action, and goal orientation that underpins all AI agent designs.
- AI agents are poised to revolutionize various aspects of our lives, offering increased efficiency, convenience, and personalization.
- By understanding the fundamental principles of AI agents, we can better appreciate their potential and navigate the ethical considerations associated with their development.
- As we move towards more sophisticated agent designs, perception, action, and goal orientation will remain central to their development and deployment.
- Ultimately, AI agents can serve humanity in meaningful and ethical ways, shaping a future where AI serves humanity.
Read Full Article
1 Like
Medium
1w
231
Image Credit: Medium
JP Morgan’s AI Revolution
- JP Morgan’s strategic use of AI agents has revolutionized the way financial fraud is addressed.
- The adoption of AI agents has saved millions and enhanced efficiency.
- JP Morgan explored new ways to combat financial fraud, and AI emerged as a potential game-changer.
- The use of AI agents revolutionized security and efficiency in finance.
Read Full Article
13 Likes
For uninterrupted reading, download the app