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Data Science News
Medium
1h
254
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Practical Guide to Time Series Analysis: Exponential Smoothing with Python
- This article is a practical guide to time series analysis using exponential smoothing with Python.
- Exponential smoothing is a powerful technique for understanding and predicting behavior over time.
- The article focuses on applying three types of exponential smoothing — simple, double, and triple — using Python and the statsmodels library.
- Time series analysis helps in identifying patterns in data such as trends and seasonality.
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15 Likes
Medium
4h
114
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Devolp Customer Data Platform using dbt part 1
- Understanding customer behavior is crucial for business growth, and developing a user-friendly BI solution can make insights quick and easy to access.
- The Event Schema provides the perfect foundation for creating a simple, scalable solution that stands out as the ideal candidate to handle all event-related data with ease.
- The dbt project ensures a well-organized, multi-layered approach to data modeling that enhances data quality and usability.
- The YAML file is used to provide more context about the data being held and improve clarity.
- An incremental model can be used for fast-changing tables to append new data only, keeping things efficient and fast.
- Tests are used to ensure data consistency across tables and confirm that each order item can be reliably linked to related records in other table.
- Business rules for pricing integrity can be incorporated with a package that allows for conditional logic similar to WHERE clauses in SQL.
- The foundational 'seeds' for the journey tables form the core structure of the activity schema, capturing the relationships between various events in the customer journey.
- Using these seeds as building blocks, we can layer in additional business logic later to refine and enrich the customer journey.
- Documentation is used in the YAML file to give context to the approach and data structure of the order journey model.
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6 Likes
Medium
9h
248
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Revolutionizing Multilingual AI: How MYTE is Redefining Text Encoding
- Multilingual AI models often struggle with handling linguistic diversity and biased results.
- Standard UTF-8 encoding fails to capture morphological differences between languages.
- MYTE (Morphology-Driven Byte Encoding) is a novel byte encoding scheme designed to address these challenges.
- MYTE leverages morphological information to improve performance and fairness of multilingual AI models.
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14 Likes
VentureBeat
9h
276
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Snowflake’s ‘data agents’ leverage enterprise apps so you don’t have to
- Snowflake Intelligence is a platform that helps enterprise users create and deploy dedicated 'data agents' for extracting relevant business insights from hosted data and third-party data sources beyond their data cloud instance.
- The data agents can take actions across different tools and applications, such as Google Workspace and Salesforce.
- The AI creation and deployment platform will live within the same cloud data warehouse or lakehouse provider, eliminating the need for another tool.
- Snowflake has integrated AI capabilities on top of its core data platform, including Document AI, Cortex AI, and Snowflake Copilot.
- Snowflake Intelligence expands these capabilities by enabling teams to set up enterprise-grade data agents that tap siloed third-party tools and structured/unstructured data sources across different systems.
- The platform uses Cortex Analyst and Cortex Search (part of Cortex AI architecture) to deploy agents that accurately retrieve and process specific data assets from both unstructured and structured data sources to provide relevant insights.
- The users interact with the agents in natural language, asking business-related questions while the agents identify the relevant internal and external data sources to provide answers.
- Once the relevant data is surfaced, the user can ask data agents to take specific actions around the generated insights, like entering the insights into an editable form or uploading the file to their Google Drive.
- Snowflake has not given a timeline on its availability, but it will go into private preview soon.
- Snowflake Intelligence will be natively integrated with the company's Horizon Catalog at the foundation level, allowing users to run agents for insights right where they discover, manage, and govern their data assets.
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16 Likes
Medium
59m
80
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Increase Trust in Your Regression Model The Easy Way
- Returning only a point prediction is not enough to determine the trustworthiness of a regression model.
- Knowing the certainty of predictions is crucial for making well-informed decisions.
- In regression problems, it is important to determine the interval within which the true value is expected to lie.
- Two approaches can be used: using a set of models that predict the interval or converting a point prediction into an interval.
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4 Likes
Medium
1h
191
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5 Shocking Ways AI Agents Are Transforming Business Operations Forever
- AI agents are becoming integral to business operations, helping companies make smarter decisions, optimize processes, and engage customers in new ways.
- AI can automate repetitive tasks, such as sorting through vast amounts of customer data, allowing human teams to focus on the more creative aspects of the work.
- AI agents are capable of answering customer inquiries 24/7 with unparalleled accuracy, anticipating needs based on prior interactions and preferences.
- AI can predict customer behavior patterns, future trends, and even craft personalized marketing campaigns.
- Focusing on key Artificial Intelligence applications delivered immediate value without overwhelming team or resources. By starting small and scaling strategically, many pitfalls can be avoided.
- Security was a primary concern, but by working with trusted AI providers, customer data remained protected.
- AI agents don’t replace jobs; it transforms them. The workforce can be shifted from mundane tasks to more strategic and creative roles, enhancing job satisfaction.
- AI agents are practical tools reshaping business operations today, crafting a new business era.
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11 Likes
Dev
3h
295
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Learn Big O Notation once and for all
- Big O notation is used to classify algorithms according to how their run time or space requirements grow as the input size grows.
- O(n) notation is meant to analyze algorithms that take linearly more time as the size of input increases. This can be seen in simple examples like finding the maximum element of an array.
- O(1) notation represents algorithms that take constant time to solve a problem, regardless of the size of input. Examples include finding the first element of an array or appending items to an array.
- O(n^2) notation represents algorithms that take quadratic time to solve a problem, which can be seen when we have to traverse two-dimensional arrays.
- O(n log n) notation is usually found in sorting algorithms and implies a combination of a logarithmic and linear runtime complexity.
- O(2^n) notation represents algorithms with an exponential complexity, commonly found in recursive algorithms that branch out in two ways.
- O(n!) notation represents algorithms that require factorial time complexity, which is extremely rare and inefficient.
- Understanding Big O notation is crucial for analyzing and improving algorithms, especially in job interviews or coding challenges.
- The cheatsheet provided by Eric Rowell can be a helpful tool for visualizing the different complexity notations and comparing their growth rates.
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17 Likes
VentureBeat
4h
183
How agentic RAG can be a game-changer for data processing and retrieval
- Enterprises have run into instances where RAG fails to deliver the expected results, prompting the development of agentic RAG, which incorporates AI agents into the pipeline.
- AI agents with memory and reasoning capabilities enable agentic RAG to retrieve data from multiple knowledge sources, going beyond fetching information from just one knowledge source.
- Agentic RAG can improve downstream LLM applications by enabling them to produce more accurate and validated responses to complex user queries.
- There are two main ways to set up agentic RAG pipelines: incorporating a single agent system; and setting up multi-agent systems with a series of specialized agents that work across their respective sources to retrieve data.
- Agentic RAG is still new and can run into occasional issues, including latencies stemming from multi-step processing and unreliability.
- The agentic RAG pipeline could be expensive as the more requests the LLM agent makes, the higher the computational costs.
- Agentic architectures are critical for the next wave of AI applications that can 'do' tasks rather than just retrieve information.
- Enterprises should explore additional capabilities such as agentic AI and Generative Feedback Loops as they continue to level up their RAG applications.
- The approach expands the knowledge base powering downstream LLM applications, enabling them to produce more accurate, grounded, and validated responses to complex user queries.
- DSPy, LangChain, CrewAI, LlamaIndex, and Letta simplify building agentic RAG systems by plugging pre-built templates together.
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Hackernoon
5h
17
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How to Build Scalable NLP-Powered Voice Agents for Seamless User Interactions
- Businesses are adopting NLP-driven voice agents to provide personalized and efficient user experience.
- Building such systems involves integrating NLP with API calls while maintaining scalability.
- This article explores building scalable NLP-powered systems, focusing on the full round-trip process.
- The round trip includes capturing voice request via speech-to-text, identifying intent via Dialogflow, fetching backend information via webhook, and delivering the result to user via text to speech.
- Dynamic API routing is introduced to handle more intents without hardcoding every interaction.
- NLP-driven APIs are expanding, making real-time communication across fintech, healthcare, and e-commerce feasible.
- Integration of NLP systems and scalable APIs provides a robust framework for creating more intuitive and responsive user experiences.
- The future could see white-labeled NLP-driven agents, replacing call centers and offering a more scalable, cost-effective way to handle customer service inquiries and internal processes.
- Advanced machine learning models, voice biometrics, and real-time analytics promise deeper personalization and improved user experience.
- NLP-driven APIs are set to revolutionize the way users interact with businesses.
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1 Like
Medium
5h
72
Image Credit: Medium
We Are Just Scaling AI And Not Coming With Novel Ideas
- The field of AI has seen significant advancements, but there is concern that innovation of novel ideas is stagnating.
- AI development focuses on scaling existing technologies rather than inventing new methodologies or concepts.
- Commercial viability and risk aversion contribute to the lack of novel ideas in AI.
- Diverse datasets, interdisciplinary collaboration, and ethical considerations are essential for fostering innovation in AI.
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4 Likes
VentureBeat
6h
48
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Qwen2.5-Coder just changed the game for AI programming—and it’s free
- Alibaba Cloud has released Qwen2.5-Coder, a new AI coding assistant.
- The release includes six model variants, making advanced AI coding accessible to developers with different computing resources.
- Qwen2.5-Coder's success stems from refined data processing, synthetic data generation, and balanced training datasets.
- Unlike its closed-source competitors, most Qwen2.5-Coder models carry the permissive Apache 2.0 license, allowing free integration into products.
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2 Likes
Medium
7h
318
OpenAI’s Ilya Sutskever: The Future of AI is Smarter, Not Bigger
- The future of AI is shifting towards smarter algorithms and strategic data selection rather than simply scaling up data size and computational power.
- AI labs are finding it challenging to make breakthroughs with large language models due to everyone having access to the same massive online datasets.
- Inferencing techniques are being developed to enable models to generate more thoughtful responses, resembling human reasoning and problem-solving.
- The shift towards more efficient inferencing hardware, such as NVIDIA's GPUs tailored for AI inferencing, suggests a new era of smarter AI processing.
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19 Likes
Medium
7h
336
9 PyTorch Layers You Must learn it
- torch.nn.Linear: Applies a linear transformation to incoming data by multiplying the input with a weight matrix and adding a bias.
- Convolutional Layer: Applies convolutional filters to input data to extract spatial or temporal patterns.
- Recurrent Layers: Used to handle sequential data by keeping information over time and learning temporal dependencies.
- Embedding Layer: Converts input indices into dense vectors of fixed size, commonly used for representing discrete items.
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20 Likes
Medium
8h
123
The Current State of Data Science and What Lies Ahead in the Next 5 Years
- The applications of AI and machine learning have moved from niche to critical components in various sectors.
- Cloud computing has provided scalable solutions that support big data analytics without massive physical infrastructure.
- Increasing scrutiny on data privacy, security, and ethical use has led to regulations like the GDPR and CCPA.
- Data-driven decision-making is now a standard, enabling a shift towards company-wide data literacy and evidence-based strategies.
- Automation within data science is advancing rapidly, with AutoML streamlining the model development process.
- Explainable AI will become a significant focus, with new tools and frameworks.
- Real-time analytics and edge computing will grow, spurring advancements in data processing.
- More stringent privacy and security measures like differential privacy will become necessary.
- DataOps and MLOps will become crucial operational frameworks.
- The demand for skilled data professionals will continue to rise.
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7 Likes
Mit
9h
146
Image Credit: Mit
3 Questions: Inverting the problem of design
- The DeCoDE Lab at MIT is combining machine learning and generative AI techniques, physical modeling, and engineering principles to tackle design challenges and enhance the creation of mechanical systems.
- Linkages, one of the group's recent projects, explores ways planar bars and joints can be connected to trace curved paths.
- The project uses the idea of self-supervised contrastive learning approaches to learn the representation of the design and how it works.
- This technique helps to incorporate precision into generative AI models and contributes to automated discovery.
- With the Linkages project, the team showed that the approach enabled solving problems more precisely and significantly faster, at 28 times less error and 20 times faster than prior state-of-the-art approaches.
- The Linkage method uses contrastive learning between the represented mechanism joints in graphs and another model that creates an embedding for the curves, and then connects these two modalities using contrastive learning.
- The method can find new mechanisms with precision through additional optimization steps beyond identifying candidate mechanisms.
- This proof of concept shows that the method is effective on discrete and continuous systems and can potentially transfer to many engineering applications.
- Future work for the project includes exploring more complex mechanical systems and more physics, as well as thinking about how precision in langue models can be incorporated.
- The project was supported by the MIT-IBM Watson AI Lab.
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8 Likes
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