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Data Science News
Medium
4w
164
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Unleashing the Power of FastAI: A Comprehensive Guide
- FastAI is a high-level deep learning library that simplifies the process of building and training neural networks.
- It offers features designed to streamline workflow and accelerate projects, catering to both seasoned data scientists and beginners.
- Built on top of PyTorch, FastAI provides a high-level interface that abstracts away complexity in training neural networks.
- FastAI enables quick prototyping and deployment of state-of-the-art models with minimal code, making it accessible and powerful.
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Medium
4w
152
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Application Network Security in Azure: Subnets, Endpoints, DNS, NSGs with Terraform Code
- The article discusses securing applications in Azure by segregating them into subnets for each service and implementing private endpoints for communication.
- Subnet delegation is essential for applying networking rules within subnets for Azure services like Azure Functions.
- Private endpoints are private IPs from virtual networks that facilitate secure communication between Azure services.
- Private endpoints cannot be added to subnets delegated to specific resources like Microsoft.Web/serverFarms; dedicated subnets are needed for private endpoints.
- Azure Private Link connects private endpoints to apps over the Azure backbone network.
- Azure Private DNS resolves domain names in virtual networks without custom DNS configurations.
- Network Security Groups (NSGs) manage networking traffic between Azure resources and enable filtering both inbound and outbound traffic.
- NSGs need to be explicitly applied to subnets hosting private endpoints, and separate rules are required for effective traffic control.
- External access to applications behind a subnet and private endpoint can still be possible due to public endpoints by default; Azure API Management (APIM) can help in exposing apps securely.
- APIM allows external access to applications by exposing a public IP for API calls with a key for connecting to the backend securely.
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Analyticsindiamag
4w
195
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Anthropic Introduces Web Search for Claude Chatbot
- Anthropic, the AI startup behind the Claude family of models, has introduced web search to its AI chatbot Claude.
- Users can enable web search in the Claude web app's settings, allowing the chatbot to pull information from the internet.
- This update puts Claude on par with competitors like OpenAI's ChatGPT and Google's Gemini.
- Anthropic plans to release voice-based conversational features in the future.
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Analyticsindiamag
4w
97
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Developers Beware! AI Coding Tools May Aid Hackers
- Researchers have uncovered a new supply chain attack vector named 'Rules File Backdoor' that enables hackers to compromise AI-generated code by injecting hidden malicious instructions.
- The instructions are injected into rule files used by AI coding assistants like Cursor and GitHub Copilot, allowing the malicious code to silently propagate through projects.
- The attack is unnoticeable to users and can affect millions of end users through compromised code, enabling hackers to override security controls and generate vulnerable code.
- To stay safe from these attacks, researchers recommend auditing existing rules, implementing validation processes, and deploying detection tools for AI-generated code review.
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Analyticsindiamag
4w
222
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OpenAI Releases New Audio Models to Power Voice Agents
- OpenAI has launched new speech-to-text and text-to-speech models in its API, providing developers with tools to build advanced voice agents.
- The speech-to-text models, gpt-4o-transcribe and gpt-4o-mini-transcribe, improve word error rate and language recognition compared to Whisper models.
- Developers can control how the text-to-speech model speaks using the gpt-4o-mini-tts model, expanding use cases in customer interactions and creative storytelling.
- OpenAI plans to enhance the intelligence and accuracy of its audio models, explore custom voice options, and expand into video for multimodal agentic experiences.
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Analyticsindiamag
4w
311
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Perplexity to Raise up to $1 Billion to Double its Valuation to $18 Billion: Reports
- Perplexity AI is in talks to raise $500 million to $1 billion and double its valuation to $18 billion.
- Perplexity, an AI-enabled search engine, provides reasoning and deep research capabilities.
- The company recently announced the development of an agentic web browser called Comet.
- A research study found that Perplexity AI answered 37% of queries incorrectly.
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Towards Data Science
4w
217
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R.E.D.: Scaling Text Classification with Expert Delegation
- Text classification can be challenging, especially with a large number of input classes and limited training data.
- The R.E.D. algorithm addresses the problem of semi-supervised learning in text classification.
- R.E.D. utilizes the concept of Recursive Expert Delegation to improve text classification performance.
- The approach involves active learning and human validation to enhance classifier outcomes.
- R.E.D. simplifies the classification task by forming subsets of training labels for better classifier efficiency.
- The algorithm focuses on pre-emptive classification and verification of samples to ensure accuracy.
- Oversampling on noise is used as a measure to prevent misclassifications in the classifier.
- Uncertainty sampling and information gain principles are employed to evaluate the classifier's performance.
- R.E.D. leverages an LLM as a human validator, mimicking the Active Labelling process.
- The algorithm has shown promising results in scaling up to 1,000 classes while maintaining high accuracy levels.
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Analyticsindiamag
4w
48
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How Tredence’s AI-First Approach Transforms the Future of Delivery Service
- Tredence, a global data science and AI solutions company, embraces an AI-first mindset to enhance decision-making and reduce operational costs for enterprises.
- The company focuses on AI-powered decision systems that deliver real business value by improving efficiency and driving measurable impact.
- Tredence is transitioning from traditional deterministic systems to dynamic probabilistic ones, enabling decision-making based on probability, risk, and contextual factors.
- This shift has led to improved project efficiency, reduced project timelines by 50%, and a 40-50% decrease in operational costs for clients through AI automation.
- Tredence emphasizes delivering substantial business impact, which is reflected in acquiring over 50 clients in the past year.
- The company focuses on continuous adaptation and learning in AI deployment, aligning with McKinsey's research indicating the need for ongoing evolution in AI strategies.
- While heavily invested in AI, Tredence also values human capital and emphasizes continuous upskilling and talent repurposing to align with AI advancements.
- Tredence's hiring focuses on adaptability and problem-solving skills, with a merit-based remote work model and initiatives like Anubhav to promote a human-centric workplace culture.
- The company integrates AI into decision-making through digital assistants that enhance speed, accuracy, and efficiency across various industries, improving processes like demand forecasting in retail and pre-diagnosis in healthcare.
- Tredence envisions a future where AI-driven assistants amplify human potential, working alongside humans to enhance service delivery, efficiency, creativity, and decision-making.
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Analyticsindiamag
4w
151
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‘Nobody Needs to Die of Breast Cancer’
- Breast cancer poses a major health challenge, especially in low-resource regions with limited access to early detection and treatment.
- Niramai has developed a novel AI-driven solution called Thermalytix, which converts thermal images of the chest into cancer health reports, making screenings widely accessible for women of all ages.
- The technology has been adopted by several hospitals, both in India and internationally, and has received regulatory clearances, ensuring its global applicability.
- Niramai plans to extend its technology beyond breast cancer detection and explore its potential for detecting other abnormalities.
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Medium
4w
115
Meta AI: Shaping the Future of Artificial Intelligence
- Meta AI is Meta’s advanced artificial intelligence initiative, developing models like LLaMA to compete with leading AI systems.
- Key features of Meta AI include LLaMA: Meta’s open-source AI model, AI integration across social media platforms, advancing AI creativity, and prioritizing AI ethics and responsible development.
- Meta AI faces competition from other AI giants like OpenAI's ChatGPT, Google's Gemini, xAI's Grok, and China's DeepSeek AI.
- Meta AI aims to continuously improve its AI capabilities and play a crucial role in shaping the future of digital interactions.
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Towards Data Science
4w
63
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Algorithm Protection in the Context of Federated Learning
- The article discusses algorithm protection measures in the context of federated learning, focusing on three critical layers.
- Two categories of protection measures have been identified after analyzing risks.
- Consideration of qualitative and quantitative characteristics of mechanisms was emphasized for selecting suitable solutions.
- Confidential Containers (CoCo) technology aims to protect algorithm code and data from hosting companies using various hardware technologies.
- TEEs, such as CoCo, face security gaps that allow skilled administrators to bypass protection mechanisms, leading to ongoing security concerns.
- Distroless container images reduce attack surfaces but do not protect algorithm code adequately.
- Compiled languages like C, C++, and Rust provide better protection against reverse engineering than interpreted languages like Python.
- Homomorphic Encryption (HE) is highlighted for securing data in federated learning but does not protect algorithms.
- The article emphasizes the importance of hardware isolation for protecting algorithms.
- Combining protection mechanisms like compilation, obfuscation, and encryption creates barriers against intellectual property theft.
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Towards Data Science
4w
389
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Mastering the Poisson Distribution: Intuition and Foundations
- The Poisson distribution is explored due to its relevance in scenarios involving binary choices that aggregate into counts, such as the behaviors in online marketplaces.
- A concrete example of a seller listing items on a platform is used to illustrate the workings of the Poisson process.
- The rate parameter (λ) in the Poisson distribution represents the average monthly listings, serving as both the expected value and variance.
- Compared to other distributions like the Normal distribution, the Poisson distribution only requires one parameter, simplifying parametric inference.
- The formula for the Poisson distribution's probability mass function (PMF) is defined, showing the relationship between observed count (k) and rate parameter (λ).
- The integration of λᵏ, e^-λ, and k! in the Poisson distribution formula is explained, highlighting the correction for overcounting due to interchangeable events.
- The Poisson process and Poisson distribution are distinguished, with the former being a continuous-time model of events occurring in intervals, while the latter describes probabilities for counts in an interval.
- Extensions of the Poisson distribution are discussed, such as allowing the rate parameter to vary over time to accommodate changing intensities or heterogeneity among multiple processes.
- The Negative Binomial distribution is introduced as a solution for overdispersion and heterogeneity in scenarios where the constant rate assumption of the Poisson distribution does not hold.
- Increased flexibility in distribution modeling comes with challenges in parameter estimation and potential overfitting, impacting statistical power in inference.
- While the Poisson distribution is useful for count data, it is essential to assess model assumptions and consider simpler approaches or Bayesian methods to address limitations and maintain statistical power.
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Medium
4w
262
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In Search of the 3%
- Startups face high risks, but Venture Capitalists (VCs) rely on them to build their strategies and identify winners.
- AI has the potential to revolutionize the VC landscape by providing a smarter way to analyze data and make investment decisions.
- While AI can expedite due diligence and assist VCs, it cannot replace human intuition in determining a company's success.
- The future of venture capital lies in a collaborative model where VCs utilize AI for efficiency while retaining their strategic thinking abilities.
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Medium
4w
115
Image Credit: Medium
Apophenia, Pattern Recognition, and AI: The Intersection of Human Perception and Machine Learning
- Apophenia is the tendency to perceive meaningful connections in random data and plays a role in human perception, creativity, and scientific discovery.
- Apophenia is also relevant in the field of artificial intelligence (AI) as pattern recognition is vital for machine learning and neural networks.
- Understanding apophenia helps balance creativity and accuracy in AI systems by minimizing false connections and biased correlations.
- Future directions in AI include refining training methods, incorporating human-in-the-loop learning, and promoting critical thinking in AI-assisted education.
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Dev
4w
400
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How does BCrypt Verification work?
- The hashing algorithm used for password verification is a one-way hashing function.
- BCrypt hashing and verification use random letters called salt and a cost variable to produce unpredictable hash results.
- BCrypt stores the algorithm, cost, and salt alongside the hash result for future verification.
- To verify a password, the algorithm, cost, and salt are extracted from the stored hash, and the same process with the extracted values is used to generate a new hash for comparison.
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