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Data Science News
Analyticsindiamag
1w
273
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Context Engineering is the New Vibe Coding
- Python developers transitioned from prompt engineering to context engineering in the tech world.
- The AI community focuses on structuring information for completing tasks successfully with context engineering.
- Context engineering involves providing necessary information for AI models to succeed in tasks.
- It emphasizes system design, memory, history, and data optimization for AI models.
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Analyticsindiamag
1w
112
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NVIDIA H20 Chip Shortage Delays DeepSeek R2 Launch
- The launch of DeepSeek's upcoming model, R2, is facing delays due to a shortage of NVIDIA's H20 chips caused by US export restrictions.
- R2, the successor to the widely used R1, is still awaiting a release date as CEO Liang Wenfeng is dissatisfied with its performance and engineers are working on improvements.
- Cloud providers hosting DeepSeek's models anticipate a shortfall in NVIDIA chips to meet the potential demand of R2, especially if it outperforms current open-source alternatives.
- Despite chip shortages, Chinese firms are finding workarounds to access NVIDIA hardware, including sending engineers to Malaysia for training AI models due to export restrictions.
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Analyticsindiamag
1w
162
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Investor With No Coding Experience Builds ‘$1.7Mn SaaS’ Using Replit
- Brad Lindenberg, co-founder of Quadpay, developed Biography Studio AI with Replit, creating biographies using voice prompts.
- Biography Studio AI removes writing friction, costs $119 for unlimited chapters and a full-length book, preserving memories in various formats.
- Lindenberg called the project a 'steep learning curve', leveraging Replit for development without prior coding experience.
- Replit, fostering 'vibe coding', has seen significant growth; Lindenberg's project showcases the potential of platforms using AI for rapid application development.
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Analyticsindiamag
1w
397
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DeepMind Launches AlphaGenome to Predict How DNA Variants Affect Gene Regulation
- DeepMind has launched AlphaGenome, an AI model to predict how single DNA variants impact gene regulation in the human genome's non-coding regions, offering high-resolution predictions on molecular processes across 1 million DNA base pairs.
- AlphaGenome outperforms existing models in sequence prediction benchmarks and variant-effect tasks, providing insights into gene function, mutation impacts, and disease mechanisms.
- The architecture of AlphaGenome combines convolutional layers, transformer models, and final layers to analyze non-coding regions, improving understanding of genomic tasks.
- Although AlphaGenome excels in many areas, it still faces challenges in predicting distant DNA interactions and hasn't been validated for personal genome interpretation or clinical use.
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Analyticsindiamag
1w
143
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The ‘Fastest Commercial-Grade’ Diffusion LLM is Available Now
- Inception Labs has launched Mercury, claiming it to be the fastest commercial-scale diffusion large language model (LLM), with a speed surpassing Gemini 2.5 Flash and comparable to GPT-4.1 Nano and Claude 3.5 Haiku.
- Mercury can be accessed on chat.inceptionlabs.ai and third-party platforms, offering over 700 tokens per second output speed. It uses a diffusion architecture for high-speed output, different from traditional models.
- The model is available via a first-party API at a cost of $0.25 to $1 per million input/output tokens. Inception Labs announced Mercury in February and recently published a technical report for the model.
- Google introduced diffusion models as a better alternative to traditional language models, allowing quick iterations and error correction during generation. Mercury aims to provide real-time responsiveness to chat applications.
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Medium
1w
338
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How DataRobot is Making AI Easy for Everyone — Even Without Coding!
- DataRobot is an automated machine learning platform that allows individuals and companies to build AI models without coding.
- Users can upload their data, select the target to predict, and let DataRobot automatically build and compare machine learning models.
- The platform provides easy-to-understand graphs and scores to assess accuracy and performance, with Explainable AI feature to understand model decisions.
- DataRobot is beneficial for beginners and business users, offering real-time predictions and is used in various sectors like healthcare, finance, retail, manufacturing, and education.
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Analyticsindiamag
1w
62
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Why LTTS is Designing AI-based Defence Tech in Texas
- L&T Technology Services (LTTS) inaugurates Engineering Design Centre in Plano, Texas.
- New facility to create 350 jobs, focused on AI, cybersecurity, digital manufacturing, and defence.
- Texas chosen for tech ecosystem, including aerospace and defence work, innovation in cybersecurity.
- Centre will focus on next-gen systems, edge AI, simulation, and ITAR-compliant development.
- LTTS plans to hire local engineers and expand globally with more design centers.
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Medium
1w
141
Making AI Accessible: How DataRobot is Transforming the Machine Learning Landscape
- DataRobot is an end-to-end enterprise AI platform that automates the process of building, deploying, and maintaining machine learning models, aiming to make AI accessible to everyone.
- The platform ingests data from various sources, selects the appropriate modeling approach, initiates parallel training across algorithms, and provides a centralized leaderboard to compare model performance.
- DataRobot emphasizes model interpretability for stakeholders to understand and trust predictions, along with deployment features like real-time scoring and continuous monitoring for model performance.
- Compared to other AutoML tools, DataRobot offers a holistic solution with features like user-friendly interfaces, automated feature engineering, deep model explainability, and one-click deployment, catering to both beginners and experienced practitioners.
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Analyticsindiamag
1w
47
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IndiaAI’s Equity in AI Startups is a ‘Fantastic’ but Risky Idea
- Indian government is taking equity stakes in AI startups like Soket AI Labs, Gnani AI.
- This move raises concerns in AI and investor circles about potential government influence.
- Equity in exchange for GPU support sparks a debate on business autonomy and accountability.
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Medium
1w
213
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How I Found My Passion for AI After Trying Everything Else
- The individual discovered their passion for Artificial Intelligence (AI) during the pandemic when their engineering journey began.
- They initially struggled with skills in DSA (Data Structures and Algorithms) but found their strengths in Python and mathematics, leading them towards AI.
- They explored AI through courses, hackathons, web development, and backend development, ultimately realizing AI was their true calling.
- After committing fully to AI, the individual became the AI/ML Lead at their college, mentored others, and secured an internship as a Data Scientist, showing the importance of following one's curiosity and passion.
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Medium
1w
188
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Breaking the Boundaries of Imagination: Md Shafiul Hasan Rejvi in a Cyberpunk Scientific Vision
- Md Shafiul Hasan Rejvi blends science and cyberpunk in a futuristic experiment, symbolizing sparks of creativity and curiosity.
- The glowing chemical reaction signifies unlocking ideas waiting within us, reflecting Rejvi's mission for technological innovation.
- The visually captivating digital artwork symbolizes a futuristic blend of fashion, science, and creativity, showcasing the direction of technology.
- Rejvi's persona as a cyber-scientist represents the future of creators, tech entrepreneurs, and dreamers, merging visual storytelling with futuristic vision.
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11 Likes
Medium
1w
424
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Prompt Engineering or Fine-Tuning: Which Is Best for Your AI Project?
- Prompt engineering guides a model's output by adjusting inputs, enhancing performance.
- For common topics and widespread knowledge, such as from large training datasets, prompt
- engineering may suffice. For specialized domains, recent data, or proprietary info, consider fine-tuning.
- Fine-tuning alters a model's internal weights for specific language patterns, yielding accuracy.
- Fine-tuning, though resource-intensive, can be more efficient for domain-specific applications at scale.
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VentureBeat
1w
90
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Walmart cracks enterprise AI at scale: Thousands of use cases, one framework
- Walmart excels in enterprise AI with a focus on trust as an engineering requirement.
- The company operationalizes thousands of AI use cases and enhances customer confidence.
- Walmart avoids generic platforms, preferring purpose-built tools for specific operational needs.
- AI deployment leads to operational efficiencies, real-time product development, and enhanced customer experience.
- Their successful AI framework can be a model for various industries facing multi-stakeholder challenges.
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Medium
1w
175
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Exploration of the day with AI: Gluttony or learning?
- The article explores a system's functioning, creation of consciousness, and body organs operations.
- Detailed discussions on nucleus production, consciousness formation, and transformation processes are provided.
- The system's logic, genetic code, control hierarchy, and external interactions are elaborated upon.
- The article concludes with insights on data management systems, role distribution, and system evaluation.
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VentureBeat
1w
117
Image Credit: VentureBeat
What enterprise leaders can learn from LinkedIn’s success with AI agents
- LinkedIn's success with AI agents includes a live hiring assistant for recruiters.
- The AI agents collaborate in a multi-agent system to streamline candidate sourcing.
- Agents interact using natural language, adapt to user preferences, and aim to improve efficiency.
- LinkedIn's approach focuses on fine-tuning models, training, and optimizing agent performance.
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