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Appsilon
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ShinyConf24 – Keynote: Decade of Shiny in Action: Case Studies from Three Enterprises
- Eric Kostello, Executive Director of Data Science at Warner Brothers Discovery, highlighted the significant role Shiny has played in enterprise projects over the last decade.
- Shiny evolved to provide interactive user interfaces for exploring data pathologies and making judgments on new and existing data, benefiting both technical and non-technical stakeholders in leveraging data insights.
- Before Shiny, integrating statistical computing with graphical user interfaces was challenging, but Shiny's framework made data science more accessible to a broader audience.
- The evolution of Shiny addressed initial limitations, introducing event-driven programming and reactive graph evaluation for greater efficiency in data processing.
- Shiny has been instrumental in various case studies, such as transitioning to online surveys, facilitating TV ratings data analysis, and enabling multi-scale applications for improved efficiency and decision-making.
- Key insights from the keynote included democratizing data science, focusing on minimum viable interactivity, emphasizing enterprise-class solutions, and choosing the right tool for the job.
- Challenges to Shiny's adoption include organizational resistance and competition from established tools, but having power users can showcase Shiny's capabilities to overcome these barriers.
- Shiny's evolution as a critical tool in enterprise data science offers powerful analytics capabilities, rapid prototyping, and flexibility for real-time insights and decision-making.
- The flexibility of Shiny allows organizations to build interactive applications that drive informed decision-making, making it a practical solution for addressing analytical needs in complex data landscapes.
- ShinyConf25 promises further insights into Shiny's role in enterprise settings, showcasing its growth and adaptability to meet the evolving demands of users in data analysis and decision support systems.
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Analyticsindiamag
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Tech Mahindra Integrates IndusQ with Qualcomm AI Hub for Enterprise AI
- Tech Mahindra has integrated its proprietary AI model, IndusQ LLM, with Qualcomm AI Hub.
- The partnership aims to enhance on-device AI capabilities for enterprises, allowing real-time insights, automation, and edge computing.
- IndusQ LLM incorporates Hindi and its 37 dialects, enabling businesses to overcome language barriers and expand into multilingual markets.
- The integration makes AI-driven solutions more inclusive, expanding Qualcomm Technologies' AI offerings and enabling next-gen solutions.
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Medium
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Quantum Computing: The Dawn of a New Technological Era That Will Reshape Our Future
- Quantum computing is a revolutionary leap that operates using quantum bits or qubits, allowing for unprecedented computational speeds.
- Superposition and entanglement are fundamental quantum principles that enable quantum computers to outperform classical computers by exploring multiple solutions simultaneously and instantly linking qubits' states.
- Quantum computers leverage quantum gates and circuits, such as Hadamard and CNOT gates, to manipulate qubits based on quantum mechanics' principles.
- Quantum computing holds promise for solving complex problems in cryptography, optimization, and AI that classical computers struggle with.
- Decoherence, error correction, and scalability are major challenges hindering large-scale quantum computing adoption.
- Quantum computing implications span industries like finance, healthcare, and materials science, offering faster optimization, improved drug discovery, and enhanced climate modeling.
- Hybrid quantum-classical systems and post-quantum cryptography are being developed to mitigate quantum computing's security risks.
- Quantum computing has led to breakthroughs in AI advancements, financial modeling, drug discovery, climate simulations, and renewable energy optimization.
- Progress in hardware stability, error correction, and quantum algorithms is essential for practical quantum applications across various industries.
- Investments in quantum education, workforce development, and equitable access are crucial for preparing for the quantum revolution's societal implications.
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Analyticsindiamag
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ElevenLabs Unveils Scribe, a Speech-to-Text Transcription Model to Rival Otter, TurboScribe, and Others
- ElevenLabs has launched Scribe, a speech-to-text tool that offers high accuracy.
- Scribe transcribes speech in 99 languages with word-level timestamps, speaker diarisation, and audio-event tagging.
- ElevenLabs tested Scribe and found it outperformed models like Gemini 2.0 Flash and Deepgram Nova-3.
- Scribe is available through the ElevenLabs dashboard, priced at $0.40 per hour of input audio.
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Medium
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The Backbone of Modern Tech + 80% Scholarship Opportunity
- Data Engineers are in high demand as they build, manage, and optimize data pipelines.
- Data Engineering focuses on collecting and processing large datasets, ensuring data quality and reliability.
- The field of Data Engineering is experiencing significant job growth and offers attractive salaries.
- To become a Data Engineer, one should master Python, SQL, cloud technologies, and big data tools.
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Medium
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Deep Learning: The Revolution of Artificial Intelligence
- Deep learning has revolutionized artificial intelligence by leveraging complex neural networks to process and analyze data, enabling breakthroughs in various industries.
- Deep learning automates feature extraction and processing, enabling advancements in image recognition, speech processing, and decision-making systems.
- Artificial neurons in deep neural networks mimic the human brain, enabling the recognition of complex patterns and generalizing knowledge across tasks effectively.
- Advancements in computational power, data availability, and algorithmic optimization have propelled the widespread adoption of deep learning in the 21st century.
- Fundamental building blocks of deep learning include neural networks, activation functions, loss functions, optimization algorithms, backpropagation, and hyperparameters.
- Applications of deep learning span across computer vision, natural language processing, healthcare, finance, and robotics, driving innovation and automation in diverse domains.
- Key types of deep learning architectures include feedforward neural networks, convolutional neural networks, recurrent neural networks, transformer networks, and generative adversarial networks.
- Activation functions like ReLU, Sigmoid, and Softmax introduce non-linearity into neural networks, influencing their learning and generalization capabilities.
- Loss functions such as Mean Squared Error and Cross-Entropy Loss quantify prediction errors, guiding the training process.
- Optimization algorithms like Stochastic Gradient Descent, Adam, and RMSprop adjust network parameters to minimize loss and enhance model performance.
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Medium
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Can AI Argue? Watch ChatGPT and Grok Clash
- AI debate between ChatGPT and Grok Clash was conducted to assess who is a better leader, Elon Musk or Sam Altman.
- The AI judges, Grok and ChatGPT, demonstrate different argument styles that reflect their training and organizational objectives.
- The experiment showcases AI's ability to simulate sharp arguments, potentially enhancing critical thinking in education and stress-testing laws in policymaking.
- While the AI biases were evident in the debate, the interaction between the bots challenges assumptions and encourages deeper thinking.
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Analyticsindiamag
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Infosys Expands Presence in Assam with New Development Centre
- Infosys has established a 230-member Project Development Centre in Guwahati, Assam.
- The move is expected to boost employment, drive digital innovation, and attract further investment in the region.
- Assam government reaffirmed its commitment to fostering a thriving technology ecosystem.
- Mukesh Ambani, chairman of Reliance Industries, also announced a significant investment in Assam.
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Analyticsindiamag
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Amazon Forms Frontier AI & Robotics Team to Revolutionise Automation
- Amazon has formed a frontier AI & robotics (FAR) team based in San Francisco and Seattle.
- The team aims to develop robotic foundation models for high-level reasoning and low-level dexterity and mobility skills.
- Other tech giants, including Meta, Apple, NVIDIA, Figure AI, Tesla, and Apptronik, are also investing in robotics.
- Amazon's FAR team is hiring for various roles in software engineering and research.
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Analyticsindiamag
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Will India’s VC Market Drive 100 AI Unicorns in the Next Decade?
- India's AI startup ecosystem is still in its early stages, with Krutrim AI being the only AI unicorn so far.
- Despite a recovery in venture capital funding—rising to $11.2 billion in 2024—AI investments remain slow, with Indian AI startups raising only $560 million in 2024, a 49.4% drop. However, with government-backed AI initiatives, incubators like Google and NVIDIA, and major VCs such as Peak XV Partners allocating ₹16,000 crore, AI funding could accelerate.
- If this momentum continues, India may see a surge in AI unicorns over the next decade, closing the gap with the US and China.
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Medium
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Machine Learning: The Future of Intelligent Computing
- Machine Learning (ML) has reshaped industries and driven innovation by enabling systems to learn from data without explicit programming.
- ML, a subset of Artificial Intelligence, focuses on developing algorithms that make predictions and optimize processes based on data patterns.
- ML's historical roots trace back to the mid-20th century, with significant advancements in the 21st century due to computational power and deep learning.
- ML principles include data-driven learning, pattern recognition, generalization, and optimization, crucial for model efficiency.
- ML models operate in supervised, unsupervised, and reinforcement learning paradigms, with applications in various fields like healthcare and finance.
- Supervised learning includes algorithms like Linear Regression and Logistic Regression for prediction and classification tasks.
- Unsupervised learning algorithms like K-Means and Hierarchical Clustering group and analyze unlabeled data for pattern recognition.
- Reinforcement learning, seen in robotics and AI, teaches models optimal actions through trial-and-error experiences.
- Deep Learning, with neural network architectures like CNNs and RNNs, revolutionizes image recognition and natural language processing.
- Application of ML in healthcare improves diagnostics, treatment planning, personalized medicine, and drug discovery for better patient care.
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Analyticsindiamag
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Bengaluru-based AI Lab LossFunk Introduces IPO, a Novel Approach to Aligning LLMs Without External Feedback
- Bengaluru-based AI Lab LossFunk introduces IPO, a novel approach to aligning LLMs without external feedback.
- IPO performed comparably better to those utilizing SOTA reward models.
- The new technique offers a more efficient and scalable method for aligning LLMs with human preferences.
- LossFunk's mission is to build a state-of-the-art foundational reasoning model from India.
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Analyticsindiamag
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Wipro Commits to $200 Million to Wipro Ventures for Startup Investments
- Wipro Ventures, the venture arm of Wipro Limited, has committed $200 million to accelerate investments in early to mid-stage startups.
- With this funding, Wipro Ventures aims to identify high-potential startups and integrate their solutions into enterprise environments.
- Over the last decade, Wipro Ventures has backed 37 startups in various fields and facilitated startup-driven solutions for over 250 Wipro customers worldwide.
- The investment aligns with Wipro's strategy of leveraging external innovation to enhance its technology offerings in the IT services sector.
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Analyticsindiamag
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Microsoft Launches Phi-4 multimodal and Phi-4-mini, Matches OpenAI’s GPT-4o
- Microsoft has launched Phi-4-multimodal and Phi-4-mini, small language models (SLMs).
- Phi-4-multimodal integrates speech, vision, and text processing, enabling natural and context-aware interactions.
- The Phi-4 multimodal model surpasses Google Gemini and is comparable to OpenAI’s GPT-4o.
- Phi-4-mini is a text-based model suitable for reasoning, coding, and long-context tasks.
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Analyticsindiamag
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How Databricks Powers Domino’s to Deliver 1.5 Million Pizzas Daily
- Domino's leverages Databricks' Mosaic AI tools to enhance customer insights and streamline operations.
- Databricks' vector search enables rapid retrieval of customer feedback for sentiment analysis and theme identification.
- Model serving endpoints in Databricks facilitate sentiment analysis and feedback classification with optimized performance.
- Domino's plans to expand AI-powered insights to other platforms and integrate additional social media channels for feedback analysis.
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