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Medium
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Crossroads of Innovation: AI’s Role in Interdisciplinary Evolution, Transforming Business and…
- AI’s interdisciplinary edge lies in its ability to integrate methodologies from diverse fields, creating powerful solutions that are greater than the sum of their parts.
- AI is revolutionizing business operations by enabling smarter, faster decisions and creating new opportunities.
- The adoption of generative AI is poised to significantly impact various industries and job markets, including manufacturing, healthcare, retail, transportation, and finance .
- Generative AI (Gen AI) is set to drive significant economic changes, primarily by boosting productivity through efficiency gains and accelerating innovation.
- However, the labor market faces challenges, including job losses in sectors where automation replaces human workers and downward wage pressures in roles that AI can perform.
- As AI continues to integrate across disciplines, it presents both opportunities and challenges.
- The diverse data sources used in interdisciplinary AI applications can amplify biases if not handled carefully, and sensitive data requires strong protections.
- Moreover, ensuring that the benefits of AI reach underserved communities remains a critical challenge.
- Looking ahead, AI’s interdisciplinary role is set to grow, with emerging fields like quantum computing and synthetic biology benefiting from AI’s capabilities.
- The future of AI holds immense promise, and our collective responsibility lies in ensuring its benefits are shared by all.
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Analyticsindiamag
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Floworks Launches New Tool Use Feature in Alisha 3
- YC-backed Indian startup Floworks has launched a new feature called ‘Tool Use’ in their AI sales development representative, Alisha.
- The feature allows customers to build custom workflows using natural language and integrate major sales tools.
- Alisha can fetch data from third-party tools like Gmail, Salesforce, and HubSpot.
- Floworks aims to strengthen the outreach layer and enhance engagement with personalized emails and communication.
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Medium
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Understanding Synthetic Data: Applications and Use Cases in the Modern Era
- Synthetic data refers to artificially generated information that mimics the statistical properties of real-world data.
- It is used in various applications such as data augmentation, bias mitigation, simulation-based training, privacy preservation, medical imaging, fraud detection, risk analysis, personalization, and demand forecasting.
- Specific use cases of synthetic data include autonomous vehicles, anti-fraud models in finance, medical AI models, e-commerce recommendation systems, and digital twins in manufacturing.
- Despite its benefits, synthetic data poses challenges in terms of accuracy, validation, and ethical considerations.
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Analyticsindiamag
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The Human in the Loop Is the Real Problem
- Large language models (LLMs) reflect the biases of their creators, warns a new study. This bias is true for models from OpenAI, Alibaba, Google, Anthropic. A dataset of over 4,000 controversial political figures was derived from Wikipedia and used to perform tests on seventeen AI models, both Western and non-Western . Non-Western models showed a preference for centralised governance, law enforcement and cultural values, and were more positive about political persons critical of liberalism, human rights, and minority groups.
- AI sceptics who claim that there will always be a human in the loop need to consider new research sparking fresh concerns about AI models and what AI scholars call LLMs. There is concern that LLMs reflect the ideological biases of their creators.
- The study shortlisted over 4,000 controversial, historical political figures Whitney summaries were available on Wikipedia. The dataset, derived from Wikipedia, carries a bias due to the platform's open-edit nature. Additionally, ideologies and societal norms evolve over time, and anything considered conventional in the past might be considered uncommon today.
- Both Western and non-Western models rated figures tagged with their ideologies and associated with their country positively. English-prompted LLMs exhibited a pro-liberal outlook while the Chinese-prompted models were favourable towards state-led economic policies.
- Chinese AI models have shaken the AI ecosystem in the past few weeks. The new DeepSeek AI challenges the reasoning capabilities of OpenAI’s o1, which is yet to release. Alibaba Qwen 2.5 Coder demonstrated strong coding capabilities, even better than Anthropic’s Claude in some cases. All of these models are open source.
- The paper’s findings can be attributed to Conway’s law that states, “organisations which design systems are constrained to produce designs that are copies of the communication structures of these organisations”.
- Dealing with bias in traditional and social media apps is not a new problem, but consumers may have to make a conscious choice in the future based on the creator of the model.
- AI models reflect the biases of their creators according to a new report. Bias is present in the datasets on which models are trained, as well as in the design of their creator organisations.
- Nonetheless, the paper’s findings can be attributed to Conway’s law and the nature of AI models in general.
- AI researcher Paras Chopra predicts that AI bias is a problem that will never be solved. Rather, users will have to make choices based on their preferred bias.
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Medium
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Smallest Number in an Array Python
- To find the smallest number in an array in Python, you can use the sorting method or write a custom algorithm.
- One approach is to sort the array and the smallest number will be at the first position.
- Another approach is to use a custom algorithm, where you assume the first element as the smallest number and iterate through the array to find any smaller numbers.
- The algorithm involves starting from index 1 and comparing each element to the current smallest number.
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Analyticsindiamag
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Oracle Quietly Becomes Every Cloud’s Best Friend
- Oracle has entered the multi-cloud era with partnerships with Microsoft Azure, Google Cloud, and AWS.
- Oracle's strategy is based on its control over data and being more open than its counterparts.
- Major companies like Vodafone and Voya Financial are using Oracle's multi-cloud services.
- Oracle's cloud services revenue grew in Q1 2025, but it still lags behind competitors AWS, Microsoft Azure, and Google Cloud.
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Analyticsindiamag
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Bengaluru Emerges as the Hotspot for GCCs
- Canadian information management giant OpenText expands its Bengaluru-based centre of excellence (CoE) in India.
- The Bengaluru CoE houses engineering and product development teams focused on OpenText's business clouds, AI, cybersecurity, and DevOps.
- Bengaluru has seen a 194% increase in OpenText's workforce in the last two years, contributing to 25% of the company's global workforce.
- Bengaluru has emerged as a hotspot for Global Capability Centers (GCCs) in India, attracting global companies with its strong IT industry, skilled workforce, and good infrastructure.
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Analyticsindiamag
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What’s Nutanix Cooking Up for New Bharat?
- At the Nutanix .NEXT India tour event, the cloud software giant elaborated on its role in transforming India into a ‘new Bharat’.
- The company is fueling innovations in sectors as diverse as banking, automotive, oil and gas, pharmaceuticals, IT/ITeS, and software.
- Nutanix powers government state data centres, strengthens hospitals, and fortifies the Indian defence forces.
- Historically known for virtual machine management, Nutanix has shifted focus toward containerisation.
- Nutanix integrates seamlessly with partners like NVIDIA for AI processing and offers robust data services.
- Nutanix has an ecosystem-driven approach, collaborating with major players such as AWS, AMD, and HPE.
- Nutanix’s exclusive channel model involves working closely with Indian resellers and managed service providers.
- One of the clients Nutanix is working to help in automating financial services is HighRadius.
- HighRadius has broadened its focus to include treasury, record-to-report, B2B payments, and account reconciliation.
- HighRadius as a Gartner Quadrant leader offers 70% efficiency improvement out-of-the-box, reaching up to 90% within six months.
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Medium
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Complete JavaScript Road Map
- JavaScript road map covers various topics like variables, data types, control structures, functions, objects and arrays, classes and prototypes, error handling, asynchronous JavaScript, modules, event handling, DOM manipulation, regular expressions, browser and web APIs, functional programming, ES6+ features, Browser Object Model, Node.js concepts, testing frameworks, and more.
- Some good resources to learn JavaScript include documentation from Mozilla MDN Web Docs and DevDocs, as well as useful YouTube channels like 'JavaScript Daily', 'The Coding Diaries', and 'Learn Programming'.
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Medium
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A-Z Complete Full-Stack web
- This news article provides a comprehensive overview of various topics related to full-stack web development.
- The web fundamentals include HTML, CSS, and JavaScript basics, while the front-end beginner level covers advanced HTML, CSS, responsive design, and various front-end frameworks.
- The back-end beginner section focuses on server-side languages, web frameworks, databases, API development, authentication and authorization, ORM/ODM, and web security.
- Other topics covered in the article include database management, API and microservices, testing, front-end and back-end advanced concepts, DevOps and deployment, mobile development, version control, and other skills like agile methodologies and soft skills.
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Fourweekmba
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Why Agentic AI Matters?
- Agentic AI aims at solving real-world problems by automating tasks and enhancing workflows.
- It involves user input, AI agents, databases, LLMs, and feedback loops for continuous learning and adaptability.
- The flow of Agentic AI includes user input, AI agent processing, databases, action execution, data flywheel, and model customization.
- This architecture ensures autonomy, customization, efficiency, and integration in AI development.
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Medium
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Water Pollution
- Encourage industries to undertake cleaner manufacturing strategies and deal with their wastewater before discharging it into water bodies.
- Invest in advanced wastewater treatment plants to filter contaminants from both domestic and commercial wastewater.
- Implement strict rules on waste disposal and pollution control, holding polluters responsible and imposing fines for violations.
- Promote sustainable farming practices, such as reducing pesticide and fertilizer use, implementing buffer zones, and erosion control to prevent agricultural runoff.
- Improve waste disposal and recycling systems to reduce plastic waste, a significant contributor to water pollution, especially in oceans.
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Dev
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Historian Hysteria
- The article discusses the process of solving Advent of Code 2024 Day 1 challenge
- The steps involved in the solution include parsing the input, making two lists of numbers, sorting each list in ascending order, and calculating the absolute difference and incrementing a tally
- The second part of the challenge involves checking for matches between two lists and building a map of numbers and their counts
- The code successfully solves the puzzle and earns two gold stars
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Dev
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Real-world Uses of Natural Language Processing (NLP) in the Business Sector
- Natural Language Processing (NLP) is impacting different industries by streamlining processes and enhancing user interactions.
- The healthcare sector uses NLP for processing medical records and providing insights into extensive medical resources, ultimately boosting patient results.
- Numerous companies in customer service use NLP-powered chatbots to respond to customer queries promptly and improve overall satisfaction.
- NLP tools assist in handling and evaluating unstructured financial data, leading to quicker decision-making and enhancing accuracy, and fraud detection.
- NLP assists online businesses in tailoring recommendations by analyzing customer data, resulting in quicker and more appropriate outcomes, aiding customers in finding precisely what they desire.
- NLP is utilized in education to automate the process of grading and provide uniform feedback to students and to develop more intelligent educational resources.
- The NLP market will hit $43.5 billion by 2027, with a 20.3% CAGR, as predicted by Grand View Research.
- The fast progress in NLP, driven by deep learning and AI, foretells more revolutionary uses in the future.
- As companies increasingly embrace and dedicate resources to NLP, we can anticipate further incorporation of this technology, enabling companies to gain a deeper insight into and cater to the needs of their customers.
- Despite its complexity, the technology behind NLP is clearly benefiting industries by making them more intelligent, efficient, and adaptable.
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VentureBeat
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The end of AI scaling may not be nigh: Here’s what’s next
- The AI scaling debate over whether bigger models are even possible or if innovation must take a different path has emerged. The concern is that scaling may not extend to the next generation of models. Future innovation may require integration, model architecture, optimisation techniques and data use rather than scaling. Similar diminishing returns were observed in the semiconductor industry. Hybrid AI models of symbolic reasoning with neural networks are expected so are agent technologies which enable LLMs to perform tasks autonomously. Quantum computing is also expected to be used soon for accelerating AI training and inference.
- AI research community has consistently proven ingenuity in overcoming challenges unlocking new capabilities. There may even be gains through novel techniques.
- Many have speculated there is a scaling wall for AI development but OpenAI CEO Sam Altman stated there was no wall. The ex-Google CEO Eric Schmidt also stated he believes scaling will continue with a factor of two, three four capability over the next five years.
- Scaling challenges dominate discourse on LLM but studies show current LLMs are highly capable, and scaling may not be the sole path forward for future innovation. Current LLMs are capable of outperforming experts in complex tasks, challenging assumptions about the necessity of further scaling for impact results.
- Gary Grossman is EVP of technology practice at Edelman and global lead of the Edelman AI Center of Excellence.
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