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Karnataka Govt Sets Bold Ambitions with $17.5 Bn AI Investment for 100K Jobs Amid Skepticism
- Karnataka Government announces $17.5 billion investment in AI initiatives to create 100,000 jobs
- Partnerships formed with global tech companies and international organizations
- Critics express concerns about job displacement and economic inequalities
- Karnataka Government remains optimistic, launches Startup Springboard Program and AI Centre of Excellence
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Hackernoon
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From Centralized to Federated: Evolving Data Governance Operating Model
- Data governance is a framework that ensures high-quality, trusted data is consistently accessible across an organization.
- A one-size-fits-all approach to data governance not only restricts scalability but also hampers the organization's ability to address unique challenges such as regulatory compliance, data quality, and cross-functional collaboration.
- There are three main data governance operating models that organizations can choose from, each with its own pros and cons: centralized, decentralized, and federated.
- Choosing the right data governance operating model requires a strategic approach that aligns with the organization's unique needs, maturity, and objectives.
- Selecting the right data governance operating model requires a strategic approach that aligns with the organization’s unique needs, maturity, and objectives.
- The ideal model must align with the organization’s data maturity, structure, and future goals.
- Too much control can stifle innovation, while too much flexibility can lead to inconsistencies, misalignments, and errors.
- The proposed blueprint for choosing the right data governance model for your enterprise includes: Assess the Current State, Define the Target State, Evaluate Model Suitability, Engage Stakeholders, and Pilot, Refine, and Scale.
- A real estate company successfully transitioned from a centralized to a federated data governance model. This shift empowered the organization to leverage its data more effectively, foster collaboration, streamline operations, and enable better decision-making.
- Shifting from centralized control to a decentralized, business-unit-driven governance framework better aligns governance with real business needs and positions data as a catalyst for innovation and growth.
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Medium
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The Impact of XBanking on the Solana Ecosystem: Boosting Crypto Adoption
- XBanking has integrated Solana into its Launchpool, boosting crypto adoption.
- By offering rewards in Solana tokens, XBanking encourages user engagement.
- The initiative drives more interest in $SOL and strengthens the Solana ecosystem.
- XBanking's incentive program lowers the risk barrier and democratizes access to cryptocurrency.
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Analyticsindiamag
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K’taka Govt Signs MOUs with Big Techs to Train 100k People, Expects $17.5 Bn Investments
- The Karnataka Government has signed MOUs with big tech companies to train 100,000 professionals and expects $17.5 billion investments in business opportunities.
- Partnerships with companies like Microsoft, Intel, Accenture, and IBM will enhance skill development and foster innovation.
- The 'Nipuna Karnataka' initiative aims to bridge the gap between industry requirements and the state's skill level.
- Despite concerns of job displacement, the government remains committed to its strategy, believing it will advance the state's technological landscape and ensure a prosperous future for the workforce.
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Analyticsindiamag
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‘We missed social media, and I take the blame,’ says former Google CEO
- Eric Schmidt, former CEO of Google, took responsibility for Google's failure to capitalize on social media
- Schmidt admitted that during the rise of Facebook, Google missed the opportunity to effectively execute on social media
- He acknowledged the success of Google's Orkut system but regretted not capitalizing on it
- Schmidt emphasized the importance of AI and expressed his fear that it may not be adopted fast enough to address global problems
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Analyticsindiamag
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Trying to Watermark LLMs is Useless
- Watermarking AI-generated content has become a popular method to identify language model outputs (LLMs) but it is flawed and essentially useless.
- The goal of watermarking, often misunderstood, is to identify text from a specific model but it cannot distinguish reliably between AI-generated and human text.
- Many companies have launched their own watermarking tools however, research from Carnegie Mellon University shows that watermarking cannot deal with AI-driven misinformation.
- Watermarking raises several difficulties including compatibility concerns with temperature settings and a total lack of accuracy and is therefore, not enough to keep pace with AI or reduce its spread.
- The problem with this approach is that only capable LLMs can be watermarked, and disingenuous actors will always access unmarked models, despite even the tiniest of hindrances in watermarked models.
- Dominik Lukes, lead business technologist at the AI/ML support competency centre at the University of Oxford claimed “Outside a school exam, the use of an LLM is no longer a reliable indicator of fraud.”
- Watermarking suffers from several restraining factors like robustness and detection difficulty, tempting bad actors to use open-source models for privacy, rendering API-based watermarking ineffective.
- Furthermore, authors may edit AI-generated text created with the help of LLMs, making the necessary distinction between human-written and AI-written text even harder.
- Even an LLM based on a model that has abolished the API will not be enough to avoid detection by AI-detection technology.
- Trivially, if watermarking would miraculously work, it would not solve the problem as not all AI-generated text is harmful and AI-generated and human-written texts are often intertwined.
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Medium
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Understanding Outliers: A Key to Robust Data Analysis
- Outliers can have a significant impact on data analysis, affecting accuracy and risk identification.
- Various methods, such as visualization techniques, statistical methods, and machine learning models, can be used to detect outliers.
- Outliers have several use cases, including fraud detection, healthcare analytics, customer insights, manufacturing, and climate analysis.
- Dealing with outliers involves determining their value, applying transformations, or removing them based on their relevance to the analysis goal.
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Medium
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AI vs. ML: Decoding the Digital Titans Powering the Future
- AI refers to machines mimicking human intelligence to perform tasks, while ML is a subset of AI that focuses on creating systems that learn from data.
- AI is the destination, and ML is the primary vehicle driving us there.
- AI and ML are being used in various industries including manufacturing, finance, and healthcare.
- Integrating AI and ML into workflows leads to automation, faster decision-making, and enhanced insights.
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Medium
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AI Integration in Daily Life:
- AI integration in daily life is reshaping how people interact with technology.
- Consumer electronics, such as smartphones and smartwatches, are equipped with AI-driven assistants to manage schedules and access information hands-free.
- Wearables, like the Amazfit Helio Ring, offer fitness tracking and act as controllers for home appliances.
- AI is transforming the transportation sector with the development of autonomous vehicles and AI-powered navigation apps.
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Analyticsindiamag
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The Year Google Showed Everyone How AI is Really Done
- Google has demonstrated its commitment to AI by embracing a bolder approach to innovation this year, shipping products like there's no tomorrow while remaining equally focused on promoting responsible AI practices.
- In 2024, Google released its Gemini AI models which revolutionised generative AI, introducing Veo, a new video generation model and extended its watermarking tool, SynthID, enabling verification if the content is AI-generated.
- Gemini series produced various iterations strengthening natural language processing capabilities and Google entered the next frontier with systems that reason across modalities.
- Google promoted transparency and collaborated with Hugging Face to open-source its research on 'Scalable watermarking for identifying large language model outputs' to address questions of authenticity and traceability in the digital content space.
- Google announced the sixth-generation Tensor Processing Unit known as Trillium which offers a 4.7-fold increase in peak compute performance per chip compared to its predecessor.
- Google created Learn About, a feature that redefined search by offering deeper, contextual learning opportunities directly within results, showcasing the company's commitment to enhancing user interactions and information accessibility.
- Expect Gemini 2.0, offering extended context, deeper multimodal capabilities, and personalised interactivity and AGI roadmap shared by CEO Demis Hassabis.
- Google I/O 2025 event is expected to redefine AI's role across industries and align innovation with responsibility for the democratization of high-quality media creation and making AI helpful for everyone.
- For developers, Gemma 2 models promised scalable AI with enhanced APIs simplifying multimodal integrations.
- Google has demonstrated a nimble and visionary approach reminiscent of its early days while promoting responsible AI.
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Medium
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10 Key steps for software development:
- Start by figuring out what problem you’re solving or what need you’re addressing.
- Talk to the people who will actually use the software and get their thoughts.
- Clearly define the project’s goals and how long you expect it to take.
- Decide which development method fits best for your project (Agile, Waterfall, or a mix of both).
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Medium
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Steve Jobs, the greatest name in the computer world.
- Steve Jobs, an American businessman and co-founder of Apple, revolutionized six industries with his artistic passion and aggressive style.
- He invented groundbreaking products like the iPod and iPhone, which became hugely popular.
- Despite his life full of ups and downs, Jobs left a lasting legacy as a creative entrepreneur.
- He was also known for his philanthropy and dedication to improving the world through technology.
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Medium
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Why Data Science is the Key to Solving Global Challenges?
- Data science plays a crucial role in solving global challenges like climate change, healthcare, social issues, and artificial intelligence.
- By predicting and analyzing climate patterns, data scientists help in creating strategies to mitigate climate impacts.
- In healthcare, data science enables predictive models for disease outbreaks, personalized medicine, and improved patient care.
- Data science also contributes to solving social issues such as hunger, poverty, and education by offering insights for effective interventions.
- Artificial intelligence and machine learning, powered by data science, are instrumental in addressing global challenges and making operations smarter and more sustainable.
- Aspiring data scientists have immense opportunities to contribute to global progress by leveraging the power of data.
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Analyticsindiamag
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‘Kaggle Grandmaster Level Agents are Total Unqualified BS’
- A recent research paper, ‘Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level,’ introduces ‘Agent K v1.0’.
- The paper claims that large language models (LLMs) can autonomously achieve a performance level comparable to Kaggle Grandmasters.
- Subsequently, a data scientist named Bojan Tunguz criticised these claims calling them 'total unqualified BS.'
- While Agent K claims to achieve a 92.5% success rate across diverse tasks, many data science professionals dispute its eligibility for the Grandmaster status.
- Kaggle competitions demand advanced technical skills, practical experience and a nuanced understanding of data science challenges.
- Although LLMs can automate certain tasks, they cannot replace the comprehensive skill set required for high-level data science competitions yet.
- According to Santiago Valdarrama, many of the competitions used in the research paper weren’t even real competitions and that it used many manual, hardcoded steps by the authors to guide the model.
- Achieving a Kaggle Grandmaster level requires consistent top-tier placements across multiple, highly-competitive challenges, often demanding insights and adaptability that LLMs currently lack.
- While Agent K demonstrates the potential of LLMs in competitive data science, achieving true Kaggle Grandmaster status autonomously remains out of reach for current AI technology.
- The sentiment that the adaptability required for consistent top-ranking Kaggle performances is still out of reach for LLMs is echoed strongly by data science professionals.
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Medium
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CNN for Image-Based Tasks
- Convolutional neural networks (CNNs) are specialized neural networks designed to handle grid-like data such as images.
- CNNs are widely used for object recognition, face detection, and medical imaging due to their ability to detect spatial hierarchies.
- These networks automatically learn important features from visual data using convolutional and pooling layers.
- Key layers in CNNs include convolutional layers, pooling layers, and fully connected layers.
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