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VentureBeat

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Meta launches open source Llama 3.3, shrinking powerful bigger model into smaller size

  • Meta has released Llama 3.3, an open-source multilingual large language model (LLM).
  • Llama 3.3 delivers top-tier performance at a fraction of the cost and computational overhead of previous models.
  • The model is available under the Llama 3.3 Community License Agreement, allowing non-exclusive, royalty-free usage.
  • Llama 3.3 offers GPU memory savings, making it more affordable and energy-efficient.

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Medium

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From Big to Small: The Rise of Small Language Models in AI

  • The AI world is shifting focus from larger to smaller language models.
  • Smaller language models offer specialization, efficiency, and accessibility.
  • They can be fine-tuned for specific tasks and consume less computational power.
  • This shift is driven by companies like Cohere, OpenAI, and Microsoft.

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VentureBeat

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Microsoft Copilot Vision is here, letting AI see what you do online

  • Microsoft Copilot now has 'vision' capabilities that enable it to browse the internet with users.
  • This feature is in the early stages of development and is currently available to select Copilot Pro subscribers.
  • Copilot Vision aims to simplify web browsing by providing assistance in reading website contents and making decisions.
  • The capability is currently opt-in and will gradually expand support to more users and websites.

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Medium

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From Zero to Expert: Your Guide to Google’s Career-Ready Online Courses

  • Google offers beginner-friendly courses designed to guide you step by step toward mastery.
  • Courses cover various areas like AI essentials, data analytics, project management, and digital marketing.
  • Each course is designed with beginners in mind, ensuring support throughout the learning process.
  • Subscribe for updates to stay informed and receive tips and recommendations for skill development.

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Analyticsindiamag

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What to Expect from Indian IT in 2025

  • Indian IT companies will continue investing in vertical AI projects focused on specific industries, as well as generative AI spending that will also increase.
  • TCS, Infosys, Wipro, HCLTech, and Cognizant are among the IT giants that reported billions of dollars' investment in the field.
  • Professional services will be enhanced to ensure rapid technology adoption and increase focus on translating generative AI PoCs into actual projects to scale them.
  • HCLTech has seen successful partnerships with Google Cloud, AWS, GitHub, and IBM to train its workforce with generative AI.
  • IT services in India will shrink as they adopt AI to improve their engineers' productivity by adopting AI as part of their core operations, according to Vaibhav Domkundwar, founder of Better Capital.
  • Some providers will enter the domain of traditional IT services, while others with stronger portfolios in business processes will see more internal integration.
  • TCS aims to onboard 40,000 freshers this fiscal year, while Infosys plans to hire 15,000-20,000, with the addition of mid-tier IT and engineering services firms targeting around 1 lakh freshers.
  • Indian IT is focused on maintaining existing projects, rather than building something revolutionary, but it is slowly changing.
  • Indian IT should currently focus on collecting data necessary for the models to build productionisation of the existing services as the data is only going to grow several times five years from now, said Viswanathan K S, former NASSCOM VP.

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Hackernoon

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What's the Deal With Data Engineers Anyway?

  • The role of data engineer is like building and maintaining the utilities for a house. It is essential to make the house functional. Building and maintaining the plumbing ensure everything flows smoothly. The role of data analyst is like people who decide how to use these utilities effectively. Data engineers are responsible for maintaining access to structured and up-to-date data.
  • A junior data engineer working for an e-scooter service company must collect data from external sources like weather and flight information. These external data sources are necessary to make informed decisions to manage the e-scooter locations and meet the changing demand of people.
  • There are two common approaches to extract the external data 'Pump from a river: Web Scraping, Connect to a central water supply: APIs'. Sometimes, the website uses JavaScript to load content dynamically, making it harder to extract. In the API approach, computers or programs communicate with each other. The APIs have API keys to authenticate requests to ensure that only authorized users can access the service.
  • Cleaning and organizing raw data into a usable format is known as Transformation. The most interesting transformation is converting colonial data from DMS format to decimal format which made data compatible with APIs.
  • Loading the cleaned data into a relational database for easy access and analysis is the final step. Excel is disconnected and inefficient as the system grows. MySQL is like a modern plumbing system that delivers water to every part of the house seamlessly and reliably. It is scalable, efficient and keeps everything linked together.
  • The automation process is essential to maintain up-to-date data. Information that rarely changes needs automation to ensure that the data is updated daily and accessible for everyone in the company. Job scheduler like cron is used to run the Python script daily to automate this process.
  • Data engineering lays the foundation for decision-makers to dive deeper, uncover insights, and build predictive models. The groundwork laid by data engineers allows data analysts to analyze the data using various techniques and models to make informed decisions.
  • Visit the author's GitHub repository to find all the scripts, functions, and documentation to help you dive deeper into the project, as he used in this project to extract, transform, and load data from external APIs.
  • The article resembles a basic learning project that enables a new data engineer to understand the process of extracting, transforming, and loading data from external APIs and websites. We learned how to collect weather and flight data to help an e-scooter company predict usage patterns and respond to challenges like weather changes or tourist activity.
  • Together, these roles (data engineers, data analysts, and data scientists) drive data-driven decision-making. The article inspires and explains the concepts in a relatable and straightforward way.

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Analyticsindiamag

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OpenAI, Perplexity Bet on Publishers to Stay Relevant

  • Perplexity and OpenAI have announced new deals with online publishers to license their content into AI systems.
  • Perplexity announced partnerships with 15 publishers, in addition to their existing partners.
  • The Perplexity Publishers' Program allows publishers to share revenue generated from advertising and access Perplexity's APIs and developer support.
  • OpenAI is expanding their publisher partnership program with Future and other notable publishers.

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Analyticsindiamag

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OpenAI to Soon Unveil ChatGPT Projects 

  • OpenAI is set to unveil ChatGPT Projects, codenamed 'Snorlax', which allows users to organize their chats, files, and custom instructions in one place.
  • The projects feature is built on custom instructions and supports a word limit of 8,000 characters. Users can add various types of content such as documents and images.
  • This feature will compete directly with Claude's Projects feature, offering a unified location to store and manage knowledge.
  • OpenAI also released the o1 reasoning model and a pro mode called ChatGPT Pro, which provides unlimited access to all models for $200 per month.

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Analyticsindiamag

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Human Clone Has Arrived!

  • Clone, a humanoid robotics startup, has announced the early release of its advanced humanoid robot designed for service and hospitality roles.
  • The limited edition Clone Alpha androids will come with pre-installed skills and a 'Telekinesis' training platform to teach the robot new skills.
  • Pre-orders for the Clone Alpha will open in 2025.
  • Clone Alpha integrates advanced artificial muscle, skeletal, nervous, and vascular systems for lifelike functionality.

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Analyticsindiamag

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AMP Robotics Secures $91 Mn Funding to Expand AI-Powered Waste Management

  • US-based AMP Robotics Corp. has raised $91 million in Series D funding, led by Congruent Ventures, to accelerate the deployment of its AI-driven sortation systems for waste management and recycling.
  • The funds will support the expansion of AMP ONE, a technology capable of sorting waste and recyclable materials at scale.
  • AMP Robotics uses AI and deep learning to improve its systems by processing millions of material images, identifying recyclables and contaminants in real-time.
  • AMP has deployed over 400 AI systems globally, processing over 2.5 million tons of recyclables, with a focus on reshaping the waste management industry.

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Analyticsindiamag

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2024: The Year English Changed the Coding Game Forever

  • English is emerging as the universal coding language
  • Large language models like ChatGPT and GitHub Copilot enable coding in natural language
  • Generative AI tools streamline software development by generating code from natural language prompts
  • English as a coding language lowers the barrier to entry, but skill and prompt engineering remain crucial

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Analyticsindiamag

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TSMC, NVIDIA in Talks for AI Chip Production in Arizona

  • Taiwan Semiconductor Manufacturing Co. (TSMC) is in talks with NVIDIA to produce its Blackwell AI chips at its Arizona facility.
  • TSMC's Arizona plant, expected to start production in 2024, would handle the front-end process of Blackwell chip production.
  • NVIDIA's Blackwell chips are in high demand for generative AI and accelerated computing applications.
  • The talks highlight the growing role of TSMC's Arizona plant in meeting global demand for advanced AI chips.

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Analyticsindiamag

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Former PayPal COO David Sacks Appointed as US AI, Crypto Czar

  • David Sacks, former PayPal COO, has been appointed as the United States' first Artificial Intelligence and Crypto Czar.
  • Sacks will work closely with Elon Musk to establish a legal framework for the cryptocurrency industry and ensure American competitiveness in AI and crypto.
  • Sacks will serve as a special government employee, working up to 130 days per year, and act as a liaison between the crypto industry, Congress, and federal agencies.
  • The AI and Crypto Czar will provide regulatory clarity for the crypto sector and guide policy for AI adoption.

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Analyticsindiamag

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Why is No One Talking About Adversarial Machine Learning?

  • Adversarial Machine Learning (AML) refers to techniques that are used to exploit the weaknesses of Machine Learning (ML) models creating subtle perturbations in input data to deceive these systems.
  • AML can become tools for attackers facilitating sophisticated and automated attacks such as phishing and malware threats.
  • AML is a bit difficult to detect as attackers use ML to disrupt machines learning models through techniques like model poisoning, model theft, and adversarial inputs.
  • Several possible threats can occur at the Model Deployment Stage such as Evasion Attacks, Poisoning Attacks, Model Extraction Attacks, and Inference Attacks.
  • The importance of securing AI systems at every stage of their lifecycle from data collection to building models to deployment is crucial.
  • By proactively addressing these challenges, we can ensure AI systems remain trustworthy and reliable.
  • Introducing adversarial examples during the training phase can help models learn to resist attacks.
  • Continuous vulnerability assessments and penetration testing can identify potential weaknesses in AI systems.
  • Building interpretable models allows developers to identify and address unexpected behaviours more effectively.
  • Organisations must prioritise security as a foundational element of AI development, not an afterthought.

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Medium

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Your First 30 Days as a Data Engineer: The Reality Check!

  • Data Engineers joining a new team often have to navigate existing complexities rather than creating something new.
  • In the first month, the focus should be on learning and understanding the current systems and stakeholders' needs.
  • Common myths include building data pipelines and transforming raw data, while the reality involves dealing with data discrepancies and inconsistencies.

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