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To install PHP on Windows, you will need to follow a series of steps to download and set up PHP…
- To install PHP on Windows, follow a series of steps to download and set up PHP.
- Select the appropriate PHP version, Thread Safe version is recommended for use with web servers like Apache.
- Choose the correct architecture, download either the x86 or x64 version depending on your system architecture.
- Extract the Zip file: Use a tool like WinRAR or 7-Zip to extract the downloaded Zip file into a directory.
- In the extracted PHP folder, copy the 'php.ini-development' file and rename the copy to 'php.ini'
- Edit the 'php.ini' file and enable required extensions.
- Set the 'extension_dir' to ensure it points to the 'ext' folder inside your PHP installation directory.
- Add PHP to the Windows PATH Environment Variable by editing the PATH variable and adding the path to the PHP folder.
- Test PHP Installation by opening a Command Prompt and typing the following command: php -v.
- Install and Configure a Web Server (Optional, if you want to run PHP on a server)
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How Data Science Transformed Industries
- Data science is a multidisciplinary field that combines principles and practices from mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data and extract meaningful insights.
- Data science involves the use of sophisticated computational methods and machine learning techniques to process and analyze big data sets, which are often too large or complex for traditional methods.
- Recent trends in data science include the integration of artificial intelligence (AI) and machine learning (ML) to enhance data processing and analysis.
- Data science is applied globally across various sectors, including business, medicine, engineering, and social sciences.
- One of the primary challenges in data science is the complexity and unstructured nature of big data, which requires sophisticated parsing for effective decision-making.
- The future of data science is promising, with significant growth projected over the next 5–10 years.
- To truly excel in data science requires a broad skill set, including learning multiple programming languages, understanding software architecture, and gaining a deep knowledge of statistics.
- Through the use of AI and cloud-based applications, data scientists are able to automate many of the tedious tasks involved in data analysis.
- Data science has transformed the way industries operate, from healthcare to finance, by driving decision-making and unlocking new opportunities.
- The importance of storytelling in data science cannot be overstated, as it helps to communicate complex insights in a way that is relatable and actionable.
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From Jaccard to SBERT: A Comprehensive Guide to Vector Similarity Search Techniques
- Similarity search methods have been researched since the 1970s in the field of NLP.
- Jaccard similarity and w-shingling are two traditional methods for text similarity search.
- TF-IDF, BM25, and Sentence BERT are popular vector-based methods for similarity search.
- TF-IDF is used to compare the importance of each word to a particular document by looking at a large set of documents.
- BM25 is an optimized version of TF-IDF that uses adjustable hyperparameters to improve results.
- Sentence BERT uses BERT to generate vector embeddings of sentences that are all of the same size and excel at semantic search.
- All of these methods can be implemented on actual problems using libraries like FAISS or vector databases like Postgres pgvector, Qdrant, ChromaDB, and Pinecone.
- Cosine similarity is used to compare vector embeddings of sentences and calculate the similarity between two sentences, ranging from -1 to 1.
- Journey through this article gave an extensive knowledge of different similarity search methods.
- The field of NLP has come a long way in text similarity search since the 1970s.
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Prioritizing Security in Software Development at Newdich Technology
- Protection of Sensitive Data: Our applications handle sensitive user data, and it's our responsibility to safeguard it from unauthorized access, theft, or exploitation.
- Prevention of Financial Loss: Security breaches can result in significant financial losses, damage to reputation, and legal liabilities.
- Trust and Credibility: Secure applications foster trust with our users, partners, and stakeholders, ensuring long-term relationships and business growth.
- Compliance with Regulations: Adhering to industry standards and regulations (e.g., GDPR, HIPAA) helps avoid costly penalties and reputational damage.
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Analyticsindiamag
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AI is Eating Fintech
- Generative AI is expected to add $200 to $340 billion in annual value to the banking sector, according to McKinsey Global Institute.
- Visa has released over 500 AI applications, investing $3.3 billion in AI and data infrastructure over the past decade, and channelling $100 million into generative AI start-ups.
- Visa’s vision for AI integration is less about workforce reduction and more about enhancing human oversight.
- GenAI comes with plenty of risks such as misinformation, IP issues, transparency gaps, bias, and security concerns limiting customer facing use cases.
- JP Morgan launched Quest IndexGPT,using GPT-4 to enhance thematic index construction for institutional investors, and introduced LLM Suite, an AI assistant for 60,000 Chase employees.
- Morgan Stanley launched AI@Morgan Stanley Debrief to boost advisor productivity and released AI@Morgan Stanley Assistant adopted by 98% of advisor teams.
- Mastercard brought in an AI technology that improves payment security by detecting compromised cards twice as fast.
- HDFC Bank in India adopted an AI-driven approach to fraud detection and launched its AI-driven banking chatbot, Eva, handling millions of queries instantly.
- ICICI Bank had introduced iPal, enabling transactions via voice commands through Alexa and Google Assistant.
- SBI announced an AI initiative in 2023 to boost decision-making and operational efficiency.
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Google Unveils ReCapture to Revolutionise Video Modeling
- Google released ReCapture, its generative video camera controls for user-provided videos, on Friday.
- ReCapture transforms user-provided videos and creates new, dynamic camera perspectives.
- Unlike prior advancements, ReCapture can recreate any existing video with enhanced angles and cinematic camera motion.
- This breakthrough opens up new possibilities for video-to-video generation and enhances user engagement.
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Why I’m Starting a Data Science Blog as a Newcomer
- Marcos, a 23-year-old from Madrid, Spain, is starting a data science blog as a newcomer.
- He has a background in Mathematics and a Master's degree in Statistics for Data Science.
- He recently got a job as a Data and AI Engineer and is working on Machine Learning and exploring Generative AI and Large Language Models.
- His blog will focus on sharing knowledge, techniques, experiences, and commentary on advancements in the field of data science.
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Top AI Initiatives by Indian State Governments in 2024
- India's central and state governments are introducing AI policies and initiatives to promote the AI revolution to serve India's unique requirements.
- Kerala aims to become a global hub for generative AI and has plans to establish an AI cluster.
- Tamil Nadu launched the Tamil Nadu Artificial Intelligence Mission project to improve governance and drive social and economic growth.
- Karnataka is betting heavily on the application of AI in education and recently launched India's first draft Global Capability Centres (GCC) Policy.
- Andhra Pradesh aims to develop Amaravati as a city centred around AI and developed an initiative, ‘AI for Andhra Pradesh, Powered by Google’.
- Telangana has partnered with Meta to harness generative AI in e-governance and signed an MoU with the World Trade Centers Association (WTCA) to host the world’s leading AI firms.
- Maharashtra's initiatives are driven by collaborations with tech giants like Google and teams up with the National Institute of Electronics and Information Technology to establish a Centre of Excellence in AI.
- The Gujarat government announced three Centres of Excellence (CoE) focused on healthcare, agriculture, and sustainable cities and partnered with IBM to launch an AI Cluster.
- The Ministry of Electronics and Information Technology has allocated INR 551.75 crore to the IndiaAI Mission for the year 2024-25.
- India needs to amplify its efforts and scale up investments to stay competitive on the world stage as global AI advancements increase at a rapid pace.
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Analyticsindiamag
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Andrew Ng Releases New Short Course on Building Agentic Memory
- DeepLearning.AI has released a free course titled “LLMs as Operating Systems: Agent Memory” to help people build agents.
- The course uses MemGPT’s research paper “Towards LLMs as Operating Systems” as a foundation.
- Learners can build an agent that can manage memory and context of an LLM using Letta’s open-source framework.
- DeepLearning.AI has released over 50+ short and free courses on its platform.
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Ferrari’s Fan Struggles Lead to IBM Partnership
- IBM has partnered with Ferrari to become the official Fan Engagement and Data Analytics Partner for Scuderia Ferrari HP.
- The partnership will focus on developing a new mobile app, set to launch in 2025, that will provide personalized content and real-time insights for fans.
- IBM will apply its data analytics capabilities to process Ferrari's data and generate custom insights to enhance the fan experience and operational efficiency.
- This collaboration aims to modernize Ferrari's fan engagement strategies and connect with younger audiences.
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HCLTech Partners with Inspeq AI to Drive Responsible AI in Enterprises
- HCLTech has partnered with Inspeq AI to aid enterprises in developing and integrating AI applications responsibly.
- The collaboration aims to embed a Responsible AI (RAI) layer into application development and address challenges in industries like financial services.
- Inspeq AI's RAIops platform, backed by advanced NLP and AI/ML, assesses and enhances models while ensuring responsible AI governance.
- HCLTech's AI offerings, including AI Force and AI Labs, are gaining traction and driving innovation across sectors.
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Medium
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The definitive guide on how data Science & AI veteran chooses his/her project’s right model.
- Choosing the right AI model requires understanding the problem and categorizing it into supervised, unsupervised, or reinforcement learning.
- The quality and nature of data play a crucial role in selecting the appropriate model.
- Consider the computational density, training time, and deployment scalability of the chosen model.
- Evaluate the model's performance, optimize hyperparameters, and have a plan for model monitoring and updates.
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Medium
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Claude: The Next Generation of AI-Language Models
- Claude is a family of large language models (LLMs) created by Anthropic, designed to engage in conversations, provide information, and generate text.
- Anthropic focuses on safety, alignment, and interpretability, aiming to develop AI that is aligned with human values and minimizes harmful outputs.
- Claude uses techniques like Reinforcement Learning with Human Feedback (RLHF) to ensure ethical and contextually relevant responses.
- Claude aims to be more interpretable and controllable, offering reliable and trustworthy AI responses across different domains.
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IBM Commits to Transparency with Granite 3.0 LLM
- IBM has released new open-source model, Granite 3.0. The datasets that are used for training are clearly disclosed by IBM. The language models are trained on Blue Vela, powered entirely by renewable energy.
- Recently, Stanford’s Foundation Model Transparency Index report stated that IBM’s Granite models achieved a 100% score in transparency and openness of a model test.
- Big tech companies including Google, Apple, and OpenAI remain tight-lipped about the data used to train their LLMs.
- IBM’s approach is to increase transparency by default. It can help businesses overcome the scepticism and negative ‘black box’ connotation attached to LLMs.
- By providing transparent models, IBM is likely to receive more recognition for playing the good guy in the world of AI.
- Deep integration of AI into technology is something that people will choose on how to use or if they want to use it.
- Granite models aim to make open source the winner in the world of the internet. People encountering good samaritans may get motivation to contribute to open source datasets.
- IBM’s generative AI book of business is up by more than $1 billion quarter to quarter.
- IBM's transparency can help businesses spend more time looking for solutions to their problems rather worry about the reliability of models they’re using.
- IBM’s training datasets are mainly open-source, which would help in research, development and enhance these datasets.
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Dev
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Day 29: Sparse Transformers: Efficient Scaling for Large Language Models
- Large language models (LLMs) face increasing computational and memory demands.
- Sparse Transformers introduce sparsity in attention mechanisms to improve efficiency.
- Key concepts in Sparse Transformers include local attention, strided attention, block sparse patterns, and dilated attention.
- Sparse Transformers offer advantages such as reduced complexity, efficiency for long sequences, and improved scalability.
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