Tag: IT AI ML

The global AI market is projected to reach over $190 billion by 2025. The top trends in AI for 2024 include the rise of generative AI, Bring Your Own Artificial Intelligence (BYOAI) in the workplace, open-source AI, AI risk insurance, AI-assisted coding, AI Trust, Risk, and Security Management, intelligent apps, and quantum AI among others. AI is revolutionizing industries, from streamlining tasks, influencing legislation, creating new jobs to reshaping customer service. Despite progress, challenges such as user reluctance, data privacy concerns, and risks like ‘AI hallucinations’ persist.

Sentiment analysis, a technique in natural language processing, identifies and quantifies emotions, opinions, and attitudes from texts such as social media posts or customer reviews. Its impact spans customer experience enhancement, marketing optimization, product development, market opportunity detection, online reputation management, and social research. Despite its strengths, sentiment analysis has challenges like subjectivity, context dependence, and language complexity. Therefore, choosing the right sentiment analysis tool based on data source, volume, language, analysis level, method, and output is paramount for accuracy and reliability.

Google and Google DeepMind have just unveiled their latest AI model, Gemini, boasting superior performance to its rival, GPT-4. The Gemini model comes in three sizes (Ultra, Pro, and Nano) and is particularly noteworthy for its natively multimodal capabilities, allowing it to process a combination of text, code, images, audio, and video. It is expected that the model will be available on various Google products in the near future. Despite its outstanding reported performance, further in-depth testing on its capabilities will be necessary.

Model optimization is crucial to efficiently use foundational models pre-trained on large datasets, especially given their computational and memory demands. A recent UC Berkeley paper suggests larger models converted to smaller versions yield better results than inherently smaller models. Model optimization enables deployment on various platforms like cloud and on-premise. Common optimization techniques include Quantization and Pruning, focusing on weight reduction and low-precision representations.

The machine learning branch of artificial intelligence aims to understand human learning and devises strategies to emulate this process. It predominantly employs three learning methods: supervised learning, unsupervised learning, and reinforcement learning. Key concepts include data processing, regression models, and clustering techniques like K-Means, all crucial for identifying patterns, assessing model performance, and preparing data for machine learning operations.

This is a detailed guide for educators on how to create an automated transcription and summarization tool using OpenAI’s Whisper and GPT-4, specifically designed for managing content from online courses. The tool transcribes lecture audio, summarizes content, highlights key concepts, performs sentiment analysis, identifies action items, provides historical context, and more. This AI-powered tool greatly enhances student engagement and comprehension while saving educators time.

Andrej Karpathy recently released a talk on large language models (LLMs), discussing their fundamentals, practical application, and future research, including the prospect of LLMs as an operating system. The speaker also addressed potential vulnerabilities and security considerations. A detailed reading list was shared for further exploration of the topics, aiming to deepen understanding in this growing field of AI. Access to weekly discussions on related papers was also offered via a group called Arxiv Dives.

The article provides a simplistic guide to creating a practical image prediction Python script using Artificial Intelligence (AI) and Machine Learning (ML) with the ImageAI library. The writer introduces the concepts of AI, ML, Deep Learning, Image prediction, and ImageAI library. The article then constitutes a step-by-step guide, from setting up the environment, loading the model to performing the image prediction. The final part details the execution and interpretation of the image prediction’s results.

The article discusses the importance of ten key libraries in data science, including NumPy for numerical computing, Pandas for data manipulation, Matplotlib & Seaborn for data visualization, Scikit-learn for machine learning, TensorFlow & PyTorch for deep learning, Statsmodels for statistical modeling, NLTK for natural language processing, Beautiful Soup for web scraping, Dask for handling big data, and Scrapy for advanced web scraping. Mastery of these libraries enhances data scientists’ capability and efficiency.

The blog provides detailed guides for building an AI-powered cover letter generation app using two techniques: Hugging Face Transformers and OpenAI API. The app uses AI models to generate contextually coherent cover letters from user inputs, including their resume and job description. The blog also includes step-by-step instructions for coding and running the app, as well as suggestions for customization.

Creating a custom GPT (Generative Pre-trained Transformer) language model can revolutionize various applications by providing increased flexibility. The process involves understanding GPT architecture, pre-training and fine-tuning, tokenization, and vocabulary design. Practical steps include defining scope, data collection, deciding model size, preparing training data, pre-training, fine-tuning, evaluation, and iteration, with applications in content generation, recommendations, code generation, and conversational agents.

A study of 425 interactions with AI chatbots like ChatGPT, Bing Chat, and Bard reveals that different types of conversations serve distinct information needs, contributing to varied user-interface designs. Six types of conversations were identified: search queries, funneling, exploring, chiseling, expanding, and pinpointing conversations. The study found there is no optimal conversation length, with both short and long interactions supporting different user goals.

Low-rank adaptation (LoRA) is an effective method for training large language models (LLMs) efficiently. It offers consistent outcomes despite the inherent randomness of LLM training. QLoRA offers 33% memory savings at a 33% runtime cost and choice of optimizer doesn’t significantly affect outcomes. The LoRA rank must be adjusted, along with the alpha value, to maximize performance. Also, using LoRA across all model layers rather than only key and value matrices improves performance. The author also answers common questions related to LoRA and its application.

This post provides a comprehensive guide on the emerging architecture of language-literal models (LLMs), and the steps to build an LLM application. Five crucial steps are discussed: focusing on a suitable problem, choosing the right LLM considering factors like licensing and model size, customizing the LLM using techniques like in-context learning and reinforcement learning, setting up the app’s architecture, and conducting online evaluations. Additionally, the article emphasises the real-world impact of LLMs in various sectors like geospatial AI and healthcare.