Artificial Intelligence
What Is Unsupervised Learning?
Unsupervised learning is a type of machine learning that relies less on human guidance and intervention and more or analyzing raw data and extracting patterns from it. It’s thanks to unsupervised machine learning that today we have so many powerful ML applications such as generative AI systems, search engines, and recommendation systems. In this article, you will learn about how unsupervised learning works and what techniques you can use to build your own ML model.
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AI Integration in Oil and Gas
With the global energy demand escalating, companies in the oil and gas field face growing demands to boost operational efficiency, cut expenses, and adhere to safety and environmental regulations. AI has emerged as a crucial solution to meet these demands. Over the last ten years, the integration of artificial intelligence into the oil and gas sector has significantly transformed the industry.
What Is Supervised Learning?
In machine learning, there are different approaches to building effective learning representations. One of them that appeared first and up to this day continues to be quite a popular way of teaching machines to learn and make predictions, is supervised learning. In this article, you will learn what supervised learning is, how supervised machine learning models are built and how they are used for real-life applications.
Bayesian Optimization Algorithm
Hyperparameter optimization plays a significant role in the development and refinement of machine learning models, ensuring their optimal performance for specific tasks. The Bayesian optimization algorithm stands out among various methods due to its efficiency and effectiveness. Unlike hyperparameter tuning methods like random search and grid search, which evaluate parameter values independently without considering outcomes from previous iterations, Bayesian optimization leverages results from previous evaluations.
Security Risks of Generative AI
Generative artificial intelligence has transformed many industries from content creation to healthcare and fintech. Because the use of generative AI has become so widespread, it introduced certain challenges for the cybersecurity of individuals and whole corporations. McKinsey Global Survey on AI shows that 40 percent of organizations plan to increase their overall AI investment because of advancements in generative AI. At the same time, 53 percent of organizations acknowledge cybersecurity as a generative AI-related risk.
Best AI Tools for Industries
Artificial Intelligence has become an essential part of various industries, providing companies with data-driven insights and personalized experiences for their customers. AI tools are quickly transforming business approaches and decision-making processes. In this article, we explore the best AI tools for various industries, including marketing, healthcare, finance, and more.
Reinforcement Learning: How It Works
Reinforcement learning (RL) is one of the most popular machine learning paradigms. RL is indispensable for teaching machines how to operate in constantly changing environments, such as games, VR, or even the real world. In this article, we will explore the learning processes, key algorithms, and practical applications that make RL a transformative force in in ML.
Editor’s pick
How to Use GitHub Copilot
GitHub Copilot is one of the most popular AI assistants for writing code. Find out how to communicate your intent to GitHub Copilot properly and write code faster, avoiding unnecessary trials and errors. In this blog post, we explain how to make the most out of GitHub Copilot and even how to gain free access.
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Top AI Conferences in Medtech 2024
AI in medtech effectively helps with medical diagnosis and treatment plan development, drug discovery, and gene modification. 86% of healthcare providers and life science companies were harnessing the power of artificial intelligence in their work already 5 years ago. Medtech conferences with a focus on AI have become essential platforms for collaboration, knowledge exchange, and the unveiling of developments. In this article, we will share top seven events not to miss if you work in the medtech or AI field.
AI Trends 2024
AI is increasingly reshaping the way we live and work. In 2024, technological advances will continue to impact all spheres of life, changing the world of software development, business patterns, and consumer habits. It will also keep raising questions about authenticity and calls for regulation. In this article, we explore key AI trends that are poised to redefine the technological landscape in 2024 based on the research conducted by three companies: Forrester, Gartner, and Bullhound.
How to Preprocess Data in Python
Before training a model, you have to preprocess data. This is necessary to transform raw data into clean data suitable for analysis. In this guide, we will cover essential steps to preprocess data using Python. These include splitting the dataset into training and validation sets, handling missing values, managing categorical features, and normalizing the dataset.
Backpropagation in Neural Networks
Backpropagation is a fundamental component of deep learning for neural networks. Its development has significantly contributed to the widespread adoption of deep learning algorithms since the early 2000s. In this post, we explore the essential concepts associated with this method, as well as its applications and history.
What Is LLaMA?
In 2023, generative artificial intelligence has become so hyped that every self-respecting Big Tech company is running to roll out their own technology. LLaMA 2 is a new open-source language model by Meta that can be seen as an opponent of ChatGPT. Releasing products that should become the “killers” of well-established products seems to be a recurring strategy of Mark Zucherberg lately. Will LLaMA have the same destiny as Threads, that has already been abandoned by most of its users? Let’s find out.
What are AI Agents?
In a [recent interview to CBS Mornings, Dr Geoffrey Hinton, the "godfather of AI," shared his concerns about the state of artificial intelligence. The person who has built the first neural nets and dedicated much of his career to the study of artificial intelligence, is worried that AI might eventually become smarter than people, and what happens next is hard to predict.