Artificial Intelligence
Chain of Thought Prompting in LLMs
Large language models are a variety of artificial intelligence that has been trained to understand and generate human language. These models are used in many fields, including programming, to help humans accomplish daily tasks. To communicate with the model effectively, you need to understand how to form requests properly. Chain of thought prompting is one of the most efficient techniques when interacting with LLMs.
Upstream posts
A Complete Guide to AGI
We have already entered the era of AI. The next step would be Artificial General Intelligence (AGI), which does not yet exist and remains a topic of debate within the scientific community. In this article, we will explore the concept of AGI and examine the current technological capabilities for its development.
How Does Character.AI Work?
Today there are hardly any people who haven’t chatted with AI via ChatGPT or tried to generate a hybrid between an intergalactic starship and a burrito using Stable Diffusion. However, this type of communication often doesn't feel natural. Yet, there is a company that has created a platform, Character AI, where you can chat with your favorite characters. Of course, these characters are just imitations of popular cartoon, movies, and book characters, as well as real people.
How Does Sora Create Videos?
Sora is a text-to-video free AI tool that allows creators to generate high-quality videos based on a text prompt. The model can be useful in SMM, cinematography, entertainment and anywhere else where quality video content is required. In this article, you will learn what to expect from this new generation AI tool.
Haskell in Production: Textcontent
In our Haskell in Production series, we interview developers and technical leaders from companies that use Haskell for real-world tasks. We cover benefits, downsides, common pitfalls, and tips for building useful Haskell products. Today's guest is Marc Scholten, the Founder and CEO of digitally induced. In this interview, we discuss their product, textcontent.ai. It is a text-generating tool that helps marketers create a distinct style and tone of voice. Marc explained how it differs from other options on the market and how they use Haskell in their product.
What Is Ensemble Learning?
In machine learning, there are various techniques for building models. One of them is ensemble learning that uses a combination of models to achieve better performance. By aggregating different predictions, ensemble models can demonstrate better results than their alternatives. In this blog post, we will talk about how ensemble learning works, review different types of ensemble learning algorithms, and learn about advantages and disadvantages of ensemble learning for various tasks.
Graph Neural Networks in Bioinformatics
In this post, we share insights from Serokell AI experts on their investigation into drug-disease interactions. Specifically, they explored whether a drug has a positive, negative, or neutral effect on the treatment of a particular disease. Serokell has collaborated with Neo7Bioscience, a molecular technology company, and Elsevier, an information and analytics firm that facilitates medical and biological research. With the data licensed from Elsevier, our specialists developed ML models that predict interactions between small molecules and diseases.
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Editor’s pick
What Is Semi-Supervised Learning?
In earlier posts on our blog, we discussed various types of machine learning, including supervised and unsupervised learning. Today, we continue the series by exploring one of the most versatile and widely used machine learning techniques—hybrid, or semi-supervised learning. In this article, you will learn about how semi-supervised learning works, what its benefits are, and what algorithms to use.
Upstream posts
How AI Is Reshaping Manufacturing
In this article, we've collected information on how AI has transformed manufacturing by automating routine tasks with never-sleeping machines and reducing operational costs. We will share some of the most promising use cases and delve into the opportunities and challenges of implementing AI.
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.
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.