Machine Learning

Theoretically, it looks simple: define a goal, collect appropriate data, train the model with this data, validate it, and then monitor and measure the model’s quality, making adjustments if necessary. In real life, however, you deal with separate and major problems at every step. Shall we begin? :) Is your defined goal clear enough? What will the model be used for? Is there a previously built model for this? Is this a research and development job, or is it solving a previously unsolved problem? Or is the main goal just keeping up with the AI trend? If it’s just for the trend, definitely don’t develop a model. Because it’s not a goal. There’s not even anything worth doing the project for. ...

Key Highlights By 2027, half of all business decisions will be augmented or automated by AI agents Gartner Data & Analytics Summit 2026 features an expanded AI agenda The summit includes a spotlight track on AI leadership for executives As the world becomes increasingly reliant on artificial intelligence (AI), businesses are facing a significant shift in how they operate. This move reflects broader industry trends, where companies are leveraging AI to drive decision-making and stay competitive. The Gartner Data & Analytics Summit 2026 is designed to help AI leaders navigate this complex landscape and make informed decisions about their AI strategies. ...

Key Highlights NVIDIA Apollo is a family of open models for accelerating industrial and computational engineering The models will enable developers to integrate real-time capabilities into their simulation software Industry leaders such as Applied Materials, Cadence, and Siemens are already using NVIDIA AI physics to improve their applications The introduction of NVIDIA Apollo marks a significant shift in the field of industrial engineering, where artificial intelligence (AI) and machine learning (ML) are being increasingly used to improve simulation accuracy and speed. This move reflects broader industry trends towards adopting AI and ML to drive innovation and efficiency. By providing a family of open models, NVIDIA is enabling developers to tap into the power of AI physics and create more sophisticated simulation software. ...

Key Highlights Yoshua Bengio, a leading figure in AI research, expresses concerns about AI’s risks to humanity Bengio emphasizes the need for safety protocols in AI development He discusses his efforts to create AI systems with built-in safety features The recent podcast episode featuring Yoshua Bengio, one of the pioneers of machine learning, has sparked a crucial conversation about the potential risks associated with AI. As the field continues to advance, it’s essential to address the concerns surrounding AI’s impact on humanity. Bengio’s comments reflect a broader industry trend, where experts are acknowledging the need for responsible AI development. ...

Key Highlights OpenAI introduces GPT-5.1 with enhanced safety features and conversational AI GPT-5.1 Instant and GPT-5.1 Thinking offer improved instruction following and adaptive reasoning Expanded safety evaluations include mental health and emotional reliance assessments The recent release of GPT-5.1 by OpenAI marks a significant milestone in the development of conversational AI. This move reflects broader industry trends towards creating more sophisticated and safe AI models. With GPT-5.1, OpenAI aims to provide a more conversational and intelligent chat experience, while also prioritizing user safety. ...

Artificial intelligence has long struggled with a fundamental problem: it treats everyone the same. While modern AI can write code, analyze data, and answer questions, it often feels impersonal—like talking to a generic chatbot rather than an assistant that truly understands you. DEEPPERSONA changes this by creating incredibly detailed, lifelike digital identities that help AI systems understand and interact with the complexity of real human beings. Why AI Needs Deep Personas Think of the difference between a form that asks for your name and age versus one that knows your career trajectory, hobbies, communication style, and life experiences. The first is functional but shallow. The second can actually be helpful in meaningful ways. ...

Scientific Machine Learning (SciML) combines data-driven learning methods with traditional physics-based modeling to tackle complex problems in science and engineering. Yet designing effective SciML models remains heavily dependent on expert knowledge—a time-intensive, labor-demanding process that creates a significant bottleneck in scientific progress. What if we could automate this expertise-driven process? Enter AgenticSciML. What Is AgenticSciML? AgenticSciML is a collaborative multi-agent system composed of over 10 specialized AI “agents” working together to discover novel solutions for scientific problems. Think of it as a brainstorming team where each member brings different expertise to the table. ...

The MLOps landscape has exploded with hundreds of tools, creating what Gartner calls a “glut of innovation.” This abundance creates a paralyzing problem: making informed choices becomes nearly impossible when tools appear similar on the surface but solve fundamentally different challenges. If you’ve felt overwhelmed trying to compare platforms, you’re experiencing the industry’s most common pitfall. This guide cuts through the confusion by focusing on the counter-intuitive lessons learned from real-world implementations. Instead of drowning in feature lists, you’ll learn to think about MLOps tools through the lens of the specific problems they actually solve. ...

As the AI landscape continues to evolve, companies are seeking ways to make their models more flexible, secure, and transparent. Anthropic’s latest move reflects this trend, introducing a new feature called Skills for its Claude AI model. This development allows developers to create modular, reusable task components that can be integrated into various applications, making Claude more versatile and efficient. The Skills feature is designed to provide a more transparent and auditable approach to AI development, aligning with Anthropic’s focus on model safety and interpretability. By defining a schema for each Skill, developers can create custom components that can be invoked dynamically through Claude’s API. This architecture enables seamless integration between the model and external systems, making it easier to adapt Claude to specialized business and research needs. ...