Deep Cognition is thrilled to unveil our website’s fresh, revamped look after months of design and development work. With a modern design, enhanced navigation and exciting new features, the website offers a user experience that reflects our innovative solutions. Deep Cognition was launched in 2017 with a clear mission to solve some of the toughest business problems using cutting-edge artificial intelligence. We created PaperEntry AI – the most accurate and configurable Document-to-Data (D2D) automation software that leverages the power of generative AI to extract, analyze and process data from the most complex documents with unparalleled speed and accuracy. Our website underwent a revamp to deliver a streamlined user experience that aligns with the company’s brand identity. The result is a visually appealing yet highly functional website that enhances the overall user journey. “We’re excited to announce the launch of our new website,” said Kurt Knapton, CEO of Deep Cognition. “As Deep Cognition continues to advance at the same rapid pace as generative AI itself, it’s important for us to upgrade our site to more effectively communicate the amazing innovation and transformative value that our PaperEntry AI platform is delivering to our growing base of commercial and government customers. We invite...

Pope Leo XIV is taking a bold stance on artificial intelligence, calling it “a challenge to human dignity, justice and labour” in his first major address since being elected leader of the Catholic Church. The new pontiff is placing AI at the center of the Church’s moral agenda, warning that we’re entering a new industrial revolution with the same threats to workers and human rights seen over a century ago. “In our own day… developments in the field of artificial intelligence pose new challenges,” Leo said, addressing the College of Cardinals on Saturday in the New Synod Hall. He echoed Pope Leo XIII, who in 1891 issued Rerum Novarum, a foundational text of Catholic social teaching written in response to the human toll of the Industrial Revolution. Pope Leo XIV meets the College of Cardinals in the New Synod Hall at the Vatican (AP) Backdrop Pope Leo XIV’s remarks follow a surge in global anxiety over AI’s role in the economy, warfare, and media integrity. The speech comes just days after: Pope Francis, who passed away on Easter Monday at age 88, had grown increasingly vocal about AI’s ethical threats, especially in military use, where he warned of “distancing from...

Maybe it’s the middle of the night. Maybe you’re not looking for anything serious — just a cheeky chat, a little fantasy, a bit of flirtation that doesn’t ask you for your email address or a blood sample. Or maybe, just maybe, you’re burned out from “Create an account first” screens and just want to jump right in and talk to an AI that gets you, no strings attached. If that sounds familiar, you’re not alone. According to recent surveys, nearly 27% of Gen Z and Millennials interact with AI chat companions regularly — and a growing slice of that pie is NSFW. The demand for adult-friendly AI conversations is skyrocketing, but here’s the catch: most of them want you to sign up, log in, verify, blah blah blah… That’s why we’ve done the legwork (and some very thorough testing, thank you) to bring you the best NSFW AI chatbots that don’t require sign up. These chatbots are fast, free to access (some have optional paid upgrades), and as spicy or romantic as you want them to be. Why Are People Looking for NSFW AI Chatbots With No Sign-Up? Let’s not dance around it: privacy matters. In an age where...

I’ve spent the last eight years working with AI, learning the ins and outs of building and applying AI solutions in business. After making countless mistakes, I created my own method for building and applying the technology. That was fine and dandy until the fall of 2022, when ChatGPT was released and gave a sudden rise in the usefulness and adoption of generative AI. For my consulting business TodAI, that meant a lot of new projects involving generative AI and a lot of learning. After several projects, I’ve identified places where generative models are clearly distinct from other AI when applying them in a business. Some are small, and others are very significant. How do these new generative AI models change the game for applied AI? Terminology It’s easier to discuss the changes if we distinguish between generative AI and predictive AI. Generative AI refers to large pre-trained models that output texts, images or sounds from user-provided prompts. The output is (potentially) unique and mimics human-generated content. It’s based on the prompt and the data used to train a large pre-trained model. Text-generating models such as OpenAI’s GPT or Googles Bard are also known as large language models (LLMs). Predictive...

Businesses today are drowning in data. A data catalog is key to organizing, finding, and using this data, benefitting various stakeholders. A data catalog serves as a single source of truth, enabling data analysts and scientists to efficiently discover, understand, and utilize datasets without wasting time searching for relevant information. Business leaders can confidently rely on accurate, well-documented data to drive informed decision-making. IT and data engineers benefit from centralized metadata management, ensuring seamless data integrity and regulatory compliance. Meanwhile, compliance teams can effortlessly oversee data usage, enforce governance policies, and mitigate risks related to data security and privacy across the organization. ​Key Features of a Modern Data Catalog Data Discovery: Allows data analysts and scientists to quickly locate and access relevant datasets, reducing the time spent searching for information and accelerating insights. Metadata Management: Captures and maintains information about data assets, including definitions, relationships, and origins, helping data engineers and IT teams maintain comprehensive metadata. Data Lineage: Tracks the data’s journey from its source to its current state, ensuring regulatory adherence and audit readiness, benefitting compliance officers and governance teams. Collaboration: Allows business users and analysts to annotate, rate, and share insights on datasets, fostering cross-functional collaboration and informed...

The process of data annotation and labeling plays a critical role in the supervised training of large language models and other types of data-driven machine-learning models. Since data annotation is both time- and resource-intensive, AI developers often outsource it to specialized providers. In this piece, we will walk you through the top data labeling companies worldwide in 2025. Best Data Labeling & Annotation Companies 2025 Cogito Tech Appen TELUS International TaskUs iMerit CloudFactory SuperAnnotate Anolytics 1. Cogito Tech Cogito Tech is a leading data solutions provider specializing in data labeling and annotation services. The company provides a wide range of data annotations for applications across computer vision and natural language processing (NLP) services to train AI and machine learning models. Its solutions extend to fine-tuning generative AI models, such as large language models (LLMs), using techniques such as Reinforcement Learning from Human Feedback (RLHF). Key Features Headquartered in Levittown, New York (US), Cogito Tech is the only DAL company to have been recognized in The Financial Times’ ranking as one of the Fastest-Growing Companies in the US (2024 and 2025) and in Everest Group’s Data Annotation and Labeling (DAL) Solutions for AI/ML PEAK Matrix® Assessment 2024. Skilled Workforce: Cogito Tech...

The Phi-4 family is Microsoft’s latest advancement in small language models (SLMs), designed to excel in complex reasoning tasks while maintaining efficiency. The Phi-4 series includes three key models: Phi-4-reasoning, Phi-4-reasoning-plus, and Phi-4-mini-reasoning. The newly released models are built with a clear focus: deliver advanced reasoning performance without the infrastructure demands of trillion-parameter models. They strike an optimal balance between size and performance using advanced techniques such as distillation, reinforcement learning, and carefully curated data. Phi-4-reasoning is a 14-billion parameter model with a 32k token context window, trained using high-quality web data and OpenAI o3-mini prompts. It excels in tasks requiring detailed, multi-step reasoning such as mathematics, coding, and algorithmic problem solving. Phi-4-reasoning-plus builds upon this with additional fine-tuning using 1.5x more tokens and reinforcement learning, delivering even higher accuracy and inference-time performance. Phi-4-mini-reasoning, with just 3.8 billion parameters, was trained on one million synthetic math problems generated by DeepSeek R1. It targets use cases like educational tools and mobile apps, proving capable of step-by-step problem solving in resource-constrained environments. What sets Phi-4 apart is not just efficiency, but sheer capability. On benchmarks like HumanEval+ and MATH-500: Phi-4-reasoning-plus outperforms DeepSeek-R1 (671B parameters) on some tasks, demonstrating that smarter training can...