“At this writing, the only serious ELIZA scripts which exist are some which cause ELIZA to respond roughly as would certain psychotherapists (Rogerians). ELIZA performs best when its human correspondent is initially instructed to”talk” to it, via the typewriter of course, just as one would to a psychiatrist. This mode of conversation was chosen because the psychiatric interview is one of the few examples of categorized dyadic natural language communication in which one of the participating pair is free to assume the pose of knowing almost nothing of the real world. If, for example, one were to tell a psychiatrist “I went for a long boat ride” and he responded “Tell me about boats,” one would not assume that he knew nothing about boats, but that he had some purpose in so directing the subsequent conversation. It is important to note that this assumption is one made by the speaker. Whether it is realistic or not is an altogether separate question. In any case, it has a crucial psychological utility in that it serves the speaker to maintain his sense of being heard and understood. The speaker furher defends his impression (which even in real life may be illusory) by...

Luc Schurgers, founder of REPLIKANT, is rethinking what it means to animate in the age of AI. In this interview, he explains why traditional workflows fall short, how REPLIKANT bridges live-action spontaneity with 3D control, and where the ethical lines are drawn in a world of synthetic media. From the risks of “AI slop” to the promise of scalable creativity, Luc offers a grounded perspective on where storytelling is headed—and why it still needs a human core. Discover more interviews here: Yuriy Adamchuk, Group CEO at Avenga — Leading a Team of 4,000 Professionals, Managing Global Challenges, Innovation in IT, Strategic Mergers Your journey in digital innovation spans viral marketing, interactive campaigns, and now AI-powered 3D animation. What pivotal moment or project inspired you to launch REPLIKANT, and how does it address a gap in the industry? There wasn’t a single “aha” moment, but rather a culmination of frustrations and insights gained over years of working on digital and interactive campaigns. I was always drawn to the energy of working on set—collaborating with actors, set designers, and cinematographers—but equally captivated by the limitless creative potential of animation. The problem was, traditional animation pipelines are slow, technical, and rigid, making rapid...

HunyuanVideo is an AI video generation model developed by Tencent. It excels at creating high-quality, cinematic videos with superior motion stability, scene transitions, and realistic visuals that closely align with textual descriptions. What sets Hunyuan AI Video apart is its ability to generate not only realistic video content but also synchronized audio, making it a comprehensive solution for immersive multimedia experiences. With 13 billion parameters, it is the largest and most advanced open-source text-to-video model to date, surpassing all existing counterparts in terms of scale, quality, and versatility. HunyuanVideo is designed to address key challenges in text-to-video (T2V) generation. Unlike many existing AI models, which struggle with maintaining subject consistency and scene coherence, HunyuanVideo demonstrates exceptional performance in: High-Quality Visuals: The model undergoes fine-tuning to ensure ultra-detailed content, making the generated videos sharp, vibrant, and visually appealing. Motion Dynamics: Unlike static or low-motion outputs from some AI models, HunyuanVideo produces smooth and natural movements, making videos feel more realistic. Concept Generalization: The model uses realistic effects to showcase virtual scenes, complying with physical laws to reduce the sense of disconnection for the audience. Action Reasoning: By leveraging large language models (LLMs), the system can generate sequences of movements based on...

“Stop using AI.” This is how Dr. Kavita Ganesan, an AI expert since 2005, begins her book The Business Case for AI. In a refreshingly direct tone, Ganesan goes on to deliver the news that, yes, you probably need to rethink your use of AI and, no, it does not need to be this difficult or this expensive. While AI is a necessary tool for businesses to remain competitive, many find themselves worried about the investment, and the consequences– what if I’m using AI to solve the wrong problems? Could it really take away my job or my employees’ jobs? Ganesan is a sharp but assuring voice in a field full of questions like these. “These worries are real,” she states before going on to describe exactly what you need to do to assuage them. Throughout the book, Ganesan tells you where you can apply AI, how you can prepare your organization for it, how to invest in the right AI opportunities, and how to measure the success of your AI initiatives, almost making it sound easy. The book does not go into technical language or AI-specific jargon that a CS professional might not be familiar with. Ganesan makes AI,...

As artificial intelligence (AI) adoption gathers pace, so does the complexity and range of its risks. Businesses are increasingly aware of these challenges, yet the roadmaps to solutions often remain shrouded in obscurity. If the question ‘How to navigate these risks?’ resonates with you, then this article will serve as a lighthouse in the fog. We delve into the heart of AI’s most pressing issues, bolstered by real-life instances, and lay out clear, actionable strategies to safely traverse this intricate terrain. Read on to unlock valuable insights that could empower your business to leverage the potency of AI, all the while deftly sidestepping potential pitfalls. 1. Bias in AI-Based Decisions The unintentional inclusion of bias in AI systems is a significant risk with far-reaching implications. This risk arises because these systems learn and form their decision-making processes based on the data they are trained on.  If the datasets used for training include any form of bias, these prejudices will be absorbed and consequently reflected in the system’s decisions. Example: Algorithmic Bias in the UK A-level Grading To illustrate, consider a real-world example that occurred during the COVID-19 pandemic in the UK. With the traditional A-level exams canceled due to health...