Humans naturally learn by making connections between sight and sound. For instance, we can watch someone playing the cello and recognize that the cellist’s movements are generating the music we hear. A new approach developed by researchers from MIT and elsewhere improves an AI model’s ability to learn in this same fashion. This could be useful in applications such as journalism and film production, where the model could help with curating multimodal content through automatic video and audio retrieval. In the longer term, this work could be used to improve a robot’s ability to understand real-world environments, where auditory and visual information are often closely connected. Improving upon prior work from their group, the researchers created a method that helps machine-learning models align corresponding audio and visual data from video clips without the need for human labels. They adjusted how their original model is trained so it learns a finer-grained correspondence between a particular video frame and the audio that occurs in that moment. The researchers also made some architectural tweaks that help the system balance two distinct learning objectives, which improves performance. Taken together, these relatively simple improvements boost the accuracy of their approach in video retrieval tasks and...

Synthetic data is on the rise in artificial intelligence. It’s going to make AI cheaper, better and less biased. It’s also very obtainable and usable. In a short while, it has gone from being an experimental technology. To something, I would not hesitate to use for production AI solutions.  To illustrate that, I will build an AI that can classify the difference between apples and bananas. I will only use images of the two classes generated by another AI – In this case, using DALL-E Mini. An Apple or Banana recognizer  I will build an image classifier using only easy-to-access, free AutoAI tools. Generating data We need around 30 images of each label, bananas and apples. We will be using DALL-E Mini, an open-source version of NVIDIAs text-to-image model DALL-E 2. To generate the images, you can go to https://huggingface.co/spaces/dalle-mini/dalle-mini. Here you can prompt the text-to-image model with queries such as: “Banana on table” “Banana on random background” “Apple on table” “Apple on random background” Try to match the background you will be testing on.

Open Source Video Generators Create Feature-Length Films Open Source Video Generators Create Feature-Length Films a concept that once seemed futuristic is now reshaping the storytelling landscape. Whether you’re a filmmaker, developer, or content creator, this shift holds compelling opportunities. With artificial intelligence (AI) tools evolving rapidly, we are witnessing an age where anyone with a vision can produce a full-length film using entirely open source software. This revolution is sparking curiosity, demanding attention, and inviting both experts and hobbyists to rethink how stories are told. Also Read: Debating the True Meaning of Open-Source AI Filmmaking has long entailed large crews, high budgets, and complex logistics. Open source video generators dramatically lower these barriers. By leveraging AI, artists can now create scenes, characters, dialogue, and even background scores from a single dashboard. These tools are powered by image generation, deep learning algorithms, and natural language processing. Generators like Stable Diffusion, RunwayML, and OpenAI’s GPT models are proving that full-length feature films can be composed using lines of code and creative direction. These engines don’t just design frames they interpret text prompts into entire sequences, enriched with stylistic elements and coherent narratives. As these platforms improve, major studios and indie filmmakers alike...

The End of the AI Safety Debate For years, a passionate contingent of researchers, ethicists, and policymakers warned about the potential dangers of unchecked artificial intelligence development. They argued about p(doom) probabilities, AI alignment strategies, and regulations that could prevent catastrophe. But as of now, that conversation has all but collapsed. The frontier AI companies—OpenAI, Anthropic, Google DeepMind, and others—have fully shifted gears. They’re no longer talking about pausing AI progress or carefully evaluating existential risks. Instead, they are racing to roll out increasingly advanced models with the primary focus being one thing: dominance. AI safety was once a core part of the conversation; now, it’s little more than a PR footnote. So, what happens now? The Cost of AI is Dropping to Near Zero One of the most overlooked aspects of AI acceleration is the rapidly declining cost of both training and inference. Just a few years ago, training a state-of-the-art AI model required billions of dollars in compute resources. Today, open-source models can be fine-tuned on consumer GPUs for a fraction of the cost. Not only that, but API-based access to the most powerful AI systems is becoming cheaper by the month. What used to cost hundreds or...

With the educational content shift taking place, publishers need to innovate. In a previous blog post, we’ve discussed why embracing content agility is key in this regard. Of course, such transitions require publishers to make investment-related decisions. With every innovation, the main question is, ‘Will we make or buy this?’ To make: independence + uncertainty Those who decide to develop a new initiative in-house will need to reach into their pockets. R&D, internal development, and maintenance are costly. Such investments do come with an advantage: once you’ve built your solution, you will have the technology, knowledge, and capability to independently manage and maintain it. You will be in control. On the other hand, this requires constant investments and a complete overhaul of the organisation. For as your innovation continues to develop, you will need to expand your R&D teams, which should specialise in AI. Meanwhile, the outcomes are often uncertain — if you set out to develop AI solutions, you’re entering a trial-by-error field. You can’t be sure your investments will pay off the moment you get started. To buy: ROI and focus + data-related concerns The alternative is joining forces with a provider that has developed a proven solution...

In a digital ecosystem where user attention is fragmented and ad-blockers are rising, media companies are finding it harder than ever to maintain let alone grow advertising revenue. Traditional approaches to targeting and monetization are hitting a ceiling, often resulting in flat or declining ROI from digital ad operations. To thrive in today’s fast-evolving media landscape, organizations must move beyond conventional segmentation, rule-based personalization, and basic A/B testing. The answer? AI-powered ad optimization. By leveraging AI to better understand audiences, predict content engagement, and dynamically adjust campaigns, media companies can unlock untapped ad revenue potential. The Limits of Traditional Ad Monetization For years, ad revenue strategies in media have revolved around maximizing impressions, increasing click-through rates, and optimizing basic user demographics. But as audiences diversify and attention spans shrink, these methods offer diminishing returns. Challenges faced by media companies today include: Ad fatigue among users due to overexposure and irrelevant targeting Inaccurate personalization based on broad, outdated demographic assumptions Inefficient monetization due to generic segmentation and static campaign strategies Difficulty scaling successful strategies across content types and platforms These inefficiencies result in missed revenue opportunities and underperforming campaigns a costly combination in a highly competitive digital environment. How AI Ad...