Narrative voice via Artlistai, News Reporter PlayAI, All other voices are V2V in Elevenlabs. Powered by (in order of amount) โHailuoAIโ, โKlingAIโ and of course some of our special source. Performance capture by โRunwayโs Act-Oneโ. Edited and color graded in โDaVinci Resolveโ. Composited with โAfter Effectsโ.
In this film, the โNewtonโs Cradleโ isnโt just a symbolic objectโit represents the fragile balance between control and freedom in a world where time itself is being manipulated. The oscillation of the cradle reflects the constant push and pull of power in this dystopian society. By the end of the film, we discover that this seemingly innocuous object holds the potential to disrupt the system, offering a glimmer of hope that time can be reset and balance restored.
Deepfake technology is a type of artificial intelligence used to create convincing fake images, videos and audio recordings. The term describes both the technology and the resulting bogus content and is a portmanteau of deep learning and fake.
Deepfakes often transform existing source content where one person is swapped for another. They also create entirely original content where someone is represented doing or saying something they didn’t do or say.
Deepfakes aren’t edited or photoshopped videos or images. In fact, they’re created using specialized algorithms that blend existing and new footage. For example, subtle facial features of people in images are analyzed through machine learning (ML) to manipulate them within the context of other videos.
Deepfakes uses two algorithms — a generator and a discriminator — to create and refine fake content. The generator builds a training data set based on the desired output, creating the initial fake digital content, while the discriminator analyzes how realistic or fake the initial version of the content is. This process is repeated, enabling the generator to improve at creating realistic content and the discriminator to become more skilled at spotting flaws for the generator to correct.
The combination of the generator and discriminator algorithms creates a generative adversarial network.
A GANuses deep learning to recognize patterns in real images and then uses those patterns to create the fakes.
When creating a deepfake photograph, a GAN system views photographs of the target from an array of angles to capture all the details and perspectives. When creating a deepfake video, the GAN views the video from various angles and analyzes behavior, movement and speech patterns. This information is then run through the discriminator multiple times to fine-tune the realism of the final image or video.
๐น 1943: ๐ ๐ฐ๐๐๐น๐น๐ผ๐ฐ๐ต & ๐ฃ๐ถ๐๐๐ create the first artificial neuron. ๐น 1950: ๐๐น๐ฎ๐ป ๐ง๐๐ฟ๐ถ๐ป๐ด introduces the Turing Test, forever changing the way we view intelligence. ๐น 1956: ๐๐ผ๐ต๐ป ๐ ๐ฐ๐๐ฎ๐ฟ๐๐ต๐ coins the term โArtificial Intelligence,โ marking the official birth of the field. ๐น 1957: ๐๐ฟ๐ฎ๐ป๐ธ ๐ฅ๐ผ๐๐ฒ๐ป๐ฏ๐น๐ฎ๐๐ invents the Perceptron, one of the first neural networks. ๐น 1959: ๐๐ฒ๐ฟ๐ป๐ฎ๐ฟ๐ฑ ๐ช๐ถ๐ฑ๐ฟ๐ผ๐ and ๐ง๐ฒ๐ฑ ๐๐ผ๐ณ๐ณ create ADALINE, a model that would shape neural networks. ๐น 1969: ๐ ๐ถ๐ป๐๐ธ๐ & ๐ฃ๐ฎ๐ฝ๐ฒ๐ฟ๐ solve the XOR problem, but also mark the beginning of the “first AI winter.” ๐น 1980: ๐๐๐ป๐ถ๐ต๐ถ๐ธ๐ผ ๐๐๐ธ๐๐๐ต๐ถ๐บ๐ฎ introduces Neocognitron, laying the groundwork for deep learning. ๐น 1986: ๐๐ฒ๐ผ๐ณ๐ณ๐ฟ๐ฒ๐ ๐๐ถ๐ป๐๐ผ๐ป and ๐๐ฎ๐๐ถ๐ฑ ๐ฅ๐๐บ๐ฒ๐น๐ต๐ฎ๐ฟ๐ introduce backpropagation, making neural networks viable again. ๐น 1989: ๐๐๐ฑ๐ฒ๐ฎ ๐ฃ๐ฒ๐ฎ๐ฟ๐น advances UAT (Understanding and Reasoning), building a foundation for AI’s logical abilities. ๐น 1995: ๐ฉ๐น๐ฎ๐ฑ๐ถ๐บ๐ถ๐ฟ ๐ฉ๐ฎ๐ฝ๐ป๐ถ๐ธ and ๐๐ผ๐ฟ๐ถ๐ป๐ป๐ฎ ๐๐ผ๐ฟ๐๐ฒ๐ develop Support Vector Machines (SVMs), a breakthrough in machine learning. ๐น 1998: ๐ฌ๐ฎ๐ป๐ป ๐๐ฒ๐๐๐ป popularizes Convolutional Neural Networks (CNNs), revolutionizing image recognition. ๐น 2006: ๐๐ฒ๐ผ๐ณ๐ณ๐ฟ๐ฒ๐ ๐๐ถ๐ป๐๐ผ๐ป and ๐ฅ๐๐๐น๐ฎ๐ป ๐ฆ๐ฎ๐น๐ฎ๐ธ๐ต๐๐๐ฑ๐ถ๐ป๐ผ๐ introduce deep belief networks, reigniting interest in deep learning. ๐น 2012: ๐๐น๐ฒ๐ ๐๐ฟ๐ถ๐๐ต๐ฒ๐๐๐ธ๐ and ๐๐ฒ๐ผ๐ณ๐ณ๐ฟ๐ฒ๐ ๐๐ถ๐ป๐๐ผ๐ป launch AlexNet, sparking the modern AI revolution in deep learning. ๐น 2014: ๐๐ฎ๐ป ๐๐ผ๐ผ๐ฑ๐ณ๐ฒ๐น๐น๐ผ๐ introduces Generative Adversarial Networks (GANs), opening new doors for AI creativity. ๐น 2017: ๐๐๐ต๐ถ๐๐ต ๐ฉ๐ฎ๐๐๐ฎ๐ป๐ถ and team introduce Transformers, redefining natural language processing (NLP). ๐น 2020: OpenAI unveils GPT-3, setting a new standard for language models and AIโs capabilities. ๐น 2022: OpenAI releases ChatGPT, democratizing conversational AI and bringing it to the masses.
– Collect: Data from sensors, logs, and user input. – Move/Store: Build infrastructure, pipelines, and reliable data flow. – Explore/Transform: Clean, prep, and detect anomalies to make the data usable. – Aggregate/Label: Add analytics, metrics, and labels to create training data. – Learn/Optimize: Experiment, test, and train AI models.
– Instrumentation and logging: Sensors, logs, and external data capture the raw inputs. – Data flow and storage: Pipelines and infrastructure ensure smooth movement and reliable storage. – Exploration and transformation: Data is cleaned, prepped, and anomalies are detected. – Aggregation and labeling: Analytics, metrics, and labels create structured, usable datasets. – Experimenting/AI/ML: Models are trained and optimized using the prepared data. – AI insights and actions: Advanced AI generates predictions, insights, and decisions at the top.
– Data Infrastructure Engineers: Build the foundation โ collect, move, and store data. – Data Engineers: Prep and transform the data into usable formats. – Data Analysts & Scientists: Aggregate, label, and generate insights. – Machine Learning Engineers: Optimize and deploy AI models.
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