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.
🔹 1943: 𝗠𝗰𝗖𝘂𝗹𝗹𝗼𝗰𝗵 & 𝗣𝗶𝘁𝘁𝘀 create the first artificial neuron. 🔹 1950: 𝗔𝗹𝗮𝗻 𝗧𝘂𝗿𝗶𝗻𝗴 introduces the Turing Test, forever changing the way we view intelligence. 🔹 1956: 𝗝𝗼𝗵𝗻 𝗠𝗰𝗖𝗮𝗿𝘁𝗵𝘆 coins the term “Artificial Intelligence,” marking the official birth of the field. 🔹 1957: 𝗙𝗿𝗮𝗻𝗸 𝗥𝗼𝘀𝗲𝗻𝗯𝗹𝗮𝘁𝘁 invents the Perceptron, one of the first neural networks. 🔹 1959: 𝗕𝗲𝗿𝗻𝗮𝗿𝗱 𝗪𝗶𝗱𝗿𝗼𝘄 and 𝗧𝗲𝗱 𝗛𝗼𝗳𝗳 create ADALINE, a model that would shape neural networks. 🔹 1969: 𝗠𝗶𝗻𝘀𝗸𝘆 & 𝗣𝗮𝗽𝗲𝗿𝘁 solve the XOR problem, but also mark the beginning of the “first AI winter.” 🔹 1980: 𝗞𝘂𝗻𝗶𝗵𝗶𝗸𝗼 𝗙𝘂𝗸𝘂𝘀𝗵𝗶𝗺𝗮 introduces Neocognitron, laying the groundwork for deep learning. 🔹 1986: 𝗚𝗲𝗼𝗳𝗳𝗿𝗲𝘆 𝗛𝗶𝗻𝘁𝗼𝗻 and 𝗗𝗮𝘃𝗶𝗱 𝗥𝘂𝗺𝗲𝗹𝗵𝗮𝗿𝘁 introduce backpropagation, making neural networks viable again. 🔹 1989: 𝗝𝘂𝗱𝗲𝗮 𝗣𝗲𝗮𝗿𝗹 advances UAT (Understanding and Reasoning), building a foundation for AI’s logical abilities. 🔹 1995: 𝗩𝗹𝗮𝗱𝗶𝗺𝗶𝗿 𝗩𝗮𝗽𝗻𝗶𝗸 and 𝗖𝗼𝗿𝗶𝗻𝗻𝗮 𝗖𝗼𝗿𝘁𝗲𝘀 develop Support Vector Machines (SVMs), a breakthrough in machine learning. 🔹 1998: 𝗬𝗮𝗻𝗻 𝗟𝗲𝗖𝘂𝗻 popularizes Convolutional Neural Networks (CNNs), revolutionizing image recognition. 🔹 2006: 𝗚𝗲𝗼𝗳𝗳𝗿𝗲𝘆 𝗛𝗶𝗻𝘁𝗼𝗻 and 𝗥𝘂𝘀𝗹𝗮𝗻 𝗦𝗮𝗹𝗮𝗸𝗵𝘂𝘁𝗱𝗶𝗻𝗼𝘃 introduce deep belief networks, reigniting interest in deep learning. 🔹 2012: 𝗔𝗹𝗲𝘅 𝗞𝗿𝗶𝘇𝗵𝗲𝘃𝘀𝗸𝘆 and 𝗚𝗲𝗼𝗳𝗳𝗿𝗲𝘆 𝗛𝗶𝗻𝘁𝗼𝗻 launch AlexNet, sparking the modern AI revolution in deep learning. 🔹 2014: 𝗜𝗮𝗻 𝗚𝗼𝗼𝗱𝗳𝗲𝗹𝗹𝗼𝘄 introduces Generative Adversarial Networks (GANs), opening new doors for AI creativity. 🔹 2017: 𝗔𝘀𝗵𝗶𝘀𝗵 𝗩𝗮𝘀𝘄𝗮𝗻𝗶 and team introduce Transformers, redefining natural language processing (NLP). 🔹 2020: OpenAI unveils GPT-3, setting a new standard for language models and AI’s capabilities. 🔹 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.
𝗪𝗵𝗼 𝗺𝗮𝗸𝗲𝘀 𝗶𝘁 𝗵𝗮𝗽𝗽𝗲𝗻 𝗮𝗻𝗱 𝗸𝗲𝘆 𝗿𝗼𝗹𝗲𝘀:
– Data Infrastructure Engineers: Build the foundation — 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.
” In this video, I utilized artificial intelligence to generate an animated music video for the song Canvas by Resonate. This tool allows anyone to generate beautiful images using only text as the input. My question was, what if I used song lyrics as input to the AI, can I make perfect music synchronized videos automatically with the push of a button? Let me know how you think the AI did in this visual interpretation of the song.
After getting caught up in the excitement around DALL·E2 (latest and greatest AI system, it’s INSANE), I searched for any way I could use similar image generation for music synchronization. Since DALL·E2 is not available to the public yet, my search led me to VQGAN + CLIP (Vector Quantized Generative Adversarial Network and Contrastive Language–Image Pre-training), before settling more specifically on Disco Diffusion V5.2 Turbo. If you don’t know what any of these words or acronyms mean, don’t worry, I was just as confused when I first started learning about this technology. I believe we’re reaching a turning point where entire industries are about to shift in reaction to this new process (which is essentially magic!).
RGBW (RGB + White) LED strip uses a 4-in-1 LED chip made up of red, green, blue, and white.
RGBWW (RGB + White + Warm White) LED strip uses either a 5-in-1 LED chip with red, green, blue, white, and warm white for color mixing. The only difference between RGBW and RGBWW is the intensity of the white color. The term RGBCCT consists of RGB and CCT. CCT (Correlated Color Temperature) means that the color temperature of the led strip light can be adjusted to change between warm white and white. Thus, RGBWW strip light is another name of RGBCCT strip.
RGBCW is the acronym for Red, Green, Blue, Cold, and Warm. These 5-in-1 chips are used in supper bright smart LED lighting products
For years, tech firms were fighting a war for talent. Now they are waging war on talent.
This shift has led to a weakening of the social contract between employees and employers, with culture and employee values being sidelined in favor of financial discipline and free cash flow.
The operating environment has changed from a high tolerance for failure (where cheap capital and willing spenders accepted slipped dates and feature lag) to a very low – if not zero – tolerance for failure (fiscal discipline is in vogue again).
While preventing and containing mistakes staves off shocks to the income statement, it doesn’t fundamentally reduce costs. Years of payroll bloat – aggressive hiring, aggressive comp packages to attract and retain people – make labor the biggest cost in tech. …
Of course, companies can reduce their labor force through natural attrition. Other labor policy changes – return to office mandates, contraction of fringe benefits, reduction of job promotions, suspension of bonuses and comp freezes – encourage more people to exit voluntarily. It’s cheaper to let somebody self-select out than it is to lay them off. …
Employees recruited in more recent years from outside the ranks of tech were given the expectation that we’ll teach you what you need to know, we want you to join because we value what you bring to the table. That is no longer applicable. Runway for individual growth is very short in zero-tolerance-for-failure operating conditions. Job preservation, at least in the short term for this cohort, comes from completing corporate training and acquiring professional certifications. Training through community or experience is not in the cards. …
The ability to perform competently in multiple roles, the extra-curriculars, the self-directed enrichment, the ex-company leadership – all these things make no matter. The calculus is what you got paid versus how you performed on objective criteria relative to your cohort. Nothing more. …
Here is where the change in the social contract is perhaps the most blatant. In the “destination employer” years, the employee invested in the community and its values, and the employer rewarded the loyalty of its employees through things like runway for growth (stretch roles and sponsored work innovation) and tolerance for error (valuing demonstrable learning over perfection in execution). No longer. …
