Lifeboat Foundation

Just as car created job for drivers, computer created job for data entry operator.robots will also create new types of high paying jobs.

For decades, the arrival of robots in the workplace has been a source of public anxiety over fears that they will replace workers and create unemployment.

Now that more sophisticated and humanoid robots are actually emerging, the picture is changing, with some seeing robots as promising teammates rather than unwelcome competitors.

Continue reading “New robots in Europe can be workers’ best friends” »

Vitaly Vanchurin, physicist and cosmologist at the University of Minnesota Duluth speaks to Luis Razo Bravo of EISM about the world as a neural network, machine learning, theories of everything, interpretations of quantum mechanics and long-term human survival.

Timestamp of the conversation:

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Image and video editing are two of the most popular applications for computer users. With the advent of Machine Learning (ML) and Deep Learning (DL), image and video editing have been progressively studied through several neural network architectures. Until very recently, most DL models for image and video editing were supervised and, more specifically, required the training data to contain pairs of input and output data to be used for learning the details of the desired transformation. Lately, end-to-end learning frameworks have been proposed, which require as input only a single image to learn the mapping to the desired edited output.

Video matting is a specific task belonging to video editing. The term “matting ” dates back to the 19th century when glass plates of matte paint were set in front of a camera during filming to create the illusion of an environment that was not present at the filming location. Nowadays, the composition of multiple digital images follows similar proceedings. A composite formula is exploited to shade the intensity of the foreground and background of each image, expressed as a linear combination of the two components.

Although really powerful, this process has some limitations. It requires an unambiguous factorization of the image into foreground and background layers, which are then assumed to be independently treatable. In some situations like video matting, hence a sequence of temporal-and spatial-dependent frames, the layers decomposition becomes a complex task.

The methods currently used to correct systematic issues in NLP models are either fragile or time-consuming and prone to shortcuts. Humans, on the other hand, frequently reprimand one another using natural language. This inspired recent research on natural language patches, which are declarative statements that enable developers to deliver corrective feedback at the appropriate level of abstraction by either modifying the model or adding information the model may be missing.

Instead of relying solely on labeled examples, there is a growing body of research on using language to provide instructions, supervision, and even inductive biases to models, such as building neural representations from language descriptions (Andreas et al., 2018; Murty et al., 2020; Mu et al., 2020), or language-based zero-shot learning (Brown et al., 2020; Hanjie et al., 2022; Chen et al., 2021). For corrective purposes, when the user interacts with an existing model to enhance it, language has yet to be properly utilized.

The neural language patching model has two heads: a gating head that determines if a patch should be applied and an interpreter head that forecasts results based on the information in the patch. The model is trained in two steps: first on a tagged dataset and then through task-specific fine-tuning. A set of patch templates are used to create patches and synthetic labeled samples during the second fine-tuning step.

This video is about, Boost Your Brain 150% With An AI Chip From Elon Musk! In 2023.

Remember the movie Limitless, now you can do it with a computer chip.

Continue reading “Boost Your Brain 150% With An AI Chip | From Elon Musk! in 2023” »

Researchers at the Electronics and Telecommunications Research Institute (ETRI) in Korea have recently developed a deep learning-based model that could help to produce engaging nonverbal social behaviors, such as hugging or shaking someone’s hand, in robots. Their model, presented in a paper pre-published on arXiv, can actively learn new context-appropriate social behaviors by observing interactions among humans.

“Deep learning techniques have produced interesting results in areas such as computer vision and natural language understanding,” Woo-Ri Ko, one of the researchers who carried out the study, told TechXplore. “We set out to apply deep learning to social robotics, specifically by allowing robots to learn social behavior from human-human interactions on their own. Our method requires no prior knowledge of human behavior models, which are usually costly and time-consuming to implement.”

The artificial neural network (ANN)-based architecture developed by Ko and his colleagues combines the Seq2Seq (sequence-to-sequence) model introduced by Google researchers in 2014 with generative adversarial networks (GANs). The new architecture was trained on the AIR-Act2Act dataset, a collection of 5,000 human-human interactions occurring in 10 different scenarios.

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When it comes to artificial intelligence (AI), the past year has been aspirational, but ultimately unsuccessful, says Athina Kanioura, who was named PepsiCo’s first chief strategy and transformation officer in September 2020. But she is optimistic about 2023.

“Think of how we started with the metaverse and the use of AI, suddenly it crumbled into pieces,” she told VentureBeat. “In AI, we tend to see what doesn’t work the first time, then we lose hope — but I think 2023 should be a year of hope and focus for AI.”

Online videos are a vast and untapped source of training data—and OpenAI says it has a new way to use it.

To be a sheep is to blindly follow the crowd. But is a sheep’s sheepiness really enough to make an entire flock walk around in a circle non-stop for days on end?

That’s a mystery the internet has pondered for about a week now and solving it has proved more difficult than you’d expect.

Continue reading “Eerie Video of Bizarre Sheep Phenomenon Has The World Running in Circles” »