Lifeboat Foundation

Researchers at the Samsung AI Center in Moscow (Russia) have recently presented interesting work called Living portraits: they made Mona Lisa and other subjects of photos and art alive using video of real people. They presented a framework for meta-learning of adversarial generative models called “Few-Shot Adversarial Learning”.

You can read more about details in the original paper.

Here we review this great implementation of the algorithm in PyTorch. The author of this implementation is Vincent Thévenin — research worker in De Vinci Innovation Center.

September 14, 2020 — The use of artificial intelligence (AI) in radiology to aid in image interpretation tasks is evolving, but many of the old factors and concepts from the computer-aided detection (CAD) era still remain, according to a Sunday talk at the Conference on Machine Intelligence in Medical Imaging (C-MIMI).

A lot has changed as the new era of AI has emerged, such as faster computers, larger image datasets, and more advanced algorithms — including deep learning. Another thing that’s changed is the realization of additional reasons and means to incorporate AI into clinical practice, according to Maryellen Giger, PhD, of the University of Chicago. What’s more, AI is also being developed for a broader range of clinical questions, more imaging modalities, and more diseases, she said.

At the same time, many of the issues are the same as those faced in the era of CAD. There are the same clinical tasks of detection, diagnosis, and response assessment, as well as the same concern of “garbage in, garbage out,” she said. What’s more, there’s the same potential for off-label use of the software, and the same methods for statistical evaluations.

When Sartre said hell is other people, he wasn’t living through 2020. Right now, other people are the only thing between us and species collapse. Not just the people we occasionally encounter behind fugly masks—but the experts and innovators out in the world, leading the way. The 17-year-old hacker building his own coronavirus tracker. The Google AI wonk un-coding machine bias. A former IT guy helping his community thwart surveillance. There are people everywhere, in and out … See More.

The scientists, technologists, artists, and chefs who are standing between us and species collapse.

TLDR: Scroll down to Conclusions.

Elon Musk has recently unveiled his company’s first Neuralink device implanted in an experimental animal — a pig.

Continue reading “Elon Musk’s Neuralink — The Dark Side” »

From the Merriam-Webster dictionary:

Avatar derives from a Sanskrit word meaning “descent,” and when it first appeared in English in the late 18th century, it referred to the descent of a deity to the earth — typically, the incarnation in earthly form of Vishnu or another Hindu deity. It later came to refer to any incarnation in human form, and then to any embodiment (such as that of a concept or philosophy), whether or not in the form of a person. In the age of technology, avatar has developed another sense — it can now be used for the image that a person chooses as his or her “embodiment” in an electronic medium.

San Francisco-based AI research laboratory OpenAI has added another member to its popular GPT (Generative Pre-trained Transformer) family. In a new paper, OpenAI researchers introduce GPT-f, an automated prover and proof assistant for the Metamath formalization language.

While artificial neural networks have made considerable advances in computer vision, natural language processing, robotics and so on, OpenAI believes they also have potential in the relatively underexplored area of reasoning tasks. The new research explores this potential by applying a transformer language model to automated theorem proving.

Automated theorem proving tends to require general and flexible reasoning to efficiently check the correctness of proofs. This makes it an appealing domain for checking the reasoning capabilities of language models and for the study of reasoning in general. The ability to verify proofs also helps researchers as it enables the automatic generation of new problems that can be used as training data.

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In the last video in this series we discussed the differences between deep learning and machine learning, how and when the field of deep learning was officially born, and it’s rise to mainstream popularity. The focus of this video then will be on artificial neural networks, more specifically – their structure.

Continue reading “How Neural Networks Work (From The Brain To Artificial Intelligence)” »

In 1998, an engineer in Sony’s computer science lab in Japan filmed a lost-looking robot moving trepidatiously around an enclosure. The robot was tasked with two objectives: avoid obstacles and find objects in the pen. It was able to do so…

The National Science Foundation has awarded a highly competitive $5 million grant to Vanderbilt University that greatly expands a School of Engineering-led project for creating novel AI technology and tools and platforms that train and support individuals with Autism Spectrum Disorder in the workplace.

The significant federal investment follows a successful $1 million, nine-month pilot grant to the same team that forged partnerships with employers and other stakeholders and produced viable prototypes through immersive, human-centric design. The multi-university team includes Yale University, Cornell University, Georgia Institute of Technology and Vanderbilt University Medical Center as academic partners.

Continue reading “Vanderbilt leads $5 million project to revolutionize neurodiverse employment through AI” »