Lifeboat Foundation

Sensing systems are becoming prevalent in many areas of our lives, such as in ambient-assisted health care, autonomous vehicles, and touchless human-computer interaction. However, these systems often lack intelligence: they tend to gather all available information, even if it is not relevant. This can lead not only to privacy infringements but also to wasted time, energy, and computational resources during data processing.

To address this problem, researchers from the French CNRS came up with a concept for intelligent electromagnetic sensing, which uses machine-learning techniques to generate learned illumination patterns so as to pre-select relevant details during the measurement process. A programmable metasurface is configured to generate the learned patterns, performing high-accuracy sensing (e.g., posture recognition) with a remarkably reduced number of measurements.

But measurement processes in realistic applications are inevitably subject to a variety of noise. Noise fundamentally accompanies any measurement. The signal-to–noise ratio can be particularly low in indoor environments where the radiated electromagnetic signals must be kept weak.

Philosophy of the future is needed.

The world is chancing fast. (AI, genome sequencing, demographics changes…)

Continue reading “A framework to a future political and economical system” »

Year 2021 face_with_colon_three

In recent years, the use of deep learning in language models has gained much attention. Some research projects claim that they can generate text that can be interpreted as human writing, enabling new possibilities in many application areas. Among the different areas related to language processing, one of the most notable in applying this type of modeling is programming languages. For years, the machine learning community has been research ing this software engineering area, pursuing goals like applying different approaches to auto-complete, generate, fix, or evaluate code programmed by humans. Considering the increasing popularity of the deep learning-enabled language models approach, we found a lack of empirical papers that compare different deep learning architectures to create and use language models based on programming code.

2022 has been a crazy year for Machine Learning and AI Research. Big Tech Companies have released a lot of amazing libraries that will benefit developers a lot. We have seen some great research papers, both from Big Tech Companies and Smaller Groups. Amongst my favorites was the research into self-assembling AI, which shows the potential of exploring alternative modes of AI.

Aerones’ robot decreases downtime by almost ten times and increases annual production by 12 percent.

Although wind turbine towers create clean electricity, they frequently leak oil, damaging the blades, increasing wind resistance, and even polluting the ground below.

Continue reading “These robots inspect and repair wind turbines, so humans don’t have to” »

A science vlogger has given snakes their legs back — and the fact that led him to his mad invention is pretty outrageous.

Robot vacuum companies say your images are safe, but a sprawling global supply chain for data from our devices creates risk.

Robots benefitted from advances in artificial intelligence, soft gels and tinier electronic sensors.

As we look back at VentureBeat’s top AI stories of the year, it’s clear that the industry’s advances — including, notably, in generative AI — are vast and powerful, but only the beginning of what is to come.

For example, OpenAI, the artificial intelligence research lab behind AI tools that exploded this year, including DALL-E 2 and ChatGPT, debuted buzzed-about advancements that drew attention from the general public as well as the tech industry. DALL-E’s text-to-image generation and ChatGPT’s new capabilities to produce high-quality, long-form content made creatives question whether they will soon be out of a job — and who owns the content these tools are creating anyway?

Meanwhile, the next iteration of advancements may not be far off for OpenAI. This fall, Ray, the machine learning technology behind OpenAI’s large-scale operations, debuted its next milestone: Ray 2.0. The update will operate as a runtime layer and is designed to simplify the building and management of large AI workloads, which will allow companies like OpenAI to make even greater strides in 2023.