Lifeboat Foundation

Stable Diffusion is a powerful open-source image AI that competes with OpenAI’s DALL-E 2. The AI training was probably rather cheap in comparison.

Anyone interested can download the model of the open-source image AI Stable Diffusion for free from Github and run it locally on a compatible graphics card. This must be reasonably powerful (at least 5.1 GB VRAM), but you don’t need a high-end computer.

In addition to the local, free version, the Stable Diffusion team also offers access via a web interface. For about $12, you get roughly 1,000 image prompts.

Researchers at Oxford University’s Department of Computer Science, in collaboration with colleagues from Bogazici University, Turkey, have developed a novel artificial intelligence (AI) system to enable autonomous vehicles (AVs) achieve safer and more reliable navigation capability, especially under adverse weather conditions and GPS-denied driving scenarios. The results have been published today in Nature Machine Intelligence.

Yasin Almalioglu, who completed the research as part of his DPhil in the Department of Computer Science, said, “The difficulty for AVs to achieve precise positioning during challenging adverse weather is a major reason why these have been limited to relatively small-scale trials up to now. For instance, weather such as rain or snow may cause an AV to detect itself in the wrong lane before a turn, or to stop too late at an intersection because of imprecise positioning.”

To overcome this problem, Almalioglu and his colleagues developed a novel, self-supervised deep learning model for ego-motion estimation, a crucial component of an AV’s driving system that estimates the car’s moving position relative to objects observed from the car itself. The model brought together richly-detailed information from visual sensors (which can be disrupted by adverse conditions) with data from weather-immune sources (such as radar), so that the benefits of each can be used under different weather conditions.

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The World Robot Conference 2022 was held in Beijing. Due to the ongoing offline pandemic, only Chinese robotics companies were represented, and the rest of the world joined in the online format. But the Chinese booths were also, as always, a lot to see. We gathered for you all the most interesting things from the largest robot exhibition in one video!

Continue reading “WRC 2022 — China’s largest robot exhibition | Robots and technologies at the exhibition in China” »

The very first industrial revolution historically kicked off with the introduction of steam-and water-powered technology. We have come a long way since then, with the current fourth industrial revolution, or Industry 4.0, being focused on utilizing new technology to boost industrial efficiency.

Some of these technologies include the internet of things (IoT), cloud computing, cyber-physical systems, and artificial intelligence (AI). AI is the key driver of Industry 4.0, automating intelligent machines to self-monitor, interpret, diagnose, and analyze all by themselves. AI methods, such as machine learning (ML), deep learning (DL), natural language processing (NLP), and computer vision (CV), help industries forecast their maintenance needs and cut down on downtime.

However, to ensure the smooth, stable deployment and integration of AI-based systems, the actions and results of these systems must be made comprehensible, or, in other words, “explainable” to experts. In this regard, explainable AI (XAI) focuses on developing algorithms that produce human-understandable results made by AI-based systems. Thus, XAI deployment is useful in Industry 4.0.

Researchers have found a better way to reduce gender bias in natural language processing models while preserving vital information about the meanings of words, according to a recent study that could be a key step toward addressing the issue of human biases creeping into artificial intelligence.

While a computer itself is an unbiased machine, much of the data and programming that flows through computers is generated by humans. This can be a problem when conscious or unconscious human biases end up being reflected in the text samples AI models use to analyze and “understand” language.

Computers aren’t immediately able to understand text, explains Lei Ding, first author on the study and graduate student in the Department of Mathematical and Statistical Sciences. They need words to be converted into a set of numbers to understand them—a process called word embedding.

In 1998 I was exposed to the term “disruptive innovation” for the first time. I read a wonderful book, “The Innovator’s Dilemma” by Clayton Christensen, where I learned the difference between incremental innovation and disruptive innovation. He analyzed the hard drive industry and showed that while many companies were trying to increase the capacity of the drives, other companies changed the form factor and made the drives smaller. This resulted in disruptive progress in the industry. We also recently witnessed dramatic advances in artificial intelligence (AI), where in 2013/2014 AI systems started outperforming humans in image recognition.

Identifying novel targets and designing novel molecules is not the only way to innovate in the biopharmaceutical industry. Sometimes, innovation in delivery systems for well-know and established therapeutics may be just as disruptive as the new targeted medicine.

Nuro, a Softbank-backed developer of street-legal autonomous, electric delivery vehicles, has struck a long-term partnership with Uber to use its toaster-shaped micro-vans to haul food orders, groceries and other goods to customers in Silicon Valley and Houston using the Uber Eats service starting this year.

People using the Uber Eats app in Houston and Mountain View, California (where Nuro is based) will be able to order deliveries using the new autonomous service this fall, with plans to expand the program to other parts of the San Francisco Bay Area in the months ahead, the companies said.

The SoftBank-backed developer of street-legal autonomous, electric vehicles, has a long-term partnership with Uber to use its toaster-shaped micro-vans to haul food orders, groceries and other goods in Silicon Valley and Houston.

Continue reading “Uber Taps Nuro’s Street-Legal Robots For Food Deliveries” »

Deepfakes are scarily simple to create. But will this technology create a reality of “alternative facts” where “truth goes to die”? No. Deepfakes are a technology, and more widespread abuse is expected with more widespread availability. Over time, we will adopt better transparency, better detection, and, most importantly, each of us users will become more aware and thus better equipped to fight the abuse of Deepfakes.

What are deepfakes?

Continue reading “Deepfakes — The Danger Of Artificial Intelligence That We Will Learn To Manage Better” »

New artificial intelligence and space radar technology has uncovered scores of vessels hiding outside North Korea’s largest port, laying bare the sheer number of ships looking to cover their tracks in unprecedented detail.

The finding comes courtesy of Global Fishing Watch’s (GFW) synthetic aperture radar (SAR) data, which reveals hundreds of vessels, many of them cargo ships, outside of Nampho in recent weeks despite not broadcasting over traditional maritime channels.

While vessels entering North Korean waters often switch off their automatic identification system (AIS) transponders to avoid detection by sanctions monitors, open-source satellite.