Lifeboat Foundation

According to the report, the global AI market will be worth US$284.4 billion by 2026.

Today, the artificial intelligence platform has become a way for computer systems to perform tasks like human intelligence including decision-making and speech recognition. Globally, problem-solving, social intelligence, and general intelligence are being achieved with the help of the artificial intelligence platform. Moreover, rising high-level computer languages are helping various industries to work efficiently on the artificial intelligence platform.

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AI startups can rake in investment by hiding how their systems are powered by humans. But such secrecy can be exploitative.

The nifty app CamFind has come a long way with its artificial intelligence. It uses image recognition to identify an object when you point your smartphone camera at it. But back in 2015 its algorithms were less advanced: The app mostly used contract workers in the Philippines to quickly type what they saw through a user’s phone camera, CamFind’s co-founder confirmed to me recently. You wouldn’t have guessed that from a press release it put out that year which touted industry-leading “deep learning technology,” but didn’t mention any human labelers.

The practice of hiding human input in AI systems still remains an open secret among those who work in machine learning and AI. A 2019 analysis of tech startups in Europe by London-based MMC Ventures even found that 40% of purported AI startups showed no evidence of actually using artificial intelligence in their products.

Artificial intelligence (AI) is a force for good that could play a huge part in solving problems such as climate change. Left unchecked, however, it could undermine democracy, lead to massive social problems and be harnessed for chilling military or terrorist attacks.

That’s the view of Martin Ford, futurist and author of Rule of the Robots, his follow-up to Rise of the Robots, the 2015 New York Times bestseller and winner of the Financial Times/McKinsey Business Book of the Year, which focused on how AI would destroy jobs.

In the new book, Ford, a sci-fi fan, presents two broad movie-based scenarios.

In 2,019 a survey from the Center for Digital Government (CDG), the National Association of Chief Information Officers and IBM found that just 13 percent of state governments reported using artificial intelligence in some non-core part of their operations. Three years later, the same survey yielded very different results.

At the NASCIO annual confference in Seattle this week, Joe Morris with CDG presented some of the study’s 2021 findings, and it was clear that the COVID-19 pandemic changed how state and local government are thinking about AI. This year, 60 percent of respondents reported AI is currently in use in their enterprise; 6.7 percent said the tech is widely used across the state, up from just 1 percent in 2019.

Continue reading “Artificial Intelligence Can Improve How People Use Tech” »

The is the world’s first biotech fine dust filter for urban spaces. Integrated moss modules bring the forest into the city and ensure that the air is verifiably and noticeably clean. We combine the natural filtering power of mosses with smart IoT technology. With an automated irrigation and ventilation system, the can clean and cool the surrounding air. Independent studies show that up to 82% of the fine dust in the air is filtered directly through the moss and the air is also cooled by up to 2.5 ° C.

Ray Kurzweil — Singularitarian Immortalist, Director of Engineering at Google, famous inventor, author of How to Create a Mind http://GF2045.com/speakers/.

A world-class prolific inventor and leading futurist author, “the restless genius” (Wall Street Journal) points to 2045 for the technological singularity when A.I. will surpass human intelligence in his New York Times best seller The Singularity is Near, Amazon’s #1 book in science and philosophy.

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Today’s world is one big maze, connected by layers of concrete and asphalt that afford us the luxury of navigation by vehicle. For many of our road-related advancements — GPS lets us fire fewer neurons thanks to map apps, cameras alert us to potentially costly scrapes and scratches, and electric autonomous cars have lower fuel costs — our safety measures haven’t quite caught up. We still rely on a steady diet of traffic signals, trust, and the steel surrounding us to safely get from point A to point B.

“If people can use the risk map to identify potentially high-risk road segments, they can take action in advance to reduce the risk of trips they take. Apps like Waze and Apple Maps have incident feature tools, but we’re trying to get ahead of the crashes — before they happen,” says He.

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Continue reading “Deep learning helps predict traffic crashes before they happen” »

From 2019: Long before we can certify that neural networks can drive cars, we need to prove that they can multiply.

This work is still in its very early stages, but in the last year researchers have produced several papers which elaborate the relationship between form and function in neural networks. The work takes neural networks all the way down to their foundations. It shows that long before you can certify that neural networks can drive cars, you need to prove that they can multiply.

The Best Brain Recipe

Continue reading “Foundations Built for a General Theory of Neural Networks” »

Despite the continued progress that the state of the art in machine learning and artificial intelligence (AI) has been able to achieve, one thing that still sets the human brain apart — and those of some other animals — is its ability to connect the dots and infer information that supports problem-solving in situations that are inherently uncertain. It does this remarkably well despite sparse, incomplete, and almost always less than perfect data. In contrast, machines have a very difficult time inferring new insights and generalizing beyond what they have been explicitly trained on or exposed to.

How the brain evolved to achieve these abilities and what are the underlying ‘algorithms’ that enable them to remain poorly understood. The development and investigation of mathematical models will lead to a deep understanding of what the brain is doing and how are not mature and remain a very active area of research.

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Continue reading “Sorry AI — The Brain Is Still The Best Inference Machine Out There” »