Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: robotics/AI

Within the next 10 years, what we eat and how it’s grown will be fundamentally transformed.

And converging exponential technologies—from materials science to AI-driven digital agriculture—are not slowing down. Today’s breakthroughs will soon allow our planet to boost its food production by nearly 70 percent, using a fraction of the real estate and resources, to feed 9 billion by mid-century.

What you consume, how it was grown, and how it will end up in your stomach will all ride the wave of converging exponentials, revolutionizing the most basic of human needs.

[Note: This article is an excerpt from my next book The Future Is Faster Than You Think, co-authored with Steven Kotler, to be released January 28th, 2020.]

Printing Food

Read more | >

The founder of robot maker Boston Dynamics, Marc Raibert, is moving from CEO to chairman as the company prepares for a “new stage of growth.” The firm, well-known for its robotic creations, has been researching and developing its technology for decades, and it started leasing its first commercial robot, Spot, in 2019.

Read more | >

Evolutionary cyberneticist and digital philosopher Alex M. Vikoulov, author of The Syntellect Hypothesis, is interviewed by Agah Bahari, host and producer of NeoHuman podcast.

On this recent podcast, Alex Vikoulov, author of The Syntellect Hypothesis, is interviewed by NeoHuman podcaster Agah Bahari. Topics include evolutionary cybernetics, computational physics, consciousness, the simulation theory, the transcension hypothesis, the Global mind, AGI, VR, AR, psychedelics, technological singularities, transhumanism, Fermi Paradox, Digital Physics, objective reality, philosophy of mind, the extended mind hypothesis, absolute idealism, physics of time, the Omega Point cosmology, mind-uploading, synthetic telepathy, and more.

Watch a short intro here ↴.

Read more | >

Machines, especially through the power of AI, will surpass humans in Intelligence, effectiveness, and functionality.

Though there are some areas where humans will hold the dominance; mostly areas that require feeling and emotion.

But overall, machines will have capabilities that far surpass even the most Intelligent of humans.

Read more | >

CS230 | Deep Learning

CS230 Deep Learning Lectures | Stanford Engineering

CS221 | Artificial Intelligence

CS221: Artificial Intelligence: Principles and Techniques | Stanford University

CS224N | Natural Language Processing (NLP)

Stanford CS224N: NLP with Deep Learning | Winter 2019

CS224N | Natural Language Processing with Deep Learning.

Deep Learning is one of the most highly sought after skills in AI. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.



Read more | >

Circa 2017

Chess isn’t an easy game, by human standards. But for an artificial intelligence powered by a formidable, almost alien mindset, the trivial diversion can be mastered in a few spare hours.

In a new paper, Google researchers detail how their latest AI evolution, AlphaZero, developed “superhuman performance” in chess, taking just four hours to learn the rules before obliterating the world champion chess program, Stockfish.

In other words, all of humanity’s chess knowledge – and beyond – was absorbed and surpassed by an AI in about as long as it takes to drive from New York City to Washington, DC.

Read more | >

Circa 2017

Thousands of years of human knowledge has been learned and surpassed by the world’s smartest computer in just 40 days, a breakthrough hailed as one of the greatest advances ever in artificial intelligence.

Google DeepMind amazed the world last year when its AI programme AlphaGo beat world champion Lee Sedol at Go, an ancient and complex game of strategy and intuition which many believed could never be cracked by a machine.

AlphaGo was so effective because it had been programmed with millions of moves of past masters, and could predict its own chances of winning, adjusting its game-plan accordingly.

Read more | >