Lifeboat Foundation

Over the last decade, military theorists and authors in the fields of future warfare and strategy have examined in detail the potential impacts of an ongoing revolution in information technology. There has been a particular focus on the impacts of automation and artificial intelligence on military and national security affairs. This attention on silicon-based disruption has nonetheless meant that sufficient attention may not have been paid to other equally profound technological developments. One of those developments is the field of biotechnology.

There have been some breathtaking achievements in the biological realm over the last decade. Human genome sequencing has progressed from a multi-year and multi-billion dollar undertaking to a much cheaper and quicker process, far outstripping Moore’s Law. Just as those concerned with national security affairs must monitor disruptive silicon-based technologies, leaders must also be literate in the key biological issues likely to impact the future security of nations. One of the most significant matters in biotechnology is that of human augmentation and whether nations should augment military personnel to stay at the leading edge of capability.

Military institutions will continue to seek competitive advantage over potential adversaries. While this is most obvious in the procurement of advanced platforms, human biotechnological advancement is gaining more attention. As a 2017 CSIS report on the Third Offset found most new technological advances will provide only a temporary advantage, assessed to be no more than five years. In this environment, some military institutions may view the newer field of human augmentation as a more significant source of a future competitive edge.

Continue reading “Biotechnology and Human Augmentation” »

AI may quickly point out a corrupt official, but it is not very good at explaining the process it has gone through to reach such a conclusion.

“We just use the machine’s result as reference,” Zhang Yi, an official in a province that’s still using the software, told the SCMP. “We need to check and verify its validity. The machine cannot pick up the phone and call the person with a problem. The final decision is always made by humans.”

Algorithmic Justice

Continue reading “China Built an AI to Detect Corruption and Officials Shut it Down” »

Neural networks can be as unpredictable as they are powerful. Now mathematicians are beginning to reveal how a neural network’s form will influence its function.

Imagine this: a driverless car cruises around in search of passengers.

After dropping someone off, the car uses its profits for a trip to a charging station. Except for it’s initial programming, the car doesn’t need outside help to determine how to carry out its mission.

That’s one “thought experiment” brought to you by former bitcoin contributor Mike Hearn in which he describes how bitcoin could help power leaderless organizations 30-or-so years into the future.

Fusion power — the process that keeps stars like the Sun burning — holds the promise of nearly unlimited clean power. But scientists have struggled for decades to make it a practical energy source.

Now, laser scientists say a machine learning breakthrough has smashed the standing record for a fusion power yield. It doesn’t mean fusion power is practical yet, but the prestigious journal Nature called the result “remarkable” and wrote that it has “major implications” — so, at the very least, it’s another hint that the long-deferred technology is starting to come into focus.

After years of promise, AI is finally becoming useful. But what usually happens to useful technologies is that they disappear. We forget about the things that just work, and we shouldn’t let that happen to AI. Any technology destined to change the world needs scrutiny, and AI, with its combination of huge imaginative presence and very real, very dangerous failings, needs that scrutiny more than most.

So, for the AI Issue at The Verge, we’re taking a closer look at some of the ways artificial intelligence and machine learning are affecting technology right now — because it’s too late to understand something after it’s changed the world.

Today, I talk not just about Exponential Technologies but also about Exponential Combinations, by combining Quantum Computing with Neural Networks we’d revolutionise both.

Facebook is working on an artificial intelligence that it hopes could one day detect people’s emotions based on their tone of their voice, aiming to alleviate the frustrations of modern voice speaker systems such as Alexa.

Engineers at the social network’s research labs are working out how to train its voice-controlled video chat device, Portal, to understand when a user is angry, an employee said during a tech conference in San Francisco.

The system could one day be used across Facebook Messenger and WhatsApp calls, but could lead to privacy fears about the scope of the company’s data collection.

Continue reading “Facebook wants to detect when you are angry” »

Neural networks have been used to turn words that a human has heard into intelligible, recognizable speech. It could be a step toward technology that can one day decode people’s thoughts.

A challenge: Thanks to fMRI scanning, we’ve known for decades that when people speak, or hear others, it activates specific parts of their brain. However, it’s proved hugely challenging to translate thoughts into words. A team from Columbia University has developed a system that combines deep learning with a speech synthesizer to do just that.

The study: The team temporarily placed electrodes in the brains of five people scheduled to have brain surgery for epilepsy. (People who have this procedure often have implants fitted to learn more about their seizures.) The volunteers were asked to listen to recordings of sentences, and their brain activity was used to train deep-learning-based speech recognition software. Then they listened to 40 numbers being spoken. The AI tried to decode what they had heard on the basis of their brain activity—and then spoke the results out loud in a robotic voice. What the voice synthesizer produced was understandable as the right word 75% of the time, according to volunteers who listened to it. The results were published in Scientific Reports today (and you can listen to the recordings here.)

Continue reading “Brain implants, AI, and a speech synthesizer have turned brain activity into robot words” »