Lifeboat Foundation

We’re one step closer to channeling our inner Harry Potters.

A Silicon Valley lobby enrolled elite academia to avoid legal restrictions on artificial intelligence.

This is the fourth instalment in a four-part series examining the brewing US-China war over the development and deployment of artificial intelligence technology.

China has had success with AI and surveillance, but when it comes to social issues such as education, health care and agriculture, there is still a ways to go.

Continue reading “China dreams of becoming an AI utopia – here’s the reality” »

So, what’s it mean for us, the humans?

Without one, it risks missing out on all the technology’s benefits—and falling behind rivals such as China.

The field of artificial intelligence has never been the subject of more attention and analysis than it is today. Almost every week, it seems, a new bestselling book comes out examining the technology, business or ethics of AI.

Yet few of the topics and debates at the center of today’s AI discourse are new. While not always recognized by commentators, artificial intelligence as a serious academic discipline dates back to the 1950s. For well over half a century, many of the world’s leading minds have devoted themselves to the pursuit of machine intelligence and have grappled with what it would mean to succeed in that pursuit.

Much of the public discourse around AI in 2019 has been anticipated—and influenced—by AI thought leaders going back decades.

Times reporters traveled to Russia, Switzerland, California and Washington, D.C., talking to experts in the commercial tech, military and A.I. communities. Below are some key points and analysis, along with extras from the documentary.

“Killing in the Age of Algorithms” is a New York Times documentary examining the future of artificial intelligence and warfare.

An international joint research team led by NIMS succeeded in fabricating a neuromorphic network composed of numerous metallic nanowires. Using this network, the team was able to generate electrical characteristics similar to those associated with higher-order brain functions unique to humans, such as memorization, learning, forgetting, becoming alert and returning to calm. The team then clarified the mechanisms that induced these electrical characteristics.

The development of artificial intelligence (AI) techniques has been rapidly advancing in recent years and has begun impacting our lives in various ways. Although AI processes information in a manner similar to the human brain, the mechanisms by which human brains operate are still largely unknown. Fundamental brain components, such as neurons and the junctions between them (synapses), have been studied in detail. However, many questions concerning the brain as a collective whole need to be answered. For example, we still do not fully understand how the brain performs such functions as memorization, learning, and forgetting, and how the brain becomes alert and returns to calm. In addition, live brains are difficult to manipulate in experimental research. For these reasons, the brain remains a mysterious organ.

Nowadays, there is an imperative need for novel computational concepts to manage the enormous data volume produced by contemporary information technologies. The inherent capability of the brain to cope with these kinds of signals constitutes the most efficient computational paradigm for biomimicry.

Representing neuronal processing with software-based artificial neural networks is a popular approach with tremendous impacts on everyday life; a field commonly known as machine learning or artificial intelligence. This approach relies on executing algorithms that represent neural networks on a traditional von Neumann computer architecture.

An alternative approach is the direct emulation of the workings of the brain with actual electronic devices/circuits. This emulation of the brain at the hardware-based level is not only necessary for overcoming limitations of conventional silicon technology based on the traditional von Neumann architecture in terms of scaling and efficiency, but in understanding brain function through reverse engineering. This hardware-based approach constitutes the main scope of neuromorphic devices/computing.