Lifeboat Foundation

When you hear the word “cyborg,” scenes from the 1980s films RoboCop or The Terminator might spring to mind. But the futuristic characters made famous in those films may no longer be mere science fiction. We are at the advent of an era where digital technology and artificial intelligence are moving more deeply into our human biological sphere. Humans are already able to control a robotic arm with their minds. Cyborgs —humans whose skills and abilities exceed those of others because of electrical or mechanical elements built into the body —are already among us.

But innovators are pushing the human-machine boundary even further. While prosthetic limbs are tied in with a person’s nervous system, future blends of biology and technology may be seen in computers that are wired into our brains.

Our ability to technologically enhance our physical capabilities—the “hardware” of our human systems, you could say—will likely reshape our social world. Will these changes bring new forms of dominance and exploitation? Will unaltered humans be subjected to a permanent underclass or left behind altogether? And what will it mean to be human—or will some of us be more than human?

AI (artificial intelligence) opens up a world of possibilities for application developers. By taking advantage of machine learning or deep learning, you could produce far better user profiles, personalization, and recommendations, or incorporate smarter search, a voice interface, or intelligent assistance, or improve your app any number of other ways. You could even build applications that see, hear, and react to situations you never anticipated.

Which programming language should you learn to plumb the depths of AI? You’ll want a language with many good machine learning and deep learning libraries, of course. It should also feature good runtime performance, good tools support, a large community of programmers, and a healthy ecosystem of supporting packages. That’s a long list of requirements, but there are still plenty of good options.

Elon Musk’s Neuralink reminds us of what is possible in the age of Artificial Intelligence — do you know who you are?

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The mentioned blog post on the gradients and its notebook are available here:
Post: https://www.wandb.com/articles/exploring-gradients
Notebook: https://colab.research.google.com/drive/1bsoWY8g0DkxAzVEXRigrdqRZlq44QwmQ

Continue reading “OpenAI’s Robot Hand Won’t Stop Rotating The Rubik’s Cube 👋” »

Laborious calculations which once took an hour can be completed in a fraction of a second with machine learning, according to scientists.

Forrester projects that artificial intelligence will severely impact jobs like cubicle workers, location-based workers, and loan processors.

Named “speech by crowd,” the Debater A.I. groups and summarizes large numbers of disparate arguments made by individuals.

This week at MIT, academics and industry officials compared notes, studies, and predictions about AI and the future of work. During the discussions, an insurance company executive shared details about one AI program that rolled out at his firm earlier this year. A chatbot the company introduced, the executive said, now handles 150,000 calls per month.

Later in the day, a panelist—David Fanning, founder of PBS’s Frontline—remarked that this statistic is emblematic of broader fears he saw when reporting a new Frontline documentary about AI. “People are scared,” Fanning said of the public’s AI anxiety.

Fanning was part of a daylong symposium about AI’s economic consequences—good, bad, and otherwise— convened by MIT’s Task Force on the Work of the Future.

The news: Alphabet X, the company’s early research and development division, has unveiled the Everyday Robot project, whose aim is to develop a “general-purpose learning robot.” The idea is to equip robots with cameras and complex machine-learning software, letting them observe the world around them and learn from it without needing to be taught every potential situation they may encounter.

For now: The early prototype robots are learning how to sort trash. It sounds mundane, but it’s tough to get robots to identify different types of objects, and then how to grasp them. Alphabet X claims that its robots are currently putting less than 5% of trash in the wrong place, versus an error rate of 20% among the office’s humans.

The big idea: Robots are expensive and confined to performing very specific, specialized tasks. Getting robots that can operate safely and autonomously in messy, complex human environments like homes or offices is one of the biggest challenges in robotics right now.