Billionaire entrepreneur Mark Cuban’s prediction for the future of the workforce includes more robots and less human workers.
“We’re about to go into a period with artificial intelligence, machine learning, deep learning, those things where we literally are going to see a change in the nature of employment,” Cuban said in an interview with CNN’s Jake Tapper.
In that same interview, he criticized President Trump’s leadership skills before calling Trump “technologically illiterate.”
A few ideas on self-awareness and self-aware AIs.
I’ve always been a fan of androids as intended in Star Trek. More generally, I think the idea of an artificial intelligence with whom you can talk and to whom you can teach things is really cool. I admit it is just a little bit weird that I find the idea of teaching things to small children absolutely unattractive while finding thrilling the idea of doing the same to a machine, but that’s just the way it is for me. (I suppose the fact a machine is unlikely to cry during the night and need to have its diaper changed every few hours might well be a factor at play here.)
Improvements in the field of AI are pretty much commonplace these days, though we’re not yet at the point where we could be talking to a machine in natural language and be unable to tell the difference with a human. I used to take for granted that, one day, we would have androids who are self-aware and have emotions, exactly like people, with all the advantages of being a machine—such as mental multitasking, large computational power, and more efficient memory. While I still like the idea, nowadays I wonder if it is actually a feasible or sensible one.
Don’t worry—I’m not going to give you a sermon on the ‘dangers’ of AI or anything like that. That’s the opposite of my stand on the matter. I’m not making a moral argument either: Assuming you can build an android that has the entire spectrum of human emotions, this is morally speaking no different from having a child. You don’t (and can’t) ask the child beforehand if it wants to be born, or if it is ready to go through the emotional rollercoaster that is life; generally, you make a child because you want to, so it is in a way a rather selfish act. (Sorry, I am not of the school of thought according to which you’re ‘giving life to someone else’. Before you make them, there’s no one to give anything to. You’re not doing anyone a favour, certainly not to your yet-to-be-conceived potential baby.) Similarly, building a human-like android is something you would do just because you can and because you want to.
In the next few years Space X and Virgin Galactic will be sending tourists into orbit and during a brainstorming session for last years SpaceApps Challenge we brainstormed some possible applications for Space Robots.
Last night on the International Space Station Astronaut Thomas Pesquet showed the SPHERES robots testing software that will be used to clean up space junk. Smaller versions of these robots could be developed with multiple ports for a Go Pro Camera linked to a SmartWatch app for Space Selfies or for a Virtual Reality 360 degree recording for the Tourists of their trip. Having wireframed for the Samsung Gear Watch App to be used on the International Space Station and with the advances in technology its easy to see how Siri/ Cortana/ Alexa could be incorporated into a SPHERE type Astromechanical robot to advise of Comms, Timetable scheduling and the other apps that are required for day to day use on the International Space Station. Fun applications that we came up with for the Space Apps challenge was a version of Space- Quidditch and Jedi Training for a SPHERE robot fitted with mini propulsion tanks.
The Annual SpaceApps Challenge is a great way of streching your tech skills and learning new ones. If you would like to host a SpaceApps event the deadline is today:
This is nowhere near the power of the biggest systems, but still allows us to participate in research and development powered by supercomputer.
The idea that a computer could deliver an increase in life expectancy arises for a number of reasons, Prof Desplat says. Major gains are expected from the emergence of personalised medicine, care specifically tailored to match your genetic make-up. This will be driven in the not too distant future by “deep artificial intelligence learning” run on a supercomputer. These will also deliver faster more accurate early diagnosis, he says.
These computers are used in a variety of ways, from weather forecasting and climate modelling to energy usage modelling, statistical processing and seismic analysis when prospecting for oil and gas.
Right now it’s easiest to think about an artificial intelligence algorithm as a specific tool, like a hammer. A hammer is really good at hitting things, but when you need a saw to cut something in half, it’s back to the toolbox. Need a face recognized? Train an facial recognition algorithm, but don’t ask it to recognize cows.
Alphabet’s AI research arm, DeepMind, is trying to change that idea with a new algorithm that can learn more than one skill. Having algorithms that can learn multiple skills could make it far easier to add new languages to translators, remove bias from image recognition systems, or even have algorithms use existing knowledge to solve new complex problems. The research published in Proceedings of the National Academy of Sciences this week is preliminary, as it only tests the algorithm on playing different Atari games, but this research shows multi-purpose algorithms are actually possible.
The problem DeepMind’s research tackles is called “catastrophic forgetting,” the company writes. If you train an algorithm to recognize faces and then try to train it again to recognize cows, it will forget faces to make room for all the cow-knowledge. Modern artificial neural networks use millions of mathematic equations to calculate patterns in data, which could be the pixels that make a face or the series of words that make a sentence. These equations are connected in various ways, and are so dependent on some equations that they’ll begin to fail when even slightly tweaked for a different task. DeepMind’s new algorithm identifies and protects the equations most important for carrying out the original task, while letting the less-important ones be overwritten.
Adam Savage gets up close with the one-of-a-kind 3D-printed endoskeleton Weta Workshop made for the upcoming Ghost in the Shell. Chatting with Weta Workshop technician Jared Haley in the studio’s 3D modeling room, Adam learns about the experimentation and prototyping necessary to make this gobsmackingly beautiful prop, which is made of several hundred individual pieces!
Shot and edited by Joey Fameli
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Massive and complete automation could enable industrializtion of the moon and space. By using some larger human colonies along with the robots then it would be more robust and less dependent on perfect automation.
Advances in robotics and additive manufacturing have become game-changing for the prospects of space industry. It has become feasible to bootstrap a self-sustaining, self-expanding industry at reasonably low cost. Simple modeling was developed to identify the main parameters of successful bootstrapping. This indicates that bootstrapping can be achieved with as little as 12 metric tons (MT) landed on the Moon during a period of about 20 years. The equipment will be teleoperated and then transitioned to full autonomy so the industry can spread to the asteroid belt and beyond. The strategy begins with a sub-replicating system and evolves it toward full self-sustainability (full closure) via an in situ technology spiral. The industry grows exponentially due to the free real estate, energy, and material resources of space. The mass of industrial assets at the end of bootstrapping will be 156 MT with 60 humanoid robots, or as high as 40,000MT with as many as 100,000 humanoid robots if faster manufacturing is supported by launching a total of 41 MT to the Moon. Within another few decades with no further investment, it can have millions of times the industrial capacity of the United States.
