Some predictions for 2045, from the Pentagon.
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I argued in my 2015 paper “Why it matters that you realize you’re in a Computer Simulation” that if our universe is indeed a computer simulation, then that particular discovery should be commonplace among the intelligent lifeforms throughout the universe. The simple calculus of it all being (a) if intelligence is in part equivalent to detecting the environment (b) the environment is a computer simulation © eventually nearly all intelligent lifeforms should discover that their environment is a computer simulation. I called this the Savvy Inevitability. In simple terms, if we’re really in a Matrix, we’re supposed to eventually figure that out.
Silicon Valley, tech culture, and most nerds the world over are familiar with the real world version of the question are we living in a Matrix? The paper that’s likely most frequently cited is Nick Bostrom’s Are you living in a Computer Simulation? Whether or not everyone agrees about certain simulation ideas, everyone does seem to have an opinion about them.
Recently, the Internet heated up over Elon Musk’s comments at a Vox event on hot tub musings of the simulation hypothesis. Even Bank of America published an analysis of the simulation hypothesis, and, according to Tad Friend in an October 10, 2016 article published in New Yorker, “two tech billionaires have gone so far as to secretly engage scientists to work on breaking us out of the simulation.”
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We have already seen the HoloLens mixed reality headset put to military use by the Israeli Defense Force for advanced battlefield planning.
Now Ukrainian company LimpidArmor has shown off a new application for the augmented reality device on the actual battlefield to improve the field of view of tank commanders without exposing them to additional risk. The technology was shown off at the Arms and Security show, held in Kiev from 11 to 14 October.
LimpidArmor’s hardware and software system uses a HoloLens integrated with a helmet and cameras mounted around the tank to give commanders a 360 degree view of their environment in both optical and thermal and makes this available in real-time.
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Story just in time for Halloween.
The prospect of artificial intelligence is scary enough for some, but Manuel Cebrian Ramos at CSIRO’s Data61 is teaching machines how to terrify humans on purpose.
Dr Cebrian and his colleagues Pinar Yanardag and Iyad Rahwan at the Massachusetts Institute of Technology have developed the Nightmare Machine.
This is an artificial intelligence algorithm that is teaching a new generation of computers not only what terrifies human beings, but also how to create new images to scare us.
Nice job Harley-Davidson when can I have my discount for my new wheels?
Lookalike modeling is a key component of lead generation, and for motorcycle brand Harley-Davidson, the tactic now goes hand in hand with artificial intelligence (AI). In March 2016, the company began working with machine learning technology provider Adgorithms to grow its ecommerce reach and hasn’t looked back since. Asaf Jacobi, president of Harley-Davidson’s New York City division, spoke with eMarketer’s Maria Minsker about the brand’s experience with AI and discussed the results he has seen so far.
EMarketer: What are some of the business challenges that drove you to try artificial intelligence?
Asaf Jacobi: One of the biggest challenges of having a business in New York City is that it’s a very competitive environment. To get the response rate brands want, they have to reach as many people as possible. That’s where artificial intelligence comes in. I started reading about how artificial intelligence boosts online marketing reach, and contacted Adgorithms. We started using their platform, Albert, for our ecommerce ads in March.
Fortifying cybersecurity is on everyone’s mind after the massive DDoS attack from last week. However, it’s not an easy task as the number of hackers evolves the same as security. What if your machine can learn how to protect itself from prying eyes? Researchers from Google Brain, Google’s deep Learning project, has shown that neural networks can learn to create their own form of encryption.
According to a research paper, Martín Abadi and David Andersen assigned Google’s AI to work out how to use a simple encryption technique. Using machine learning, those machines could easily create their own form of encrypted message, though they didn’t learn specific cryptographic algorithms. Albeit, compared to the current human-designed system, that was pretty basic, but an interesting step for neural networks.
To find out whether artificial intelligence could learn to encrypt on its own or not, the Google Brain team built an encryption game with its three different entities: Alice, Bob and Eve, powered by deep learning neural networks. Alice’s task was to send an encrypted message to Bob, Bob’s task was to decode that message, and Eve’s job was to figure out how to eavesdrop and decode the message Alice sent herself.
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In Brief:
- Researchers have demonstrated how electrons travel on different elliptical paths by using a quantum crystal kept at low temperatures.
- The discovery could lead to a new class of microchips far beyond the capabilities of today’s silicon chips.
New developments from Princeton University and the University of Texas-Austin have revealed odd behavior in electrons that could lay the foundation for a new generation of faster microchips, according to a study published in Science.
Soon, we see Legos that self assemble from 4D printers, printers that can recycle robots & devices and produce a more improved robot and/ or devices. The days of manually working on equipment, autos, etc. will be gone except for the eccentric hobbyist.
Open-source hardware could democratize the future of robots.
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