How could AI disrupt the music and commercial media industries?
Artificial intelligence may be set to disrupt the world of live music. Using data driven algorithms, AI would be able to calculate when and where artists should play, as well as streamline the currently deeply flawed means through which fans discover concerts happening in their area.
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Guest Post by Cortney Harding on Medium
Posted in robotics/AI
Posted in media & arts, robotics/AI
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Toyota have unveiled a new robot that can play the violin.
Once strictly an extremely expensive tool used only by law enforcement and the military, thermal cameras are now accessible to anyone with a smartphone and a $250 accessory. But starting with Caterpillar’s new rugged S60, thermal imaging sensors are starting to be built right into smartphones.
The FLIR ONE thermal camera started life as a bulky case for the iPhone 5, but was eventually streamlined into a compact dongle that connected to the microUSB or Apple Lightning port on the bottom of iOS or Android smartphones. With the new CAT S60 smartphone, however, the Lepton sensor that allows FLIR cameras to see in total darkness has finally been integrated into the device itself, alongside its standard rear camera.
Rapidly advancing robot appendages are in vogue lately, and they’re going to change how we do everything.
When it comes to robot design, there are the pragmatists and the biomimetics. The pragmatists will design a robot that efficiently carries out its designated function without much worry about what the finished product looks like. As long as it can complete its repetitive task day-in and day-out, who cares about the cosmetics? An industrial robot arm that holds a welding torch, for example, isn’t much of an “arm” at all, but rather something that could easily be mistaken for a sci-fi villain.
But the biomimetics school of thought aims to duplicate a lifelike quality in its robots. Consider the Replicants of Blade Runner, robots so lifelike that they are routinely mistaken as biological humans. They are fiction, however; contemporary robotic technology has to overcome all kinds of challenges in order to move like a human and exhibit convincing fluid motion.
This is a good baseline around common known issues — the real problem is cyber terrorists (as I call them) learns from each attack they instigate and like an artist, they constantly are fine tuning their own skill. So, the attacker’s approach and execution may be done one way, and by the next attack they can easily have changed their whole attack model completely which makes it very cumbersome for experts to trace at times. If we believe this is bad now; wait until AI is more widely available and adopted. Or, Quantum ends up in the hands of these guys.
Cybercrimes in today’s technologically advanced society have become much more sophisticated and progressive. We can thank mobility for the ease of extended access to our personal data, as with every use of our mobile phones, laptops or tablets in public areas we further increase our risk and vulnerability. As business owners, online shoppers, students, employees and even house wives, we remain at high risk for intrusion of our virtual systems. In this digital day in age, our personal data is used everywhere from when we make an online banking transaction to buying a new shirt at the mall, and even working on a project at the local coffee shop. It is hardly responsible to think that your information is safe anywhere.
Protecting Yourself
Lucky for us, there are many effective and efficient opportunities for protecting ourselves virtually. When it comes to building a good defense against malicious cyber attackers the best mode of attack is a good offense. This means, educating yourself and setting up parameters that protect your system and therefore your personal data from all angles. In the grand scheme of things, knowledge is power and the more power you have, the more you can leverage such as a way to build a good defense against cybercrime. Here are five facts about cybercrimes that you might not be aware of:
One of the largest drawbacks in robotics is the rigid parts and movements of the robots. Well, soon that maybe changing due to non-Newtonian fluids.
(Inside Science) — By using fluids similar to Silly Putty that can behave as both liquids and solids, researchers say they have created fluid robots that might one day perform tasks that conventional machines cannot.
Conventional robots are made of rigid parts that are vulnerable to bumps, scrapes, twists and falls. In contrast, researchers worldwide are increasingly developing robots made from soft, elastic plastic and rubber that are inspired by worms, starfish and octopuses. These soft robots can resist many of the kinds of damage, and can squirm past many of the obstacles, that can impede hard robots.
However, even soft robots and the living organisms they are inspired by are limited by their solidity — for example, they remain vulnerable to cutting. Instead, researcher Ido Bachelet of Bar-Ilan University in Israel and his colleagues have now created what they call fluid robots that they say could operate better than solid robots in chaotic, hostile environments. They detailed their findings online Jan. 22 in the journal Artificial Life.
This maybe true in the UK. However, I am in the US. In the US, if I have a robot representing me and I lose my case; can I claim improper representation? I believe that I can. Also, which states and counties/ cities recognize a robot as an attorney? What federal/ state/ county/ and city ordinances and laws will need to be changed for robots to be recognized as attorney in the US? Just having a robot that interprets laws is not enough in the US.
http://mic.com/articles/135693/this-robot-lawyer-can-get-you…t-for-free
It has already saved people millions of dollars.
Seeking to “push the limits of what humans can do,” researchers at Georgia Tech have developed a wearable robotic limb that transforms drummers into three-armed cyborgs.
The remarkable thing about this wearable arm, developed at GT’s Center for Music Technology, is that it’s doing a lot more than just mirroring the movements of the drummer. It’s a “smart arm” that’s actually responding to the music, and performing in a way that compliments what the human player is doing.
The two-foot long arm monitors the music in the room, so it can improvise based on the beat and rhythm. If the drummer is playing slowly, for example, the arm will mirror the tempo.
Fujitsu Laboratories today announced that it has developed deep learning technology that can analyze time-series data with a high degree of accuracy. Demonstrating promise for Internet-of-Things applications, time-series data can also be subject to severe volatility, making it difficult for people to discern patterns in the data. Deep learning technology, which is attracting attention as a breakthrough in the advance of artificial intelligence, has achieved extremely high recognition accuracy with images and speech, but the types of data to which it can be applied is still limited. In particular, it has been difficult to accurately and automatically classify volatile time-series data–such as that taken from IoT devices–of which people have difficulty discerning patterns.
Now Fujitsu Laboratories has developed an approach to deep learning that uses advanced mathematical techniques to extract geometric features from time-series data, enabling highly accurate classification of volatile time-series. In benchmark tests held at UC Irvine Machine Learning Repository that classified time-series data captured from gyroscopes in wearable devices, the new technology was found to achieve roughly 85% accuracy, about a 25% improvement over existing technology. This technology will be used in Fujitsu’s Human Centric AI Zinrai artificial intelligence technology. Details of this technology will be presented at the Fujitsu North America Technology Forum (NAFT 2016), which will be held on Tuesday, February 16, in Santa Clara, California.
Background
In recent years, in the field of machine learning, which is a central technology in artificial intelligence, deep learning technology has been attracting attention as a way to automatically extract feature values needed to interpret and assess phenomena without rules being taught manually. Especially in the IoT era, massive volumes of time-series data are being accumulated from devices. By applying deep learning to this data and classifying it with a high degree of accuracy, further analyses can be performed, holding the prospect that it will lead to the creation of new value and the opening of new business areas.
