How do YOU *really* feel about A.I.?

Evan asked about this on the Q&A, and I wanted to re-post it here since people might miss seeing it on the Q&A.

http://lovelace.augustana.edu/q2a/index.php/1582/what-are-your-actual-feelings-about-ai

It's an excellent question.  Naturally, you don't have to answer... but it could be interesting to discuss.

You can either talk about it on the Q&A post, or (in case you fear being voted up/down on Lovelace), you can discuss it in the comments section of this blog post here...

NXT Maze Solver that Remembers the Path Back to the Start

I found this video helpful when working on my final project. Check it out

https://www.youtube.com/watch?v=apQhBppWDLw

The blog is informative too....https://decibel.ni.com/content/blogs/ILabVIEW/2012/04/22/nxt-maze-solver-that-remembers-the-shortest-path#comment-47189

Evolving Mario's Brain?

For another application of BOTH genetic algorithms AND artificial neural networks (much like my swarm robotics project), check out this awesome video about evolving a brain to play Super Mario.

It should also help reinforce the idea about how artificial neural networks work, which I explained briefly during class...

Robots Taking Our Jobs

Photo Source

In this article by Ivana Kottasova, she talks about the likelihood of A.I. and robots taking the job market from humans.  The robots that we currently have perform mostly manual labor, but experts say as A.I. advances the more skilled jobs will start to disappear as well.  She says that a study done by Bank of America stated robots are likely to be performing 45% of manufacturing tasks by 2025 compared to the 10% performed today.  In this study they also looked at the falling prices of computers and robots over the past decade and how they will continue to get cheaper over the next decade, and this is one of the main reasons the artificial work force so appealing to employers.  Another study done by Oxford University stated that nearly half of all U.S. jobs will be at high risk of being taken over by computers and an additional 20% of the jobs facing medium risk.

Ivana also said another reason why our jobs are becoming more at risk of being taken over by computers is because of the current advances in voice and facial recognition, machine learning, and simply because they are getting easier to use.  According to experts, economic inequality will only increase as more jobs are taken by robots.  This is not the first time that technology is causing a change in the global economy, just look at the industrial revolution for example, but this time it is much different.  Workers will have the choice to take jobs that they are overqualified for or simply stay unemployed.  Ivana also wrote another article in late 2015 in which she talked with the chief economist of the Bank of England Andy Haldane, and he said "These machines are different.  Unlike in the past, they have the potential to substitute for human brains as well as hands."

Our last debate in class was about whether or not the development of advanced A.I. would be beneficial for humans or not, and these articles only help to provide evidence that it would not help us.  When the A.I. that we create becomes more intelligent than we are, who knows how the A.I. will act.  We can try to put safeguards in place to make sure we are always the most advanced creatures on the planet, but as we try to evolve programs with genetic algorithms who knows what will remain and what will be taken out.  I personally think that advanced A.I. most likely will cause the collapse of our economy and possibly human kind.  Once the job market is taken by computers and the economic gap is widened even further, the majority of the population will become poor and out of work.  Once that happens I'm not entirely sure what will happen to society.  We are trying to create robots to help us complete tasks and make our lives easier, but what happens when we create the A.I. that can do every task that we can do also?  At that point what need is there for humanity?

If you think Google's/Facebook's image recognition is impressive, that's only the start.

I'm fairly appalled - almost creeped out - every time I upload a picture on Facebook, the site gives correct tag suggestions on almost everyone that's featured in the photo. Even then, I'm still not used to having the capability to use Google as a reverse image search engine, and that has been around for quite a bit. These are some pretty ground-breaking features that came about within the last 4-5 years. I would just sit there and try to imagine how a search algorithm could trace through photos and give return you a result. It's already hard enough for me to comprehend the search algorithms used by Google for just basic text-based search. There's so many factors like relevancy to keywords, domain names, exact names, or determining worthy sources. But apparently, that's only scratching the surface.

computer vision from http://www.mathworks.com

A recent Wired article calls the image/facial recognition computer vision. The identification process these search algorithms use are part of what's known as deep learning, a "breed", as Wired calls it, of artificial intelligence. Deep learning goes on to represent a branch of machine learning used to model high-abstract concepts. The article continues to talk about a historical 2012 image recognition competition for computers called ImageNet being won by the University of Toronto, introducing the use of deep neural nets, which is technology that uses mass collections of images to learn to identify another image. It sets up its own rules to find a result versus using human-influenced rules.

exmaple of a neural net from http://e-lab.github.io

The article features a more recent breakthrough. A team of researchers from Microsoft has found a way to expand on that concept. They recently won the next ImageNet competition with their new approach called the deep residual network, which is essentially their version of a complex neural net that spans 152 layers of mathematical operations. This is tremendous since most nets use 6-7 layers. Those few layers itself are often difficult tasks for programmers to have them communicate together within their networks. With 152 layers, the Microsoft researchers resolved the problem by skipping a signal across layers within a network that was deemed unnecessary and saves them for when they are needed later. The process alone allows the signal to be much stronger and span through more layers than any other network.

I'm impressed by Microsoft's findings. Their research is going to affect not only the future of image recognition, but also areas of A.I. such as speech recognition or language understanding. Even then, I can't even imagine myself where this deep residual network can possibly lead us.

Self-aware? Evolving robots?

A TED talk about robots and self-awareness... also about evolution and robots...

https://www.youtube.com/watch?v=lMkHYE9-R0A