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This one was weird, I was doing the round of my bases and spaceships, transmit and restock science labs and checking status. On Munbase Beta I refueled I science rover while uploading science, tabbing from kerbal on eva to base I passed an life support tank who was left after a bit hard landing. This had clipped trough the ground and was falling down towards Mun center. Falling Falling faster, negative attitude now. Starting to get fast Looks like I got close to the singularity, insane acceleration, next I know I was out of the moon going fast. KSP has the tank registered as landed and moving over surface so I had to revert. After this I let it fall and it disappeared.

This post will be very long, but I think it is important that we get involved, which does not mean that we can do something about it, but we can be more prepared to face that final step as human beings. (Sent me a pm if you find some English mistakes that makes a sentence unclear) Introduction: If I need to make a prediction, I like to include as many variables and data as my brain allows me. But there is always a particular variable which I choose to ignore, because if it is unleashed, it destroys any possibility of accuracy in the prediction. That variable is the moment when our technology escape from the limits imposed by our brain. In where the research and conclusion is done by the same technology which can improve itself in a positive feedback loop; generating an exponential explosion. To understand how big will be this change, we need to know first how our technology and human capacity evolved in the last 50000 years. Our brains hardly changed in this time frame. We already had language to help us to share discoveries, but it was not until the writing that we became more efficient in knowledge accumulation. Machines, population, cheap energy; all played an important role in transforming our linear slow growth in something more exponential. But our brain is still the same, our intelligence did not develop for visualize and understand complex concepts beyond our everyday reality, due this, we depend a lot on the experimentation to move on. The new age: We already enter in the age of self machine learning using neural networks and evolution principles. In case someone doesn't know, all the latest biggest software advances like speech recognition, image recognition, concept understanding, new search algorithms; between others, was achieved by these new neural networks structures. The trick was to mimic the things we know about real neurons and our way to learn, which is all based on how data are related. How neural networks work: Brain vs computers: In the past it took us a lot of code engineering and hundreds of experts working by many years, just to try to make an algorithm to identify objects in a picture or a song in the radio. They first started with 2 % of accuracy, then 5%, 8%.... many years later 25%, the first year Deep Learning go out (a new NeuNet algorithm that needs less human intervention and other characteristics) already achieve a 40% in a very short time without those hundred of engineers. Now the % of efficiency in any of its task was increased considerably. There are some small hardware chips which recreate the structure of an already trained NeuNet that can identify people, cars, and other objects in a video surveillance camera only consuming few miliwatt of power, whereas for the same task with normal programs would consume a lot of power. In 2011 IBM win the Jeopardy game using Watson, a supercomputer base in NeuNet, who was able to read Wikipedia and relate all its content, then it keep learning in other areas as Medicine, analytics, cooking, sport, advisor; helping to researches in a way that until now nobody could. Watson Links: Jeopardy, How it works?, as Advisor, Learning to see, General knowledge We can feed these algorithms with raw data as pixels in the screen, without teaching rules or nothing; the computer will learn what to do by itself just looking the screen. In this case; learning to play video games. There are two drawbacks with this technology. 1- Learns on its own, the acquired knowledge is not fully controlled by us. 2- We don’t really understand why it produces an outcome, because "it’s a complicated machine", then we cannot predict what it will do. Google in recent times bought the company DeepMind, which it has as goal to create a true AI in where our friend Elon Musk also invested some money to ensure that the necessary security measures are taken, this also gave him the opportunity to keep an eye on the development of this technology. Examples of today breakthrough with Deep Learning: The human conclusion To see if these neural networks really show intelligence traits in their results and what it is needed to achieve consciousness, first we need to find a better definition of what we call intelligence and consciousness. Michio Kaku makes a good job answering this question in the first 10 min of this video from a physicist's point of view, I recommend. Taking a look to deepmind papers and last breakthrough in neuroscience with the different brain mechanism, we are close to create an algorithm that would learn in a similar way as the brain; this does not mean that it needs to be equal, just needs to work. We can make airplanes that fly well without the need to imitate all the complex movements from birds. I realized that when looking for news in this field by selecting the option “this year” is not enough; you need to select months or even weeks given how fast it progresses. So this take us to our final question: -and then what? Well, we will reach the time when our brain is not longer the limit to our technology, our slow way to learn, test and developing will be over. Our technology at this point allow us to create a learning machine smart enough to improve its own design, this point in time is called “THE SINGULARITY”. Even today, it is becoming more difficult to make predictions, but once we're in the singularity, all our predictions collapse, we can no longer see the future, neither (to a certain degree) the machine that is driven this. At this point all resources are mostly focus to improve the power of this Hard IA, any other application of the new acquired technology will become in a waste of time and resources. Why? Because the knowledge will increase so fast, that any application that we might think of as useful, it will be outdated in few months by the new discoveries, a Hard IA does not need experimentation to prove new theories (which is something that consume us a lot of time), it can do it only by deduction. We will reach a time when we (or it / Hard IA) will double all human knowledge progress in just 1 year, then we double again in 1 month, then again in just a week. There is not hard to imagine no matter how complex the universe, all possible questions will be answered in a very short amount of time after the singularity, this means jump from a limited knowledge to a godlike knowledge without middle app steps. So, when will this happen? They made this same question to many scientists and people working in the field in 2012; the average answer was by 2040, but many of those specialist was not even able to predict the grade of success that deep learning has today in just a period of three years. Elon Musk said it may happen in 5 to 10 years. if I have to make a prediction, I would say that in 10 to 15 years, even 15 years looks like an eternity at this accelerating rate. We saw many signs like this in the past, but this certainly reflects the end of predictions, with respect to the Time magazine issue, that note was in fact about the singularity, it was released in 2011. So this make us think; what about all our silly predictions about in how long mankind will begin to colonize other worlds?, or the technology needed for a Von Neumann probe?, how long until we reach another star? what about our life plan of have grandchildren and die from old?, Global warming really matter? We were always so wrong to ignore this variable in all our predictions, but well, maybe now we are more prepared to explore and enjoy these last years of life as we know it.