The Ape Machine
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Elon Musk is really doubling down on the idea of introducing regulation in the field of artificial intelligence and machine learning.
This all started during the National Governors Association back in July 2017.
Nevada Rupublican Governor Brian Sandovall asked Elon Musk if robots will take everyone’s jobs in the future and Musk answers went far beyond the question asked.
Besides saying that “robots will do everything better than us,” his worries extended into the realm of what is currently still all based in nothing but science fiction.
So you want to know more about machine learning, and how to get started?
Maybe you want to get into the field of technical artificial intelligence, or learn more about the current state of A.I. philosophy.
In this post I will give you some invaluable pointers to resources you can use today to get a better grip on this new and exciting field of research that is taking the world by storm.
Whether you are a programmer, data scientist, mathematician, or in any other way interested in machine learning, there should be something in here for all of you.
So China has made the news recently when announcing their plans to be the world leader in artificial intelligence technology by the year 2030, and I for one am 100% not suprised at all by this news.
Think about it for a second, besides those freaky machines made at the Boston Dynamics labs which recently were in the spotlight—mostly because of the footage of them being kicked around by their creators, while subsequently failing to perform the challenges set for them outside of the lab by DARPA—what other country do you know to be often in the limelight when it comes to advances in robotics and machine learning?
Further more, I suspect any country that has money and human resources to spend on this will have the same goal as China and America, which is to become world leader in the most advanced technologies possible, and this has been going on since the dawn of man.
Demis Hassabis, founder of DeepMind, recently spoke out about his vision on how to reach the "next level," of artificial intelligence.
His strategy is, predictably, to reconnect with the field of neuroscience, to study natural intelligence, in the hope of mimicking these processes inside the machine.
While I have shortly cover my opinion on this before, I want to take another pass over this topic to see if the opinions of multiple high ranking experts are able to make me change my mind about human like artificial intelligence.
So, Facebook researchers have taken their machine learning algorithm offline, which as far as I can tell was being used as an experimental chatbot program.
Of course most of the media is jumping on this story with the usual sensationalism, and are quick to connect this to the remarks made by Elon Musk, and Mark Zuckerberg, trying desparately to use this as fodder to determine which one was right, and which one was wrong.
The answer to that last part, by the way, is: Both, and neither.
A great many articles are written every day about everything to do with artificial intelligence, machine learning, deep learning, etc.
Sometimes these terms are used with great care, and applied in exactly the right context, other times not so much.
If you are very familiar with this field, eiter through practice or study, you may already know exactly what I am talking about, and might even be able to correct misuse of terms on your own, but for those of you who want to get a finar grain understanding of what is going on, keep reading.
You may be wondering: Has the Singularity happened? Has artificial intelligence turned itself against us?
Then you may be relieved when I tell you nothing even close to this has happened, but over the span of a very short test, I did turn my new best friend against me, and my new best friend was indeed a chatbot...
If you have ever messed around more with chatbot services like api.ai or recast.ai, and got beyond the "order a pizza" example, you might be wondering: Why does my chatbot start messing up when I train it more?
We all know the scenario, starting with a simple bot, and adding the small-talk intent to it that all of them provide out of the box. Things are looking great so far!
Adding more intents and features to our bot is working out too, and we are soon dreaming of building a general solution to all of our customer service related problems.
What's more, many companies are sprouting up targetting businessnes big and small, promising them solutions for customer service, employee onboarding, and other tasks that sitll require some human finesse.
Sadly, things are not that simple...
By now you are probably aware of Elon Musk's performance at the National Governors Association's meeting, where he proceeded to express his deep concerns about artificial intelligence, and that there is a need for regulation and for researchers and companies to "slow down."
No doubt about it, things are moving fast, and some people calling themselves experts are even saying that we are moving a lot faster than earlier predictions by other experts. Whether that is true or not, I think we can all agree that we need to start actively and openly discussing the safety concerns surrounding A.I.
Meanwhile, we also need to start identifying the true valuable resources in this field when it comes to expertise, because sure: Elon Musk may have invested in start OpenAI, but that does not make him an expert, and after his remarks recorded on video, most of the truly important machine learning researchers have spoken out against him.
Elon Musk is first and foremost a businessman, and we need to realize that when taking in his words, and question what his motivations are when he say he thinks there should be more regulation, and other (competing) companies should slow down their research.
For the most part researchers think that before we can have artificial super intelligence, we need to go past human intelligence first. But is this a good idea?
Governments around the world are getting more and more interested in artificial intelligence, and starting to think about regulations and control. Is this a good thing, or should we start arguing our case for a free and open structure in the field of A.I. research?
A look at hacking in the world of artificial intelligence, and how to trick machine learning algorithms.
While the term "human compatible artificial intelligence" is used a lot by researchers and thinkers in the field, I believe this whole concept to be an absolute myth.
Humans creating artificial intelligence flies directly in the face of our evolution as a species. In this article we look at why it is strange that we, as the species all the way at the top of the food chain, are trying to make something even higher than us.
Elon Musk is of the opinion that humans should be outfitted with a so-called neural lace, which would create a neural link between man and machine. This article takes a closer look at the very real dangers such an interface brings with it, and why we should consider this very carefully, before letting our science fiction desires take over logical reasoning.
Let me show you some ways that A.I. can go horribly wrong, and way before we even reach AGI, ASI, or any kind of singularity.
The threat lies not so much in intelligence, but in over-connectivity to real-world environments.
George Kingsley Zipf's law states that given a large sample of words used, the frequency of any word is inversely proportional to its rank in the frequency table. In this article we look at applying these techniques to adaptive text compression, which while a little of a naive approach, is actually a lot of fun, and a great learning experience.