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7 ways to reduce your AWS costs

Are you spending more than planned on AWS? Or maybe you just want to spend less? What can you do? With the great variety of services and pricing options that AWS offers, you can build unimaginable networks of servers in the cloud, something very difficult and expensive to do with traditional IT infrastructure. But with that power in your hands it is really easy to go far from what you exactly need, ending up with a lot of underused or overused running resources which are difficult to keep track of.   Most AWS users are initially attracted to the service because of its pay-as-you-use pricing model. Like running water or electricity, you only pay for what you. But as your usage increases so does your billing size. On-demand is great so far as your pockets go. But with careful budget planning you can benefit from other models and save a lot of money on the long run. There are several ways to save yourself from paying high bills on AWS. Once you are able to define or re-define your origina…

What is the difference between AI, ML and deep learning?

The Difference Between Artificial Intelligence, Machine Learning and Deep Learning

Once the domain of Sci-Fi geeks and film script writers, Artificial Intelligence or A.I. is considered well above and beyond fantastical subject these days. Anyone with the slightest interest in tech, no doubt knows that corporations like Microsoft and Google are running not just one, but multiple A.I. projects concurrently to address some of the most challenging problems known to mankind. Each approaching the problem from a slightly different angle.
And like any emerging technology, the development of working (albeit limited) A.I. has spawned a whole plethora of new buzzwords such as Machine Learning and Deep Learning. But what do these terms mean? A quick and dirty explanation could look like this:
  • Artificial Intelligence – the top-level container for all things related to creating at the very least, a synthetic “mind” able to solve problems in a heuristic manner.
  • Machine Learning – the human mind uses experience and knowledge to develop new capacities. Machine Learning is the phrase that sums up the need for any A.I. to be able to learn how to solve a set of problems on its own based on past experience and new input.
  • Deep Learning – is a term used to describe a mathematical model for Machine Learning that emulates a human brain, creating a large neural network. It is, in fact, a subset of Machine Learning, or a refinement if you like.
There we have A.I. 101 as the tech stands today. But of course, these simple definitions do not really do the subject justice. A more in-depth analysis is required.

What is Artificial Intelligence?

If we believe what we see at the movies, A.I. is Skynet building Terminators, and HAL 9000 turning psychotic. The current truth is far from this. Although we could safely say that the overall goal of A.I. research is to develop a self-aware machine, current A.I. goals are far simpler. The NSA has a set of three custom-built A.I. that it uses to test encryption techniques. Would we call these machines intelligent? They have been built to run a very limited thought process. But it is a recognizable thought process nonetheless. And one that has been demonstrated to have a capability to learn heuristically. This is a good example of what is termed “narrow A.I.” heuristic devices designed to perform a limited function. This is what cutting edge A.I. is today.

What is Machine Learning?

If you have read this far, you probably realize by now that you simply can’t have an A.I. without having Machine Learning. If the measure of a successful operating A.I. is its ability to come up with solutions to a problem or series of problems, without being given any instruction on how to do so, then it must have the ability to learn.
Why is this such an important factor? Consider this, one of the most famous A.I. projects of all time is IBM’s Deep Blue. The very first A.I. project to start hitting news headlines in the mid-90s. But this was the early days of A.I. Deep Blue had to be taught, it had no way to learn. After being taught how to play the game, Deep Blue managed to beat several world masters at chess. But had it not been taught, it could never have played a game of chess at all, let alone won.
On the other hand, we have the much more modern A.I. that has been developed by Google and named DeepMind. This is an A.I. that does incorporate Machine Learning. In 2016, DeepMind taught itself how to play Go. It then went on to beat one of the top Go players in the world.

What is Deep Learning?

Deep Learning is something like the evolution of Machine Learning. Or we could say that ML was the concept, with no real way to implement it. DL is the final fruition of ML, a proven way to build synthetic, heuristic learning machines. Indeed, DeepMind uses DL techniques, and as mentioned above, it has proven effective.
Deep Learning comes back to a very old concept, that of a neural network. We have known for a few decades that we need to develop a machine that works the same as a human brain, to get it to learn without restrictions. It has only been in recent years that our hardware has become powerful enough to create such a platform. So how does the human brain work? It’s all about pattern recognition.
Show somebody a cat, tell them it is a dog. Show them another cat, and tell them it is a cat. They will begin to learn the pattern of what a cat is. By pattern, we mean the state that the neurons of our brain are in, based on the effect of sensory input. Put simply, when we look at a cat, the network neurons of our brain light up in a specific way, a specific pattern. A neural net, an organic one.
DL works the same way, it doesn’t try to store recognisable data, perform calculations or run algorithms. It simply saves entire snapshot patterns of a digital neural network and correlates them with specific inputs. So, when DeepMind beat Lee Sedol at Go, it did so by recognising the pattern of the game turn by turn, and choosing from other known patterns of the game that would produce the optimal outcome each move. This might sound very simple, but this is how every thought a human mind comes up with, every decision we make, and every emotion we feel is processed. We are not that complicated, we are just a very fast, organic pattern recognition engine.

An Oranges and Apples Comparison

Our original question, “what is the difference between A.I, ML and DL?” is something of a misnomer. They are not comparable. A.I. is a goal, ML is a requirement to meet that goal, and DL is a solution that enables ML.
An Artificial Intelligence, by its very definition, must be able to learn (ML). The most effective way to accomplish Machine Learning (that we know of right now) is to create a neural network that emulates the pattern recognition capabilities of the human brain (DL).
If you are willing to listen to the scaremongers, the development of a fully functional A.I. would be a singularity event for humanity. A moment in time when we have entirely changed the fate of the race. But right now, this is still Sci-Fi. The most complex A.I. currently operating, which has cost billions of dollars to develop, can play a decent game of Go.
We are still very far from creating a self-aware machine. But technologies such as Deep Learning could eventually get us there. Evolving as it has, from the almost trial and error approach of original A.I. experiments, which in turn, led to the realisation that Machine Learning was a requirement.

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  1. Gartner estimates that greater than 50 p.c of enterprises can have adopted a hybrid cloud strategy by this yr. This is great blog. If you want to know more about this visit here AWS Cloud Certified.

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