Learning machine Examples

Take a look at this diagram the picture on the top left is the data set the data is classified into two categories red and blue data is hypothetical however it could represent almost anything coin weights and their diameters the number of petals on a plant and their width clearly there are some definite groupings here everything in the upper left belongs to the red category and the bottom right is blue however.

In the middle there is some crossover if you get a new previously unseen sample which fits somewhere in the middle does it belong to the red category or the blue category the other images show different algorithms and how they attempt to categorize a new sample if the new sample lands in a white area then it means it can’t be classified method the number on the lower right shows the classification accuracy one of the buzzwords that we hear from companies like Google and Facebook is neural net.

A neural net is a machine learning technique modeled on the way neurons work in the human brain the idea is that given a number of inputs the neuron will propagate a signal depending on how it interprets those inputs in machine learning terms it is done by matrix multiplication along with an activation function the use of neural networks has increased significantly in recent years and the current trend is to use deep neural networks with several layers of interconnected neurons.

During Google i/o 2015 during the keynote it was explained how much machine learning and deep neural networks are helping Google fulfill its core mission to organize the world’s information and make it universally accessible and useful to that end you can ask Google now things like how do you say Kermit the Frog in Spanish and because of neural networks Google is able to do voice recognition natural language processing and translation currently.

Google is using 30 layer neural nets which is quite impressive as a result of using these neural networks Google’s error rate for speech recognition has dropped from 23% in 2013 to just 8% in 2015 so we know that companies like Google and Facebook use machine learning to help improve their services .

So what can be achieved with machine learning one interesting area is picture annotation here the machine is presented with a photograph and asked to describe it here are some examples of machine generated annotations the first two are quite accurate although.

I’m not sure there’s a sink in that first picture and the third is interesting in that the computer managed to detect the box of donuts but it misinterpreted the other pastries as a cup of coffee what is it it’s a banana no it isn’t try again what is it it’s a banana no it isn’t what is say it’s an orange this is an orange of course the algorithms can also get it completely wrong look at this first picture those men in hard hats seem to be doing some work however the computer thinks they’re lounging around in a couch and that motor scooter doesn’t look like a fire hydrant to me and I don’t think that horse will be very happy as being described as a surfboard it’s a small off-duty Czechoslovakian traffic warden.

Another example is teaching machines how to write Cleveland amore an American author reporter and commentator once wrote in my days a school taught two things love of country and penmanship now they don’t teach either I wonder what you think about this the above handwriting sample was produced by a recurrent neural network to train the machine its creators are 221 different writers to use a smart whiteboard and copy out some text during the writing the position of their pens was tracked using infrared this resulted in a set of X&Y coordinates which were used for supervised training as you can see from the results they’re quite impressive.

Movie subtitles

in fact the machine can actually write in several different styles and at different levels of untie deenis google recently published a paper about using neural networks as a way to model conversations as part of the experiment the researchers trained the machine using 62 million sentences from movie subtitles as you can imagine the results are quite interesting at one point the machine declares that it’s not ashamed of being a philosopher while later when asked about discussing morality and ethics it said and how

I’m not in the mood for a philosophically debate so it seems if you feed a machine a steady diet of Hollywood movie scripts you get a moody philosopher unlike many areas of AI research machine learning isn’t an intangible target it is a reality that is already working to improve the services we use in many ways it is the unsung hero the uncelebrated star which works in the background trolling through all our data to try to find the answers.

We are looking for and like deep thought from Douglas Adams Hitchhiker’s Guide to the galaxy see sometimes it is the question we need to understand first before we can understand the answer my name is Gary Simms mandrel authority and I hope you’ve enjoyed this video if you did please do give it a thumbs up also please use the comments below to tell me what you think about machine learning also don’t forget to subscribe to Android authorities YouTube channel and as for me I’ll see you in my next video

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