We are getting  more and more to the human realm of artificial intelligence you see this guy splits in two and he's inside his hidden networks and machine learning programs, deep learning which is a subcategory of machine learning .Deep learning provides artificial intelligence the ability to mimic a human brains neural network it can make sense of patterns noise and sources of confusion in the data let's try to segregate different kinds of photos using deep learning so first we have our pile of photographs much more organized in my boxes in my closet of photographs the machine goes through the features of every photo to distinguish them this is called feature extraction. It figures out the different features in the photos and so based on those different features labels of photos it says these are landscapes these are portraits these are others so it separates them in there if you've ever been on Google photos .





We have the two kind of orange &  red layers here and there's all these lines in there those lines are the weights each one of those represents a usually a float number or a decimal number and then it multiplies it by the value in the input layer and you'll see that as it adds all these up in the hidden layer each one of those dots in the hidden layer that has a value based on the sum of the weights and then it takes those and puts it into another hidden layer and you might ask well why do we have multiple hidden layers well the hidden layers function as different alternatives to some degree so the more hidden layers you have the more complex.



Features Of Artificial Intelligence



  •  Data that goes in and what can they can produce coming out the accuracy of the predicted output generally depends on the number of hidden layers ,we have so there we go the accuracy is based on how many hidden layers we have and again it has to do with how complex the data is going in the output layer gives us the segregated photos so once it adds up all these weights and you'll see there's weights also going into the output layer based on those weights it'll say yes it's a portrait no it's a portrait yes it's a landscape no it's a landscape and that's how we get our setup in there so now we were able to label our photos let's take a look and see what that looks like in another domain.
  •  We have photograph domain now let's look at it in the airline ticket. So let's predict the airline ticket prices using machine learning these are the factors based on which we're going to make the predictions and choosing your factors is so important but for right here we'll just go ahead and take a look what we got we have Airlines.
  •  We have the origin airport the destination Airport the departure date here are some historical data of ticket prices to train the machine so we pull in the old data this is what's happened over the years now that our machine is trained let's give it new data for which it will predict the prices if we remember from our four different kinds of machines .we have machines with memory well this is the memory part it remembers all the old data and then it compares what you put in to produce the new data to predict the new prices the price is 1,000 that's an expensive flight of course if that is a US dollars if it's India.
  •  I don't know what the price range is on that hopefully it's a good deal wherever you're going so we predict the prices and this is nice because if you're looking lay ahead it'd be nice to know if you're planning a trip for next year how much those tickets are gonna cost and they certainly fluctuate a lot so let's take a look at the applications of artificial intelligence. We're gonna dive just a little deeper .
  •  We talked about photos we've talked about airline ticket prices kind of very specific you get one specific number but let's look at some more things that are probably more in-home more common right now speaking of in-home this young gentleman is entering his room of course makes a comment like we all do we walk into a room this room is dark isn't it let's see what happens when .
  • I enter it the sensors in my room detect my presence and switch on the lights this is an example of non memory machines okay you know it senses you it doesn't have a memory whether you got it or not some of the new models start running a prediction as to whether you're in the room or not when you show up when you don't so they turn things on before you come down the stairs in the morning especially if you're trying to save energy might have one of those fancy thermostats which starts guessing when you get up in the morning so it doesn't start the heater until it knows you're gonna get up so here he comes in here and this is one of the examples of smart machine and that is one of the many applications of artificial intelligence one of the things we're developing here in our homes okay bye abruptly he leaves leaving his nice steaming cup of coffee there on the table and when he leaves the sensors detect he's gone and turn off the lights let's watch some.
  •  TV did someone say TV the sound sensors on the TV detect my voice and turn on the TV sounds kind of strange but with the Google dongle and a Google home mini you can actually do just that you can ask it to play a movie downstairs on your downstairs TV true also with the Amazon fire stick and all the different artificial intelligent home appliances that are coming out  pretty amazing time to be alive and in this industry here you have one more application of artificial intelligence and you can probably think of dozens of others and are right now on the cutting edge of development .
  • So let's jump into my favorite part which is a use case predict if a person has diabetes and in the medical domain if you read any of these A CA predict a person has diabetes but in the medical domain we would want to restate this as what is the risk is a person high risk of diabetes it should just be something on their radar to be looking out for and we'll be helping you out with the use case there we are with a cup of coffee again.

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