Machine Learning Applications In Today’s World
Machine Learning Applications have been getting a lot of interest and for a good reason. This is a part of the machine learning community and is also referred to as computer vision.
So there is a lot of interest in teaching computers how to see like humans. Cameras do that, that is what cameras are for. But beyond seeing pixels, we want the camera to be able to understand what it is seeing. So it will be able to identify, say, pedestrians, or vehicles and the road, and identify where the lane is.
If you are in the computer vision business or you want the computer to be able to get all the data. The data can come from either the radar, the LIDAR, or some other sound. But all the sensors around the vehicle need to decide to turn left or turn right or stop. So all this is one application of computer vision.
How Facial Recognition Works
Another application of computer vision is in facial recognition. Now, the current smartphones these days look at your face and know that it is you. Yeah, it is fun. But under the hood, there is an algorithm. There is an AI or machine learning algorithm that trains to identify certain points on your face.
So, the camera scans your face and matches those points on your face to the model it has built for your face. That is another application. The police these days are also thinking about adding AI algorithms to their cameras. This is so they can be running facial recognition as they move around. If there is a fugitive that they have been looking for, the AI will tell them that the person is a fugitive. Then they can make an arrest if necessary.
An App That Knows Every Language
With computer vision, there is a lot of talk around privacy and security. But whether we like it or not, it is here and we will apply it for the betterment of society.
Natural language processing is another machine learning application that is gaining traction. There is a translation app now that enables you to communicate in your own language. The machine learning application will translate what you are saying. It translates it into a new language in real-time.
So imagine you travel to a different country and you don’t know what everyone around you is saying. If you want to communicate, all you have to do is bring out your phone. You say something, and in real-time, there is a two-way translation between you and who you are talking to.
Machine Learning Applications Are Story Tellers
Text generation is another area where machine learning applications are gaining traction. We have AI algorithms now that might, in a few years, push authors out of business because they are writing text. They are writing texts that are indistinguishable from what a human wrote. So we have machine learning applications that write stories. This means kids in the future could be reading stories written by AI and that would be awesome.
The Innovation Of Machine Learning Applications
We also have AI algorithms now that are generating faces and objects that never existed. This is cool. I know many people worry about all the nefarious applications in the area of deepfake, as they call it now. But it is a good thing because it means we can apply this in the future. This might be an important tool in the future for helping AI to be innovative, as the case is. A machine learning application able to generate something thay has not seen before is art.
In logistics, there is a lot of machine learning applications. One thing we have done is to use machine learning applications for pricing predictions. The machine learning applications use historical data to recommend a price. It can do this for a specific shipment.
Machine Learning Applications Protect Your Company
We also have algorithms that will tell you in the future if a customer is going to run away with your money or not. So, you can start taking steps to stop that from happening. There is a lot of applications and this is not counting self-delivery systems now.
Amazon and many other companies are working on same-day self-delivery systems. You sit in front of your computer and click a few buttons. Then the shipment gets somewhere close by and a drone or a robot drops it at your doorstep and then takes off again.
Helpful In All Parts Of Society
So there is an unlimited list of machine learning applications. There are even in drug discovery. Scientists these days are using machine learning applications. They use it to discover new drugs and find solutions that they haven’t explored before.
What else? In agriculture, satellite imaging is now deciding when to water a crop. In the past, it was you watered a crop based on how long ago the crop was last watered. So if it had been five days since you last watered the crop you watered it now. Now there are machine learning applications applied to agriculture.
With satellite imaging, you can know when to exactly water the crop. You even know what quantity of water you need to apply. This thereby saves a lot of wastage as far as water goes, and improves crop yield. There is an optimization of energy generation.
A company recently used machine learning applications to apply algorithms to solar concentrators. The solar concentrators were able to focus all the rays of the sunlight to one specific spot. They were able to generate temperatures ranging to around 1000 degrees celsius. Iron melts at a temperature not far from that.
The Tip Of The Iceberg
There are no limits to what we can do as far as the applications of AI goes. I’m thinking about healthcare, for example. Most of us wear smartwatches, and they collect data on what we eat, our physical exercise rate, and more.
In the future, they will be able to serve as real-time doctors always going around with you. They will be telling you to take your medicine. There are so many machine learning applications as far as AI goes. We haven’t even scratched the surface yet. We’re only seeing the tip of the iceberg.
Where Does It Start?
It all starts with the data. So data is the raw material for machine learning. It has to be not only data, but it also has to be the right data for the application you want. It has to be labeled data that as well. So when you get labeled data, if the data wasn’t labeled before, you have to go through the process of labeling.
After that labeling process, you now have to carry out preprocessing on the data. Get rid of the noise and make changes. Find the type of data, the ones that are categorical or non-categorical, or numerical. Then you now have to normalize the data. So there’s a lot of preparation process.
The job, most data scientists spend the bulk of their time in data preprocessing. They hate that part of their jobs because there is a lot of bending and tweaking and cleaning of the data. Now, when the data is ready you have to decide what machine learning applications to apply. There are a bunch of machine learning applications.
Think about a deep neural network or a Bayesian model. Is there a regression or classification problem? So you now have to decide what kind of problem are you going to solve. Then you have to pick the right algorithm to solve that problem. There are thousands of algorithms to go through. They all have their advantages and disadvantages as well.
Now, when you get done picking the right model, you take the data and pass it through the algorithm. This is what the training process is. So it’s more or less a back-propagation of this is the right answer. The machine predicts the wrong answer. Then it finds the difference between the right and wrong answers. It tries to go as close as possible to the right answer. After some time the machine comes very close to the right answer. If you like the result you stop the training process and then you deploy that model.
Deployment Of Your Model
Now, the deployment has to do with how you want to consume the model that you have built. If it is a computer vision model, you have to prepare. You need to know how you are passing the pictures you want it to predict if it’s a real-time camera. So you have to deploy that model either to the cloud or to the edge.
A lot of people are thinking about machine learning applications at the edge. This is where the models sit on the devices themselves on the edge and not in the cloud anymore. So that is the pipeline from data to cleaning up the data. You build the model, you train the model, you get your output, and then you deploy.
Testing the Model
There are many things to consider when you are testing, one is the distribution of the data. The distribution of the data used to train the model has to be the same as the one you apply the model.
So let’s say I am trying to build a facial recognition software. All the data that I have on all the faces are all people from only one race. I now want to apply it to an environment where there is more than one race in that environment. So the machine is not going to function right. So you have to test your model. You have to get the environment and samples from the environment that you want to apply it to. Then see how close your predictions are to that distribution and the environment.
Like I said, another example is if I train my model with one breed of dogs, only chihuahuas, for example. Most of the pictures I have for dogs are chihuahuas. And I train my model with that set of data. If the distribution that I want it to make predictions on is on bigger dogs then my model will fail. So that is one thing to consider when testing.
Tips You Need To Know
Testing involves how close your model’s prediction is to the right answers. That is how close it is. If it is very close to the right answer, you have very high accuracy. If it is not too close to the answer, then your accuracy is going to go down. What most machine learning practitioners do is divide their datasets into three. The training set, the cross-validation set, and then the testing set. It’s very, very important that these come from the same distribution.
Machine learning applications are able to do many things as far as AI algorithms go. They are gaining traction and the interest of many people and for good reason. Machine learning applications are very advanced forms of computer vision.