AI uses a simple concept of machine learning or supervised learning to train machines on how they can perform certain tasks without human intervention. We train machines using the concept of A to B mapping or A-B mapping. Let us understand this using an example of a machine that is used to find out if the image input made to it is a cat or not.
After the initial coding is done, with both AI and machine learning, the program is made to learn how an image of a cat looks. For this purpose, lots of images are added to it initially and each image is identified as ‘a cat’ or ‘not a cat’. This process of educating the machine on what an image of a cat looks like is called A-B mapping. Here, all the images initially demarcated as ‘cat’ or ‘not cat’ are A and the output, which in this case is ‘a cat’ or ‘not cat’, constitutes to B. Hundreds of images have to be added initially, and each image must be classified as ‘cat’ or ‘not cat’ for a sophisticated machine to be created. The larger the data set that is used for training, the better the machine is at distinguishing whether the image is a ‘cat’ or ‘not a cat’. Using the principles of A-B mapping, the machine is essentially learning the difference between a cat and all other objects.
Now that we have created a mechanism to train the machine, we need data to do so. Where does one get such large quantities of data?
There are three major ways to do so,
1. Manual labelling :
This is a time-consuming and mundane way to collect data but does not involve and cost and is very accurate as we are labelling data exactly what we want it to be. In the above example, we can extract images from Google and manually label each image as ‘cat’ or ‘not cat’.
2. Observing behaviour:
In this method, the behaviour of the specific object we are trying to observe is noted, and its corresponding output is recorded. For example, if we want to create a program which helps us find out whether a machine in the production department is about to fail or not, we can observe the behaviour of other such machines in other departments and record values such as machine temperature, pressure, etc. and a corresponding output such as machine failure or not. This way, we can develop a program that warns us when its obverse eve behaviours make it likely for the machine to fail considering the records.
3. Download from websites:
We have millions of catalogues online, which help one train a machine quickly. These catalogues are usually paid and are the most expensive way to train a machine but are also the quickest way to do so if time is a constraint.
Working with large quantities of data has its own set of problems. Some of the most common problems with data are:
4. The phenomenon of ‘garbage-in, garbage-out’:
Most project engineers do not know what kind of data is best required for training the machine. AI engineers come in handy when one is trying to figure out what kind of data is best suited for the training of the machine. For example, if one is collecting data from a machine every one hour. Also, it is trying to figure out if it is going to need a break because its temperature is too high while using a program, an AI engineer could tell him to collect information about the machine every five minutes as it is more apt for finding out when to stop the machine due to high temperatures.
5. Wrong labels and missing values:
When training a machine, one uses large quantities of data, which usually comes with certain values, which are wrong or missing. For example, one inputs ‘cat’ for a picture that is not of a cat or types ‘cat’ in front of a blank canvas. The machine, in both cases, will give a wrong analysis of the input image, it will output ‘cat’ for all blank canvases and may not be very accurate because of the wrong inputs initially.
6. Unstructured data:
Machines also cannot interpret unstructured data, which usually comes in the form of images or voice messages. Any machine, which is not specially made to analyse any of the data types, will show an error message or may crash every time the input is of a data type it cannot analyze.
I am sure you have heard of the terms ‘data science’ and ‘deep learning’. Let us get a brief understanding of what these terms are.
AI is a very broad field. A more focused stream inside AI is called Machine Learning. Machine learning can also be subdivided into many streams, one of which is deep learning. Deep learning uses neural networks, like those, which are inside our mind, to connect many data aspects and come to meaningful conclusions. Deep learning can speed up the process of A-B mapping if used properly and can help speed up many such tasks.
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Data science is used to extract knowledge and insights from data. Data science is one of the last stages in the process of data analysis. Once data is collected, and filtered one can make sense of it and come to meaningful conclusions using it, this is called data science.
Let us look at the differences between a data science project and a machine-learning project to understand data science better and why joining an AI ML training is important.
Steps of a data science project
- Collect data
- Train model
- Analyze data
- Deploy model
- Suggest hypothesis/ actions
We all realize AI is the future and greatly increases efficiency, so let us get straight to the point and try to understand how one can implement AI in their organization. Some of the broad steps in doing so are:
1. Execute pilot projects to gain momentum:
AI is relatively new to everyone, and it becomes very difficult to implement a broad AI strategy suddenly in the organization. To gain momentum, one must execute pilot projects to ensure that the organization is ready for a change. These projects do not have to be very elaborate; they can focus on a job or two. They set the pace for AI development in an organization.
2. Build an in-house AI team:
Most of the initial in-house AI projects have to be outsourced as the organization lacks an AI team. Once the initial projects have been expected, the organization can work towards developing an In-House AI team. This will help the organization speed up the process of AI development in the organization as they can run projects without having to worry about additional outsourcing charges, and data collection becomes easier with an in-house AI team. The usual roles in an AI team include software engineers, data scientists, data engineers, machine learning engineers, machine learning researchers and lastly, AI product manager. This list of roles is not exhaustive and can be changed depending on the organization and its AI requirements.
3. Develop an AI strategy:
An organization should then define a broad AI strategy, so all its employees and stakeholders are aware of what changes they expect to see in the organization shortly. All goals and objectives can then be aligned to this overall strategy, creating a productive synergy.
4. Divide the automation process into small AI projects:
To ensure that an AI project is launched satisfactorily, one needs to ensure that a cross-functional team with AI ML training is created for its development. These teams usually consist of domain experts, AI experts, and engineers, this way, one can develop programs after considering the requirements of the department and the capability of AI.
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While we run AI projects, we should consider that jobs cannot be automated, we can only automate individual tasks, each job can be broken up into many tasks, and the tasks which are mundane and repetitive can be taken up for automation.