Since last year, Artificial Intelligence (AI) has expanded and improved various technologies. AI Automation has transformed multiple industries with a vast variety of applications. Artificial Intelligence also contributes to economic growth and productivity of multiple enterprises. The technology has removed the gap between machines and domain knowledge.
While the demand for skills of production of such machines from scratch is high, there is comparatively less number of businesses that are capable of building these models.
Ruchir Puri, in his keynote, discussed the automation of Artificial Intelligence, which means automating the tasks to build, deploy, and manage. The Artificial Intelligence revolution, which is accelerating with each passing moment.
While Artificial Intelligence has changed the way a lot of work is done in various industries through automation, there are still some functions that require human touch for their operation and learning.
Automated Artificial Intelligence changes the way we need various data scientists and professionals to sought day to day actions and makes it easier to use machines.
The building and handling of these machines are systematically carried out on raw data and makes the accessibility of data science across the organization easy.
Construction of an Artificial Intelligence model requires domain knowledge, expertise in mathematics and computer and coding skills, which is way more than to expect from a single organization.
Apart from this, there is a constant chance of human errors, which affects the accuracy of the model. Automated AI uses the knowledge of various data scientists and helps improvise data science investments.
Artificial Intelligence and automation combines the mathematics and computer coding areas of the model and combines to form a single unit.
Automation of Artificial Intelligence helps the use of this technology in various industries, including banking, healthcare, marketing, fintech, and many more, which was earlier constricted to big enterprises with large amounts of resources.
Automation of these tasks helps organizations to deploy and manage the solutions quickly and help free up precious time of data scientists which they can use for more complex tasks.
AI designing AI
AI Optimizing AI
AI correcting AI
Let us know about each one in detail.
Many highly skilled data scientists are needed to build these custom neural networks, which is both time-consuming and can cause errors. So the new area is the designing of neural networks automatically, which are more accurate and does not require any coding. With this, you can also optimize the model to evaluate its neural network accordingly
The optimization of AI needs not just expertise in the field and knowledge of coding but also a significant amount of time spend on trial and error. The technology of AI optimizing AI, however, is based on learning patterns between data classification and transformation, exploration and combining intelligent enumeration and selection of features. It helps in automation of the task of trial and error, which is the most time consuming of all.
This best can be explained through an example. Suppose you prepared a dish using all the right ingredients but forgot one or used the wrong one. Now, in the long run, you have to remake the whole meal to get the required results. But the situation can also be handled by just putting in the forgotten ingredient without compromising the taste. AI correcting AI works on the same principle. It fixes the problem without compromising with accuracy.
Automation of Artificial Intelligence has helped in a lot of ways from making the model error free to freeing up data scientists time. It has also helped carry out the most challenging task of trial and error by optimization. Through the automation of AI, real-time models can also be corrected without compromising with accuracy. So automation of AI sure is a trigger in revolutionizing the technology.
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