The domain of science and technology is an ever-evolving one and the progress has been noteworthy in the last two decades. Among all the highlights, the development of artificial intelligence (AI) and machine learning (ML) has been a notable one. The reason is that their widespread application is making a tangible difference in everyday human life and playing a part in making it better.
The field of AI and data science is one such domain involving machines like computers and robots to perform complex thinking activities like human beings. In short, it is the simulation of human intelligence in machines. An engineer in this field works with traditional machine learning techniques to build models powered by artificial intelligence.
Read on to know more about these two fields, their difference, and their importance.
What is Machine Learning?
Machine learning is an offshoot of artificial intelligence focusing on better handling of computer data and algorithms. It is probably the most important component of the eternally growing field of artificial intelligence. In addition, statistical methods are added with data and algorithms to make trained predictions with data mining projects and key insights.
Further, machine learning has turned out to be an important arena for most of the leading companies of this day. It renders to each of these companies a comparative analysis to view the trends in consumer behavior in response to operational patterns in the business.
Generally, there are four basic approaches to machine learning, including supervised, semi-supervised, unsupervised, and reinforcement learning. Data scientists choose either of the four approaches to plan a route map to their labeled inputs and desired outputs. Machine learning has turned out to be the important backbone in a wide range of applications. It leads to the development of business intelligence and the management of customer relationship patterns, human resource information systems, and virtual assistants.
What is Artificial Intelligence?
The field of artificial intelligence centers around digesting volumes of data by computer and analyzing it like human intelligence. This digestion includes elaborate scrutinizing patterns to make predictions about future situations.
A common instance is the use of a chatbot that is fed with examples of typical replies to its customers. It is also equipped with some image recognition tools that can be helped to identify different images by looking at millions of examples.
The total crux of artificial intelligence is to transform data into actionable information. It uses reasoning and self-correlation processes to copiously tune the algorithms to provide the most accurate possible results.
This has led to efficiency on the part of organizations. Earlier, it would have been quite difficult even to concretize the idea of connecting customers to booking their rides or having a personalized view of the webpage according to the customer's preferences.
AI Engineer Roles and Responsibilities
Artificial Intelligence and machine learning has rapidly evolved, becoming an integral part of our daily lives, powering virtual assistants like Siri and Alexa, and recommendation systems on online platforms. Behind the scenes, AI engineers play a crucial role in designing, developing, and implementing these intelligent systems.
AI Model Development and Implementation
AI engineers develop and implement machine learning models, selecting appropriate architectures, preparing data for training, and fine-tuning model parameters for desired outcomes.
Data Pre-processing and Analysis
AI engineers pre-process and analyse data to ensure its quality and relevance. They excel in data wrangling, cleaning, and feature engineering to prepare data for model training.
AI Model Evaluation and Testing
AI engineers assess model performance using various metrics and validation techniques, identifying issues and fine-tuning models for better results.
Deployment and Maintenance
AI engineers deploy models into production environments, ensuring seamless integration and handling maintenance and updates.
Collaborating with Cross-functional Teams
AI engineers collaborate with data scientists, software engineers, product managers, and domain experts to integrate AI solutions effectively. Read difference between data science and Ai for more.
Problem-solving and Troubleshooting
AI engineers overcome challenges during development and deployment with strong problem-solving skills.
Types of Artificial Intelligence and Their Importance
There are four types of artificial intelligence, namely, reactive machines, theory of mind, limited memory, and awareness of the self. These variants of AI are incorporated into a host of different technologies. And, the following are some prime examples –
- Machine Learning: Through the algorithms of supervised learning, unsupervised learning, and reinforcement learning, machine learning involves the science of predictive analysis.
- Automation: A successful coupling of automation tools and artificial intelligence can expand the horizon of the tasks destined to be performed. As a result, it provides positive data when there is a sudden change in the process.
- Machine Vision: It is a type of technology that allows a machine to see. It captures visual information using a camera with the facility of analog to digital conversion, along with digital processing of the signal. This machine-based processing of the image is much like the human eye.
- Robotics: This growing arena of engineering uses artificial intelligence to work on designing and manufacturing robots. They deliver consistent tasks that are apparently difficult for humans. Researchers use the domain of artificial intelligence to build robots that are well equipped to interact with humans in a social setting.
- Natural Language Processing: Also known as NLP, it is the processing of human language with the aid of a computer program. A common usage of this is the detection of spam messages and e-mails by artificial intelligence. It does this by reading the text and the subject line of the e-mail.
- Self-driven Cars: These cars use a combination of computer vision and image recognition to automate a car by staying in a given lane and avoiding pedestrians or unwanted obstructions on the road.
The major advantage of artificial intelligence is that it includes deep learning with large volumes of data with easy predictions and much faster than humans. As a result, it delivers consistent results and reduces the time that is usually taken to work with this data in large volumes.
Difference Between Artificial Intelligence and Machine Learning
Both machine learning and artificial intelligence are parts of computer language that are correlated with one another. Both are equally important in the creation of intelligent systems. However, there is a hairline difference between them.
Artificial intelligence is a human-made thinking power incorporated into systems to celebrate human intelligence. It helps in solving complex problems. It is also known to have a larger scope. On the contrary, machine learning is an offshoot of artificial intelligence that paves the way for computer systems to make predictions on some sort of historical data programming. In short, it uses algorithms to make fruitful predictions for various organizations.
However, it has a limited scope and can only perform activities in which the system is trained. While the system of artificial intelligence is involved in the maximization of success, machine learning deals with the accuracy of diverse patterns.
What Does an Artificial Intelligence Engineer do?
An AI engineer is a skilled professional who complies with the traditional techniques of machine learning with the natural processing of human language to build AI models.
What are the Skills Needed?
In terms of education, an artificial intelligence engineer must have a degree in the following fields:
- Computer science
- Applied mathematics
- Cognitive science
In addition, the following technical skills are required:
- Programming: Artificial intelligence engineers must be proficient in programming languages like Python and R along with a degree in computer science.
- Math/Statistics: Artificial intelligence engineering involves machine learning models centering on statistics and probability. This is much needed to determine the validity and accuracy of models.
Furthermore, a number of soft skills are required for an artificial intelligence engineer to be successful:
- Creativity: Artificial intelligence engineers, along with their technical prowess, must have a creative and thoughtful mind. They should be comfortable in thinking out of the box when required and have the necessary skill set to implement them.
- Business Knowledge: It is necessary for any artificial intelligence engineer to know the business tactics so as to add value to the company. He or she has to have sound knowledge from a strategic level to a tactical level.
- Communication: Communication skills are also a priority to establish a proper connection. Otherwise, it will become difficult to pull everybody in the same direction and put in the best team effort. As a result of poor communication, the productivity of the organization will go down, which is not a welcoming thing for anyone.
Salaries of Artificial Intelligence Engineers
The field of artificial intelligence is very much growing and the salaries of trained personnel can go as high as Rs 50 lakh per annum. However, a point to bear in mind here is that this will depend on a few factors like the qualification, work experience, and achievements of the personnel. Moreover, the salary structure of the company one is joining will play a part here.
Artificial intelligence engineers are always in high demand simply for the high potential that is offered by this domain. This job is always the most in demand and is increasing the employment opportunities with each passing day. What is more to it? It absorbs passionate workers both at the entry level as well as at levels of high expertise.
AI engineers are at the forefront of the AI revolution, shaping the future with their expertise. With a passion for technology and a drive to create intelligent systems, aspiring AI engineers can embark on a rewarding and impactful career in AI. The trends in artificial intelligence and machine learning are becoming increasingly crucial in various industries, making it an attractive and promising career choice.
The domain of AI and ML holds great growth prospects. Resultantly, the career opportunities are equally excellent in this segment. So, if you are interested in this technology, then go ahead and complete a course that will help you understand the basics of it and get you employed.
In this regard, the Hero Vired Artificial Intelligence program is one of the best in the market to this day. It provides a practical understanding of concepts, offers deep knowledge of case studies, and presents analysis accordingly. Completing it will help you to get the necessary knowledge about the domain and become a better professional.