Where does one start to get a career in Artificial Intelligence?
With the rise in digitalization, artificial intelligence has become a rich and welcoming branch of computer science that deals with smart machines. Artificial intelligence is constantly rising. Hence it offers several career opportunities.
In the beginning, undergraduate courses are good, but a Master’s degree is essential for growth and promotions. Primarily you can buy books and keep working on your mathematical and statistical skills, or you can straightaway go for an Artificial Intelligence training program.
To be an AI Developer, there is a skill set required, which includes the following essentially:
- Statistical skill: Artificial Intelligence starts with excellence in reading and understanding statistics and graphs.
- Mathematical skills and probability: To have a perfect understanding of probability and profits is essential.
- Programming skills: When it comes to smart machines, it includes programming, programming languages like java, python, C++, etc.
To step into the field of Artificial intelligence, you need to go for Artificial Intelligence training. Being a wide branch, it requires an equally extraordinary amount of knowledge, and hence, there are various Artificial Intelligence certification courses you can opt for.
Career paths in Artificial Intelligence include:
- Data Analytics
- User experience
- Natural Language Processing
- Software Engineer
- Artificial intelligence engineer
- Data Scientist
- Data Mining
What are the advantages and disadvantages of artificial intelligence?
- Ethical at detail-oriented jobs
- Saves time for data-heavy tasks
- Offers consistent results; and
- AI-powered virtual agents are always available
- Requires deep technical expertise
- A limited supply of qualified workers to build AI tools;
- Only knows what it’s been shown; and
- Lack of ability to generalize from one task to another.
Strong AI vs. Weak AI
AI can be categorized as either weak or strong.
Weak AI is an AI system that is designed and trained to complete a specific task. Industrial robots and virtual personal assistants, such as Apple’s Siri, use weak AI.
Strong AI describes programming that can replicate the cognitive abilities of the human brain. When presented with an unfamiliar task, a strong AI system can use fuzzy logic to apply knowledge from one domain to another and find a solution.
Applications of AI:
Nowadays, AI is used everywhere, even when you can’t imagine a music system, smart assistants like Alexa, Siri, robot cleaners, and email spam filters. Netflix’s recommendation also involves AI in them.
AI in law
The discovery process — sifting through documents — in law is often overwhelming for humans. Using AI to help automate the legal industry’s labor-intensive processes is saving time and improving client service. Law firms use machine learning to describe data and predict outcomes, computer vision to classify and extract information from documents, and natural language processing to interpret requests for information.
AI in manufacturing
Manufacturing has been at the forefront of incorporating robots into the workflow. For example, the industrial robots that were programmed to perform single tasks and separated from human workers increasingly function as cobots: Smaller, multitasking robots that collaborate with humans and take on responsibility for more parts of the job in warehouses, factory floors, and other workspaces.
AI in banking
Banks successfully employ chatbots to make their customers aware of services and offerings and handle transactions that don’t require human intervention. AI virtual assistants are being used to improve and cut the costs of compliance with banking regulations. Banking organizations also use AI to enhance their decision-making for loans, set credit limits, and identify investment opportunities.
AI in transportation
In addition to AI’s fundamental role in operating vehicles, AI technologies are used in transportation to manage traffic, predict flight delays, and make ocean shipping safer and more efficient.
AI and machine learning are at the top of the buzzword list security vendors use today to differentiate their offerings. Those terms also represent truly viable technologies. Organizations use machine learning in security information and event management (SIEM) software and related areas to detect anomalies and identify suspicious activities that indicate threats. By analyzing data and using logic to identify similarities to known malicious code, AI can alert new and emerging attacks much sooner than human employees and previous technology iterations. The maturing technology is playing a significant role in helping organizations fight off cyberattacks.
Types of AI
Artificial intelligence is of four types:
- Reactive Machines
Reactive machines are the simplest level of robot. They cannot create memories or use information learned to influence future decisions — they are only able to react to presently existing situations.
2. Limited Memory
As the name might suggest, a limited memory machine can retain some information learned from observing previous events or data. It can build knowledge using that memory in conjunction with pre-programmed data. Self-driving cars, for instance, store pre-programmed data — i.e., lane markings and maps, alongside observing surrounding information such as the speed and direction of nearby cars or the movement of nearby pedestrians.
3. Theory of mind
It is this theory of mind that allows humans to have social interactions and form societies. Theory of mind machines would be required to use the information derived from people and learn from it, informing how the machine communicates in or reacts to a different situation.
As a conscious being, this machine would not just know of its internal state but also predict the feelings of others around it. For instance, if someone yells at us, we assume that person is angry because we understand that is how we feel when we yell. Without a theory of mind, we would not be able to make these inferences from other humans.