What is Artificial Intelligence & Machine Learning

by - March 12, 2019

Artificial Intelligence


Artificial Intelligence (AI) is a field of study of computer science that makes machines seem like as if they have human intelligence.
In short, AI can be defined as “a machine with human thought process.”

 A person called together a group of scientists and mathematicians to see if a machine could learn just like a child does using trial and error to develop formal reasoning and problem-solving skills.
An AI machine (or program) follows 3 basic steps viz.,
   1.  Learn
   2.  Act
   3.  Adapt
It then repeats these steps over and over again and gets much smarter as more and more data are fed to it.
Selective Focus Photography of Two Danbo and Star Wars Stormtrooper Robot Toys

Types of AI
1.  Analytical AI
2. Human-inspired AI
3. Humanized AI
Machine Learning
Types of ML algorithms
1. Regulated and semi-managed learning
2. Unsupervised learning
3. Reinforcement learning
Applications of AI
1. Healthcare
2. Automotive
3. Government
4. Video Games
5. Advertising
6. Art
Questions on AI
Getting Started in AI
1. Newbie
2. Programmer
3. Data Scientists
Jobs in AI
2. Data Scientist
3. Business Intelligence (BI) Developer
4. Research Scientist
5. Big Data Engineer/ Architect
1. Critical Thinking

2. Creativity

3. Leadership
Artificial Intelligence can be classified into 3 different types of AI systems viz.,
It has the characteristics of acquiring knowledge and processing thoughts.
It has the ability to process emotions and feelings along with logics.
It has logical reasoning, emotional feeling and social intelligence.

Reasoning, planninglearningnatural language processingperception and the ability to move and manipulate objects are the traditional goals of AI research. General intelligence is the long-term goals. Many tools used in AI, are based on statistics, probability and economics such as search and mathematical optimization, artificial neural networks, and methods. The AI field expands to computer science, information engineering, mathematicspsychology, philosophy, and many more.

Machine learning (ML) may be defined as a set of Artificial Intelligence that focuses on the scientific study of applied mathematics models and algorithms that pc systems use to effectively perform a particular task while notusing explicit instructions.
In short, ML means “training an AI”.

The algorithms differ with each other on the basis of “type of data” and “type of task”.Regulated learning calculations are known to manufacture a scientific model of a lot of information that contains both the sources of info and the ideal yields, which prepares information, and comprises of a lot of preparing precedents.These algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points and therefore learn from test data that has not been labelled, classified or categorized. These algorithms identify commonalities in the data and react based on the presence or absence of such commonalities.Reinforcement learning is generally focussing on how software agents ought to take actions in an environment. They use dynamic programming techniques.

Gray and White Robot
AI is capable of doing any intellectual task as long as it is given enough amount of data to consume and produce the most probable outcome.
But, when an AI technique reaches mainstream use and becomes a tool of daily routine, then it is neglected and no longer considered as an artificial intelligence which is known as the AI effect. For e.g., an optical character reader is now no longer considered as a unique helpful device as it has become a general-purpose device. As we continue to make more advanced AI machines (or software), the previous ones will fall to the prey to AI effect and will sadly no longer be considered as an Artificial Intelligence device.
AI is being used in vehicles (self-driving cars, drones), online assistants (Google Assistant, Siri, Alexa, Cortana), art (making paintings and writing poetries), medical science, online search engines (Google, Bing), playing games and quizzes.

AI can correctly determine the accurate dose of drugs to be given to patients. It can predict which combination of drugs will be most effective for a person. IBM Watson (AI computer of IBM) gained worldwide recognition for successfully diagnosing a woman who was suffering from Leukemia. In the years to come, AI will be making its place above highly trained doctors and surgeons.
Today, there are over 30 companies utilizing AI in their driverless cars, some of them are tech giants such as Google, Apple, and Tesla. Recent developments have made the innovation of self-driving trucks possible. Tesla has recently launched Model-T which is a driverless car under the budget of $30,000.00 only which makes it viable to use for common public too.
Applications of AI to the public sector are growing around the world. It can be used for public policy objectives (emergency services, health and welfare), as well as assist the public to interact with government (virtual assistants for public queries). AI can be used to automate the repetitive paperwork at government offices and hence can help us gain some more time for other policies. 
In video games, it is generally used to generate dynamic purposeful behaviour in non-player characters (NPCs). Modern video games make the use of Virtual Reality (VR) and Augmented Reality (AR) along with Artificial Intelligence (AI) to produce real-life like scenes and realistic graphics.

Major publishers now use artificial intelligence (AI) technology to post stories more effectively and generate higher volumes of traffic, AI is being used as an effective tool to target specific audience for a specific niche with appropriate and relevant online advertisements.

A highly trained AI is capable of creating a masterpiece much more perfect than the original one. It can learn patterns and lines and specific words and colours to be used for the artwork to be made. A perfectly trained AI can do stuff far beyond the capabilities of an artist’s mind. It can go places with amazing possibilities. Recent exhibitions showcasing the usage of AI to produce art include the Google-sponsored benefit and auction at the Gray Area Foundation in San Francisco, where artists experimented with the deepdream algorithm. The exhibition named "Unhuman: Art in the Age of AI” which took place in Los Angeles and Frankfurt in 2017.

As AI is growing at such an exponential rate, it is obvious that some questions are being raised to its capabilities and intentions, some of which are as follows.
1.  Can a machine solve any problem that a human being can solve using intelligence?
2.  How can we ensure that machines behave ethically and that they are used ethically?

Just the fact that five out of six people use AI services in one form or another every day proves that this is a viable career option.  AI has been the most preferred career choice for a while now because of the growing adoption of the technology across industries and the need for trained professionals to do the jobs created by this growth. It is assumed that AI will create close to 2.3 million jobs by 2020. It is estimated that this technology will wipe out over 1.7 million jobs, resulting in about half a million new jobs worldwide. AI also offers many unique career opportunities. You should get your hands on this opportunity before the time fades away.

There are different paths for different types of professionals interested in learning AI.

If you are new to the field of computer science, then firstly you should start with mathematics and some basic coding experience (like C++ or Python), and then move to the complex algorithms.

If you are well-aware about the world of programming and have enough experience, then you can move straight into the algorithms and start coding.

If you are moving from a data analysis to machine learning, you must know how to prepare data, as well as have good communication skills and business knowledge, and be proficient at model building and visualization; all along with great programming skills.

No matter where you are starting from, just remember that, An AI never stops learning, so you can’t stop learning either.

If career in artificial intelligence appeals you and you want a piece of the AI pie, then what kind of jobs should you start looking for? What skills would you need to get hired? Let’s take a closer look.
1. Machine Learning Engineer
Role: Their role is at the heart of AI projects and is perfect for those who hail from a background in applied research and data science. They are responsible for building and managing platforms for machine learning projects.
Annual Average Salary: $114,856
Skills required: Java, Python

Role: Data scientists do the work of collecting, analysing, and interpreting large, complex datasets by both machine learning and predictive analytics. They also play a very important role in developing algorithms that enable the collection and cleaning of data which help in its further analysis.
Annual Average Salary: $120,931
Skills Required: Hive, Hadoop, Perl, Python, Scala, SQL

Role: BI developers analyse complex data sets to identify business and market trends. They are typically responsible for designing, modelling, and maintaining complex data in highly accessible cloud-based data platforms.
Annual Average Salary: $92,278
Skills Required: Data warehouse design, Data mining, SQL (Queries, Server Integration Services, Server Reporting Services).

Role: Research Scientists are experts in multiple AI disciplines, including applied mathematics, machine learning, deep learning, and computational statistics.
Average Annual Salary: $99,809
Skills Required: Benchmarking, Parallel computing, Distributed computing.

Role: Big data engineers and architects play a vital role in developing an ecosystem that enables business systems to communicate with each other and collate data, most companies prefer professionals who have completed a Ph.D. in mathematics, computer science, or a related field.
Annual Average Salary: $151,307
Skills Required: C++, Java, Python, Scala, Data mining, Data visualization, Data migration.

Along with the above-mentioned skills, an AI professional must have the following soft skills as he/she needs to work in a team to complete an AI project.

Critical Thinking allows the employee to use AI to make meaningful decisions. For example, a computer can model climate change, but it takes human beings to devise and enact policies to stem it.
Creativity helps employees develop innovative solutions to problems. It requires openness to innovation and mental flexibility. In a future when AI efficiency and productivity are givens, the organizations that permit original ideas to flow freely will distinguish themselves. Nick Seneca says, “Breakthrough creativity is fundamentally organic, not algorithmic.” 
Leadership skills help provide any employee with the ability to take charge of a situation and ensure it is solved or brought to a successful conclusion. It about “getting work done” through others. Today, if you are operating in machine learning, you're possibly operating as a part of a team, and this team would comprise people that have direct interaction with the business. Hence, it directly implies that if you want to be successful as a machine learning practitioner today, you must be ready and able to interact with the business and be a team player.

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