« Back CERTAIN THINGS YOU NEED TO KNOW ABOUT MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE


By Harshita  |  Machine Learning Training  |  On 12/13/2018 3:58:24 AM

CERTAIN THINGS YOU NEED TO KNOW ABOUT MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

Let us know what they mean and how they work:

 

Machine learning is considered as an application of AI which helps in providing the system the ability to learn automatically and also at the same time improve from such experiences without being programmed explicitly. It mostly aims of focusing on the development of the computer program which can help in assessing of data and also use it to learn. While AI is an area of computer science focuses on the creation of machines which tend to work exactly like humans. Though AI and machine learning is used as a synonym, but it is not the same thing. The difference between two is that machine learning is a process through which a computer tends to learn a skill while AI I the computer that works on its own.

Thus, AI is a very broad concept that represents how computers can work as humans and carry out the smart tasks, while machine learning adheres to a specific subset of algorithms od AI which helps one in transferring knowledge which helps the system to act accordingly without being programmed. These two are very different things yet they served the same goal which is why they are often used together or used as synonyms for each other. Though to use such machine learning techniques, requires training and learnings where tech data solutions seem to excel. The best ones are the machine learning training in Mumbai and machine learning training in Pune that masters all.

There are certain things we don’t know about machine learning which are:

It Is Mostly About Data

There has been a lot of advance in the sector of machine learning algorithm and deep learning. But the main ingredient is data which has made machine learning possible. Machine learning is possible without an algorithm but not without data.

Stick To Simple Models If There Is Not Much Data

It tends to train models through patterns in the data which explores the space of models which is basically defined by parameters. If the parameters are huge then one will tend to overfit the training data which will ultimately lead to the training of a model that does not generalize beyond it. More math is required than a detailed explanation though it is suggested to keep the models simple.

It Is Only Good As The Data Used To Train It

The patterns that are present in the training data are the only ones discovered by machine learning. A richly featured and correctly labeled a robust collection of training data is required for supervised machine learning tasks. Seeking for Data Science Training in Mumbai & Pune locations, I recommend you to TechData Solutions.

Only Works If Training Data Is Representative

“Past performance has no guarantee of future results”. This phrase is applicable to machine learning as well. According to machine learning, it’s only going to work if the data is generated only by that distribution which had generated its training data. It is advised to always look keep on retraining the models so that they don’t become obsolete.

Data Transformation Is The Main Hard Work

It has been publicized that AI is all about tuning and selecting algorithms when we talk about new machine techniques. But in reality, most of the time is occupied in feature engineering and cleansing of data which means the transformation of raw features into features that can represent the signal in the data.

Now that we have come across machine learning, there are certain things we should know about AI as well:

We Use AI Every Day

AI is all around us and we use it in our everyday life as it is all around us. Cortona, Siri, and Google now are some obvious examples of AI. They are also founded in cars, vacuum cleaners, video games, lawnmowers, etc. The special effects in Hollywood, medical research, market software, and international financial markets are some of the other very common examples of AI. It was once said that once it works, no one calls it AI anymore, which is why we do not know that we have been using it every day!

Robots Will Take Human Jobs

It has been forecasted that by 30 years all the human jobs will be done by robots which will lead to employment more than 50%. It does sound bad when we talk about it, but it is said that this technological employment will lead to a time where people will tend to work for pleasure and not because of necessity. Universal basic income is the proposal that defines the support structure from a society which could help in making it happen.

According To AI, The Computer Will Be As Smart As Humans By 2040

According to a survey executed by the Artificial intelligence department, it was observed that by 2040 there is a 50/50 chance of computers being as smart as the humans. The researches said that it is expected that there is a great chance of it happening very soon which could bring a lot of growth in the near future.

Artificial Intelligence Cannot Be Evil

AI cannot be evil, it is just a concept build by humans. It is true that the AI can do some horrific things, but it does so as it has been designed or programmed to do so, it doesn’t do them out any wickedness or for any evil intentions. It was said by Stephen Hawking, that an intelligent AI can achieve goals at a tremendous level, though if they aren’t aligned with our goals, we can be in trouble.

Ways How Superintelligent Artificial Intelligence Can Work

In the book Superintelligence: Paths, Dangers, Strategies, it has been defined by the author Nick Bostrom, that how the superintelligent can operate in three ways. :

• First is the oracle that can help in answering questions with a great level of accuracy

• Second is the genie that would fulfill all the commands

·         And third is the sovereign, that would be assigned a goal, along with allowing them to operate such goals in the world, as well as make decisions regarding the same.

Here is another article which might interest you, All you need to know about machine learning.