The Best Technology in Software Industry 2019 – MACHINE LEARNING !!
The Best Technology in Software Industry 2019 – MACHINE LEARNING !!
Have you listened to people speaking about machine learning but only have a fuzzy idea of what that implies? Are you curious about machine learning but has no idea where to start? If you are nodding Yes!, Then this post is exclusively for you!!
What are the latest technology in Software Industry 2019?
These technologies are worth to watch closely in 2019.
-
Machine Learning will advance Artificial Intelligence (AI)
-
Quantum Computing (Super computing)
-
Augmented Reality (AR) and Virtual Reality (VR)
-
Global Internet of Things (IoT) security breach
-
Block chain technology
-
Edge Computing
-
Cyber Security
-
Robotic Process Automation or RPA
The Machine Learning market is accomplished to grow to $8.81 billion by 2022. Machine Learning applications are applied for data analytics, data processing, and pattern recognition. On the patron finish, Machine Learning powers net search results, time period ads, and network intrusion detection, to call solely some of the various tasks it will do.
In addition to completing infinite tasks on our account, it’s creating jobs. Machine Learning jobs place among the highest rising jobs on LinkedIn, with implicitly 2000 job listings denote. And these jobs pay well.
Machine Learning jobs embody engineers, developers, researchers, and knowledge scientists.
What is Machine Learning?
Well, Machine Learning is a concept which allows the machine to learn from examples and experience, and that too without being explicitly programmed. So instead of coding on your own, what you do is you feed data to the generic algorithm, and the algorithm/ machine creates the logic based on the given data.
The process of learning starts with observations or data, such as examples, direct exposure, or instruction, in order to look for patterns in data and make better choices in the future based on the examples that we afford. The main aim is to allow the computers to learn automatically without human mediation or assistance and adjust actions respectively.
What is the Difference Between Artificial Intelligence and Machine Learning?
Artificial Intelligence (AI) and Machine Learning (ML) are two very furious buzzwords right now, and often seem to be used reciprocally.
They are not quite the very thing, but the perception that they are can sometimes drive to some uncertainty.
In short, the ablest answer is that: Artificial Intelligence is the wider concept of machines being capable to carry out tasks in a way that we would think “smart”.
And,
Machine Learning is a current purpose of AI based around the concept that we should really just be able to provide machines access to data and make them acquire for themselves.
Evolution of Machine Learning
Two important inventions led to the emergence of Machine Learning as the vehicle which is driving AI progress forward with the pace it currently has.
One of these was the recognition – credited to Arthur Samuel in 1959 – that
“rather than teaching computers everything they need to know about the world and how to carry out tasks, it might be likely to teach them to learn for themselves.”
The second, more latterly, was the evolution of the internet, and the tremendous increase in the amount of digital data being generated, stocked and made open for analysis.
Once these innovations were in place, engineers realized that rather than teaching computers and machines how to do everything, it would be far more efficient to code them to think like human beings, and then plug them into the internet to give them access to all of the information in the world.
Applications of Machine Learning (ML)
1. Virtual Personal Assistants
Siri, Alexa, Google Now are some of the common examples of virtual personal assistants. As the name implies, they assist in obtaining information, when questioned over voice. All you need to do is stimulate them and ask, “What is my agenda for today?”, “What are the trains from Delhi to Mumbai”, or related questions. You can even command assistants for specific tasks like “Set an alarm for 7 AM next morning”, “Remind me to visit Visa Office day after tomorrow”.
For answering, your personal assistant looks out for the data, recalls your detailed queries, or send a request to other resources (like phone apps) to get info.
Machine learning is an essential part of these personal assistants as they gather and polish the information on the basis of your prior involvement with them.
2. Predictions while Commuting
-
Traffic Forecasts:
We all have been using GPS navigation help. While we do that, our current locations and speeds are being stored at a central server for controlling traffic. This data is then utilized to create a map of the current traffic.
While this helps in anticipating the traffic and congestion analysis, the underlying difficulty is that there is less number of cars that are provided with GPS. Machine learning in such situations helps to predict the regions where congestion can be found on the basis of daily experiences.
-
Online Transportation Networks:
When booking a cab, the app determines the price of the ride. When sharing these services, how do they minimize the deviations?
The answer is “ Machine learning ”
Jeff Schneider, the engineering lead at Uber ATC reveals in an interview that they use ML to define price surge hours by predicting the rider demand. In the complete circle of the services, ML is performing a significant role.
3. Social Media Services
From personalizing your news feed to more suitable ads targeting, social media platforms are using machine learning for their own and user privileges. Here are a few samples that you must be seeing, practicing, and enjoying in your social media accounts, without recognizing that these amazing features are nothing but the applications of ML.
-
People You May Know:
Machine learning runs on a simple concept: understanding with experiences. Facebook continuously sees the friends that you connect with, the profiles that you hit very often, your interests, workplace, or a group that you share with someone etc.
On the basis of constant learning, a list of Facebook users are recommended that you can become friends with.
-
Face Recognition:
You upload a picture of you with a friend and Facebook instantly recognizes that friend. Facebook compares the poses and projections in the picture, notice the unusual features, and then match them with the people in your friend list.
The entire process at the back end is complex and takes care of the accuracy factor but seems to be a mere application of ML at the front end.
-
Similar Pins:
Machine learning is the kernel element of Computer Vision, which is a technique to derive useful information from images and videos.
Pinterest uses computer vision to recognize the objects (or pins) in the images and suggest similar pins accordingly.
4. Product Recommendations
You shopped for a product online a few days back and then you keep receiving emails for shopping suggestions. If not this, then you might have seen that the shopping website or the app prescribes you some items that anyhow suit with your taste.
Assuredly, this improves the shopping experience but did you know that it’s machine learning creating the magic for you? On the basis of your action with the website/app, earlier purchases, items wished or added to cart, brand preferences etc., the product suggestions are made.
5. Online Customer Support
Plenty of websites nowadays give the option to chat with customer support spokesperson while they are driving within the site.
However, not every website has a live executive to solve your queries. In most of the crises, you chat to a chatbot. These bots manage to secure information from the website and present it to the customers. In the meantime, the chat bots advance with time.
They tend to follow the user queries better and serve them with more reliable answers, which is plausible due to its machine learning algorithms.
Future with Machine Learning
Google says “Machine Learning is the future,” and the future of Machine Learning is going to be very bright. As humans shift more addicted to machines, we’re signatories to a new revolution that’s taking over the world, and that is going to be the future of Machine Learning.
In modern years, self-driving transports, digital assistants, robotic warehouse workers, and smart cities have determined that smart machines are possible. AI has transformed most industry sectors like retail, manufacturing, finance, healthcare, and media and continues to invade new territories.
The world is simply being reshaped by machine learning.
As machine learning is becoming more complicated, we’ll see extended usage of robots. Robotization depends on machine learning for achieving various purposes, including robot vision, self-supervised learning, and multi-agent learning.
Soon, we suspect robots to become more intelligent at performing tasks. Drones, robots in manufacturing positions, and other types of robots are likely to be adopted frequently to make our lives more prosperous.
Machine learning is one of the most groundbreaking technologies of the 21st centenary. Although this technology can still be held to be nascent, its future is intense.
In the upcoming years, we are inclined to see more high-level applications that amplify its capabilities to unbelievable levels.
Best Jobs of 2019
According to a statement from job site Indeed, machine learning engineer is the best job of 2019 due to rising demand and great salaries.
The career boasts a current average salary of $146,085 with a growth rate of 344 per cent last year.
Roles such as software developer continue to rank highly due to a high number of job openings, but machine learning engineer roles claim the number one spot due to higher salaries and faster growth.
Hurry ! Machine Learning Engineers are in demand !!
Due to the growing use of AI in companies’ works, the report foresees this growth to sustain accelerating in subsequent years.
