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The future of robotics: 10 predictions for 2017 and beyond

What does the future hold for robotics? It's hard to say, given the rapid pace of change in the field as well as in associated areas such as machine learning and artificial intelligence. But one thing seems certain: Robots will play an increasingly important role in business and life in general.

1. Growth of "robot as a service." By 2019, 30 percent of commercial service robotic applications will be in the form of a robot-as-a-service (RaaS) business model. This will help cut costs for robot deployment.

2. Emergence of the chief robotics officer. By 2019, 30 percent of leading organizations will implement a chief robotics officer role and/or define a robotics-specific function within the business.

3. An evolving competitive landscape. By 2020, organizations will have a greater choice of vendors as new players enter the $80-billion information and communications technology market to support robotics deployment.

4. The coming robotics talent crunch. By 2020, robotics growth will accelerate the talent race, leaving 35 percent of robotics-related jobs vacant, while the average salary increases by at least 60 percent.

5. Robotics will face regulation. By 2019, government entities will begin implementing robotics-specific regulations to preserve jobs and to address concerns about security, safety, and privacy.

6. Rise of the software-defined robots. By 2020, 60 percent of robots will depend on cloud-based software to define new skills, cognitive capabilities, and application programs, leading to the formation of a robotics cloud marketplace.

7. More collaborative robots. By 2018, 30 percent of all new robotic deployments will be smart collaborative robots that operate three times faster than today's robots and are safe for work around humans.

8. Arrival of the Intelligent RoboNet. By 2020, 40 percent of commercial robots will become connected to a mesh of shared intelligence, resulting in 200 percent improvement in overall robotic operational efficiency.

9. Growth in robots outside the factory. By 2019, 35 percent of leading organizations in logistics, health, utilities, and resources will explore the use of robots to automate operations.

10. Robotics for Ecommerce. By 2018, 45 percent of the 200 leading global ecommerce and omni-channel commerce companies will deploy robotics systems in their order fulfillment warehousing and delivery operations.

To become a Data Scientist:

Education: 88% have a Master’s Degree and 46% have PhDs
In-depth knowledge of SAS and/or R: For Data Science, R is generally preferred.
Python coding: Python is the most common coding language that is used in data science along with Java, Perl, C/C++.
Hadoop platform: Although not always a requirement, knowing the Hadoop platform is still preferred for the field. Having a bit of experience in Hive or Pig is also a huge selling point.
SQL database/coding: Though NoSQL and Hadoop have become a major part of the Data Science background, it is still preferred if you can write and execute complex queries in SQL.
Working with unstructured data: It is most important that a Data Scientist is able to work with unstructured data be it on social media, video feeds, or audio.

To become a Big Data professional:

Analytical skills: The ability to be able to make sense of the piles of data that you get. With analytical abilities, you will be able to determine which data is relevant to your solution, more like problem solving.
Creativity: You need to have the ability to create new methods to gather, interpret, and analyze a data strategy. This is an extremely suitable skill to possess.
Mathematics and statistical skills: Good, old fashioned “number crunching”. This is extremely necessary, be it in data science, data analytics, or big data.
Computer science: Computers are the workhorses behind every data strategy. Programmers will have a constant need to come up with algorithms to process data into insights.
Business skills: Big Data professionals will need to have an understanding of the business objectives that are in place, as well as the underlying processes that drive the growth of the business as well as its profit.

To become a Data Analyst:

Programming skills: Knowing programming languages are R and Python are extremely important for any data analyst.
Statistical skills and mathematics: Descriptive and inferential statistics and experimental designs are a must for data scientists.
Machine learning skills
Data wrangling skills: The ability to map raw data and convert it into another format that allows for a more convenient consumption of the data.
Communication and Data Visualization skills
Data Intuition: it is extremely important for professional to be able to think like a data analyst.

Now let’s talk about salaries!

Though in the same domain, each of these professionals, data scientists, big data specialists, and data analysts, earn varied salaries.

The average a data scientist earns today, according to Indeed.com is $123,000 a year. According to Glassdoor, the average salary for a Data Scientist is $113,436 per year.

The average salary of a Big Data specialist according to Glassdoor is $62,066 per year.

The average salary of a data analyst according to Glassdoor is $60,476 per year.

Now that you know the differences, which one do you think is most suited for you – Data Science? Big Data? Or Data Analytics?

If you’d like to become a complete expert in Data Science or Big Data – check out our Masters Program certification training courses: the Data Scientist Masters Program and the Big Data Architect Masters Program.

With industry recommended learning paths, exclusive access to experts in the industry, hands-on project experience, and a Masters certificate on completion, these packages will give you need to excel in the fields and become an expert.
So what are you waiting for? Get out there, and get certified, today at Spaecare.

Machine Learning: What it is and why it matters?

A recent news item went as follows: ‘Apple buys machine learning firm Perceptio Inc., a startup, in an attempt to bring advanced image-classifying artificial intelligence to smartphones by reducing data overhead which is typically required of conventional methods’. Another recent development was that MIT researchers were working on object recognition through flexible machine learning. Yet another tech enthusiast, David Auerbach, claims that, ‘Machine learning is starting to reshape how we live and it’s time we understood what it was and why it matters. So, what IS Machine learning and why has it got everybody talking? Read on to learn all you need to know!

Pro-Tip: To fast-track your learning, consider Simplilearn's comprehensive Machine Learning Advanced Certification Training. With modules in supervised and unsupervised learning, deep learning, Spark, and live industry projects, you become a job-ready machine learning specialist in a matter of a few weeks!

Machine learning is a core sub-area of artificial intelligence as it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, computer programs, are enabled to learn, grow, change, and develop by themselves.

SAS, the North Carolina-based, American developer of analytics software comes with a definition on it: ‘Machine learning is a method of data analysis that automates analytical model building’. In other words, it allows computers to find insightful information without being programmed into where to look for a particular piece of information. This, it does by using algorithms that iteratively learn from data.

While the concept of machine learning has been around for a long time, (one might be reminded of the notable example here – Alan Turing’s famous Enigma Machine) the ability to automatically apply complex mathematical calculations to big data – iteratively and quickly – is gaining momentum only in recent times.

This emphasizes the iterative aspect of machine learning – the ability to independently adapt to new data.

While the concept of machine learning has been around for a long time, (one might be reminded of the notable example here – Alan Turing’s famous Enigma Machine) the ability to automatically apply complex mathematical calculations to big data – iteratively and quickly – is gaining momentum only in recent times.

This emphasizes the iterative aspect of machine learning – the ability to independently adapt to new data.

This is made possible as they learn from previous computations and make “pattern recognitions” in order to produce reliable results.

To understand better about the uses of machine learning, we might want to consider some of the instances where machine learning is applied: the self-driving Google car, cyber fraud detection, online recommendation engines - like friend recommendations on Facebook, movie recommendations on Netflix and offers recommendations from Amazon – are all examples of applied machine learning.

All of this echoes the vitality of the role machine learning can play in today’s data-rich world. A recent report from Mckinsey Global has asserted this fact by claiming that machine learning will be the driving factor behind the big wave of innovation in the coming times. Obviously, if machines can aid in filtering useful pieces of information that help in major advancements, and if machines can learn through programmed algorithms, all by themselves, then the technology is bound to find implementation in a wide variety of industries.

Why Machine Learning?

With the constant evolution of the field, there has been a subsequent raise in the uses, demands, and importance of machine learning. The answer to the question as to why one has to adopt machine learning would be: ‘High-value predictions that can guide better decisions and smart actions in real time without human intervention’ (Source: SAS).

Thus, if big data is gaining all the importance for the contributions it does, machine learning as a technology that helps analyze these large chunks of big data, easing the task of data scientists, in an automated process is equally gaining prominence and recognition. Machine learning has also changed the way data extraction, and interpretation is done by involving automatic sets of generic methods that have replaced traditional statistical techniques.

Uses Of Machine Learning

Some instances of machine learning applicability were mentioned previously. To understand the concept of machine learning better, let’s consider some more examples: web search results, real-time ads on web pages and mobile devices, email spam filtering, network intrusion detection, and pattern and image recognition. All these, are by-products of applying machine learning in the analysis of huge volumes of data.

So, how drastically is machine learning revolutionizing the data analysis avenue?

Traditionally, data analysis has always been characterized by trial-and-error, an approach that becomes impossible when data sets are large and heterogeneous. It is for the very same reason, that big data was criticized as being an overhyped technology. Availability of more data is directly proportional to the difficulty of coming up with predictive models that work accurately. Also, traditional statistical solutions are focused on static analysis that is limited to the analysis of samples that are frozen in time. This could obviously result in inaccurate and unreliable conclusions.
Machine learning comes as the solution to all this chaos. It proposes clever alternatives to analyzing huge volumes of data. It is a step forward from all of statistics, computer science and all other emerging applications in the industry. By developing fast and efficient algorithms and data-driven models for real-time processing of data, machine learning is able to produce accurate results and analysis.