What Every Software Engineer Should Be Learning For 2018 | Robotics

Since scientists devised the term Artificial Intelligence (AI) more than six decades ago, men have entertained the idea of soon seeing and interacting with a self-thinking computer in business, in schools, and in their day-to-day activities. Nowadays, that idea is actually being applied and many companies believe that with sufficient talent and technology, Artificial Intelligence can possibly become an actual product.

Recently, the tech world is echoing with talks of Machine Learning (ML) and Artificial Intelligence (AI). Big names in the tech industry like Google, IBM, Apple, and Facebook to name a few are now offering high profile consumer products like self-driving cars, smart assistants, and even image and face recognition apps. Business initiators are now trying to weigh the impact of Machine Learning and Artificial Intelligence in Business Intelligence and even in Analytics.

The Search is On

With the latest buzz in the tech world on the impact of Machine Learning and Artificial Intelligence, tech companies are now looking for technology professionals who can make a workable Artificial Intelligence platform. These types of tasks require specialized knowledge and experience to successfully accomplish, making it all the more challenging to select and delegate to the right individual or team. Doug Henschen, Vice President and Principal Analyst of Constellation Research Inc., notes that starting 2018 and beyond, there will be an increasing list of smart capabilities fueled by machine (ML) and Artificial Intelligence (AI).

He adds that there will be a steady outpouring of announcements in throughout 2018 and beyond on ML used to tasks. This includes cleansing and linking data, ascertaining new data, and even offering fresh mixes of data that could unravel crucial insights. The business executive also states that there are more than 20 startups and around 14 BI and analytics vendors who have already made investments in advancing the field. This includes data preparation, prediction, and discovery analysis.

Shortage of Talent

There is a surging demand for professionals with skills in Machine Learning and Artificial Intelligence. According to Gary Kazantsev, Bloomberg’s Head of Machine Learning, although Artificial Intelligence and Machine Learning are now becoming ordinary, there is still a noticeable shortage of talent. He adds that the shortage is now becoming worse since many enterprises create their own AI groups and even include AI in their corporate strategy. Businesses are currently creating ways on how to fill this skill gap.

Learning and Training People

Joel Dodge, Software at Infer shares that they expect everyone on their team to do the learning on-site to fill the skill gap. The engineer believes that it is uncommon nowadays to get a candidate who has years of experience with machine learning dilemma. Abdul Razack, Senior VP and Head of Platforms at Infosys shares that their approach to solving the skill gap problem is by hiring a specialist and teaching the workforce the needed skills and expertise. This could mean getting a statistical programmer and training them in data strategy. It could also mean inculcating more statistics to a personnel skilled in data processing.

There are also organizations like Allen Institute for Artificial Intelligence who are working on solutions to solve this problem. Companies like Google and Facebook have been recently open-sourcing high-powered Artificial Intelligence and Machine Learning tools like TensorFlow.

While the tech world is currently on abuzz about the impact of Artificial Intelligence and Machine Learning in the business and in the world as a whole, the question lies on the availability of programmers who understand the algorithms. It is the ripe time to be a engineer skilled in both AI and ML as right now, the world is still in the early stages of applying Artificial Intelligence.

Written by Lindsey Patterson

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