Whether you're recruiting or applying for new roles in emerging technologies, understanding where innovation is going and how businesses are implementing hiring and skills strategies is crucial.
We've gathered some tips from The Future of Jobs Report from the World Economic Forum to get you up to speed about what you need to know about tomorrow's tech jobs. This article is the second in a four-part series, with upcoming posts on blockchain, cloud computing and big data analysis.
Machine Learning: Cornerstone of the Fourth Industrial Revolution
Machine learning, perhaps more than any other emerging technology, is at the center of the Fourth Industrial Revolution for its ability to radically change the nature of jobs, job roles and skills required.
The task of finding meaningful patterns in data is automated by machine learning platforms, making it easier to get insights from extremely large data sets. This, in addition, facilitates the implementation of artificial intelligence (AI), automation and widespread industry-wide innovation.
As more activities are automated, it will be important for companies to develop existing positions and create new ones to meet the increasing demand.
Each business needs to invest in its workers to adapt to machine learning instead of being overwhelmed by it in order to weather this next industrial revolution.
The effect of machine learning through industries
In businesses that recognize the potential impact of machine learning on business processes and job roles, it is not a matter of whether — but how much — they are going to use machine learning to accelerate growth and innovation.
Among various applications, businesses are rapidly growing AI programs to advance autonomous driving, data security, fraud detection and customer experience personalization. And they don't seem to slow down.
The Future of Jobs Report found that, in the next three years, 73 percent of all businesses plan to adopt machine learning in some way. The effect will be felt most quickly in the ICT market, with 91% of survey respondents planning to implement machine learning by 2022.
The other two rapidly shifting sectors toward adopting machine learning are the automotive, aerospace, supply chain and transport and consumer sectors, with 87 percent and 82 percent of companies in these industries expecting to adopt machine learning in some form by 2022.
New technologies (such as machine learning) might, according to some estimates, displace 75 million jobs over the next three years. Yet, the potential for new roles to emerge is even greater, representing a forecast 133 million jobs — a significant net job growth.
Changing tasks and new jobs in machine learning
According to The Future of Jobs Report, this massive shift towards adoption of machine learning will require a retraining of at least 54 percent of the current workforce, as well as broad support for education and training to accommodate the new roles.
Organizations overwhelmed by the prospect of implementing large-scale machine learning technology should focus on finding value and time savings in sensitive organizations where implementation is most smooth, and use those successful test cases as a guide.
Starting by automating simple, repeatable tasks in IT, for example, would free people from repetitive work and allow them to spend time on strategic and innovative activities. These activities will then fuel ongoing innovation in technology.
In other words: human imagination, hard work and activities that are cognitively taxing get a boost when manual administrative demands such as data entry, bookkeeping and accounting are handled by automation.
While the reality of automation becoming the norm poses very real concerns about job loss, companies should prepare by devoting resources and attention to the growth of key roles related to machine learning, including data analysts and scientists,
AI and machine learning specialists, process automation experts and interaction designers. This will also result in additional positions such as robotics engineers, blockchain experts, and information security analysts for machine learning?
Next steps for machine learning
Profiting from machine learning involves methodical preparation and redeveloping skills. When determining the company's automation capacity through machine learning and AI, here are three industry-specific factors to keep in mind:
Machine learning in Information Technology
Machine learning could speed up the ability of organizations to simplify the operation and maintenance of ICT networks and services by, for example, making evolving 5 G networks more effective.
But the applications reach beyond pure network expansion and productivity into other business areas; a combination of best practice in machine learning and data science may help companies refine pricing models to help maximize revenues as well as develop capabilities for threat detection.
As with all communications sectors, the question remains: How will you turn over traditionally human-driven roles to machines without dehumanizing them?
Machine learning in automotive, aerospace, supply chain and transport
Companies must increasingly rely on data scientists and experts in AI and machine learning to leverage opportunities to improve customer experience and achieve greater efficiency and productivity across the supply chain.
As well as feeding the growth of autonomous driving, the automotive, aerospace, supply chain and transportation industries will use machine learning in other revolutionary ways — including predictive maintenance of large machines and equipment, delivery forecasting, and human-robot collaboration.
Machine learning in consumer
Would companies be able to use machine learning to predict what consumers want before the customer actually does?
Machine learning will accelerate the ability of companies in this industry to predict purchasing habits, foresee and avoid consumer churn and personalize shopping experience while also driving higher overall efficiency, with a clear roadmap that integrates skills and skills reskilling the workforce.
The TechWorld is changing rapidly
What kind of hard and soft skills do businesses need to succeed in 2020? Here's what you should pay attention to and invest in as you plan your next year (and next decade) tech approach.
It was still possible to argue a few years ago whether machine learning would eventually become a transformative technology in the general business world. There's just no more debate.
What we have seen in machine learning is a development close to that of other groundbreaking technology,it may not change your enterprise or life completely in any single year, but seeing the steady, inexorable development and effect in the longterm, machine learning can not be overlooked.
As this area matures, what we see now is a greater understanding of what capabilities are really important — allowing us to shift our focus from the technical implementation details of the technology themselves into how we can use them to have greater understanding and perspective.
The state of machine learning
There have been myths in recent years about what qualifications companies need to make inroads in machine learning and data science, contributing to a frenzied rush to attract PhDlevel experience without questioning whether that level of expertise was appropriate or even attractive.
But just as most companies don't need their engineers to write database management systems, cryptographic libraries or video decoders (but rather programmers who can incorporate and extend existing platforms and functionalities), most organisations don't need their own special battery of PhDs in computer science and computational analytics that can write machine learning algorithms.
What's much more relevant is to cultivate the interdisciplinary ability to know when and how to use algorithms, to use technical skills as an engine for existing business acumen and domain-specific knowledge of machine learning systems and frameworks.
So instead of accreting these knowledge and experience only around specific "data scientist" positions, we see a trend toward the democratization of data science itself — a more general adoption of machine learning skills and abilities.
For example, it has become popular for business users in different roles over the past two decades to use software such as Excel or PowerPoint to create and display historical data graphs and charts.It is so widespread that it is now called a "general business capability" rather than a specialist ability confined to analysts. Yet providing forward-looking, insightful data – the type of analysis that machine learning can provide – is still uncommon for the average business customer.
This is the next step. In democratizing machine learning skills, we will hit a new frontier once the use of predictive data is as accessible and common as is the use of historical data right now.
What you need to be effective with machine learning in future
If you're still in the early stages of machine learning (as a person, as a team or as a company), understanding where to start can be frustrating. Here are a few areas of focus that will help you build on machine learning over the next 10 years:
It is less important that you are an expert on a specific technology, such as TensorFlow, or a specific machine learning cloud platform, and much more important that you are familiar with the general skillset, vocabulary and machine learning concepts.
Recommending a new programming language is always a controversial task, but the main recommendation is easy for anyone wanting to get involved with machine learning and AI: learn Python.
Yeah, there are other languages in the machine learning community that are common, and if you're either operating in an area or using a specific technology that supports another language — R, for instance — then of course use that.
Otherwise, Python is never a bad choice, particularly for those who have no intention of being practitioners.
One challenge is the immense difficulty of recruiting and maintaining employees with machine learning expertise. If you can consider outside applicants, they lack the knowledge of internal business and strategic history to produce insights. Upgrading the existing staff is not a privilege-it is a requirement.
It's important to remember that insights into machine learning will complement and educate the people, not act as a replacement for their expertise, meaning and experience.Now that we see machine learning taking an increasingly important position at the business strategy table, we need to be ready to rapidly update our teams — and ourselves.
Any company that takes the time to better understand the potential of machine learning in their field and establish a clear strategy to transform the value chain in response will find that they are better prepared to harness the vast potential of machine learning.
It will also be equally important to become real about the ability to meet these new skills on their local labor market, and to create a skills approach to help address the shift in the workforce.