Why 'Skilled in Machine Learning' Should Be the New 'Proficient in Excel' On Your Resume
The only difference? What you bring to table will be more valuable than a pivot tables or color-coded pie charts.
It won’t be long before “skilled in machine learning” becomes the new “proficient in Excel” as a standard bullet point on your resume. The only difference? What you bring to table will be more valuable than a pivot tables or color-coded pie charts.
The day when any average Joe can train an algorithm along with his morning coffee is well within reach. The experience of using artificial intelligence is becoming more accessible, and choosing an algorithm to create an end-of-year report will soon be as simple as selecting a template in Microsoft Word. New hires will see promotions come quicker, startups will see faster growth, and the traditional enterprise businesses will see efficiency integrated into their corporate culture, whether they like it or not.
We’re already building machine-learning skills on a daily basis. When we flag a spam email or skip video ads on YouTube, we are training algorithms to apply statistical methods to data so computers can learn what humans want and serve us better content without requiring constant intervention. Training algorithms like this is called “machine learning,” and as this process becomes easier to deploy in more places, we will be able to train AI to do more than just sort our emails or filter our ads.
Citizen developers are contributing daily to open-source projects and crowdsourced algorithms that incrementally generate more ways to use AI and reduce the training time for popular tasks. As developers manipulate algorithms the way you or I would edit a sentence, bots emerge that can master complete high-level tasks such as sending boardroom updates.
Before you know it, the PR intern will flag relevant news articles and generate descriptions of their tone and impact on public perception in seconds; the marketing manager will create a client presentation compiling ideas from a shared Google folder with the click of a button; and the startup CEO will assess the risk-versus-reward of any partnership, acquisition, merger, or IPO on the horizon without batting an eye.
As workplace bots become more valuable, the act of training them gets easier. Machine-learning models like neural networks, a type of deep learning, have strengthened AI to a point where bots can now learn through observing their users. The more natural words, phrases or utterances an algorithm is exposed to, the more accurate and impactful it becomes over time. In turn, the bots that run on top of your selected algorithms get smarter with every word you speak, virtually removing the skill requirement from the rest of the machine-learning process.
With natural language in the driver’s seat, it won’t take a data scientist to unlock the potential for artificial intelligence. As training algorithms becomes easier, the spectrum of what machines can do will expand, one conversation at a time. With every interaction, the value of AI-powered personal assistants becomes more defined, which brings a new level of efficiency to all of us, not just those in the tech industry.
Alexa and Siri currently dominate the personal-assistant space, but once people realize they can train a chatbot to enhance other aspects of their daily lives beyond ordering toilet paper and checking the weather, we’ll enter the next era of user-focused AI. With a refreshed approach to chatbots and personal assistants, a new-found relationship with emerging technology takes shape. As AI puts time back into our schedules, CEOs can focus on refining their companies’ visions and managers can work on coaxing creative ideas from their employees.
The future of AI won’t be ominous or all-consuming. More likely, it will be highly practical. But with less expertise needed to train AI, a new credential is about to hit every resume on HR’s desk—and it won’t be long until you need a new way to stand out from the crowd.