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According to one Gartner report, by 2021, chatbots will take over, with more than 50% of enterprises spending more per annum on bots and chatbot creation than traditional mobile app development. And this is especially the case for competitive learning and development (L&D) and Human Resources (HR) departments, which will rely heavily on chatbots.
2019 is quickly approaching, and with it comes the predictions of what organizations can expect for the following year. So what will 2019 have in store?
Artificial intelligence (AI) has changed our world in a number of ways that we often don’t realize. The impacts range from personal assistants, like Alexa or Siri, or personalized ads that come our way based on our shopping and browsing habits to changes in the way businesses are run, including the obsolescence of certain job […]
The prospect of greater automation in a variety of industries understandably has many workers worried about their long-term employment prospects. What is the likelihood of a given employee’s job being rendered obsolete by advancing technology? Even if the role doesn’t disappear entirely, the need for human labor could be drastically reduced.
Fears of artificial intelligence (AI) and automation taking people’s jobs are increasingly common as new technologies emerge that seem to make many aspects of certain jobs—especially routine, low-skilled labor jobs—potentially obsolete. We’ve written a lot about how these fears are often overblown.
Yesterday’s post detailed what deep-learning artificial intelligence (AI) is, as well as its benefits and drawbacks for learning and development (L&D) professionals. Today’s post will cover more information about how you can implement deep-learning AI and upcoming trends.
The International Data Corporation (IDC) predicts that the compound annual growth rate (CAGR) for global sending via artificial intelligence (AI) will be 50.1%, reaching $57.6 billion by 2021. And the three most in-demand AI-related skills are currently machine learning, deep learning, and natural language processing. Additionally, the deep learning market, which is a subset of […]
In yesterday’s post, we discussed the idea that using artificial intelligence (AI) or big data in the recruiting process doesn’t eliminate problems with discrimination and bias. Perhaps counter-intuitively, these methods can actually emphasize bias if we’re not careful because the machine doesn’t know any better. It can only assess the (often imperfect) traits it is told to […]
Instinctively, it would seem that using a machine, data, or artificial intelligence (AI) to review job applicants would create a process that is fairer by default.
Artificial intelligence (AI) in the workplace is rapidly progressing from fantasy to a pervasive reality impacting the entire employee journey, from hire to retire. It’s an exciting time for our industry, with great opportunities ahead. However, I also believe that our industry should not breathlessly rush to deploy ever more powerful yet poorly understood capabilities […]