Most current AI models are trained through вЂњsupervised learning.вЂќ This means that humans must label and categorize the underlying data, which can be a sizable and error-prone chore. LetвЂ™s explore five interconnected ways in which these limitations, and the solutions emerging to address them, are starting to play out. In such cases, users sometimes need to know the вЂњwhysвЂќ behind the workings, such as why an algorithm reached its recommendations-from making factual findings with legal repercussions to arriving at business decisions, such as lending, that have regulatory repercussions-and why certain factors (and not others) were so critical in a given instance. A black box, even one that does what itвЂ™s supposed to, may have limited utility, especially where the predictions or decisions impact society and hold ramifications that can affect individual well-being. In some domains, they may simply not be available, but even when available, the labeling efforts can require enormous human resources.ДЇurther, it can be difficult to discern how a mathematical model trained by deep learning arrives at a particular prediction, recommendation, or decision. Obtaining large data sets can be difficult. Since these systems are вЂњtrainedвЂќ rather than programmed, the various processes often require huge amounts of labeled data to perform complex tasks accurately. A critical step is to fit the AI approach to the problem and the availability of data. Stuart Russel et al., вЂњResearch priorities for robust and beneficial artificial intelligence,вЂќ AI Magazine, winter 2015,. While much progress has been made, more still needs to be done. These neural networks learn through the use of training data and backpropagation algorithms.

The most common networks are called convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

Deep learning uses large-scale neural networks that can contain millions of simulated вЂњneuronsвЂќ structured in layers. Arguably the most exciting developments in AI, these advances are delivering jumps in the accuracy of classification and prediction, and are doing so without the usual вЂњfeature engineeringвЂќ associated with traditional supervised learning. Challenges, limitations, and opportunitiesДЄ useful starting point is to understand recent advances in deep-learning techniques. If you want to become a leader who understands some of the critical technical challenges slowing AIвЂ™s advance and is prepared to exploit promising developments that could overcome those limitations and potentially bend the trajectory of AI-read on. WhatвЂ™s more, they must appreciate the relationship and distinctions between technical constraints and organizational ones, such as cultural barriers a dearth of personnel capable of building business-ready, AI-powered applications and the вЂњlast mileвЂќ challenge of embedding AI in products and processes. In other words, they need to understand not just where AI can boost innovation, insight, and decision making lead to revenue growth and capture of efficiencies-but also where AI canвЂ™t yet provide value. Please email us at: hoping to narrow the gap must be able to address AI in an informed way. If you would like information about this content we will be happy to work with you. We strive to provide individuals with disabilities equal access to our website. As recent McKinsey Global Institute research indicates, thereвЂ™s a yawning divide between leaders and laggards in the application of AI both across and within sectors (Exhibit 1). It is also disappointing when AI efforts run into real-world barriers, which can lessen the appetite for further investment or encourage a wait-and-see attitude, while others charge ahead.

Simply put, AIвЂ™s challenges and limitations are creating a вЂњmoving targetвЂќ problem for leaders: It is hard to reach a leading edge thatвЂ™s always advancing. WeвЂ™ve sought to distill this experience to help executives who often, in our experience, are exposed only to their own initiatives and not well calibrated as to where the frontier is or what the pace setters are already doing with AI. Our perspectives rest on a combination of work at the front lines-researching, analyzing, and assessing hundreds of real-world use cases-and our collaborations with some of the thought leaders, pioneering scientists, and engineers working at the frontiers of AI. Along the way, we will also highlight promising advances that are poised to address some of the limitations and create a new wave of opportunities.

This is an article about those limitations, aimed at helping executives better understand what may be holding back their AI efforts. A reality sometimes lost amid both the fears and the headline triumphs, such as Alexa, Siri, and AlphaGo, is that the AI technologies themselves-namely, machine learning and its subset, deep learning-have plenty of limitations that will still require considerable effort to overcome.

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