Sunday, February 11, 2018

AI SPECIAL ....What AI can and can’t do (yet) for your business PART II


What AI can and can’t do (yet) for your business PART II

Limitation 4: Generalizability of learning
Unlike the way humans learn, AI models have difficulty carrying their experiences from one set of circumstances to another. In effect, whatever a model has achieved for a given use case remains applicable to that use case only. As a result, companies must repeatedly commit resources to train yet another model, even when the use cases are very similar.
One promising response to this challenge is transfer learning. In this approach, an AI model is trained to accomplish a certain task and then quickly applies that learning to a similar but distinct activity. DeepMind researchers have also shown promising results with transfer learning in experiments in which training done in simulation is then transferred to real robotic arms.
As transfer learning and other generalized approaches mature, they could help organizations build new applications more quickly and imbue existing applications with more diverse functionality. In creating a virtual personal assistant, for example, transfer learning could generalize user preferences in one area (such as music) to others (books). And users are not restricted to digital natives. Transfer learning can enable an oil-and-gas producer, for instance, to expand its use of AI algorithms trained to provide predictive maintenance for wells to other equipment, such as pipelines and drilling platforms. Transfer learning even has the potential to revolutionize business intelligence: consider a data-analyzing AI tool that understands how to optimize airline revenues and can then adapt its model to changes in weather or local economics.
Another approach is the use of something approximating a generalized structure that can be applied in multiple problems. DeepMind’s AlphaZero, for example, has made use of the same structure for three different games: it has been possible to train a new model with that generalized structure to learn chess in a single day, and it then soundly beat a world-champion chess program.
Finally, consider the possibilities in emerging meta-learning techniques that attempt to automate the design of machine-learning models. The Google Brain team, for example, uses AutoML to automate the design of neural networks for classifying images in large-scale data sets. These techniques now perform as well as those designed by humans. That’s a promising development, particularly as talent continues to be in short supply for many organizations. It’s also possible that meta-learning approaches will surpass human capabilities and yield even better results. Importantly, however, these techniques are still in their early days.
Limitation 5: Bias in data and algorithms
So far, we’ve focused on limitations that could be overcome through technical solutions already in the works, some of which we have described. Bias is a different kind of challenge. Potentially devastating social repercussions can arise when human predilections (conscious or unaware) are brought to bear in choosing which data points to use and which to disregard. Furthermore, when the process and frequency of data collection itself are uneven across groups and observed behaviors, it’s easy for problems to arise in how algorithms analyze that data, learn, and make predictions. Negative consequences can include misinformed recruiting decisions, misrepresented scientific or medical prognoses, distorted financial models and criminal-justice decisions, and misapplied (virtual) fingers on legal scales. In many cases, these biases go unrecognized or disregarded under the veil of “advanced data sciences,” “proprietary data and algorithms,” or “objective analysis.”
As we deploy machine learning and AI algorithms in new areas, there probably will be more instances in which these issues of potential bias become baked into data sets and algorithms. Such biases have a tendency to stay embedded because recognizing them, and taking steps to address them, requires a deep mastery of data-science techniques, as well as a more meta-understanding of existing social forces, including data collection. In all, debiasing is proving to be among the most daunting obstacles, and certainly the most socially fraught, to date.
There are now multiple research efforts under way, as well as efforts to capture best practices, that address these issues in academic, nonprofit, and private-sector research. It’s none too soon, because the challenge is likely to become even more critical, and more questions will arise. Consider, for example, the fact that many of these learning and statistically based predictive approaches implicitly assume that the future will be like the past. What should we do in sociocultural settings where efforts are under way to spur change—and where making decisions based on past behavior could inhibit progress (or, worse, build in resistance to change)? A wide variety of leaders, including business leaders, may soon be called upon to answer such questions.
Hitting the moving target
Solutions to the limitations we have described, along with the widespread commercial implementation of many of the advances described here, could be years away. But the breathtaking range of possibilities from AI adoption suggests that the greatest constraint for AI may be imagination. Here are a few suggestions for leaders striving to stay ahead of—or at least not fall too far behind—the curve:
Do your homework, get calibrated, and keep up. 
While most executives won’t need to know the difference between convolutional and recurrent neural networks, you should have a general familiarity with the capabilities of today’s tools, a sense of where short-term advances are likely to occur, and a perspective on what’s further beyond the horizon. Tap your data-science and machine-learning experts for their knowledge, talk to some AI pioneers to get calibrated, and attend an AI conference or two to help you get the real facts; news outlets can be helpful, but they can also be part of the hype machine. Ongoing tracking studies by knowledgeable practitioners, such as the AI Index (a project of the Stanford-based One Hundred Year Study on Artificial Intelligence), are another helpful way to keep up.
Adopt a sophisticated data strategy. 
AI algorithms need assistance to unlock the valuable insights lurking in the data your systems generate. You can help by developing a comprehensive data strategy that focuses not only on the technology required to pool data from disparate systems but also on data availability and acquisition, data labeling, and data governance. Although newer techniques promise to reduce the amount of data required for training AI algorithms, data-hungry supervised learning remains the most prevalent technique today. And even techniques that aim to minimize the amount of data required still need some data. So a key part of this is fully knowing your own data points and how to leverage them.
Think laterally. 
Transfer-learning techniques remain in their infancy, but there are ways to leverage an AI solution in more than one area. If you solve a problem such as predictive maintenance for large warehouse equipment, can you also apply the same solution to consumer products? Can an effective next-product-to-buy solution be used in more than one distribution channel? Encourage business units to share knowledge that may reveal ways to use your best AI solutions and thinking in more than one area of the company.
Be a trailblazer. 
Keeping up with today’s AI technologies and use cases is not enough to remain competitive for the long haul. Engage your data-science staff or partner with outside experts to solve a high-impact use case with nascent techniques, such as the ones discussed in this article, that are poised for a breakthrough. Further, stay informed about what’s possible and what’s available. Many machine-learning tools, data sets, and trained models for standard applications (including speech, vision, and emotion detection) are being made widely available. Sometimes they come in open source and in other cases through application programming interfaces (APIs) created by pioneering researchers and companies. Keep an eye on such possibilities to boost your odds of staking out a first-mover or early-adopter advantage.

The promise of AI is immense, and the technologies, tools, and processes needed to fulfill that promise haven’t fully arrived. If you think you can let the technology develop and then be a successful fast follower, think again. It’s very difficult to leapfrog from a standing start, particularly when the target is moving so rapidly and you don’t understand what AI tools can and can’t do now. With researchers and AI pioneers poised to solve some of today’s thorniest problems, it’s time to start understanding what is happening at the AI frontier so you can position your organization to learn, exploit, and maybe even advance the new possibilities.
By Michael ChuiJames Manyika, and Mehdi Miremadi McKinsey Quarterly January 2018
https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/what-ai-can-and-cant-do-yet-for-your-business?cid=other-eml-alt-mkq-mck-oth-1801&hlkid=ebe57c83f2bf4531a29b965fdb8d3493&hctky=1627601&hdpid=d3bf26bf-7f43-48fd-8f9d-9770bfb5f550

No comments: