Organizations need Machines that Learn

Stephen DeAngelis

October 30, 2018

Every day new articles are published about the importance of artificial intelligence (AI) for businesses hoping to remain competitive in the coming decades. The term “AI” is broad and often ambiguous. More often than not, those articles are referring to a subset of AI known as machine learning. Udai Chilamkurthy, lead architect for retail and logistics at Sainsbury’s, bluntly states, “AI in some senses is a meaningless term, it doesn’t say anything about anything.”[1] He adds, “The next time somebody mentions AI ask them what they’re really talking about, and by the time you’ve reached the third question you’ll see they literally have no idea, or they have an idea that might make sense for a specific use case.” Like any business tool, AI/machine learning must be tailored to the task at hand. Chilamkurthy explains, “Let’s say someone wants to use AI to optimize the supply chain. That doesn’t even mean anything. You have to think about what the exact problem is in the supply chain, what is the intelligence you want to gather that your team is not able to gather at this point, how are you going to do it and what will be the wider consequences?” Most enterprises don’t have to search very hard to find problems where insights gleaned by AI/machine learning can be of help. Nevertheless, Chilamkurthy’s point is well made.

 

Artificial intelligence and machine learning

 

“People often use the term ‘artificial intelligence’ without really understanding what it’s supposed to mean,” writes Rahul Sharma (@Im_RahulSharma). “We can’t blame them. AI can mean what you want it to. It’s an abstract term, to a good extent. And if you talk to experts in the science of machine learning, you might even learn that they don’t really recognize artificial intelligence as a technology but more as a marketing buzzword used to sell machine learning.”[2] Machine learning has grown in importance since the dawn of the Digital Age. The Digital Age is all about generating data — Big Data — and machine learning can’t exist without data. Sharma explains, “Machine learning is mostly based on using lots and lots of training data and good algorithms. Though there’s a lot of excitement in technology circles about sophisticated algorithms … it must be understood that most applications of machine learning are a result of good data. Machine learning could exist without good algorithms, but it can’t exist without good data.”

 

Ben Lorica (@bigdata), Chief Data Scientist at O’Reilly Media, insists organizations are in the early stages of machine learning implementation. Like Chilamkurthy, he asserts machine learning only makes sense when discussed in relationship to a specific challenge. He explains, “Machine learning is typically used to enable some level of automation. From a rule-based fraud detection system to a complex model that automatically learns from examples. Machine learning for an organization is only valuable when there are benefits, which could be anything from improving decision making to increasing revenue or engagement among employees or customers.”[3] He suggests the best way to understand how machine learning can be useful is to examine ongoing use cases. He writes, “By exploring how these areas and technologies are being implemented by both companies just starting out and those farther along on their machine learning journey, we can better understand where barriers for adoption exist and what’s ahead for this powerful technology.”

 

Machine learning use cases

 

Even though machine learning remains in the early stages of implementation in most economic sectors, Princy Lalawat asserts, “Machine learning is the new stepping stone which has moved towards the mainstream. It is growing at an astronomical rate, and businesses irrespective of [their] background are now moving towards making the best out of this technology.”[4] Lalawat goes on to note, “Top market giants like Amazon, Microsoft, Apple or Google are leading their industry sectors in machine learning and artificial intelligence investments. Each of them is individually designing machine learning tools to improve customer service and increase the overall business growth.” Randy Bean (@RandyBeanNVP), CEO of NewVantage Partners, insists, “For organizations seeking to compete on data, machine learning has reached the stage of providing a critical business edge.”[5] Steve Moore, an IBM strategist, lists 10 sectors in which machine learning can prove valuable.[6] They are:

 

1. Healthcare (patient diagnosis)
2. Finance (fraud detection)
3. Manufacturing (anomaly detection)
4. Retail (inventory optimization)
5. Government (smarter services)
6. Transportation (demand forecasting)
7. Networks (intrusion detection)
8. E-commerce (recommendation services)
9. Media (interaction & speed)
10. Education (research insight)

 

Moore makes it clear his list is notional rather than exhaustive. He explains, “[The listed examples] are tremendous demonstrations of the power of the technology —  and in aggregate, they give a sense of machine learning’s pervasive presence in our lives. But the convenience of go-to examples might come at a cost. In particular, citing the same handy examples might keep us from noticing the wide diversity of machine learning use cases within individual sectors.” Sharma adds, “Examples of machine learning in action are all around us. E-commerce websites use it to recommend suitable products to users, Gmail uses it to fight spam, music streaming and entertainment apps use it to show good content to people, Facebook uses it for photo tagging, and Google uses it to rank web pages for search keywords.”

 

Summary

 

A modern day adage is “artificial intelligence sells, but machine learning delivers.” When it comes to how machine learning can be applied to everyday business problems, imagination is the only limit. Enormous amounts of data are being generated by organization’s each and every day. Machine learning platforms offer the only way organizations can put that data to its best use. Lorica concludes, “It’s clear there’s still a lot to learn when it comes to enterprise machine learning, but it’s encouraging that more organizations are starting to embrace the business value it can provide.”

 

Footnotes
[1] John Leonard, “Sainsbury’s lead architect: ‘The next time somebody mentions AI ask them what they’re really talking about’,” Computing, 1 October 2018 (registration required).
[2] Rahul Sharma, “Why AI is ‘Artificial’ Intelligence without Machine Learning,” Techgenix, 10 September 2018.
[3] Ben Lorica, “The machine learning hype is real, but enterprises are still in the early adoption stages,” ITProPortal, 21 August 2018.
[4] Princy Lalawat, “Machine Learning becomes a Mainstream Enterprise Technology,” Analytics Insight, 9 September 2018.
[5] Randy Bean, “The State of Machine Learning in Business Today,” Forbes, 17 September 2018.
[6] Steve Moore, “Top 10 Machine Learning Use Cases: Part 1,” KDnuggets, August 2017.