Artificial intelligence and machine learning are having a considerable impact on the changing tide in manufacturing. The change is so impressive it is actually playing a role in many industries. Its impact is so impressive that for this column I am going to dig into the difference between AI (artificial intelligence) and machine learning.
Artificial intelligence is “intelligence demonstrated by machines…” This is in contrast to the “natural intelligence” that we have as humans. AI has become a bit of an umbrella term often used to describe machines doing things that we associate with human brains, like problem solving and learning. The term “machine learning” was coined by Arthur Lee Samuel in the late 1950s.
Samuel pioneered the Samuel checkers-playing program, which was a successful early demonstration of self-learning and artificial intelligence. Machine learning is an application of AI in which computer systems “learn” by making data-driven decisions. The applications for AI and machine learning are as numerous as they are exciting.
In a myriad industries, these technologies can be used to drive efficiency gains and create insights that enable personalization of the customer experience. They also contribute to the creation of new business models driven by data-driven insights.
So how do you monetize AI and even machine learning? It’s hard, but Gartner says $2.9 trillion worth of new business value opportunities will be attributable to AI by 2021. The research firm also says AI will help recover 6.2 billion hours of worker productivity. That is 6.2 billion hours of worker productivity regained as a result of AI adoption.
What’s more, startups focusing on artificial intelligence and machine learning seem like they’re everywhere, and they’re drawing a lot of equity funding. CB Insights’ most recent list of 100 AI startups, for instance, raised $11.7 billion in equity funding—and that’s just 100 startups.
We’re seeing AI startups in categories like cybersecurity, healthcare, and enterprise IoT, as well as a ton of cross-industry AI companies. In general, AI and machine learning are making inroads in just about every industry.
It’s pretty clear AI and machine learning are becoming a trend these days. CB Insights points to a couple of examples that I absolutely love because they illustrate this point so clearly.
A company called IntelligentX brewing Co. is working on leveraging machine learning to brew beer. The company uses complex machine-learning algorithms to improve its product based on customer feedback. After customers try one of the company’s beers, they can go online and provide feedback, and the algorithm uses this data to brew the next batch. Now this is a genius application of AI, especially if you’re a beer lover.
Another company called Weedguide recently unveiled a new search platform that relies on machine learning and artificial intelligence to provide personalized cannabis-related recommendations and search results to its customers.
The company secured $1.7 million in seed funding before unveiling its AI-powered search platform. There is demand for more intelligent information about weed—medicinal and recreational.
Another couple of trends worth mentioning are personal assistant technologies and AI’s expanding role in cybersecurity. Adoption of AI-enabled personal assistants and the devices they empower is growing by leaps and bounds.
The smart audio report by NPR and Edison Research says one in six American adults currently owns a smart speaker—that’s 39 million people. People are using these smart speakers to control their homes and offices, and it’s looking like these devices are acting like spring boards encouraging smart home adoption. AI cybersecurity is also a key trend.
Since cybercriminals are constantly evolving their attack methods, our defenses must constantly evolve too, and this adaptability is where AI and machine learning shine.
The use of industrial robots powered by AI is another important trend, and this brings us back to our overarching theme this month: manufacturing. AI and machine learning are in many ways what make smart manufacturing “smart.”
These technologies help increase the efficiency and speed of manufacturing processes. And, in doing so, they reduce labor costs. They also contribute to the bottomline in other ways, such as shortening the duration of equipment downtime.
Along with preventing or decreasing downtime, AI can also help decrease capital expenditure costs by helping technicians track down and fix faulty machine parts more quickly. It’s all part of the value gained by automated data collection and monitoring.
You could say that much of the value in Industry 4.0 is based on the possibilities brought forth by AI and machine learning. This is because it’s not the data itself that’s changing industries; it’s the ability to analyze this data to generate insights that’s really changing the game. AI and machine learning provide this crucial capability.
While there are many opportunities for AI, manufacturing is perhaps one of the greatest opportunities for its ability to create personalized products. A few weeks ago, in this column. I addressed the concept of “batch size 1” and “mass personalization” in manufacturing.
Just like AI-brewed beer and AI-enabled weed recommendations, AI will power the next generation of automated design adaptation, allowing manufacturers to offer unprecedented personalization. These are truly exciting time for manufacturing and I am going to be right here in the thick of it all reporting it and sharing how AI and machine learning can finally give manufacturing the boost they deserve. The sky is the limit. Can you imagine what’s next?
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