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Six Strategies for Building Resilient Supply Chains with Multi-Domain MDM
Posted in: Master Data Management - Jun 08, 2021Join this TDWI Webinar to learn how multi-domain MDM helps you reduce data chaos and gain faster and easier access to diverse but accurate and complete data. top
Six Strategies for Building Resilient Supply Chains with Multi-Domain MDM
Posted in: Master Data Management - Jun 08, 2021Join this TDWI Webinar to learn how multi-domain MDM helps you reduce data chaos and gain faster and easier access to diverse but accurate and complete data.top
AI vs. Machine Learning: Their Differences and Impacts
Posted in: AI, Big Data, Innovation, machine learning - Jun 08, 2021
Artificial intelligence (AI) vs. machine learning. Just the words can bring up visions of decision-making computers that are replacing whole departments and divisions—a future many companies believe is too far away to warrant investment. But the reality is, AI is here, and here to stay. And particularly at the enterprise level, a growing number of companies are tuning in to the productivity and promise of machines that can think for themselves.
In fact, a recent study by McKinsey showed that by 2019, venture capital investment in AI had already topped $18.5 billion. And IDC predicted that by 2023, global spending on AI and Machine Learning solutions will reach nearly $98 billion.
All this development promises to have a tremendous impact on every corner of industry. McKinsey recently released figures predicting that by 2030, 375 million workers—about 14 percent of the total global workforce—will need to switch occupations as robots and algorithms take over tasks once done by humans. Yet most analyses project net job gains as a result of AI—like this report from Gartner, which predicts that in the US, AI will displace as many as 1.8 million jobs in the near future, yet experience a net gain of at least 500,000 to two million new jobs as companies expand to absorb the new productivity.
So, with all that in mind, how do you understand dial back the AI vs. machine learning hype? And how should you be thinking about what cognitive computing can do for your business? Let’s take a closer look.
Defining Artificial Intelligence
Artificial intelligence is a computer system designed to think the way humans think. That means more than just doing one task well, like say, Alexa, who responds to your voice command to play your favorite song. True artificial intelligence has the ability to parse data, make decisions, and learn from those decisions to create something new.
AI has been famously used to tackle big problems, like testing drug compounds for curing cancer. Alibaba uses AI not just for predictive advertising on their sites, but also for monitoring cars and creating constantly changing traffic patterns, or helping farmers monitor crops to increase yield. Amazon Go is using AI to rethink the future of retail, creating unmanned convenient stores that monitor your shopping experience and charge you automatically when you walk out the door with an item.
Experimental AI has written novels (badly), played chess against world masters (very well), and parsed the world’s medical literature to help doctors make better and more complete diagnoses (and saved lives.) With AI platforms like Microsoft Azure, Google Cloud, and many others, developers now have the resources they need to think creatively about AI for their own businesses. Further, AI in the cloud significantly reduces a company’s infrastructure costs for the massive computing capacity AI needs to be most useful.
Defining Machine Learning
Sometimes, machine learning is used interchangeably with artificial intelligence, but that’s not quite correct. Machine learning is actually a subset of artificial intelligence. Machine learning refers to a program which does one task really well by parsing and analyzing data over time. It is only as good as the data flowing into it. However, examples of machine learning are all around us, from Alexa on our tabletops, to the dynamic pricing that goes up or down on a website based on your personal information, or the email that gets automatically filtered to your inbox, and the chatbot that responds when you ask a question on a website.
Go deeper with Datamation‘s Top Machine Learning Companies.
Seeing the big picture
Artificial Intelligence has promise, and is becoming more feasible for companies to incorporate into their systems, says Sitima Fowler, vice president of marketing for national IT consulting firm Iconic IT. But she recommends most companies start small.
“AI is trendy right now, definitely. But the reality is, most companies will be starting with machine learning, such as bots that parse their user traffic, for instance, to mine data. They might use it for chatbots on their website to direct consumer inquiries to the right information. From there, many companies can use the AI development tools available in the cloud from services like Amazon and Microsoft to develop AI that powers their consumer facing apps, and so much more. We’re all very excited about the future of where artificial intelligence can take us. But it’s important to take it one step at a time, so the rest of your systems can integrate and keep up,” Fowler said.
“For example, at Iconic IT, we use AI to prevent cyber security breaches. Just simply installing an antivirus and email spam filter on your computer isn’t enough. The bad guys have figured out ways around this software. So we incorporate AI on top of these software so it looks as the person’s normal behavior and interactions with other people. Over time it learns a user’s email habits, communication styles, contacts to determine if a particular email is legitimate or potentially harmful,” she added.
Background Reading on AI vs. Machine Learning
Enterprise security is having a year: Here are 2021’s hottest cybersecurity startups.
Want to learn more about AI vs. machine learning? Check out these resources from CIO Insight and Datamation.
The post AI vs. Machine Learning: Their Differences and Impacts appeared first on CIO Insight.
topTDWI Checklist Report | Building Resilient Supply Chains with Multidomain MDM
Posted in: Master Data Management - Jun 08, 2021This checklist offers six key steps for developing a multidomain MDM strategy to improve supply chain resilience.top
Data Digest: Master Data, Data Quality, Data Privacy
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