Artificial intelligence (AI) vs machine learning. Words alone can spark visions of BIs replacing entire departments and divisions – a future that many companies consider too distant to warrant an investment. But the reality is, AI is here, and here to stay. And particularly at the enterprise level, a growing number of companies are turning to productivity and the promise of machines capable of thinking for themselves.
In fact, a recent McKinsey study showed that by 2019, venture capital investments in AI had already exceeded $ 18.5 billion. And IDC has predicted that by 2023, global spending on AI and machine learning solutions will reach nearly $ 98 billion.
All of this development promises to have a huge impact on all sectors of the industry. McKinsey recently released figures predicting that by 2030, 375 million workers, or roughly 14% of the total global workforce, will need to change occupations as robots and algorithms take over tasks once performed. by humans. Yet most analyzes project net employment wins thanks to AI, like this report from Gartner, which predicts that in the United States, AI will displace up to 1.8 million jobs in the near future, but see a net gain of at least 500 000 to 2 million new jobs as companies grow absorb the new productivity.
So, with all of that in mind, how do you understand the hype between AI and machine learning? And how should you think 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 like humans think. It means more than doing a task well, like Alexa, responding to your voice command to play your favorite song. True artificial intelligence has the ability to analyze data, make decisions, and learn from those decisions to create something new.
AI has been known to tackle big problems, like testing drug compounds to cure cancer. Alibaba uses AI not only for predictive advertising on its sites, but also to monitor cars and create ever-changing traffic patterns, or help farmers monitor crops to increase yield. Amazon Go is using AI to rethink the future of retail, creating unmanned convenience stores that monitor your shopping experience and automatically bill you when you go out with an item.
Experimental AI has written novels (badly), played chess against world masters (very good), and analyzed the world’s medical literature to help physicians make better and more comprehensive diagnoses (and save lives). With AI platforms like Microsoft Azure, Google Cloud, and many more, developers now have the resources they need to think creatively about AI for their own businesses. Additionally, AI in the cloud dramatically lowers a company’s infrastructure costs for the massive compute capacity AI needs to be most useful.
Define machine learning
Sometimes machine learning is used interchangeably with artificial intelligence, but that’s not entirely correct. Machine learning is actually a subset of artificial intelligence. Machine learning refers to a program that performs a task very well by analyzing and analyzing data over time. It’s only as good as the data flowing through it. However, examples of machine learning are all around us, from Alexa on our tables, to dynamic pricing that goes up or down on a website based on your personal information, or email that is automatically filtered. in your inbox, and the chatbot that responds when you ask a question on a website.
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See the big picture
Artificial intelligence shows promise and is becoming increasingly feasible for companies to integrate into their systems, says Sitima Fowler, vice president of marketing for national IT consulting firm Iconic IT. But she recommends that most businesses start small.
âAI is all the rage right now, definitely. But the reality is that most businesses will start with machine learning, like bots that analyze their users’ traffic, for example, to extract data. They can use it for chatbots on their website to direct consumer inquiries to the right information. From there, many companies can use the AI ââdevelopment tools available in the cloud from services like Amazon and Microsoft to develop AI that powers their consumer applications, and more. We are all very excited about the future where artificial intelligence can take us. But it’s important to take it step by step, so the rest of your systems can integrate and keep pace, âsaid Fowler.
âFor example, at Iconic IT, we use AI to prevent cybersecurity breaches. It is not enough to install antivirus and spam filter on your computer. The bad guys have found ways to get around this software. So we integrate AI on top of this software so that it looks like normal person behavior and interactions with other people. Over time, it learns a user’s email habits, communication styles, contacts to determine whether a particular email is legitimate or potentially dangerous, âshe added.
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