Article comments, interest in AI in the 1980s led to a bit of a boom. However, because it lacked a viable business use, “AI winter” ensued. Today, increased data and immense processing power have made AI possible—for real this time. As a result, investments in AI are booming. As The Times mentions: “Funding for AI startups has more than quadrupled to $681 million in 2015, from $145 million in 2011, according to market research company CB Insights. The company estimates that new investments will reach $1.2 billion dollars this year, which represents an increase of 76% compared to last year.

There are currently many uses for this technology, but I think we are just scratching the surface of the potential impact it can have on our lives. The combination of internet connectivity and the spread of mobile devices results in billions of connected devices that can benefit from AI, in the service of humanity. Our life is about to change very quickly. Here are four ways AI will change everything: 1. The Internet of Things Have you noticed how computers are getting smaller and smarter at the same time? They are also cheaper. Nowadays, there’s a computer inside anything with an on/off button.

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Smart devices—from toasters to Bulgaria mobile phone numbers toothbrushes to thermostats to light bulbs to cars—are networking and communicating with each other, with businesses, and with consumers. Why shouldn’t your car tell your house that you’re coming so your house can tell the oven to preheat to the right temperature for that fish it already knows you just bought because you bought it with your phone, and you phone informed you? Behind every device is a customer, and the next generation of customers we expect a connected and intelligent experience. We’re talking about a lot of connected things: 6 billion things that Gartner says will require support in 2018.

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Those billions of connected things mean gigantic volumes of customer data. 2. Data and intelligent analysis A huge gap is developing between companies and customers. Of all the data that customers create, less than 1% is analyzed, which is why 77% of customers say they do not feel that the companies they interact with have a relationship with them. There are so many ways to read the data and so many conclusions to draw about customer habits and preferences, and yet most of those potential insights fall by the wayside because companies don’t prioritize analyzing those data. data.

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Useful insights about the customer. Those perspectives exist along a spectrum of intelligence: the most basic tools require you to extract information from them, while the most intelligent tools deliver information to you and anticipate what you want to know. For the latter we use machine learning. 3. Machine learning With machine learning, computing systems can take all that customer data and supplement it, to operate not only on what has been programmed, but also to adapt to changes. Algorithms adapt to data, allowing them to perform previously unprogrammed behaviors.

Learning to read and recognize context means a digital assistant could scan emails and extract what you need to know. Inherent in that learning is the ability to make predictions about future behavior . To know the customer more intimately . And to be not just proactive, even prescriptive. 4. Prediction Big data and analytics produce patterns, and When the smartest machines are able to read patterns and learn from them.  They can decipher what follows, deduce rather than assume, and conclude rather than guess. The promise of a “digital assistant” is not the robotic voice that answers your questions . About the weather and movie times, but rather that it knows your patterns.

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