Inside AI<\/span><\/h2>\n\n\n\nAt the heart of AI is machine learning (ML), a process that has the ability to learn on its own without being explicitly programmed. Machine learning uses data to detect patterns in data and adjust actions accordingly so that, when it’s exposed to new data, it develops programs that adapt to that information. ML algorithms are closely related to a number of computational methods, such as computational statistics and mathematical optimization.<\/span><\/p>\n\n\n\nWhile this may sound rather boring, especially for those that never really liked math that much or were garbage at it, the reason why we are mentioning all the math babble is because ML is a standard method used to create complex algorithms that possess predictive powers. You might know this as predictive analytics, a number of analytical models that uncover insights through learning from trends and historical information in the data set.<\/span><\/p>\n\n\n\nPredicting prices<\/span><\/h2>\n\n\n\nMachine learning has many approaches that constitute it – different types of learning if you will. It\u2019s in your Facebook\u2019s News Feed. It\u2019s making Tom Cruise\u2019s life a living hell in Minority Report. It can even predict when you are going to <\/span>buy soup<\/span>. Thus, we\u2019ll spare you the nitty-gritty of it and focus on how the technology helps with price prediction.<\/span><\/p>\n\n\n\nIn a nutshell, you have pricing analytics<\/a> software whose machine learning component is using a technique based on a certain statistical model (<\/span>Gaussian process regression<\/span><\/a>,<\/span> Bayesian linear regression<\/span><\/a>, multi-task learning and a number of other models) to create algorithms that automatically identify patterns from the data and predict prices based on that information. Patterns from huge data sets range from competitors\u2019 pricing and inventory, purchase histories, product preferences to product demand, and anything closely related to pricing.<\/span><\/p>\n\n\n\nAs you can imagine, these parameters are constantly in flux, which is where machine learning comes in and adds a bit of nuance to the whole process that goes beyond simple price history. Suddenly, you have an accurate prediction of customer behavior, a whole system built around the individual, and its needs. All of this is followed by high levels of automation, where the execution of produced data-driven insights is instantly applied.<\/span><\/p>\n\n\n\nIt\u2019s actually a practice that has been around for a few years. Back in 2014, Amazon was granted a<\/span> predictive stocking patent<\/span><\/a> that allowed the online retailer to cut down on delivery time and cost by predicting what buyers are going to buy before they actually buy it. That\u2019s just one of the ways the company is deeply integrated with AI. For instance, sophisticated sorting algorithms are in charge of their warehouses. When an order comes through, the system almost immediately works out where the item is in its inventory and then dispatches a human worker to go fetch it.<\/span><\/p>\n\n\n\n![\"Pricing](\"https:\/\/no-cache.hubspot.com\/cta\/default\/857779\/eb086aed-933d-4459-97f6-3c4ccdeeeda1.png\")
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While this may sound rather boring, especially for those that never really liked math that much or were garbage at it, the reason why we are mentioning all the math babble is because ML is a standard method used to create complex algorithms that possess predictive powers. You might know this as predictive analytics, a number of analytical models that uncover insights through learning from trends and historical information in the data set.<\/span><\/p>\n\n\n\n Machine learning has many approaches that constitute it – different types of learning if you will. It\u2019s in your Facebook\u2019s News Feed. It\u2019s making Tom Cruise\u2019s life a living hell in Minority Report. It can even predict when you are going to <\/span>buy soup<\/span>. Thus, we\u2019ll spare you the nitty-gritty of it and focus on how the technology helps with price prediction.<\/span><\/p>\n\n\n\n In a nutshell, you have pricing analytics<\/a> software whose machine learning component is using a technique based on a certain statistical model (<\/span>Gaussian process regression<\/span><\/a>,<\/span> Bayesian linear regression<\/span><\/a>, multi-task learning and a number of other models) to create algorithms that automatically identify patterns from the data and predict prices based on that information. Patterns from huge data sets range from competitors\u2019 pricing and inventory, purchase histories, product preferences to product demand, and anything closely related to pricing.<\/span><\/p>\n\n\n\n As you can imagine, these parameters are constantly in flux, which is where machine learning comes in and adds a bit of nuance to the whole process that goes beyond simple price history. Suddenly, you have an accurate prediction of customer behavior, a whole system built around the individual, and its needs. All of this is followed by high levels of automation, where the execution of produced data-driven insights is instantly applied.<\/span><\/p>\n\n\n\n It\u2019s actually a practice that has been around for a few years. Back in 2014, Amazon was granted a<\/span> predictive stocking patent<\/span><\/a> that allowed the online retailer to cut down on delivery time and cost by predicting what buyers are going to buy before they actually buy it. That\u2019s just one of the ways the company is deeply integrated with AI. For instance, sophisticated sorting algorithms are in charge of their warehouses. When an order comes through, the system almost immediately works out where the item is in its inventory and then dispatches a human worker to go fetch it.<\/span><\/p>\n\n\n\nPredicting prices<\/span><\/h2>\n\n\n\n
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