2020 is all about AI and machine learning. If you are looking for a development agency, have a look at these top 10 machine learning development companies based in Toronto.
Artificial intelligence’ is a term that sounds exciting and high-tech, but the problem is that it can mean anything. Calling that your company does something with artificial intelligence – also known as AI, artificial intelligence or AI – is like saying that you are in the automotive industry: it sounds nice, but it does not make clear whether you are developing self-driving cars or whether you are developing a self-driving car. Muddy scrap yard.
Why Machine Learning?
Yet the excitement about artificial intelligence (AI) and machine learning (ML) is certainly not unjustified. Because everything possible with these techniques is developing at a rapid pace, certainly when you consider that it has been worked on since the 1950s. Science had just discovered then that our brain works with networks of neurons. Psychologists, linguists and computer scientists began to wonder if it was not possible to imitate the working of the human brain with machines.
The starting point was the question that mathematician and computer pioneer Alan Turing asked in the late 1940s: “Can computers think?” The answer to that question has still not been found. What makes it difficult is that it is not clear what intelligence is. People with an IQ of 132 cannot necessarily do everything well, and people who are incredibly good at calculating, for example, can be very bad at remembering names at the same time.
Although demanding was the starting point for the first AI researchers, that question is no longer so relevant. “The question of whether machines can think is about as relevant as the question of whether a submarine can swim,” says well-known computer scientist Edsger Wybe. We owe him, among other things, the algorithm with which practically all route navigation systems work.
In the search for serious artificial intelligence, the comparison with human intelligence has always caused considerable disappointments. The research has always followed the same pattern: a few people are far too optimistic about what will be possible, investors and governments are wildly enthusiastic about it, then the results are disappointing and as a result, the budgets are drying up, until new developments are nevertheless there which starts the cycle again.
What is Machine Learning?
Machine learning is a form of artificial intelligence (AI) that is focused on building systems that can learn from the processed data or use data to perform better. Artificial intelligence is an umbrella term for systems or machines that mimic human intelligence. Even though machine learning and AI are often mentioned in the same breath and the terms are sometimes used interchangeably, they do not mean the same thing. An important difference is that while machine learning always falls under AI, AI does not always fall under machine learning.
Today, machine learning is used everywhere around us. When communicating with banks, shopping online or using social media, machine learning helps ensure that our experiences are efficient, smooth and safe. Machine learning and the technology around it are evolving rapidly, but we are only just beginning to understand what options they all have to offer.
Business Goal: To Display the Lifelong Customer Value in a Model
Modeling lifelong customer value is not only important for e-commerce companies, there are many more sectors where this is applied. In this type of model, machine learning algorithms attempt to identify, fathom and retain the most valuable customers for a company. These value models select huge amounts of customer data to identify the people who spend the most, the most loyal proponents of a brand or combinations of these types of characteristics.
Lifelong customer value models are especially effective in predicting the future revenue that an individual customer will generate for a company in a given period. With this information, organizations can target their marketing activities specifically to customers who add a lot of value to encourage them to choose their brand more often. Lifelong customer value models also help organizations to align their acquisition spending more focused on attracting new customers that closely resemble these existing ‘value-generating’ customers.
Business Objective: Harnessing the Power of Image Classification
Machine learning can be used in various other usage scenarios in addition to retail, financial services and e-commerce. But also for application in science, healthcare, construction or the energy sector, machine learning offers great opportunities. For example, machine learning algorithms are used in image classification to assign a label from a certain series of categories to each image recording. This allows companies, for example, to create 3D building plans based on a 2D design, to enable the tagging of photos on social media or to make better substantiated medical diagnoses.
Deep learning methods, such as those in which the neural system is simulated, are often used for image classification because it is the most effective way to identify the relevant characteristics of an image in the presence of potential complications. With these methods, variations in the point of view, the exposure, the scale or the size of a mass can be included on the image, whereby these deviations can be adjusted so that it yields the most relevant and qualitatively best information.
Create Unparalleled Tangible Business Value
Machine learning offers many important business usage scenarios. But how does it provide a competitive advantage? One of the most attractive features of machine learning is the ability to automate and shorten the decision-making process, and to create value faster. This starts with better visibility of the company and improved cooperation.
“We saw in the past that people were unable to work together,” said Rich Clayton, vice president of product strategy at a leading tech company. “By adding machine learning to Analytics Cloud, people can now better organize their work and it is possible to develop, learn and implement these data models. It is a tool for collaboration with the added value that processes go faster and different departments can work together to improve performance and you get better models to implement. ”
For example, the Finance department is often and repeatedly charged with the same process for performing variance analyzes, whereby forecasts are compared with the actual result. This is an application that requires little cognitive ability and can greatly benefit from machine learning.
