A European approach to artificial intelligence Shaping Europes digital future

Artificial means that a non-organic thing, such as a computer, collects and interacts with the data. Intelligence is defined as acquiring and applying knowledge, whether by an organic being or a computer. Deep learning uses a multi-layered structure of algorithms called the neural network. Algorithms are trained to make classifications or predictions, and to uncover key insights in data.

The other type of AI would be symbolic AI or “good old-fashioned” AI (i.e., rule-based systems using if-then conditions). Below we attempt to explain the important parts of artificial intelligence and how they fit together. At Sonix, we are specifically focused on automatic speech recognition so we explain the key technologies with that in mind. The insights we provide regarding AI vs. ML vs. DL applications connect directly to the work we perform for our clients.

Machine Learning (ML)

Running AI/ML software requires massive amounts of compute power and data–close to where the data is being generated. Low latency is critical when it comes to transmitting data to and from these cars, to ensure the necessary reaction time and avoid collisions. Powerful hardware can be provisioned quickly in colocation facilities such as Equinix IBX data centers–directly from Equinix or our partners. In ninth grade, I discovered that relying on standard definitions limited my ability to understand new concepts. When my math tutor asked for my definition of a circle, I gave the textbook answer.

He immediately challenged me to think of it differently, revealing a completely new path to discovering math. So, let’s take the mystery out of AI/ML textbook definitions by using simpler terms. Unfortunately, those two terms are so often used synonymously that it’s hard to tell the difference between them for many people. But even though both are closely related, AI and ML technologies are actually quite different from one another. As such, in an attempt to clear up all the misunderstanding and confusion, we sat down with Quinyx’s Berend Berendsen to once and for all explain the differences between AI, ML and algorithm.

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These classic algorithms include the Naïve Bayes Classifier and the Support Vector Machines, both of which are often used in data classification. In addition to classification, there are also cluster analysis algorithms such as the K-Means and tree-based clustering. To reduce the dimensionality of data and gain more insight into its nature, machine learning uses methods such as principal component analysis and tSNE.

AI is versatile, ML offers data-driven solutions, and AI DS combines both. The “better” option depends on your interests and the role you want to pursue. Say someone is out in public and sees someone wearing a pair of shoes they like. They can’t identify a brand name, so they take a picture of the shoe using Google Lens. It scans the image for recognizable features and characteristics and searches the internet for a match, eventually driving the searcher to the exact pair of shoes.

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• Artificial Intelligence aims to create a computer that could “think” like a human person and solve complex problems.
• Maximising resources and coordinating investments is a critical component of AI excellence.
• Our technology then assesses and categorises the severity of each dent separately and provides data that can be used to accurately estimate the cost of repair in an automated manner.
• Accordingly, engineers commonly use them for data segmentation, anomaly detection, recommendation systems, risk management systems, and fake images analysis.
• The image below captures the relationship between machine learning vs. AI vs. DL.