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Artificial intelligence is everywhere these days, but the fundamentals of how this influential new technology works can be difficult to wrap your head around. Two of the most important fields in AI development are machine learning and its sub-field, deep learning, although the terms are sometimes used interchangeably, leading to a certain amount of confusion. Heres a quick explanation of what these two important disciplines are, and how theyre contributing to the evolution of automation.

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Proponents of artificial intelligence say they hope to someday create a machine that can think for itself. The human brain is a magnificent instrument, capable of making computations that far outstrip the capacity of any currently existing machine. Software engineers involved in AI development hope to eventually make a machine that can do everything a human can do intellectually but can also surpass it. Currently, the applications of AI in business and government largely amount to predictive algorithms, the kind that suggest your next song on Spotify or try to sell you a similar product to the one you bought on Amazon last week. However, AI evangelists believe that the technology will, eventually, be able to reason and make decisions that are much more complicated. This is where ML and DL come in.

Machine learning (or ML) is a broad category of artificial intelligence that refers to the process by which software programs are taught how to make predictions or decisions. One IBM engineer, Jeff Crume, explains machine learning as a very sophisticated form of statistical analysis. According to Crume, this analysis allows machines to make predictions or decisions based on data. The more information that is fed into the system, the more its able to give us accurate predictions, he says.

Unlike general programming where a machine is engineered to complete a very specific task, machine learning revolves around training an algorithm to identify patterns in data by itself. As previously stated, machine learning encompasses a broad variety of activities.

Deep learning is machine learning. It is one of those previously mentioned sub-categories of machine learning that, like other forms of ML, focuses on teaching AI to think. Unlike some other forms of machine learning, DL seeks to allow algorithms to do much of their work. DL is fueled by mathematical models known as artificial neural networks (ANNs). These networks seek to emulate the processes that naturally occur within the human brainthings like decision-making and pattern identification.

One of the biggest differences between deep learning and other forms of machine learning is the level of supervision that a machine is provided. In less complicated forms of ML, the computer is likely engaged in supervised learninga process whereby a human helps the machine recognize patterns in labeled, structured data, and thereby improve its ability to carry out predictive analysis.

Machine learning relies on huge amounts of training data. Such data is often compiled by humans via data labeling (many of those humans are not paid very well). Through this process, a training dataset is built, which can then be fed into the AI algorithm and used to teach it to identify patterns. For instance, if a company was training an algorithm to recognize a specific brand of car in photos, it would feed the algorithm huge tranches of photos of that car model that had been manually labeled by human staff. A testing dataset is also created to measure the accuracy of the machines predictive powers, once it has been trained.

When it comes to DL, meanwhile, a machine engages in a process called unsupervised learning. Unsupervised learning involves a machine using its neural network to identify patterns in what is called unstructured or raw datawhich is data that hasnt yet been labeled or organized into a database. Companies can use automated algorithms to sift through swaths of unorganized data and thereby avoid large amounts of human labor.

ANNs are made up of what are called nodes. According to MIT, one ANN can have thousands or even millions of nodes. These nodes can be a little bit complicated but the shorthand explanation is that theylike the nodes in the human brainrelay and process information. In a neural network, nodes are arranged in an organized form that is referred to as layers. Thus, deep learning networks involve multiple layers of nodes. Information moves through the network and interacts with its various environs, which contributes to the machines decision-making process when subjected to a human prompt.

Another key concept in ANNs is the weight, which one commentator compares to the synapses in a human brain. Weights, which are just numerical values, are distributed throughout an AIs neural network and help determine the ultimate outcome of that AI systems final output. Weights are informational inputs that help calibrate a neural network so that it can make decisions. MITs deep dive on neural networks explains it thusly:

To each of its incoming connections, a node will assign a number known as a weight. When the network is active, the node receives a different data item a different number over each of its connections and multiplies it by the associated weight. It then adds the resulting products together, yielding a single number. If that number is below a threshold value, the node passes no data to the next layer. If the number exceeds the threshold value, the node fires, which in todays neural nets generally means sending the number the sum of the weighted inputs along all its outgoing connections.

In short: neural networks are structured to help an algorithm come to its own conclusions about data that has been fed to it. Based on its programming, the algorithm can identify helpful connections in large tranches of data, helping humans to draw their own conclusions based on its analysis.

Machine and deep learning help train machines to carry out predictive and interpretive activities that were previously only the domain of humans. This can have a lot of upsides but the obvious downside is that these machines can (and, lets be honest, will) inevitably be used for nefarious, not just helpful, stuffthings like government and private surveillance systems, and the continued automation of military and defense activity. But, theyre also, obviously, useful for consumer suggestions or coding and, at their best, medical and health research. Like any other tool, whether artificial intelligence has a good or bad impact on the world largely depends on who is using it.

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Machine Learning vs. Deep Learning: What's the Difference? - Gizmodo