Artificial intelligence is everywhere these days, but how this impactful new technology works can be confusing. The two most important fields in artificial intelligence development are “machine learning” and its subfield “deep learning”. Here is a quick explanation of these two important disciplines and how they contribute to the development of automation.
First, what is artificial intelligence?
It’s worth reminding ourselves what artificial intelligence actually is.Proponents of artificial intelligence say they hope it will one day be possible Create a machine that can “think” For yourself.The human brain is a wonderful instrument, capable of calculations far exceeds the capacity of any existing machine. Software engineers involved in the development of artificial intelligence hope to eventually create a machine that can not only do everything that human intelligence can do, but also surpass humans.At present, the main applications of artificial intelligence in business and government fields are Equivalent to prediction algorithmthat kind Suggest your next song on Spotify or try to sell products similar to yours Bought on Amazon last week. However, AI evangelists believe the technology will eventually be able to reason and make more complex decisions. This is where machine learning and deep learning come in.
machine learning, explanation
Machine learning, or ML, is a broad category of artificial intelligence that refers to the process of “teaching” software programs how to make predictions or “decisions.” Jeff Crume, an IBM engineer, explain Machine learning is “a very complex form of statistical analysis.” According to Krum, this kind of analysis allows machines to “make predictions or decisions based on data.” “The more information that’s fed into the system, the more accurate forecasts it can give us,” he said.
Different from the general programming where the machine is located Designed to accomplish a very specific task, machine learning revolves around training algorithms to identify patterns in the data itself. As mentioned earlier, machine learning covers a wide variety of activities.
deep learning, explanation
deep learning yes Machine learning. It is one of the previously mentioned subcategories of machine learning that, like other forms of machine learning, focuses on teaching artificial intelligence to “think.” Unlike some other forms of machine learning, deep learning seeks to let algorithms do most of the work. Deep learning is driven by mathematical models called artificial neural networks (ANN). These networks attempt to mimic processes that occur naturally in the human brain, such as decision-making and pattern recognition.
Key Differences between ML and DL
One of the biggest differences between deep learning and other forms of machine learning is the level of “supervision” provided by the machine.In less complex forms of machine learning, computers may be involved supervised learning—The process in which humans help machines identify patterns in tagged structured data, thereby improving their ability to perform predictive analysis.
Machine learning relies on large amounts of “training data.”This data is typically compiled by humans through data labels (many of which are The pay is not high). Through this process, a training data set is created, which can then be fed into an AI algorithm and used to teach it to recognize patterns.For example, if a company is training an algorithm Identify specific car brands in photos, which will feed the algorithm a large number of photos of that model that have been manually labeled by staff. Once the machine is trained, a “test data set” is also created to measure the accuracy of the machine’s predictive capabilities.
At the same time, when it comes to deep learning, a machine Engage in a process called “unsupervised learning”“.Unsupervised learning involves machines using their neural networks to identify so-called patterns Unstructured or “raw” data– This is material that has not yet been tagged or organized into a database. Companies can use automated algorithms to sift through large amounts of unorganized data, thereby avoiding the need for extensive human effort.
How neural networks work
Artificial neural networks are composed of so-called “nodes”. According to MIT, an ANN can have “thousands or even millions” of nodes. These nodes may be a little complicated, but the simple explanation is that they relay and process information just like the nodes in the human brain. In neural networks, nodes are arranged in organized forms called “layers.” Therefore, “deep” learning networks involve multiple layers of nodes. Information moves through the network and interacts with its various environments, which aids the decision-making process of machines when prompted by humans.
Another key concept in artificial neural networks is “weights”, One commentator compared Synapses to the human brain. Weights are simply numerical values that are distributed in the neural network of artificial intelligence and help determine the final outcome of the final output of the artificial intelligence system. Weights are inputs that help calibrate a neural network to make decisions.In-depth research at MIT About Neural Networks Explained like this:
The node assigns each incoming connection a number called a “weight.” When the network is active, a node receives a different data item (a different number) through each of its connections and multiplies it by the associated weight. The resulting products are then added together to produce a number. If the number is below the threshold, the node does not pass the data to the next layer. If that number exceeds a threshold, the node “fires,” which in today’s neural networks usually means sending that number (the sum of the weighted inputs) along all its outgoing connections.
In short: Neural networks are structured to help algorithms draw their own conclusions from input data. Based on its programming, the algorithm can identify useful connections in large amounts of data, helping people draw their own conclusions based on its analysis.
Why is machine learning important for artificial intelligence development?
Machine and deep learning help train machines to perform predictive and interpretive activities that were previously only possible by humans. This may have many benefits, but the obvious drawback is that these machines can (and, let’s be honest, will) inevitably be used for nefarious rather than just useful things, such as government and private surveillance systems, as well as ongoing automation. But, obviously, they are also useful for consumer advice or coding and, in the best cases, for medical and health research. Like other tools, whether artificial intelligence will impact the world for good or bad depends largely on who is using it.
