The basic framework of artificial intelligence existed in the 1940s and since then various organizations have been innovating in the development of artificial intelligence. In recent years, big data and advanced deep learning models have pushed the development of artificial intelligence to an unprecedented level. Will these new technological components eventually produce the intelligent machines envisioned in science fiction or will they maintain the current trend of artificial intelligence, simply "put the same wine in the more upscale bottle?"
"It's actually a new wine, but there are a variety of bottles and different years," said James Kobielus, lead analyst for data science, deep learning and application development at Wikibon.
Kobielus added that in fact most of the old wine is still quite palatable; the new generation of AI uses the previous methods and builds on them. For example, Apache's big data framework Hadoop technology used.
However, the fanatical enthusiasm for artificial intelligence today is due to the lack of specific development of some former AI candidates. According to Kobielus, existing technology brings us closer to machines that look "human like" thinking. "The big thing is big data," he said at CUBE's studio in Marlborough, Mass. Why does big data sparked interest in AI? Because it is a tremendous help in training a deep learning model that makes it possible to make more human-like inferences. Kobielus and Dave Vellante have made technological breakthroughs in AI and machine intelligence. Dave Vellante is Wikibon's lead analyst and co-host of SiliconANGLE's live studio.
The artificial intelligence revolution will be algorithmic
Artificial intelligence in the smart dialogue made significant progress, but also reflects its rapid revenue growth. A survey by research firm Tractica LLC shows that in 2016, the market for artificial intelligence software will be 1.4 billion U.S. dollars and will increase to 59.8 billion U.S. dollars by 2025. "Artificial intelligence has applications and use cases in the verticals of almost any industry and is considered to be the next major technology shift similar to what has happened in the past, such as the industrial revolution, the computer age and the smartphone revolution," said Tractica LCC's research director Aditya Kaul said. Some of these verticals include the financial, advertising, healthcare, aerospace and consumer sectors.
The next industrial revolution will revolve around artificial intelligence software, which may sound like an imaginative nerdy fantasy. But even in Silicon Valley, the mood is spreading. Time magazine recently featured a special feature entitled "Artificial Intelligence: The Future of Humanity." However, this artificial intelligence vision has existed for decades in the wild swamps of science fiction and technology. Has technology developed so fast in the past few years? What can we get from reality in today's artificial intelligence and the foreseeable future?
First of all, artificial intelligence is a broad tag - in fact more of a hot phrase than an accurate technical term. Kobielus said artificial intelligence refers to "anything that helps machines think like human beings." However, in the strictest sense, is not machine "thinking" a completely different mindset from the human brain? Machines do not really think, are not they? It depends on the situation. If the synonym for "thinking" is "inferred," then the machine may be thought of as equivalent to the brain.
When people talk about artificial intelligence, they usually talk about the most popular way of artificial intelligence - machine learning. This is a mathematical application, the principle is to infer a certain pattern from the data set. Kobielus said: "For a long time, people used software to deduce patterns from their data." Some existing reasoning methods include support vector machines, Bayesian logic, and decision trees. These technologies have not disappeared and are continuing to be used in the growing field of Artificial Intelligence. Machine learning models or algorithms trained on the data can make their own inferences, often referred to as artificial intelligence output or insights. This inference does not need to be pre-programmed on a machine, and only the model itself needs to be programmed.
The inference of the machine learning model is based on the possibility of statistics, which in a way resembles the process of human understanding. Inferences from data can come in the form of predictions, correlations, classifications, classifications, recognition anomalies, or trends. For machines, the learning model is hierarchical. Data classifier named "perceptron", by layering the perceptron, they form an artificial neural network. This neural network relationship between perceptrons activates their functions, including non-linear perceptrons, such as tangents. Through this neural process, the answer or output of a layer becomes the input of the next level. The final output is the final result.
Deep learning layer of neurons
Deep learning networks are artificial neural networks with a large number of perceptual layers. The more layers of the network, the greater its depth. These extra layers raise more questions, deal with more input, and produce more output, thus abstracting higher levels of data.
Facebook's automatic face recognition technology is driven by a deep learning network. By combining more layers together, images can be more abundantly described. "You might ask, is not this a face? But if it's a scene-recognition deep learning network, it might recognize it as a face corresponding to a person named Dave, who happened to be this Father in family scene, "Kobielus said.
Now that we have a neural network with 1,000 layers of sensors, software developers are still exploring the capabilities that deeper neural networks can achieve. The latest Apple iPhone face detection software relies on a 20-layer convolutional neural network. In 2015, Microsoft researchers won the ImageNet Computer Vision Competition through a 152-layer deep residual network. According to Peter Lee, director of research at Microsoft, thanks to a design that prevents data dilution, the network can gather information from images beyond the typical depth-residual network of 20 or 30 levels. He said: "We can learn a lot of subtle things."
In addition to image processing, new cases of artificial intelligence and deep learning are also emerging, from law enforcement to genomics. In a study last year, researchers used artificial intelligence to predict the verdict in hundreds of cases before the European Court of Human Rights. They predicted that the final decision of human judges reached 79% accuracy.
Have the ability to "think", and have a wealth of resources, and even machines more accurately than people concluded. Recently, Stanford researchers' depth learning algorithms are better at diagnosing pneumonia than human radiologists. This is called "CheXNet
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