The world becomes more advanced if humans and machines can keep learning and improving, work collaboratively, making breakthroughs to reach the next level of universal wisdom.
Also, Deep Learning is a hot concept in information computing and part of Machine intelligence which can be viewed as the ability of a computer to learn and reason. Learning would generate a hypothesis or output for a certain input data set. At the same time, reasoning can be seen as deciding whether or not to act upon those learned hypotheses. Nowadays, Deep Learning has been categorized into the following disciplines for building a more intelligent society.
Cognitive analysis: Cognition is a group of mental processes that includes the attention of working memory, producing and comprehending language, learning, reasoning, problem-solving, and decision-making. Machine learning is a set of processes that imitate human learning scenarios and simulate programable patterns for collecting information, retrieving information, and doing a certain level of analysis & reasoning.
Cognition is a faculty for processing information, applying knowledge, and changing preferences. Cognitive gaps will cause the blind spots for either defining the real problem or solving it. Deep learning is a subcategory of machine learning; it also simulates such a process, uses weight or bias and other analytics techniques to understand the nature of human language, and analyzes the large set of data to make better decisions. But in most circumstances, machine learning is good at collecting sufficient information about certain subjects at high speed. Human experts are better at analyzing, especially synthesizing for making sound judgments. They need to work collaboratively to improve cognitive abilities in decision-making.
Predicting & Forecasting: The prediction of the future is based on the analysis of the past. Every single prediction depends on some kind of model. Machine intelligence is very useful to analyze historical data, discover patterns, and predict future events with a certain level of accuracy. So deep learning can be applied in various industries, especially for analyzing customer sentiments, doing certain industry trend forecasts, improving risk intelligence, etc.
The optimal prediction system based on deep learning should have the best results in different areas according to specific or customized quality criteria. However, machine intelligence has its limitations. Human experts with management skills should keep in mind, that some trends are more significant than others in their impact on business growth. Ultimately, they need to make sound judgments on critical issues with coherence.
Process Optimization: Due to information overflow and multidisciplinary human interaction, business processes are also becoming complex, and dynamic. There are all sorts of process complexities such as step complexity - the number of process steps involved; process flow complexity - the number of splits, joins, and human steps complexity. Deep learning machine intelligence enables organizations to do process optimization via analyzing, consolidating, modernizing, integrating, optimizing, etc.
Deep learning helps businesses improve process intelligence and develop intelligent processes. Also, the quality of processes impacts corporate capacity management. By embedding marching intelligence into process management, the process intelligence can be implemented to analyze the historical management data, estimate current inventory levels, and forecast future demands, so they can manage assets, resources, talent, IT, etc, with the primary goal of ensuring that the organizational capacity meets current and future business requirements cost-effectively.
Intelligence amplification is another digital phenomenon. For both humans and machines, learning needs to go deeper and deeper, and understanding should become more profound and interdisciplinary. (learning is essential to the function of the machine). Also, intelligence is multidimensional and subjective; Different people have cognitive differences and perceive things via different lenses such as moral, ethical, emotional, intellectual, promotional, etc. Different deep learning frameworks and models have their strengths and weaknesses, with technique limitations. The world becomes more advanced if humans and machines can keep learning and improving, work collaboratively, making breakthroughs to reach the next level of universal wisdom.
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