The Power of Deep Learning
The rapid advancement of artificial intelligence has prompted discussions amongst field experts of the potential wide-scale applications of machine learning technologies, algorithmic models designed to process large swaths of data and detect patterns and trends in those datasets. As these strides have accelerated with society’s shift towards the increasing reliance on digital technology, opportunities have started opening up for more in-depth applications of artificial intelligence technologies. For example, as a result of the developing capabilities of AI technologies, it has become possible to now use open source, seemingly innocuous, aggregated data, to infer quantitative indicators of population level change.
This particular process in engineering and science circles is called deep learning, a subset of machine learning in artificial intelligence (AI) that allows for unsupervised learning through the utilization of unstructured or unlabeled data. All of this may pique the interest of a data scientist or an engineer, but why should the average person with little to no technical knowledge of artificial intelligence be curious about the trajectory of deep learning research?
What Deep Learning Seeks to Achieve
Bernard Marr provides a great layperson definition of what deep learning is: “Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Similarly to how we learn from experience, the deep learning algorithm would perform a task repeatedly, each time tweaking it a little to improve the outcome. We refer to ‘deep learning’ because the neural networks have various (deep) layers that enable learning. Just about any problem that requires “thought” to figure out is a problem deep learning can learn to solve.”
The onset of the digital era, concurring with the advancement of artificial intelligence technologies, brought about a huge global data revolution in its process. The emergence of the digital era resulted in a massive data explosion across the globe in regards to the new forms that data could now assume, and the new data channels that were opened up due to the evolution of communications and the interweb.
As a result of these developments, “big data” could now be “mined” from various open channels such as social media, financial markets, and blockchain ledgers; this data can be collected and analyzed – whether that data are images, text, or sound – and put to use for real-world applications.
Before the days of artificial intelligence and machine learning, humans were not avail of the capacity to automate large-scale data collection processes and had to deal with the inefficiencies associated with manual methods of data collection and analyzation. Today, however, the promise of artificial intelligence technologies has led to large-scale data collection processes now being predominantly automated.
Deep Learning: Advancing Frontiers
Simply put, deep learning is a machine learning technique that teaches computers to do something that humans do naturally: learn. One example of a commercialized application of deep learning technology is the driverless car. The deep learning features built into driverless cars allows for the cars to distinguish between pedestrians and light posts, and enables them to recognize stop signs and other traffic signals.
Another example of deep learning technology is the Amazon Alexa virtual assistance, which provides its users with music, smart-home, and digital-assistant abilities without the inconvenience of the intermedial usage of another device. Overall, as a result of the advancements of deep learning, society has achieved results today that were previously thought of as impossible or unattainable yesterday.
The general public may naturally be pretty excited to attain such advancements and may be eager to experience the commercialized applications of newer deep learning technology. The impressive results that society has already achieved in deep learning are owed to the increased accuracy of data collection (deep learning). To continue to push the envelope for deep learning, AI experts must continue to make the process of data collection more accurate. The accurate processing of data for deep learning is contingent upon four main factors: 1) Data, 2) Algorithms, 3) Algorithm Tuning, 4) Ensembles. The performance of deep learning technology will only be increased with improvements in all four of these categories.
The improvement of deep learning technology = More cutting-edge deep learning applications = More cool technology for us to marvel over!