Deep learning: The Most Important Software Breakthrough

Author: Joyce Chen

The BRB Bottomline

Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. We believe that deep learning could be the most important software breakthrough nowadays and is expected to drive high potential revenue growth in the future years.


Until recently, humans programmed all software. Deep learning, a form of artificial intelligence (AI), uses data to write software. By “automating” the creation of software, deep learning could turbocharge every industry and is expected to add $30 trillion to the global equity market capitalization during the next 15-20 years. It is therefore expected to be the most significant software breakthrough nowadays. 

Overview of Deep Learning

From the 1950s to 2012, we had software 1.0 which involved a programmer building applications line by line, instructing the computer to perform specific behaviors. With more objective-oriented programming, the software had increasing scale and capabilities. In 2012, deep neural networks won the ImageNet challenge, marking the beginning of the deep learning or “software 2.0” era. With deep learning, the software is written in the internal parameters of a neural network. These parameters are found by specifying the desired behavior of the program and optimizing the network to reproduce that behavior. With more codes being written by data, in 2020, deep learning empowered almost all large-scale internet services including search, social media, and video recommendations. During the next decade, it is expected that the most important software will be created by deep learning, enabling self-driving cars, accelerated drug discovery, and more.

Deep Learning is Creating the Next Generation of Computing Platforms

The emergence of conversational AI, self-driving cars, and consumer applications are all powered by AI and expected to drive high potential market growth in the future. 

Deep learning empowers conversational AI and helps design more cost-efficient digital experiences. Conversational AI allows artificial intelligence technologies like chatbots to interact with people in a humanlike way. By bridging the gap between human and computer language, it makes communication between the two easy and natural. In 2018, the conversational AI market was valued at $3.2 billion, and is anticipated to reach a value of $15.0 billion by 2024, registering at a CAGR of 30.2% during the forecast period. Conversational AI bots have had a positive impact on businesses in a number of ways, prominently in improving the customer experience. The bots can enhance user engagement by resolving the basic queries of the customers instantly and enhance employee productivity by passing on complex problems to the human agents. A bot can engage in multiple conversations at a time solving multiple queries at an instance; hence, the need to hire employees at a large scale is eliminated. This results in huge cost savings for businesses.

Additionally, deep learning empowers self-driving cars and helps reduce the costs of human workload and labor costs. The global self‑driving cars and trucks market size is expected to be approximately 6.7 thousand units in 2020 and is anticipated to expand at a CAGR of 63.1% from 2021 to 2030. With the advancement of deep learning and automotive technologies, the U.S. is anticipated to witness high adoption of driverless technology in transportation in the future five years. 

Furthermore, AI consumer applications such as Tiktok have already demonstrated the power of deep learning and suggested the high potential revenue growth that deep learning may bring. Powered by AI, TikTok can engage its average new users up to 10 minutes that is three times the capability of Instagram

Key Takeaways

Deep learning scales up the market sizes in conversational AI, self-driving cars, and consumer applications. With the advancement in AI technologies, I believe that deep learning could create more economic value than the internet did in the future years. Therefore, I believe that we should invest in companies that are centered around deep learning, especially for companies that focused on conversational AI, self-driving cars, and AI-powered consumer applications.

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