Deep Learning, a subset of Machine Learning, is the driving force behind modern Artificial Intelligence (AI). Deep Learning uses layers of algorithms that mimic the functioning of the human brain, enabling applications to learn and think as humans do.
Such layers of algorithms, technically referred to as Artificial Neural Networks, allow computers to process data, understand human speech, and visually recognize objects. Artificial Neural Networks need extensive training before they can start solving AI problems.
Apart from a host of eminent universities, research at many companies has driven advances in Deep Learning over the last century. While AT&T Bell Labs and IBM staged some of the early breakthroughs in Deep Learning, the most important company in the field currently perhaps is Google, which pioneered the TensorFlow framework for creating Deep Learning models.
Beyond that, other notable companies with Deep Learning missions: OpenAI, Nvidia, Microsoft, Intel, Amazon, Qualcomm.
Deep Learning now easily surpasses other Machine Learning approaches in performance and accuracy and constitutes the backbone of Big Data processing and modern AI. But this didn’t happen quickly — it took nearly a century of considerable efforts, and Deep Learning still has a long way to go.
Remembering the history of its growth helps us keep things in perspective and respect the thousands of folks who have contributed to this exciting field’s evolution.
What Exactly is Deep Learning?
Deep Learning developers today address a variety of AI goals: correct classifications, trustworthy analysis, accurate predictions. To meet these goals, developers have to rise to multiple challenges:
- Computer vision
- Natural language processing
- Personalized content or product recommendations based on prior user activity
- Content analysis and filtering
- Pattern recognition and anomaly detection.
That developers are flying high means Deep Learning directly or indirectly affects our daily lives in several ways. Governments use the technology for various purposes: facial recognition, identity theft detection, satellite imagery, weather forecasts. Law enforcement agencies further use it to analyze transactional data for the sacred purpose of identifying fraudulent or criminal activity.
The following list enumerates prominent industries that serve consumers with the silent power of Deep Learning in the background, quite commonly without consumers’ awareness.
- Financial Services — Banks and credit-card companies use Deep Learning to prevent fraud. Banks also use Deep Learning to assess risks related to loan failure and client bankruptcy. Predictive analytics can further identify investment opportunities and help investment firms know when to buy or sell stock.
- Healthcare — Sensors attached to a patient help Deep Learning algorithms track the patient’s health. Healthcare organizations further use Deep Learning to detect, diagnose and treat diseases.
- Retail — Retailers use Deep Learning to capture and analyze data for tailoring your shopping experience, price optimization, and inventory management. Business websites use Deep Learning to decide what online ads to show you.
- Energy — Energy companies use Deep Learning to locate new energy sources and analyze minerals. Oil companies use it to streamline oil distribution for higher efficiency and cost savings. Electricity companies use it to predict demand loads.
The use of Deep Learning in industries is expected to shoot up further in the next three years, with billions of dollars being invested in AI solutions globally. Two specific industries in which Deep Learning will play enhanced roles in the coming days are manufacturing and cybersecurity.
Manufacturing companies will increasingly use Deep Learning for logistics and for robots that tackle hazard-prone tasks, while Deep Learning models will assist cybersecurity organizations in preventing and detecting intrusions and malware, as well as network traffic analysis.
How Deep Learning Was Born
Deep Learning history goes back to 1943 when Warren McCulloch and Walter Pitts created a computer model based on neural networks in the human brain.
Since then, research from thousands of individuals, universities and companies has allowed Artificial Neural Networks to reach impressive heights.
Among the most notable achievements along the way, Frank Rosenblatt, an American psychologist, built the Perceptron machine for image recognition in the 1950s. In 1989, Yann LeCun at AT&T Bell Labs applied a backpropagation technique letting neural networks read handwritten zip codes.
But the modern wave of Deep Learning is widely credited to Geoffrey Hinton. In 2006, Hinton made a crucial breakthrough — Greedy Layer-wise Training to train Deep Belief Networks — to fundamentally alter how neural networks are trained.
Since then, Deep Learning has seen massive gains. These gains have partly been facilitated by explosive increases in computing power and the emergence of enterprise-class GPUs.
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