“Men should not waste their time on trivial games. They should play Go,” said Confucius.
Go is an ancient Chinese board game that uses uncomplicated features (circle and line, black and white, stone and wood), along with easy-to-grasp rules, to create a notoriously complex strategy game that’s fascinated players for 2,500 years. It became a favorite game to crack with AI researchers because of its infinite number of outcomes, as compared to other strategy games like Chess.
When Deep Learning became the God of Go
In 2019, Lee Sedol, the South Korean grandmaster of Go announced his retirement. At 36, he was too young to hang his boots. But Sedol said that AI had created an opponent he couldn’t defeat. “Even if I am number one, there is an entity that cannot be defeated,” he said, in a post-retirement interview.
The South Korean world champion lost 4 to 1, in a set of matches to AlphaGo, an artificial intelligence program developed by DeepMind, a Deep Learning startup.
AlphaGo was programmed using deep neural networks, which are inspired by biological brains. The networks have millions of neuron-like connections that AlphaGo rearranged as it played. Its undisputed victory over the world’s best human Go player highlights the growing capability of AI to take on mankind at complex tasks.
Deep Learning is a highly advanced subset of machine learning that creates an Artificial Neural Network (ANN), which enables it to learn and make intelligent decisions on its own. It’s what’s powering the most human-like artificial intelligence and could create path-breaking opportunities for businesses that incorporate ML into their decision-making systems.
Deep Learning Goes Mainstream
AlphaGo’s developers say the technology can be used to conquer problems in industries across the board, from medical imaging, climate modeling, driverless automobiles to restaurant recommendations.
How Deep Learning has seamlessly integrated into our lives:
- Deep Learning in our daily lives: Every time you log in to Amazon, it pops up new recommendations that are exactly what you’ve been meaning to watch. It’s Deep Learning algorithms at work. Uber has built Deep Learning applications for diverse scenarios: customer classifications, self-driving cars, and predicting driver demand
- Medicine: From disease and tumor diagnoses to personalized medicines created specifically for an individual’s genome. Deep neural networks aid in medical imaging: detecting metastasized cancer in pathological scans; diagnose eye diseases.
- Self-driven cars: AI and Deep Learning-powered self-driven cars are already a reality. They’ve proven to be high on safety compliances. By scanning sensor data, on-board computers take driving decisions in split-seconds.
- Environment protection: Deep learning can analyze environmental data from millions of sensors to provide weather and pollution forecasts. It will allow city planners to model future environmental scenarios and find ways to mitigate them.
- Language Aid: Be it Alexa, Siri or Google Translate, Deep Learning is used to understand speech and language, and automatically translate between languages.
Will Deep Learning Drive the future of Marketing?
If Deep learning can solve so many problems, Can it solve the number one challenge of marketers – Marketing ROI measurement and advertising waste?
Marketing ROI measurement struggles with four aspects
Lack of understanding of interconnectedness in planning
Lack of agility in measurement and media planning
A comprehensive view of ROI for above the line, online, and below the line activities
Actionable insights at a granular level
The inherent nature of Deep Learning can uproot these old-world problems as the system will understand the causal relationship between different elements of marketing on the business KPI at a very granular level. As deep learning systems are continuous learning engines, they will bring in a new era of agile planning which became the norm in 2020.
Unlike the sub-optimal paradigms available, deep learning systems will solve the problem of actionable insights at a very granular level.
Data Poem’s proprietary deep learning application has already started to show its impacts. We are seeing a min of 20% lift in the marketing ROI through proper allocation and cost optimization.
Solution marketers are looking for ages is now a reality. Now it’s time for the marketing to adopt it and make its impact felt in the board rooms with an accurate measurement of ROI and effective planning.
