Automation, deep automation, and artificial intelligence (AI); while we have seen automation in industries for decades now, AI and deep automation are recent buzzwords. So, what exactly do these concepts entail? In this third article of our blog series, we will explore these technologies and how they are expected to shape our industries in the coming years.
Automation is the use of systems or machines to perform tasks that would otherwise require human effort. It’s about optimizing processes, improving efficiency, and reducing manual labor. Automation, of course, is not a new concept, as we’ve seen automation in various forms in the industrial revolution with assembly lines, for instance.
On the other hand, deep automation takes this concept to an entirely new level. It is the integration of AI and machine learning techniques into automation processes. The “deep” in deep automation often refers to the use of deep learning, a subset of machine learning, to make these systems more intelligent and capable.
Lastly, artificial intelligence, as discussed in previous posts, is the simulation of human intelligence in machines. These machines are programmed to think like humans and mimic their actions.
Deep automation is part of the evolution towards more sophisticated AI systems.
The Mechanics Behind Automation and AI
Automation is driven by a set of rules or an algorithm that guides the system to perform tasks. These algorithms are based on a predefined set of instructions that the machine follows.
On the other hand, AI is much more complex. It uses deep learning and neural networks to mimic human intelligence. These networks are designed to learn and improve from experience, much like the human brain. They analyze data, identify patterns, make decisions, and learn from their mistakes. This ability to learn and adapt is what sets AI apart from traditional automation. When the principles of AI are applied to automation, we get intelligent or deep automation.
Deep Automation: The Next Step in Automation’s Evolution
Deep automation represents the next step in the evolution of automation. It’s about machines not just doing tasks but understanding them. Deep automation uses AI and machine learning to handle complex processes that require a level of intelligence and decision-making capability. Think of deep automation as automation with a brain. It’s about machines that can understand, learn, and adapt. They can analyze data, identify patterns, make decisions based on that analysis, and learn from their experiences. Deep automation is the driving force behind industry 4.0 and digital transformation. It’s changing the way industries operate and revolutionizing the way we work. It’s not just about doing tasks faster and more efficiently; it’s about doing them smarter.
The Fine Line: Automation, Deep Automation, and AI
There’s often confusion about the difference between automation, deep automation, and AI. While they’re related, they’re not the same thing. Automation is about machines doing tasks based on a set of rules. AI is about machines that can think and learn like humans. Deep automation, on the other hand, is a combination of the two. It’s about machines that can do tasks and make decisions based on their understanding and learning. It’s the integration of AI in automation processes. While automation can handle repetitive tasks, deep automation can handle complex tasks that require a level of understanding and decision-making capability. And while AI can think and learn like humans, deep automation applies this intelligence to automation processes.
Case Study: Automation vs. Deep Automation in Gaming
Let’s examine the case of two historically significant game engines, IBM’s Deep Blue and Google DeepMind’s AlphaGo, to delineate the difference between automation and deep automation.
A traditional automated chess engine, exemplified by Deep Blue, would follow a set of predefined rules or algorithms to make its moves. In its historic match against Garry Kasparov in the late ’90s, Deep Blue assessed millions of potential moves each second using its preprogrammed algorithms, choosing the move that appeared optimal based on these analyses. It showcased an impressive feat for AI at the time, but this approach heavily relied on raw computational power (or brute force) and didn’t incorporate any form of learning from past games or adapting its strategy based on the specific patterns or playstyle of its opponent.
On the other hand, deep automation is best exemplified by Google DeepMind’s AlphaGo. Unlike Deep Blue, AlphaGo leverages AI, specifically deep learning, to understand the game, make decisions, and learn from its experiences. It begins by analyzing the moves of its opponents, identifying patterns, and developing strategies. AlphaGo’s deep learning framework enabled it to constantly refine its internal model, learn from its experiences, and adapt its gameplay strategy. As a result, AlphaGo was able to defeat the world champion Go player by making unexpected and innovative moves that demonstrated a deep understanding of the game’s strategy.
This progression from Deep Blue to AlphaGo illustrates the evolution from traditional automation to deep automation in game-playing AIs and demonstrates the potential of deep automation in a variety of other fields.
The Transition: Moving from Automation to Deep Automation
The transition from automation to deep automation is a significant paradigm shift. It’s not just about machines performing tasks anymore; it’s about machines understanding tasks, making intelligent decisions, and learning from their experiences. This shift is being enabled by advancements in AI and machine learning technologies, which are making machines smarter and more adaptable.
Let’s consider a concrete example from the telecommunications/ cybersecurity industry. Email providers have always faced the challenge of spam filtering. Earlier automated systems could filter out spam based on predefined rules, such as blocking certain phone numbers or identifying typical spam phrases in text messages. However, these rule-based systems struggled with the ever-changing strategies employed by spammers.
With the advent of deep automation, telecom companies are now integrating AI and machine learning into their spam filtering processes. These advanced systems can learn from vast amounts of data, identifying complex patterns that indicate spam, even as spammers change their tactics. They can also adapt over time as they encounter new types of spam, continually improving their accuracy. This is an example of moving from traditional automation to deep automation.
Another relevant example for us pertains to CCTV systems and, specifically analytics. Traditional automated CCTV systems adhered to predefined rules to alert for recognized threats. However, these systems were limited by their ability to only detect threats they were explicitly programmed to recognize. With deep automation, these systems are being enhanced with AI and machine learning capabilities. They can analyze complex scenarios, adapt to new threats, and provide more accurate alerts, all while reducing false positives. We will delve deeper into CCTV analytics and how AI transforms it in a dedicated upcoming post.
Moreover, the drive toward deep automation is fueled by the need for greater efficiency and productivity. As industries become more competitive, there’s a growing need for systems that can perform tasks faster, more accurately, and with less human intervention. In the case of spam filtering, this means fewer false positives and negatives, leading to improved customer satisfaction and trust.
The transition to deep automation not only enhances current capabilities but also opens up new possibilities that were beyond the reach of traditional automation. It’s a transformative shift that’s reshaping industries and offering enormous potential benefits.
The Future: The Impact of Deep Automation on Workforce and Industries
Deep automation is set to have a profound impact on the workforce and industries. It’s changing the way we work and the skills we need. As machines become more intelligent, they’re able to handle more complex tasks. This means that some jobs will become obsolete, while others will become more important. However, it’s not all doom and gloom. While deep automation will replace some jobs, it will also create new ones. And these new jobs will be more interesting and rewarding. They’ll be about supervising machines, interpreting data, and making strategic decisions. Deep automation is also transforming industries. It’s making them more efficient, productive, and competitive. It’s driving innovation and creating new business opportunities.
Conclusion: Embracing the Future with Deep Automation
As we move into the future, deep automation will assume an increasingly important role. It represents integrating AI into automation to make our systems more intelligent and capable. It’s an opportunity to make our work more interesting, our industries more productive, and our lives more comfortable. So let’s embrace the future with deep automation. Let’s make the most of this incredible technology and the opportunities it brings. And let’s prepare ourselves for the exciting changes ahead. Before proceeding, I would like to share two fascinating videos: Legendary chess champion Gary Kasparov’s TED talk in 2017, ‘Don’t fear intelligent machines. Work with them’, where he recounts his experiences of matches with Deep Blue supercomputer and what he learned from them. The second is a full-length documentary movie, ‘AlphaGo – The Movie’, on AlphaGo, Google DeepMind’s AI-powered Go engine, which defeated the human world champion at this incredibly complex game.
In the next post, we will cover the two fundamental areas of AI Machine Learning and Deep Learning; see you then!