The 21st century has been defined by stunning breakthroughs in artificial intelligence (AI) that have taken the concept out of the realm of science fiction and turned it into one of the most promising and disruptive technologies of our time. What had been languishing for decades as an elusive goal has rapidly become a part of everyday life, changing how we interact with technology in ways unimaginable just a short time ago.
In this article, we will explore the key factors that have unleashed the recent explosion of progress in AI after years of slower incremental advancements. We will look at how the combination of increased computing power, big data, and new algorithms have enabled AI systems to finally achieve human-like capabilities in areas like computer vision, natural language processing, and more. The real-world impacts of applied AI will also be examined, from virtual assistants like Alexa to autonomous vehicles and everything in between.
Additionally, we will delve into some of the risks and ethical concerns raised by the emergence of intelligent machines with increasing autonomy. AI has the potential to transform our societies and economies to an extent that is both inspiring and frightening. Understanding the pros and cons of this technology is crucial as its influence continues to rapidly expand.
The recent AI revolution hasopened the door to tremendous possibilities, from helping tackle some of humanity’s greatest challenges to changing how we live day-to-day life. As we will explore, coming to grips with this game-changing technology may be one of the defining tasks of the 21st century.
The Exponential Growth of Computing Power
One of the most important factors underlying the recent explosive progress in artificial intelligence (AI) is the exponential increase in computing power that has occurred over the last few decades. This rapidly accelerating pace of advancement is encapsulated by Moore’s Law, the famous trend first noted by Intel co-founder Gordon Moore in 1965.
Moore predicted that the number of transistors on an integrated circuit, and therefore its processing power, would double approximately every two years. This compounding doubling effect leads to exponential gains in key computer capabilities like processing speed, memory capacity, and data storage over time.
In the early pioneering days of AI research in the 1950s-1970s, computers were extremely limited in their capabilities. Early AI programs ran on mainframe computers that were so large they filled entire rooms, yet these massive machines had less processing power than even a typical digital watch today.
These severe resource constraints meant that early AI systems were very brittle, narrow in their capabilities, and unable to process large amounts of data. A game like chess, which seems simple to modern AIs, was completely inconceivable to early chess-playing programs. The limited power of early hardware severely crippled what AI systems could accomplish.
However, as the decades passed, Moore’s Law held true and computers became exponentially more powerful with each passing year. By the 2000s, consumer PCs had enough processing power, memory, and data storage to run far more flexible machine learning algorithms on large datasets.
The advent of graphical processing units (GPUs), which were specialized for parallel processing, provided another boost to AI capabilities. This massive expansion of computing resources finally allowed AI algorithms to be trained on the vast quantities of data needed for them to learn complex tasks and achieve human-level performance in many domains. Tasks like image recognition, natural language processing, and strategic gameplay that were once impossible for computers became possible.
The exponential growth of computing shows no signs of stopping, with top supercomputers today surpassing speeds on the order of petaflops (quadrillion floating point operations per second). Quantum computing promises another giant leap if practical systems can be built.
This ongoing explosion of processing power is what has taken AI from a mostly academic field limited by resources to the transformative real-world technological force it is today. However, it also raises concerns about how an unfathomable expansion of AI capabilities could lead to unpredictable futures as progress continues its exponential arc. Understanding the power of exponential growth will be vital for anticipating the impacts of future artificial intelligence.
In addition to more powerful computers, breakthroughs in AI required access to vastly larger datasets than were previously available to researchers. These came from sources like e-commerce, social media, and the digitization of everything from finance to healthcare to government records.
For example, machine translation systems rely on datasets of hundreds of millions of human-translated sentence pairs to learn associations between words and phrases across languages. Image recognition systems are trained on hundreds of millions of labeled images.
Recommendation systems crunch user data from billions of purchases, clicks, and ratings. The avalanche of digital data that has come online in recent decades provided the critical raw material needed to train AI to perform at human levels of accuracy across diverse domains.
Critically, more data enables deeper neural networks that can capture more complex patterns. Training on larger datasets also makes the systems more robust. In contrast, earlier AI systems built on limited data tended to break easily outside narrowly defined tasks. The big data revolution was essential for creating versatile, powerful AI.
Crowdsourcing and Data Labeling
While the exponential growth in computing power enabled the training of powerful AI models, actually training those models required massive labeled datasets, which were not easily available. In order for an AI system to learn to recognize images, translate text, make recommendations or predictions, it needs examples that are manually labeled with the relevant features, categories, languages, ratings, and other attributes. Manually labeling millions or even billions of data samples is no easy feat.
Fortunately, the rise of crowdsourcing and distributed labor markets over the internet provided a solution to generating enormous labeled datasets quickly and economically. Companies like Amazon Mechanical Turk created platforms that allow large numbers of online crowd workers to be paid small amounts to complete simple tasks like labeling images, transcribing audio clips, or translating sentences.
By splitting up massive annotation and labeling jobs into microtasks done by thousands of workers, datasets of previously unthinkable sizes could be labeled with human accuracy at minimal cost. For example, companies might pay Mechanical Turk workers just a few cents to label 100 images, then integrate the labels from multiple workers to come up with high-accuracy ground truth data for training computer vision algorithms.
In addition to paid crowdsourcing marketplaces, open crowdsourcing platforms like Wikipedia have also provided crucial training data for AI systems. On Wikipedia, millions of users contribute and discuss content, providing a vast corpus of text, images, and translations curated by humans.
Social media platforms are similarly flooded with billions of posts, images, videos, and conversations that can be utilized to train AI models after appropriate filtering. The hyper-connected crowdsourced nature of the web has enabled levels of data annotation that are key for advancing modern AI.
Of course, crowdsourced training data also introduces risks such as bias, human error, and gaming the system that must be accounted for. But on the whole, crowdsourcing has allowed the previously painstaking task of data labeling to be accomplished on a Web-scale, enabling breakthroughs in data-hungry deep learning algorithms that are powering AI progress today.
This distributed labor approach has demonstrated that just as interconnected computing resources can create an exponentially more powerful computational whole, interconnected human intelligence can be aggregated to create massively annotated datasets.
A technique called deep learning emerged as a key breakthrough that allowed AI systems to achieve dramatically better performance in the 2010s. Deep learning utilizes neural networks, computing systems inspired by the biological neural networks in human brains. These contain layers of simple computing nodes that pass data progressively between layers and are capable of learning complex relationships within large datasets.
In the past, neural networks were limited to only a few layers because hardware was too slow to train deeper networks and computations failed to converge. However, researchers discovered that graphics processing units (GPUs) designed for parallel processing were remarkably efficient at training deep neural networks orders of magnitude faster. This fueled an explosion in deep learning starting in 2012.
With deep learning, AI systems were able to achieve new state-of-the-art results across diverse tasks from computer vision to speech recognition and natural language processing. Deep learning proved exceptionally powerful at finding patterns and extracting features within massive datasets that allowed AI to match or surpass human capabilities on many problems for the first time. This key innovation was critical to the leap forward in practical results from AI research in recent years.
Sample Real-World Impacts of AI
The combination of exponentially more powerful computers, massive datasets, scalable data processing, and deep learning algorithms finally allowed AI to achieve human-level performance on tasks that had confounded earlier attempts for decades. This breakthrough led to AI rapidly moving out of research labs into real-world applications with transformative effects on society, the economy, and daily life.
Some examples of the real-world impact:
- Computer vision AI can now identify objects in images and videos with accuracy rivaling human perception. AI vision systems can also match or exceed people at facial recognition, picking out individual faces from huge databases. Algorithms can identify emotions, age, gender and more from facial images.
- Natural language processing AI enables real-time translation between languages, reaching near-human accuracy. Chatbots utilize NLP to engage in natural seeming conversations.
- AI programs have surpassed the greatest human players at games like chess, Go, poker by learning from millions of past games. The humble board game has been a vital proving ground for AI capabilities.
- In healthcare, AI is analyzing massive sets of patient medical records to spot patterns and improve diagnoses. Pharmaceutical companies use AI to analyze molecular data and discover promising new drug candidates faster.
- AI digital assistants like Siri, Alexa and Watson engage in voice conversations, answer questions, perform tasks, and control smart devices in homes and offices. Their capabilities continue to grow more human-like.
- Recommendation engines on sites like Amazon and Netflix use AI to predict preferences and customize content to individual users. Targeted recommendations keep users engaged.
- AI is automating knowledge work, reading and analyzing documents, handling customer service queries, completing legal research and more. This is reducing costs but also affecting employment since machines can handle many tasks done by humans.
The examples of revolutionary change enabled by AI already go on and on, and the future promises even greater disruption as data and computing resources continue to advance. While technical and ethical challenges remain, the scalability and accelerating innovation of AI systems suggest they will significantly alter economics, labor, warfare, and potentially even cognition in coming decades. The AI genie is decisively out of the bottle.
Conclusion
After decades of slow progress, AI advanced enormously in a short period thanks to the scalability unlocked by new technologies. Computing power grew exponentially under Moore’s law, providing the processing muscle to handle ever-larger datasets and neural networks. Vast amounts of digital data shared online gave AI the raw material needed to learn.
Crowdsourcing scaled up data labeling at low cost via the internet. Deep learning enabled breakthrough results not possible with earlier techniques. Together these innovations allowed AI systems to achieve human-level performance in diverse capabilities that found widespread real-world use.
The ability to scale up data, computation, and model complexity unleashed the AI revolution impacting society today and driving future progress.
James is a writer who specializes in writing about AI and education for our blog. He believes in the power of lifelong learning and hopes to inspire his readers to take control of their education.
James is passionate about self-education as a means of personal growth and fulfillment, and aims to empower others to pursue their own paths of learning.