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Self-learning AI Development Methods and What Can AI Learn on Its Own
Artificial Intelligence is everywhere. AI’s, as we’re promised, are predicting weather conditions, helping companies find unhappy customers, making ourselves better in selfies, arranging our holiday pictures, scheduling appointments with doctors, and what not. Supervised Machine Learning, an AI technique, takes labeled data as an input to produce desired output for unseen data. In a similar manner we teach children to recognize a cat or a dog, supervised learning technique teaches the system to see patterns.
As businesses keep integrating AI into their applications, technophiles are brainstorming to take AI development beyond supervised machine learning. This is where the AI system owns capabilities like humans and is able to learn on its own.
Interesting Read: How AI and ML in Retail Will Boost Ecommerce Sale This Holiday Season.
Is Artificial Intelligence Capable of Learning its Own?
Humans can understand and learn from different situations. We adapt to different environments. We can figure out how to solve a given problem by learning from historical experiences. While AI cannot replicate human brains- the Terminator scenario of AI ruling the world is still far to see- general intelligence gets us the closest. This critical element could help AI to reach the level of human intelligence.
It’s Time to Go Beyond Supervised-Learning
Supervised learning depends on labeled data including images, audio, text, etc. Being highly data-dependent, such models require intensive computational power. To prevent the exponential usage of computational power, AI development has to utilize other machine learning approaches namely self-supervised learning to augment AI with general intelligence. This kind of learning technique does not learn from human-trained data sets, but from the data environment where it is placed into.
What Can AI Learn
Top AI companies like Facebook and Google have remarkably done well with the applications of AI such as web search and Facial Recognition respectively. Even more, companies in different domain alike, XpresSpa, for example, has worked out an intelligent solution to accurately forecast daily sales for their airport spa business by leveraging AI techniques. Businesses nowadays are heavily investing in AI to solve operational problems that previously were stagnating their growth.
But moving further, scientists are planning to surface AI to take on much tougher challenges that require common sense. For example, chatbots that can learn itself to explain the news, autonomous cars that can handle dense traffic on roads, and robotic assistants that nurse elderly.
Here's what can AI learn on its own.
AI powered robots will take a leap from discretely defined environments to explore real-world environments and just play within their new settings and learn. For example, a Toyota Human Support Robot (HSR), designed to perform physical tasks at home, works on instincts like humans. It possesses two humanlike instincts. First, the HSR decomposes challenges into smaller pieces just like human does. And the second is to reason about goals that are being asked to perform, the way human’s instinct works toward a goal. To surprise, upon asking HSR to get a red cup, when there are only a black cup and a red plate in the surrounding, it uses good sense to pick the black cup and avoid the red plate.
Scientists are working on interaction networks to empower AIs with the same intuitive behavior that babies have. Just like infants deconstruct the world into interacting entities, AI systems quickly learn objects' properties and relationships to make sense in the real-world scenarios.
Different Methods to Develop Self-learning AI
The AI is learning to achieve general intelligence to match the level of human intelligence by developing common sense through promising techniques such as reinforcement learning and generative adversarial networks.
Reinforcement learning is a machine learning technique that trains algorithms using a system of reward for desired behavior and punishment for undesired behavior, respectively. This unsupervised form of learning allows algorithms to learn tasks simply by a trial-and-error basis. Set an objective, and reinforcement learning will start work toward that objective through trial-and-error method until it starts getting rewards in a uniform manner. With repetition, the behavior of the algorithm improves and hence performance improves. In most of the scenarios, the learning intelligence surpasses the human abilities as long as the environment is representative of the real-world.
One befitting example is AlphaGo Zero, an AI system built by DeepMind using reinforcement learning, which defeated its predecessor AlphaGo. The system works on a neural network that has no understanding about the game. It then plays against itself by placing stones randomly on the board. Over time, the neural network gets better at evaluating board positions and identifying the best moves possible. It also learns various canonical elements of Go strategy and finds new strategies all its own.
Generative Adversarial Networks (GANs)
This semi-supervised machine learning technique involves two networks – the Generator and the Discriminator. The first network, the Generator, attempts to create forgeries of human-created work, say the work of Claude Monet. The second network, the Discriminator, works on distinguishing the forgeries (fake images) from the real-world images. With practice, the two networks compete against each other and gradually get better to refine their understanding about a concept.
The power of GANs to generate increasingly realistic data can reduce the need of vast labeled data created by humans. In fact, this ability is matching with human creative thinking to harness knowledge from one context and then applying it within another. For example, University of California, Berkeley, is working on ‘cycle-consistent adversarial network’ to transform video or horses into the one of zebras. In this context, AI is detecting the basic shape of a horse in the first video and can work on the aesthetic of that image, and then seamlessly swapping the brown skin with white and black stripes while the image is in motion.
Such work is taking a stride in autonomous driving field, the AI that can enable self-driving cars to adapt to unfamiliar road conditions and prevent accidents. Also, using GANs, the AI can generate realistic images of different types of tumors, helping scientists in the diagnosis process.
AI is Learning on Its Own
Yes, AI is learning on its own to see, think and take action. Self-supervised learning methods like reinforcement learning and GANs are making AI systems to explore their learning environment and build up the general knowledge. This suggests that evolved version of AI work not only on the rules, but also in the real-time situations – much like amateurs who grow into artists by practicing over and again.
The promise of AI is enormous; and the tools, techniques, processes involved are taking this innovation a way further. Whatever you are struggling in your business, you can consult with our AI experts and strategically plan growth.