4 emerging AI trends to watch
AI-powered solutions can do amazing things, from offering products based on previous purchases to voice assistants that can order pizza or suggest the next movie to watch on Netflix.
From a business perspective, AI solutions are designed to improve business processes while making them less time-consuming and more qualitative. From the technology perspective, AI is not a single technology, but a whole set (including deep and machine learning) designed to teach computers to mimic the way a human thinks.
Excitement over AI is growing year after year. It’s no wonder that this technology is widely used behind the scenes of the world’s leading companies, including IBM, Salesforce, American Express, Burberry, Netflix and many more.
With all hype around AI, we’ve narrowed it down to four of the most promising AI and machine learning trends that will transform the world we live in.
Networks with memory
Memory allows us to retain knowledge and acquire new skills based on our previous experience. With the increased amount of information available thanks to technology, we no longer have to rely on just our memory for recall.
Traditional neural networks are unable to constantly learn new things while maintaining previous memories. This drawback occurs because the principles used to perform task X are not working for task Y. Modern networks can be trained to operate in the way that humans store and remember information. Specific types of architectures including, but not limited to, networks with long short-term memory or progressive neural networks are used to cope with the subject of memory.
Networks with memory can be applied to robotics, autopilots, financial analytics, and natural language processing.
Sometimes it’s necessary to not only identify an object, but also learn to produce a new one. For example, you could distinguish a photo of an apple from a pear and then produce a new picture with the pear that was not initially in the data set.
Today, generative models are able to create fake objects. Here’s an example of how a neural network mimicked speech.
Generative models are all about understanding the main principles of a certain process while trying to repeat it. These models are widely used for image style transfer, texts and text-to-speech solutions (chatbots).
Learning from small data
In order to perform at a scale, neural networks need to process and learn a huge amount of data. Without big data, networks aren’t able to optimize and qualitatively perform complicated tasks, such as image recognition or natural language processing. But what if you’re not an industry giant like Google, Amazon or Apple? Or what if you need to find large amounts of medical data?
Most deep learning algorithms require a large amount of data, but it is still possible to program them via small data without losing accuracy. This approach makes the process of training networks more efficient.
Reinforcement learning is considered to be one of the most promising areas of machine learning. It can be used in many different fields, from robotics to modeling buyers’ behavior and healthcare solutions.
In short, this method can be described as a trial-and-error approach. Within this paradigm, an agent doesn’t have enough data about the system but has the opportunity to take different actions within this system. These actions bring the system into a new state while the model gets feedback on what is good and what is not. By analyzing the results of actions taken, a model learns what it is expected from it in order to receive a reward.
The main difference between reinforcement learning and classic machine learning lies in the fact that the network is trained by interacting with the environment, but not on historical data.
Bonsai, a California-based startup, created a reinforcement learning platform containing a rich AI toolchain designed to train autonomous systems in accomplishing various tasks. The solution is applicable for building intelligent manufacturing, energy, supply chain, and automotive solutions. Notably, that startup was acquired by Microsoft.
AI-powered solutions at iTechArt
If you’re looking to create an AI-powered solution, we can help! We've been developing neural networks and complex configurations for solutions powered by natural language and image processing for more than 10 years.
We started with the development of neural networks for a kids’ app. The task was to identify what a child draws on the screen. We successfully developed an app powered by neural networks that are able to analyze the educational tasks completed by kids and evaluate their performance. We are now working on functionality that will allow for the creation of personalized educational programs based on a child’s abilities. For example, if a child is good at math, the app will give him or her tasks that will further develop mathematical skills.
We also successfully trained neural networks for a project related to the healthcare industry. The solution recognizes ECG signals even more precisely than a cardiologist does. In the future, these neural networks can be trained for any specific medical sphere be it ophthalmology, radiology or oncology.