In a recent post, we covered five of our favorite artificial intelligence (AI) applications that you can see in action today. It delved into how organizations around the world are leveraging Narrow AI, data mining, computer vision, and more. In case you missed it, you can read it here.
But one article certainly wasn’t enough to cover all that AI is capable of accomplishing! For this reason, we’ve decided to revisit this topic with another round of AI applications being used right now. In this post, we’ll cover machine learning, deep learning, neural networks, and natural language processing.
Machine learning is a subset of AI that arose from the idea that computers could learn how to perform specific tasks without being explicitly programmed to do so. It relies on data to train off of and pattern recognition to get the job done.
Basically, if you feed a machine the right data in the right way, it can learn from this information, identify patterns, and make decisions, all without human intervention. In this sense, it’s really a data analysis method which automates analytical model building.
Iteration is imperative to making machine learning work. By training machine learning systems repeatedly and exposing them to new data, they can continually improve and independently adapt. This relentless repetition is what allows these systems to achieve reliable results.
Machine learning has actually been around for decades. So why did it take so long to explode in popularity? Because of lack of computing power. Today, we have the data resources and computing power needed to rapidly apply complex mathematical calculations to big data. This has opened up opportunities for an array of machine learning applications.
Machine learning is responsible for making autonomous vehicles possible, giving your phone’s GPS directions pinpoint accuracy, virtual voice assistants, facial recognition, targeted advertisements, and much more — this is really just the tip of the iceberg.
If we dig deeper into machine learning, we’ll find deep learning, an important subset within this field. Deep learning focuses on teaching computers how to learn rotely. Put another way, deep learning enables machines to loosely mimic the way a human mind learns by categorizing factors like text, sound, or images.
Deep learning actually started as a fringe idea based on this concept. AI pioneers Yoshua Bengio, Geoff Hinton, and Yann LeCun wondered if it was possible to construct software that mimicked how our brains’ neurons process data. Bengio explains, “I fell in love with the idea that we could both understand the principles of how the brain works and also construct AI.”
At first, many computing experts dismissed this thought as nothing more than absurd. But recently, Bengio, Hinton, and Yann won the 2018 Turing Award, also known as the highest honor in computer science. And for good reason — deep learning is the part of machine learning that’s behind virtually every type of AI you encounter in your day-to-day.
With mountains of data in the form of hundreds of hours of video, thousands of images, and enough text to fill a million novels, tech developers from San Francisco to Beijing are utilizing deep learning’s self-teaching ability to improve any AI they employ, from voice assistants to self-driving cars.
Neural networks are at the core of deep learning’s success. Like machine learning, neural networks can trace their roots back several decades, all the way to the late 1950s. AI researchers back then took inspiration from rudimentary brain neuron models and formed a network of nodes which could interpret data by classifying and clustering it.
Fast forward to now, and neural networks have become the top technology for object recognition. If you feed a neural network enough images of similar objects (e.g., animals, street signs, etc.), it will identify patterns within these objects and learn how to categorize future images based on these observations.
Convolutional Neural Networks
Convolutional neural networks (CNNs) are a type of neural network that excels at analyzing visual imagery. Also known as convnets, CNNs rely on multilayer perceptrons to accomplish this feat. Perceptrons are algorithms which learn how to distinguish between numerous shapes on one screen.
LeCun showed the world the potential that neural networks offered for object recognition. The AI developer solidified this technology’s validity by creating an ATM check-reading software powered by neural networks. Today, you can find CNNs being utilized to identify objects within larger images as well as text.
Generative Adversarial Networks
By this point, we’ve discussed many of the AI technologies that are applied for object recognition. But what about deepfakes? How are these realistic false images created? To answer this, we must turn our attention to another type of neural network: Generative adversarial networks (GANs).
By taking photographic data from several images, GANs can use these elements to create realistic-looking pictures of any object, whether it’s a person, animal, or place. A prime example of this is Nvidia’s Style-Based Generator Architecture for GANs. Known as StyleGAN, this neural network creates artificial imagery gradually. First, it forms a pixelated, low-quality picture. Then it shapes this into a realistic, high-resolution image over time.
By borrowing elements from actual images of an object, StyleGAN can create uncanny pictures with an enormous amount of detail. Aspects like eye color, facial hair, and even skin pores are all accounted for in this process.
Natural Language Processing
Of course, identifying and generating images is only one of AI’s many talents. Another area in which the technology excels is natural language processing (NLP). Through NLP, AI helps computer systems process, analyze, and understand human language. It’s the closest thing we’ve got to the conversational AI you’ll often see in works of science fiction.
Applications like speech-to-text transcription, smart assistants, word processors, chatbots, and translation apps all rely on NLP.
AI Is Everywhere
And that wraps up our coverage of some of our favorite AI applications you can see in action today. If you’ve reached this point in our post, it has probably become obvious to you that AI is already everywhere! But this is really just the beginning.
In a few years, this disruptive technology will advance in a myriad of ways and open up unprecedented opportunities for innovators to make an impact in the world.
What AI application ideas do you have? Hopefully, this post has helped you figure out how to make them a reality! As always, let us know your thoughts in the comments.Tags: AI, AI and machine learning, AI and ML, AI App Developer, AI app developer San Francisco, AI App Development, AI applications, AI apps, app developers san francisco, benefits of machine learning, machine learning, machine learning app developer San Francisco, machine learning app development San Francisco, mobile app developer San Francisco, mobile app developers San Francisco, mobile app development San Francisco