Core Data Science and Machine Learning
Cutting Edge AI
Cutting Edge AI
Each year companies are investing more and more in AI technology. In the short period between 2015 and 2019, the number of enterprises using AI grew by an eye-watering 270%! This trend shows no sign of stopping, and it's predicted that by 2030, AI could be a 15 trillion dollar industry.
A study of executives found that enhancing features, functions, and performance of products was the primary goal of AI in business. However, the use cases of AI in industry are wide-ranging. You get everything from spam filters, smart email, process automation, surveillance to advanced conversational AI, chatbots, smart personal assistants, and more.
As AI continues to gain popularity, its use cases are expanding and evolving into new areas. But use cases aren't the only thing that's evolving. AI itself continues to grow more advanced every year. However, there's a problem. Approximately 60% of businesses cite a "shortage of data science talent" as the primary barrier to realizing their AI potential. That's where AI Superior comes in.
What Does It Include?
Our team of experts is continually sharpening their skills in the latest AI technology. We seek out cutting-edge AI technology and master it to bring more value to the businesses we work with. Here are just some of these examples of advanced AI we have been working with recently.
The human brain consists of an advanced network of cells called neurons. Deep Learning, a cutting-edge subfield in machine learning, aims to model algorithms similarly to how the brain does.
DL is excellent at adding value through supervised learning from labeled data - something that businesses have a lot of. To put it simply, deep learning can handle more data, create bigger models, and handle more computation. This leads to better algorithms and advanced new insights. Deep learning models are highly flexible and scalable, which means they can continue to advance far beyond their original creation.
Reinforcement Learning is the process of training machine learning models to make the correct decision through punishment and reward. To train the model, the AI usually gets rewards or penalties for each action it performs. Through reinforcement, it becomes better at achieving the desired outcome.
Generative Adversarial Networks (GANs)
GANs have been described as the "most interesting idea in the last ten years in Machine Learning." GANs can generate new content by learning patterns in the training data. The machine learning algorithm is usually unsupervised and will have two core components. Firstly, it will train to generate new content. Secondly, a discriminator model will try to classify whether that content is real or fake. If you can fool the model, you can have a high degree of confidence in the content.
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