Machine learning (ML) is a technology that automates analytical model building. From autonomous cars to speech translation, machine learning is leveraging AI’s capabilities to provide more order and convenience to our chaotic and unpredictable real world.

But what exactly is machine learning, and why is the technology gaining momentum in today’s world?

What is Machine Learning?

Machine learning — a branch of artificial intelligence (AI) — is based on the idea that software applications can learn from data, identify patterns, and offer predictable outcomes without being explicitly programmed. With this technology, systems become more precise at predicting results with minimal human intervention.

Machine learning algorithms use previously-fed data as input to predict new values as the final output. One of the most popular uses for ML is recommendation engines. Other well-known use uses include predictive analysis, fraud detection, business process automation (BPA), spam filtering, and malware threat detection.

The major difference from traditional software applications is that the system is instructed by a human developer to derive meaning from the data fed into the system. While in machine learning, a model is taught how to predict outcomes based on a large amount of data reliably.

Data, and only data, is the key behind the majority of machine learning models.

What is Machine Learning Used For?

Machine learning is all around us. The technology is used to recommend which series or movie you may want to watch next on Amazon Prime or a product you may want to buy on the internet. The Google Search Engine uses multiple machine learning models to understand your buying patterns, thereby personalizing your results. Similarly, Gmail’s spam recognition systems use trained models to prevent your inbox from being flooded by rogue messages.

One of the most powerful use cases of machine learning is virtual assistants, including the Google Assistant, Amazon’s Alexa, Microsoft’s Cortana, and Apple’s Siri. Each one depends on the technology to support their voice recognition and understand natural language processing (NLP).

Nevertheless, beyond these obvious uses of machine learning, these models are beginning to find use in every sector. These manifestations include: facial recognition for surveillance; detecting tumours; helping researchers spot genetic sequences related to illnesses; computer vision for autonomous cars, drones, and robots; drug discovery; speech and language recognition and synthesis; providing an accurate translation of speech for meetings, etc.

What are the Top Machine Learning Trends in 2020?

Below are the top Machine Learning trends that you need to watch out for in 2020 and the coming years.

Labelled Data

More research in human-in-the-loop learning and machine learning needs to be conducted, where smart data collection is a part of the ML feedback loop. Self-supervision has shown tremendous opportunities for self-supervised learning. Be prepared to see more development in these areas!

Controllable Generative Models

Controllable generative models for data sources, including voice, text, videos, and images, will need to be considered. Memorization cannot accomplish disentanglement of control inputs and will require significant attention. Current generative models such as GANs fail to provide good uncertainty estimates (uncertainty quantification), and hence will be another significant challenge that needs to be addressed.

Synthetic Simulations for Data-limited Applications

Synthetic simulations will be fundamentally used for training data in applications such as self-driving cars and robotics. Since data cannot be perfectly precise, algorithms will be implemented for fine-tuning the existing information.

AI Will Get More Advanced

Artificial intelligence will become more advanced, which will require real-time processing and sophisticated (and aggressive) model compression. Not all AI training will be conducted on the cloud, but some will move to the edge. Additionally, algorithms that can quickly adapt to changes in environmental conditions, such as data distribution, will need to be further developed.



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