Top Machine Learning Trends to Look Out for in 2021

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In this day and age, technologies emerge and evolve every day. But only a few come about that revolutionize everything from our lifestyles to applications across industries. Artificial intelligence is often compared to electricity for its potential to revolutionize every aspect of human life. It will transform every industry and create huge economic value. ML is a subset of AI that allows machines to learn from data without being programmed explicitly.

To give you a perspective of how important Machine Learning is in our lives, a study showed that 77% of devices that we presently use are utilizing ML. From our interactions with Google Assistant, Alexa, or Siri to our mail services putting a phishing email directly into the spam folder, it’s all ML. The AI-ML industry is moving at a lightning-fast pace because of the higher availability of data and developments in computation power.

Keeping up with this pace is crucial for everyone, regardless of the industry that they’re working in. It will help them leverage machine learning to come up with innovative and disruptive solutions for problems and pain points of their respective industries. Here are a few machine learning trends that you should be on the look-out for in 2021:

  • Hyper-Automation

You might be familiar with the quote, “Technology like supervised learning is automation on steroids.” This has become the central idea behind Hyper-automation, an IT mega-trend identified by Gartner. It refers to the likeliness that virtually everything in an organization can be automated. Machine learning can be used at every step of the way in managing workflows and processing across the organization.

The pandemic has significantly accelerated the adoption of the concept in various organizations across different industries. AI and ML key segments and significant drivers of hyper-automation, coming together with other technologies like task automation and process automation tools. Incorporating ML solutions in hyper-automation initiatives make the automated business processes more adaptive and resilient to changing circumstances.

  • Amalgamating IoT and Machine Learning

Internet of Things, as the name suggests, is a network of several devices that can interact with each other. Advances in communication and 5G have allowed IoT devices to communicate at much faster speeds with exceptionally lower latency. Put simply, the room for innovation and growth in IoT has expanded from the size of a pebble to the size of a boulder.

The function of IoT devices is to collect data that can be processed and used further for wide-ranging applications. This is where ML comes into the picture, for using that data. With both technologies moving at a pace faster than ever, we’ll see a lot of applications that amalgamate both technologies to develop disruptive solutions. In addition, the usage of ML for making IoT devices and services smarter and more secure is also one of the emerging machine learning trends.

  • Faster Computing Power

Artificial neural networks are a fascinating subject of research with new architectures and methods coming up every year. AI researchers and analysts are making revolutionary algorithmic breakthroughs and finding new problem-solving mechanisms that are more computationally economical. Furthermore, as third-party cloud service platforms support the deployment of machine learning solutions in the cloud, we’re moving towards a new era of computational excellence.

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  • Reinforcement Learning

Reinforcement Learning is one of the budding machine learning trends that can generally be used by businesses across different industries in the future. It is a newfound use of deep learning to enhance the effectiveness of collected data by factoring in its own experiences. Essentially, it is the training of machine learning models to make a sequence of decisions using their own experiences.

A chatbot for customer service is the perfect example of that. In reinforcement learning, the AI solution is programmed with a set of conditions that determine what kind of activity will be performed by the software. The software then tries to achieve the ultimate objective by auto-learning from its own actions and outcomes.

ML solutions for cybersecurity is another one of the promising machine learning trends in 2021. ML algorithms find their applications in cyber threat detection, combating cyber-crime, enhancing available antivirus software, and much more. ML-based antivirus software are known for their ability to detect malware or virus by detecting irregular behavior and anomalies. Smart antivirus leveraging advanced machine learning algorithms are much more reliable, compared to their signature-based counterparts.

There are various other machine learning trends that you must follow. To name a few, automated machine learning, deep learning for business intelligence, augmented intelligence, and conversational AI are starting to shape up and drive innovation. But, these five machine learning trends will be the compass of innovation, disruption, and growth in 2021 across industries.

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