Top Machine Learning Trends you Need to Look Out for in 2021
Machine Learning (ML) is a part of Artificial Intelligence (AI) that enables computers to learn on their own without a programmer writing a detailed code. While Artificial Intelligence attempts to develop human-like intelligence in machines, machine knowledge aids machines to extract knowledge from the given data.
The increasing demand for Machine Learning can be associated with the growing adoption of cloud-computing services. Since the data is becoming unstructured on these cloud systems, machine learning solutions are required more than ever to analyze and organize this data.
The term Machine Learning was coined in 1952 by Arthur Samuel, however, for many, it continues to be a new concept. Machine Learning involves the scientific study of complex algorithms and statistical models that usually rely on inferences and patterns. It is a technology that can function independently without any explicit instruction.
In today’s world, machine learning is being used for multiple purposes such as image classification, facial and speech recognition, spam, and malware filtering.
Machine learning has captured the attention of all industries and has transformed them for the better. It is changing enterprise technology rapidly and helps businesses to identify patterns in their data that in turn, gives them a competitive edge in the market.
Here are the 10 Machine Learning Trends You Need to Look out for in 2020:
1.ML in Voice Assistance
Voice assistants offer services to users by making use of voice recognition, Natural Language Processing (NLP), and Speech Synthesis through an application.
Alexa, Siri, and Google Assistant are the top voice assistants in the market today. This is only because they have tapped into Machine Learning.
Machine Learning essentially helps computers to comprehend the speech and habits of the users and also allows them to construct their own personalities that can reduce the discomfort users feel while interacting with a voice assistant.
By making use of machine learning algorithms these bot assistants can start giving more authentic responses based on the information the writer has programmed into them. Many tech companies are hiring writers to build the personality of their voice assistants.
2.Machine Learning to Improve Cybersecurity
Cyberattacks are becoming more and more common in this digital age and these attacks threaten the safety of businesses, institutions, and even individuals.
Cybercriminals can break into any system or network from anywhere by creating and using malware. With cyberattacks becoming more complex, traditional methods of detecting these threats are failing. Cybercriminals are using smarter methods to bypass firewalls and gain access to highly secure networks.
Machine learning technology helps keep underperforming security systems to stay alert to these threats. This technology adds additional security layers by automating complex tasks and reacting to security breaches instantly without any need for human intervention. Since Machine Learning can collect, process, and evaluate large data sets, also be used to develop Biometric Security solutions.
3.ML in Predictive Analytics
Businesses seek to reach their objectives through data-based decision making. To make such informed decisions businesses use machine learning-based predictive analytics. Predictive Analytics studies the historical and current data to predict future outcomes.
Previously, Machine Learning and Predictive Analytics were considered to be unrelated and were seen as separate concepts. Now, machine learning is used for predictive analytics extensively to process large sets of deals accurately and identify the patterns.
Companies are employing machine learning-based predictive analytics to stay ahead of the market. This technology enables retailers to understand customer behavior, help in customer segmentation, identify purchase patterns, and also prevent any kind of fraudulent transactions and activities within finance companies.
By integrating past consumer experience data points with industry dynamics, machine learning predictive analytics provides a 360-degree picture of the potential customer.
Instead of merely taking a risk, the use of predictive algorithms has helped businesses reach higher goals and streamline their sales and marketing efforts into a data-based undertaking.
4.Automated Machine Learning
Automated Machine Learning or AutoML provides methods and processes that make machine learning accessible for those who are not ML experts.
Many off-the-shelf AutoML packages have been developed in recent years such as AutoWEKA, TPOT, MLBoX, and H2OAutolML. Major cloud service providers are also offering AutoML such as Google’s AutoML and the Azure Automated Machine Learning.
Today, AutoML has become a significant trend in the data science industry as this technology accelerates the data science processes allowing data scientists to be more productive.
AutoML allows data scientists a way to scale their work within days rather than months. In the next 3-5 years, AutoML will handle data cleaning processes, become human competitive, scale to larger data sets, and vastly develop deep learning.
5.Generative Adversarial Networks (GANs)
GANs are primarily used to formulate or extract new data from in a manner that it accurately resembles the original data. By creating new yet similar data Generative Adversarial Networks can be used to produce synthetic photos of a real human face.
It is essentially a Machine Learning model wherein two neural networks compete to generate more accurate predictions. These two neural networks that constitute a GAN are referred to as the generator and the discriminator.
The key function of the generator is to create synthetic outputs from real data, while the discriminator helps in distinguishing between real and artificial data.
Though this type of technology seems innovative, it can also be misused for defaming or hurting someone. The deep fake scandal proves that GAN is an advanced technology that needs to be monitored.
The process of capturing digital images and videos using computers to extract some useful information is called computer vision.
In other words, Computer Vision can automate those tasks that can be achieved through human vision. Computer vision encompasses motion estimation, video tracking, and object recognition.
When coupled with machine learning, computer vision can be used for video tracking, creating self-driving vehicles, and to improve the broadcast of sports on tv.
Computer vision is simply the method of understanding the digital formats of the images and videos available. Computer vision is used in Machine Learning ( ML) to train the model to recognize such patterns and store data in its artificial memory to use the same to predict the effects in real-life use.
7.ML in Healthcare
Over the years, the healthcare industry has greatly benefited from the advancements in Machine Learning technology. This technology has been instrumental in accurately detecting diseases at their earlier stages and in turn reducing the number of readmissions in healthcare facilities.
Machine Learning has played an important role in developing and discovering new drugs that can help patients with complex conditions. Though human intervention is still required for critical surgeries, Machine Learning has greatly improved the robot-assisted surgery area.
Machine learning algorithms are being developed in a way that they can start providing real-time data, vital statistics, advanced analytics in terms of the patient’s ailment and even lab test such results such as clinical trial data, family history, blood pressure, body temperature, and so on, to doctors.
8.Foundation for Next-Gen Logistics Technologies
The backbone of the next generation of logistics technology is machine learning-based algorithms, with advanced resource scheduling systems making the most remarkable gains.
The most improvements are being made in those areas where Machine learning can help solve complex constraints, costs, and delivery challenges faced by businesses today. For instance, ML speeds up network management and predictive demand efficiencies, prompting merchandisers to be more proactive.
In fact, McKinsey believes that the most important contribution to machine learning would be to provide supply chain operators with more important insights into how to enhance the performance of the supply chain, predicting anomalies in logistics costs and performance before they take place. Machine learning also offers insights on where automation can have the most benefits in terms of scalability.
9.Reinforcement Learning (RL)
Reinforcement Learning (RL) is going to be the biggest Machine Learning trend in 2021. This technology is used to stimulate human-like creativity in machines by running different possible scenarios.
RL essentially attempts to maximize rewards by taking suitable actions such as identifying sales leads or transferring the user to relevant service provider websites.
No inputs are programmed into the RL agent and it learns how to maximize rewards by repeating tasks. It is applied by machines and computers to optimize their behavior and identify which path to take in a specific scenario.
The best example of RL applications is self-improving chatbots. Utilizing RL development companies can make them more resourceful by adding sequential conditions to it.
Reinforcement Learning is being used for industrial automation robotics, aircraft control, robot motion control, and business strategy planning.
The above trends prove that Machine Learning has a wide scope. In 2021, Machine learning will be utilized to create more personalized services, aid in decision making, and forecasting demands.
For programmers and developers Machine learning will be a blessing as they can focus more on innovation instead of wasting their time on writing extensive programs.
Machine learning will improve search-engine browsing experiences by ensuring that users find relevant content based on their queries and profiles. This technology is certainly pivotal for technological advancement.