Future of Machine Learning

Future of Machine Learning

Has anyone in the last decade thought that technological advancement will one day show us this world that we are currently living in? Have you thought about what this world would look like after a decade? Well, no one would have predicted the progress that machine learning has made in the last decade. And to know what opportunities the future holds for us, we need to know the future of Machine Learning.

While it has become the need of the time to learn all the technological details, it is also important to see how this technology would be useful for us in the future. It is not necessary that the algorithm that you’re studying now will be of great need in the future. You need to develop yourself with this continuously advancing techno-driven world.

As an estimation, the global machine learning market is predicted to grow from $8.43 billion in 2019 to $117.19 billion by 2027. In 2020, $3.1 billion in funding was raised for machine learning companies. Machine learning has the power to bring transformative changes across industries.

And to do that, this article from PythonGeeks will guide you through the various advancements in the field of Machine Learning. You’ll now be able to study the algorithms not only for the present, but also grasp the future opportunities. Let’s look at some of the major advancements that could take place in the future using Machine Learning and how it’d benefit you.

Problem with Machine Learning Today

In order to understand how the algorithms can develop, we first have to understand what is wrong with the current system. While many experts around the globe believe that technological advancement is up to the mark, many still argue about its efficiency. To elaborate, let’s look at the general scenario of these algorithms to solve a detected problem.

Whenever you encounter a problem, the Machine Learning algorithms tend to solve the problem by choosing the right dataset and then training the models accordingly to achieve a particular result. But many experts now argue that by following this methodology, the algorithm is always starting its process from the beginning. To explain this in easier words, it’s like humans becoming infants again and gaining all the necessary logic they require to solve a problem that they encountered today. This makes these models quite inefficient and expensive for computation.

Due to these issues, there is still scope for the exponential future advancement of Machine Learning. Let us quickly take a look at how these advancements can take place in some of the Machine Learning Arenas. To give you a sneak peek, we’ll be looking through some of the most interesting aids like self-driving automated vehicles, quantum computing, fully automated self-learning systems, and more.

1. Surge in Quantum Computing

The most fascinating and accurate development in the field of Machine Learning has to be Quantum Computers. Experts believe that quantum computing has a scope to boost the potential of Machine Learning and increase its manifolds. As an interesting fact, Google’s quantum processor in 2019 performed a task in 200 sec. The astonishing part of this fact is that the best supercomputer in this world would require 10,000 years to accomplish the same task. Interesting right? With this you’d certainly get an idea about the power of Quantum Computing.

As of now, there is no quantum computer that is available commercially. Many big names in the field of technology are investing hefty amounts of investments for the development of Quantum Computers. The sole reason behind this is the accuracy and efficiency of the Quantum computers. With this, the existing machine learning algorithms can attain nearly accurate data analysis and get clear perception of the scenario.

Such increased performances can help us in getting more reliable solutions to the current problems. Prediction algorithms would be getting more and more reliable if the dataset analysis is accurate. Motion detection and behavior analysis would get much enhanced if you train the model with accurate datasets.
Thus, if you are looking for better future opportunities in this field, Quantum Computing can help you in achieving so.

2. Concept of AutoML

Don’t you think that for manipulating data science models to work on your accord, you need to be an expert in Machine Learning algorithms? But won’t that reduce the reach of the technology to a limited community? Well, this drawback is what gives rise to Automated Machine Learning or more commonly referred to as AutoML. With this, you can automate the process of employing machine learning algorithms to complete day-to-day tasks.

AutoML integrates the machine learning models and techniques to apply them in real life without being an expert or having zero knowledge of coding. As a result of which, the industry has witnessed a surge of 82% in the leading users for deploying Machine Learning models.

Corporations could fine-tune their understanding of their target audience using machine learning to inform the enhancement of the existing products, new product development, merchandising, and gross revenue. As a developer, you can customize products far more precisely than ever before with algorithms to break down exactly how you can use your products, maximizing value for both the organization and the clients.

It thus caters to the need of a wider audience range and in turn enhances its potential.

Some of the stages of development and deployment of AutoML are:

a. Data pre-processing: In this stage, raw unstructured data is processed with the help of data cleaning, data transformation and data reduction.

b. Feature engineering: Based on the input data provided, you can deploy machine learning algorithms to automate more adaptable features.

c. Feature extraction: In order to improve the end results, you can use existing models and features to develop new and enhanced features.

d. Feature selection: Out of the existing and new features, you can choose your favorable features to attain advantageous results.

e. Algorithm Selection and hyperparameter optimization: The models can now automatically choose the beneficial parameters and algorithms.

f. Model deployment and monitoring: you can now deploy the final model based on the selected framework and monitor conditions through the dashboard.

3. Industries that can look for the machine learning disruption

a. Healthcare and Pharma: Every minute, the healthcare industry generates a gigantic amount of data. Applying machine learning algorithms to these datasets can help to flourish this industry at an exponential rate.

Some of the examples are prediction of diseases by processing the datasets about the person’s weight, age, height and previous medical history, you can deploy new drugs beforehand if the prediction of disease becomes accurate and reliable, machine learning algorithms like Natural Language Processing (NLP) and image processing can help you to convert the patient data into a more useful record.

b. Manufacturing: Since the manufacturing domain hasn’t deployed the machine learning models to such an extent yet, this domain has a scope of leveraging the machine learning models to a large magnitude. In 2020, only 9% of survey respondents have stated that they are employing artificial intelligence in their business processes.

As you’d have observed earlier, using automated algorithms reduces the scope of human error to a large extent. Among the many other benefits, deploying machine learning algorithms in this sector can help you to reduce labor costs, enhance the quality of your control over the system and also enrich the supply chain management.

4. Automotive and Self Driving Vehicles

Amongst the various boons of machine learning, autonomous or self-driving vehicles are seeming like a miracle. An autonomous vehicle is one that is skilled to function and perform necessary controls without any human assistance. Though a fully automated vehicle is yet to be developed, semi-automated vehicles have taken over the vehicle market today. Deep learning, a class of machine learning, can help you to deploy such trained models. These models can then help the system to accurately improve perception and navigation.

You can train the models in such a way that they can accurately plan a precise path for the desired destination. It can then ascertain an obstacle and pedestrian detection could be done precisely. This can in-turn reduce the chances of pedestrian mishaps and ensure a hassle-free drive.

5. Improved Unsupervised Algorithms

Unsupervised algorithms are probably the most heard term in the field of Machine Learning. Being used in numerous industries, improving this algorithm can certainly shape the future of Machine Learning algorithms.
When these algorithms are left on their own, you can use them to discover the fascinating hidden patterns or groupings within a dataset. In the future, the integration of robust learning and unsupervised learning will help you to achieve better results.

According to the expert Jeff Dean, ” The progress that has been made from 26% error in 2011 to 3% error in 2016 is hugely impactful”. The way he likes to think is, computers have now evolved manifolds.

6. Accuracy in Search Results

Have you ever experienced that the results of your web search aren’t accurate? That you’re looking for things apart from the specified topics but you can’t get through the numerous web pages to get your desired result?

The hierarchy of these results are determined purposefully to enhance your search results. There is a great scope for Machine Learning to filter these results according to your needs. By using neural networking with the trained Machine Learning models, the future of these search engines is vast and powerful. You can train future search engines to carve a path of useful and efficient results with precision.

7. Improved Responsiveness

Without any second thoughts, we know that with the widespread use of cognitive services across major industry giants, it is definitely going to shape the future of Machine learning in the coming days.

By training the models on certain patterns, cognitive services allow you to include intelligent capabilities into your applications. You can instruct various cognitive features such as visual recognition, speech detection, and speech understanding in your apps using Machine Learning.

Thus, you can make applications that are more interactive and intelligent than ever before. All thanks to Machine Learning! With the help of cognitive services automated by Machine Learning, you can make applications and devices more responsive in the future.

8. Surge in the Robotics

Even before the surge in automation of things using Machine Learning, Robotics has been a fascinating topic of discussion. As an amazing fact, In 1954, George Devol invented the first programmable robot named Unimate. With the surge in automation, you can train robots using the models of Machine Learning algorithms. This will enhance their performance and automate them for lesser human intervention.

A Market Research Engine report has estimated that, “ The Global Service Robotics market is expected to reach almost $24 billion by 2022. The market is projected to have a compound annual growth rate (CAGR) of more than 15%.”

Conclusion

Thus, we conclude Machine Learning algorithms will continue to dominate the techno-world. Apart from the above-mentioned advancement that could take place in the coming time. But to be ready for future opportunities, you can practice these technologies and gain a prior hand compared to others. The Machine Learning advancement will stay for quite long and thus, help in reducing human efforts.