Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think Artificial Intelligence and Machine Learning will transform in the next several years – Andrew Ng
And that’s true enough! In these dynamic times, there is a dramatic increase in the platforms, tools, and applications that are based on Machine Learning. And these technologies are not only impacting the software industry but industries all across the spectrum like healthcare, automobile, manufacturing, entertainment, agriculture, etc. like Andrew Ng rightly stated.
And this advancement in Machine Learning technologies is only increasing with each year as top companies like Google, Apple, Facebook, Amazon, Microsoft, etc. are heavily investing in research and development for Machine Learning and its myriad offshoots. Keeping this in mind, let’s see some of the top Machine Learning trends for 2019 that will probably shape the future world and pave the path for more Machine Learning technologies.
1. Digital Data Forgetting Using Machine Learning (Rather Machine Unlearning!)
These days data is the new oil in Computer Science! We are producing more and more data every day and this means that we are fast running out of places to store the data! Regular software systems cannot handle Big Data and while Cloud Computing is very helpful, the overall costs to manage large amounts of data are insane!
And so, there are some times when it is much more beneficial than some data is conveniently forgotten by the system. This can occur in situations when organizations want to control their data related expenditure or maybe when users want their data and lineage forgotten by the system because of privacy risks and so on. In such situations, it is better to use Machine Learning to thoroughly understand the scenarios and identify the unnecessary data so it can be deleted or rather forgotten (In other words Machine Unlearning!).
2. Interoperability among Neural Networks
Artificial Neural Networks are a part of Machine Learning that are inspired by, amazingly enough, biological neural networks (So we were inspired by ourselves basically!!!) But one of the major challenges in creating Artificial Neural Networks is choosing the right framework for them. And Data scientists are spoiled for choice among various options like PyTorch, Microsoft Cognitive Toolkit, Apache MXNet, TensorFlow, etc.
But the problem is that once a Neural Network is trained and evaluated on a particular framework, it is extremely difficult to port this on a different framework. This somewhat diminishes the far-reaching capabilities of Machine Learning. So to handle this problem, AWS, Facebook and Microsoft have collaborated to create the Open Neural Network Exchange (ONNX), which allows for the reuse of trained neural network models across multiple frameworks. Now ONNX will become an essential technology that will lead to increased interoperability among Neural Networks.
3. Automated Machine Learning
For those who are not experts in the mysterious world of Machine Learning, Automated Machine Learning is godsent! It allows the application of Machine Learning solutions much easier for ML non-experts and may even be able to easily handle the complex scenarios in training ML models.
So a tool like AutoML which can be used to train high-quality custom machine learning models while having minimal machine learning expertise will surely gain prominence. It can easily deliver the right amount of customization without a detailed understanding of the complex workflow of Machine Learning. However, AutoML is not a silver bullet and it can require some additional parameters that can only be set with some measure of expertise. (So you will have to learn some Machine Learning!)
4. The convergence of Internet of Things and Machine Learning
Machine Learning and the Internet of Things is like a match made in Tech Heaven!!! According to Business Insider, there will be more than 64 billion IoT devices by 2025, up from about 9 billion in 2017. All these IoT devices generate a lot of data that needs to be collected and mined for actionable results. Now, this requires the expertise of advanced Machine Learning models that are based on deep neural networks.
So the Internet of Things is used to collect and handle the huge amount of data that is required by the ML algorithms. In turn, these algorithms convert the data into useful actionable results that can be implemented by the IoT devices. This convergence of IoT and ML can transform industries and help them in making more informed decisions based on the mammoth data available every day which will result in new value propositions, business models, revenue streams and services.
5. Rise Of Natural Language Processing for Customer Support
It’s obvious that humans can converse with each other using speech but now machines can too! This is known as Natural Language Processing where machines analyze and understand language and speech as it is spoken (Now if you talk to a machine it may just talk back!). And now NLP is extremely popular for customer support applications, particularly the chatbot. These chatbots use ML and NLP to interact with the users in textual form and solve their queries. So you get the human touch in your customer support interactions without ever directly interacting with a human.
And that’s not all! NLP and ML are also invaluable in actually parsing through different conversations and understanding what the users are saying. This allows the company to acquire strategic information about the users such as their preferences, buying habits, sentiments, etc. which can then be analyzed to understand market trends, operational risks, etc.
- Top Machine Learning Applications in 2019
- Learning Model Building in Scikit-learn : A Python Machine Learning Library
- Artificial intelligence vs Machine Learning vs Deep Learning
- Azure Virtual Machine for Machine Learning
- How to Start Learning Machine Learning?
- Machine Learning in C++
- ML | What is Machine Learning ?
- 30 minutes to machine learning
- Regularization in Machine Learning
- What is AutoML in Machine Learning?
- Firebase Machine Learning kit
- Clustering in Machine Learning
- How Does Google Use Machine Learning?
- An introduction to Machine Learning
- Demystifying Machine Learning
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