Latest Trends to Observe in Machine Learning
Understanding the latest trends and developments taking place in the field of machine learning and artificial intelligence can have a good impact in the way we try to learn about the ML use cases and tools.
When we look at the present, there are powerful applications of artificial intelligence in diverse fields such as in agriculture, retail, manufacture and automobile industries. Furthermore, there are newer and much more sophisticated applications being developed in the aeronautics industry as well along with many others. In light of this, it is often an added advantage when one is familiar with the latest trends so that they are able to reflect upon it along with taking actions whether they be working on a consulting firm, organization or starting their own company. Having the knowledge about the use cases of artificial intelligence can be quite handy and useful to consider in many scenarios.
In this article, we would be exploring some of the useful trends and general insights in artificial intelligence and how learning them can add a lot of value to the organization as well. Let us now quickly go over some of the latest developments in the field of AI so that people could understand them and take the right steps and actions along the way.
Machine Learning Democratization
We often hear the terms machine learning and deep learning being used by many industries and also advertised as well. To put things in context, Google has spent a good amount of their revenue to develop machine learning solutions to build the language models that can be useful for natural language understanding and inference. In light of these developments, there are still many other areas that are explored by the likes of Facebook, Amazon and others. Nonetheless, there are still many startups that have not yet leveraged the use of ML solutions and the profits that they would be making based on the model predictions. Hence, there is this additional step of democratization of AI so that it reaches wider audience and could be used for the benefit of the society as well.
Growth of Machine Learning Operations (MLOps)
Since applications in artificial intelligence and machine learning have been growing at a rapid pace, a large number of companies are moving towards the deployment aspect of machine learning. While it could be great that ML models have produced a lot of value by giving the right predictions, failing to give those predictions to the end user would hinder its progress overall. Hence after the models have been built, it can also be important to deploy them and also monitor them so that they can have a good impact overall. There is a growing trend in the development of various deployment tools so that they can be very handy for building cool applications that are powered by artificial intelligence. There are popular deployment tools such as Kubeflow that have suddenly become popular due to the demand of deployment of models in real-time.
Ethics in Artificial Intelligence
There has been a lot of talks lately about the ethics of AI and how it is shaping the way at which this technology is produced and also questioned. Some of the examples include not being able to accurately predict the chances of various outcomes occurring for particular set of classes while doing well on the remaining classes. If the machine learning models are not able to identify a few classes in the dataset quite as well as those of the other classes, it could be said that the models are not performing quite well in the ethical point of view. While there have been greater advances in improving the interpretability of the models which is basically letting the users know why the model came up with a particular outcome in the first place, there still is room for advancement and there are a lot of technologies and tools developed in this area to ensure that we are building the right products.
One Shot Learning
We often hear the news that companies are generating a ton of data that could potentially add a lot of business value to them. Although we see this news almost in most of the top tech companies, what we fail to understand is the usefulness of the data in solving various machine learning operations and functions. In order words, there is a strong requirement for quality data that can be especially handy for the ML operations and ensuring that the use of artificial intelligence is leveraged. With one shot learning, it is possible to train the models with less data and also expect them to be performing quite well as if we have trained with a large amount of data. There are a few revolutionary steps taken by various companies in this effort to promote and use one shot learning where it becomes especially time consuming to gather the right data for the tasks at hand. Therefore, we also see an increase in demand in this direction as well.
Conclusion
All in all, we’ve seen various trends that are being developed as a result of building and deploying machine learning to reach the end users. We’ve seen how companies are taking the steps to democratize the use of artificial intelligence and also ensuring that there are ethics followed along the process. Furthermore, there are newer technologies that are learning to leverage one shot learning with which it can be quite easier to train the models with less data and expect them to be performing quite well. Thank you for taking the time to read this article.
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