Machine Learning

Machine Learning 2.0: HCI, NLP, Auto ML and Federated ML


The advancements that we have made in the domain of artificial intelligence have been a direct outcome of machine learning techniques. The path breaking applications like self driving cars, chatbot technology, computer vision and Quantum computing have travelled this far as a result of machine learning techniques. 

Machine learning did not have a sudden origin. It was a result of a long scientific pursuit and historical discourse with technology. That said, the agenda of machine learning to transform various technological sectors is not yet complete. 

Machine learning is slowly progressing into new domains and ventures. Consequently, the need for trained professionals in this domain is also increasing. Various institutions are offering advanced machine learning courses to fill this void. Machine learning courses in Delhi are being offered by Analytixlabs which is one of the best institutions in this domain. 

The importance and popularity of AI and Machine Learning in Riyadh can be envisaged from the fact that a large number of upcoming applications would be directly or indirectly dependent upon it. Let us analyze it in deeper detail. 


Human computer interaction: HCI

The process of human computer interaction is more complex when we look at its architectural aspects. There is usually a dialogue box that communicates with the server and ensures effective communication between humans and the machine. In addition to this, there is also an inbuilt data repository from which the dialogue box sources data, if necessary. 

There is also the need for real time processing of data to ensure effective and meaningful communication. Machine learning techniques help in the classification of different types of data sets that are quantifiable in nature. On the other hand, deep learning helps to distinguish between objects, figures and shapes, thereby, helping the machine give a feedback mechanism to the dialogue box.


Natural language processing: NLP

The various aspects of natural language processing are dependent upon the type of language model that is followed. It is with the help of language models that algorithms are applied as machine input so that a meaningful context can be derived. Natural Language Processing is not only about understanding the context of the interaction. It is also about the capacity to participate in a meaningful dialogue and anticipate various answers to different questions. 

The advancements in natural language processing can be understood from the fact that Google uses the BERT model in its search ranking algorithm. This model has made considerable advances in the overall natural language capabilities and enhanced the efficiency of the overall search results. It needs to be mentioned at this point in time that transformer based models are also used to enhance natural language capabilities. In this way, overall improvement in natural language capabilities has had a profound effect on numerous domains.


Automated machine learning: Auto ML 

Automated machine learning is a technology that focuses on deriving solutions to real world problems by automating tasks and applying machine learning methodology. In this way, automated machine learning is a way of applying artificial intelligence to the automation process itself. Automated machine learning not only allows design thinking and predictive analytics but also enables us to achieve efficiency in various types of orthodox tasks. 

Right from the step of processing raw data sets to the stage of analytics and advanced computing, automated machine learning helps in rapidly increasing the efficiency of these steps.


Federated machine learning 

Federated machine learning is one of the most useful sub branches of machine learning. It helps in training a model by sourcing data across distributed databases or clients. The model of federated machine learning is becoming popular because it lays emphasis on data security and data privacy. This is one of the unique machine learning categories that is relatively immune from cyber attacks. 

In addition to this, the use cases of federated machine learning are numerous. Firstly, in the age of industry 4.0, federated machine learning can fuel smart manufacturing. Self driving cars and the data processing technologies that are related to this category can be handled by federated machine learning. Ethical data challenges are also minimised when we deal with federated machine learning. 

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The way ahead

The data processing across various health stacks and databases can be effectively done with the help of federated learning technologies. In a time when the whole world is gripped in a pandemic, the formation of a Global Health repository has been dubbed as a prospective way forward. Global Health repositories would need to source information across various databases and this problem can be effectively handled by federated machine learning.

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