Artificial Intelligence (AI) is a computer science technique which enables machines to learn from their experience and work like intelligent human beings. Machine learning (ML) is an application of AI used to impart automatically learning capability to machines and improve from experience. ML is attained by developing computer programs that access data and use it to remember for themselves.
Speech recognition, email spam and malware filtering, chatbots, and virtual personal assistants are some examples of machine learning. Even top social media sites like Facebook, Twitter, Instagram, etc. use machine learning to enhance user experience. The latest trends reveal that machine learning also has an important role in mobile app testing. AI and ML technologies are also used in healthcare sector, agriculture, automobile, manufacturing, and many other industries.
Many top IT organizations like, Apple, IBM, Google, etc. are doing research to develop powerful ML algorithms for various purposes. In this article, we present the latest twelve trends of AI machine learning in 2019.
Top 12 trends of AI Machine learning:
1. AI-based Chabot with NLU and conversational capabilities
This is one of the most important trends in 2019. First let’s know about the difference between the terms NLP, NLU, and NLG.
Natural Language Processing (NLP) is a term used to explain a machine’s ability to absorb what is said by a human, decide the necessary action, and then respond back to the user. Natural Language Understanding (NLU) is a part of NPL which accepts jumbled inputs and converts them into a systematic form that can be understood by a machine. Natural Language Generation (NLG) is used to convert structured data into text.
NLP is the underlying base for the creation of AI-based chatbots. The process involves:
(A) Text-to-knowledge technology to understand the meaning of text
The key role of NLP is to find out and deliver the relevant information. The text input which is simply a collection of character strings is arranged in a machine-readable form by NLP. The NLP applications use information searching techniques to extract the required information from a bulk of information and quickly presents it in a human-readable form.
These applications are developed by using advanced indexing technique for extracting semantic information from large volumes of text and presenting it a convenient user-readable form.
(B) Speech reorganization technology for a natural language ICT interface
Natural Language Processing (NLP) performs an important role in natural language based computer interface. The exchange of information between humans and computers is usually through a specific communication format consisting of icons, menus, and commands given by keyboard or mouse. But the development of natural language based information and communication technology (ICT) interface allows people to readily use ICT and avail its benefits. Secondly, it facilitates a smooth correlation environment between human beings and AI to coexist in society.
An ideal example is a customer-service support terminal in place of multiple telephone operators at contact centers of a company which listens to the requests and quickly delivers the required information. It is able to handle customer’s queries more efficiently and also saves time.
(C) Informal conversation technology to carry out human-like conversation
A person and an AI agent become more familiar with each other with the number of times conversation occurred between them. Hence such functions should also be included in developing ML so that computers can behave and learn like humans.
A system based on informal conversation technology generates huge volumes of content on the Internet using NLP and creates two types of database for storing data. First utterance database is created to store or input what the user says. Then this system searches for user’s question or queries on Internet to get the required words/phrase and stores them in a knowledge database for system utterance.
There is a database of keywords and corresponding to each keyword, there is an integer value showing the rank of keyword. So when a user writes something, the ML logic finds the keywords from the sentence and then searches for similar keywords from database to understand the meaning of sentence.
In case of multiple keywords, NLU gives priority to the keyword with the highest rank value and yields the output accordingly. Now NLG comes into action which displays meaningful output in the form of natural language. If some keyword is not found in database, NLG interacts with user by generating statements like “enter more information”, “tell me more…” etc.
2. Increased usage of AI-enabled chips
AI depends a lot on specific processors that align with CPU. Additional hardware is required to quickly perform tasks like facial recognition and object detection. In 2019, the major chip manufacturers like Intel, Qualcomm, and NVIDIA are making and shipping specialized chips for faster execution of AI-enabled applications.
Even the foremost infrastructure companies like Microsoft, Amazon, andhave increased their investments in custom chips based on application specific integrated circuits. These chips are very useful for various domains to boost query processing and predictive analysis.
3. Minimizing the time required for training
The time taken to train a model can be reduced significantly by optimizing the required hardware infrastructure. Google Cloud Platform offers a cloud-based tailored environment for creating very effective machine learning models. The leading Graphics card manufacturers like Nvidia are making graphics processing unit (GPUs) because they are more useful in machine learning than CPUs.
4. Feature detection
A feature describes a column in a table that is used to train data and feature detection is an important part of machine learning model. Feature engineering emphasizes on regulating and presenting useful data for easily extracting the necessary features. The data prepared for machine learning models is empowered by visual capabilities so we can quickly choose how much and what we exactly want.
5. Development of powerful algorithms
Powerful machine learning algorithms can extract the necessary insights from data proving very useful for any business. The impact of such algorithms can be seen in the emerging new app store where app innovators rely a lot on the automated process to promote their products or services.
6. Choice of cloud platform
The ML algorithms hosted on cloud enable businesses to reduce the data storage and management costs significantly. Robot bosses can play a crucial role in large manufacturing units like automobile and airline manufacturing plants. The businesses are choosing cloud service providers for their data center activities because cloud brings agility to businesses while AI & ML have a huge positive impact on business outcomes.
7. Developing ML for Autonomous vehicles
There are many major economic and social benefits of automating the driving processes such as more efficient organization of transport, automated vehicles capability to drive relentlessly for many hours, to fuel usage and route optimization, better time and energy management, etc.
and many other companies have built and tested their autonomous vehicles on roads while some are still in a process of developing the necessary ML technology, forming an important AI ML trend in 2019.
8. AI Machine learning will be more productive
The requirement for data scientists is increasing because of the popularity of big data solutions and machine learning. The data scientists have proved to be very useful in developing ML solutions for the internal purposes of several organizations and ML techniques are also being used to science-related problems. So the adoption of such AI tools for better productivity is also a major trend in 2019.
9. Transparent AI models
AI can be efficiently used to power-up the daily operations of a company. For example, Amazon has used AI for preprocessing resumes of candidates applying for technical positions. The need for transparent AI is rising with the increased adoption of ML models in various corporations. ML powered applications are getting wide usage in various fields including healthcare. ML tools are being used in the diagnosis of medical data to improve the treatment process.
10. Merging of Internet of Things (IoT) and AI at the edge
This year AI merges with IoT at the periphery of computing layer and nearly all models trained in public cloud will unfold at the edge. Advanced ML models can perform predictive analysis and will be able to deal with speech synthesis, video frames, unorganized data generated by devices like microphones and cameras. These edge devices will be furnished with special AI chips serving a lot of purposes.
11. Elimination of incomparability among neural networks
A major challenge to AI is the absence of interoperability among neural network toolkits. There are many tools for data scientists such as Microsoft Cognitive Toolkit, Apache MXNet, Caffe2, etc. Once a ML model is trained and evaluated in a particular framework, it is hard to port that model to another framework. To overcome this challenge, Microsoft, AWS, and Facebook have jointly made an Open Neural Network Exchange () ecosystem which allows trained neural network models to be reused across multiple frameworks.
12. Automation of DevOps by AIOps
Artificial Intelligence in IT Operations (AIOps) will provide better assistance in prediction and infrastructure management. AIOps will automate DevOps so the teams can perform accurate root cause analysis much faster than before. Enterprises and public cloud vendors will get a lot of benefit with the integration of AI and DevOps.
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Paru Saxena, Sales head at TechIngenious – A business development with leading organizations. When I am free, I like to observe the nature in its wide diversity of forms.. Writing is my passion and I am writing from the past 5 years. However, my experience lies in digital marketing and web development. I have a proven track of record in sales &