AI And ML Turns Possibilities into Opportunities in 2019

Let’s scan through the escalating trends that follow as AI and ML re-defines the ways infrastructure is managed in 2019!

While it is easy to embrace technology, waiting for the financial return on investment can be challenging. Sometimes, it can be even more thwarting to wait for any significant improvements or breakthroughs.

Artificial Intelligence (AI) has proven to be a double-edged sword of modern technology. Human intelligence has flourished even more after combining machine learning (ML) concepts with AI. Cyborg technology, automated transportation, diffusing bombs, solving issues of climatic changes (might however seem like a tall order from a robot but machines are likely to have more access to data than humans in coming times), gaming companions like C-3PO and Pepper and elder care – AI technology has taken big steps in influencing our future and shouldering professionals across the industry and this trend will grow in the coming years. AI can be effectual enough in enhancing internal and external operations, increasing sales, reduce costs, optimizing processes, building data strategy and promoting better business decisions.

Artificial Intelligence and Machine Learning, Virtual Reality (VR), Internet of Technology (IoT) Platforms and Blockchain Software-defined security are the key mainstream technologies to create a transformative impact in coming years. Out of the emerging AI trends, deep learning, cognitive computing, and machine learning trends will prove to be trendsetters in years to come. More trends to follow in 2019:

Virtual Assistants and Chatbots

These work on one simple principle: Processing natural language. While it is a small script that understands the text; when combined with speech recognition solutions, these stimulate understanding along-with usable solution to deliver business value. Chatbots have gradually become the face of any business, due to their 24/7 availability and human-like responses.

Reduces the Time Needed for Training

AI-based academic work focusses on reducing time and computing power that is required to train a model effectively with a goal to make daily work increasingly affordable. Out of the various ways to optimize the time required to train a model are to optimize the required hardware infrastructure. Google Cloud Platform offers a cloud-based tailored environment, for building machine learning models. Scaling and redesigning the architecture of neural networks via Google’s Gpipe infrastructure to make use of existing resources is another way in which performance is optimized.

Powering Autonomous Vehicles

AI enabled autonomous vehicles can see, hear, think and of course drive just like normal human drivers do. They are complemented with sensors, cameras and communication systems to generate a massive amount of data and make appropriate decisions while on road.

AI-enabled chips will become prominent

AI is based on very specialized processors that complement CPU. It is often difficult for even most advanced CPU to improve the speed of the training on the AI model. It requires additional hardware to perform complex mathematical computations to speed up tasks such as object detection and facial recognition. Recognized chip makers like Intel, NVIDIA, AMD, ARM, and Qualcomm are going to ship specialized chips that will speed up the execution of AI-enabled applications.  These chips will be helpful in optimizing specific use cases and scenarios related to computer vision, natural language processing, and speech recognition. AI in healthcare, automobile, and finance industries will as well escalate.

IoT and AI Bring Competitive Advantage

IoT is going to be the best driver of artificial intelligence in the enterprise. This will involve the advanced ML models based on deep neural networks to be optimized to run at the edge. Right from performing outlier detection, root cause analysis, predictive maintenance of the equipment, speech synthesis and time-series data, edge devices will be equipped with the special AI chips based on FPGA’s and ASIC’s.

Neural Networks are Interoperable

Selecting the correct framework to develop a neural network model was important as it was not possible to port the trained model to another framework. AWS, Facebook and Microsoft recently collaborated to address the above challenge and built Open Neural Network Exchange (ONNX). This enables the reusability of trained neural network models across multiple frameworks.

Automated Machine Learning Will Outstand

Automated Machine learning models will outshine the traditional process of training ML models. These perfectly align between cognitive API’s and custom ML platforms thus delivering the right level of customization without forcing the developers to navigate entire workflow. This model is flexible and portable.

AI will automate DevOps through AIOps

This convergence will help teams perform precise and accurate root cause analysis and benefit public cloud vendors and enterprises.


After learning the importance and effectiveness of machine learning and artificial intelligence, top app development companies have adopted these techniques for developing applications for various industries like healthcare, manufacturing, automobile, agriculture, and finance, etc. Large organizations like Amazon, Google, Apple, Facebook, Microsoft, and IBM have already invested huge sums in research and development to fill in the gap between consumer and AI. Adding more to the top trends in AI and ML discussed above, some less prominent and even lesser known trends will keep up with their progress. These include: Facial Recognition, Deep Learning, Increase in public cloud providers, AI-enabled chips, and AI coupled with GDPR standards to bring in more secrecy and protection of user’s digital data that will eventually promote safety and awareness of the complicated technologies of AI.

All these trends will give a boost to topical business applications, and make them focus on business value instead of cost-efficiency. As the power of data is democratized with the widespread adoption of analytics and data-driven decision making, it will become central to enterprises plan and execute strategy.