The ongoing year will see a lot of technical evolution with Artificial Intelligence (AI) being the center of attraction.
This technical transformation will function in almost all sectors bringing viable changes. The new developments taking place due to AI interference will create a new future for the generations to come.
Let’s see what are the trends that AI will follow in 2019.
AI chips for Specific Scenarios
Artificial Intelligence needs peculiar processors that will enable CPU to work. In order to perform tasks like detection of the object and facial recognition, the model will be required to have additional hardware.
This year, some of the chip manufacturers like Qualcomm, AMD, and Intel will bring to life specialized chips that will help in speeding up the implementation of AI-based applications. These chips will be modified for specific situations like for Natural Language Processing (NLP), computer vision and recognition of speech. The applications of next generation in industries like healthcare and automobiles will depend on these chips.
Reformation in Medicine
The capability of AI to interact with data will allow researchers of the medical field to achieve impossible things like processing of medical images to identify relevant information and effectiveness of the therapy used on a patient.
Hike in Demand for Data Scientists
With the presence of AI in the picture, the jobs for data scientists will see a rise. According to the sources, the demand for advanced level analysts is supposed to grow by 28% till 2020 with the estimated growth of AI. Professionals who will be more proficient in handling large data will be most sought after during the implementation of AI.
AI will play a big role in businesses that are communication-based. The tools will record conversations to make the process of sales efficient providing analysis for the future.
Automated Machine Learning (ML) to Grow
For ML-based solutions, AI will give power to business analysts and dealers to recreate ML models. This will help notice complex situations without undergoing the regular process of ML models training.