The tech industry is expected to place a strong emphasis on AI and machine learning research.
Every year, the budget is announced, and it attracts attention from throughout the country. This will undoubtedly remain a heated topic for some time, and progress toward what has been promised will undoubtedly be monitored.
This time, too, the budget drew a lot of attention. A lot has been stated and promised in the agricultural sector, education, technology, and healthcare. The topic around AI and ML (artificial intelligence and machine learning) appears to have stood out from the others.
According to Gartner’s latest forecast, global IT investment would reach US$4.2 trillion in 2021, up 8.6% from 2020, and US$4.4 trillion in 2022. (5.3 per cent up on 2021). CIOs want partners who can think beyond the digital sprints of 2021 and help them be more deliberate in their digital transformation efforts in 2022.
This entails developing new technology and services, as well as further distinguishing their company in a congested market.
Even before the budget was presented, much had been saying and speculated about how much weight AI and analytics should be given. Some believe that the establishment of robotics centres of excellence, incubation centres, continuous research funds for robotics R&D, and the continuance of the income tax credit could be major drivers in accelerating research and development and technology.
Furthermore, warehouse automation should be prioritised because it leads to an increase in consumer traction.
In addition, steps have been taken to use deep analytics and artificial intelligence to identify tax evaders and phoney billers, and special operations have been started against them.
Many initiatives have been taken in the past to combat tax evasion, including the use of machine learning algorithms. With so much being promised in terms of using AI, machine learning, and analytics to improve our lives, all eyes are on the implementation side now.
According to AI specialists, the IT sector in 2022 will place a strong emphasis on making AI and Ml development processes repeatable and adaptable to changing business settings.
COVID demonstrated how significant changes in user behaviour and consumption patterns reduce the accuracy of machine learning models.
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