Machine Learning is an algorithm-based programming method that allows computers to deduce inferences from patterns of data. This allows systems to forage through massive amounts of unstructured data to bring about search results, shopping preferences or predict movies or shows we may be interested in based on previous streaming experiences.
Machine Learning algorithms can perform a range of autonomous functions by training data through different kinds of learning models – supervised, unsupervised and reinforcement learning. Supervised learning is when algorithms seek to solve problems according to the ready-reckoner answers already tagged to them. Unsupervised learning involves algorithms combing data to find patterns on its own is widely used to identify cluster searches like types of holiday spots and, as per a recent discovery, detect cybertheft. Reinforcement learning emphasizes on trial-and-error learning models so that algorithms can achieve clear objectives like playing games with human counterparts or against itself.
The concept of self-thinking machines pervading 21st century fast-paced routines is nothing but Machine Learning in its numerous avatars. Self-driving cars, smart home appliances, voice assistants and automated devices are an extension of human mental and cognitive abilities transferred to machines.
In 1997, IBM’s Deep Blue beat the then reigning World Chess Champion Gary Kasparov in a six-match game, making it the first instance of human losing to computer. History repeated itself two decades later when Google’s DeepMind’s AlphaGo won against South Korean professional Go player Lee Se-dol in March 2016.
The ancient board game, played like chess using black and white stones on a chequered board, allows for complex board positions that are greater than the number of atoms in the universe, according to The Atlantic. This made AlphaGo’s victory a stunning phenomenon, elevating AI’s dominance in intelligent gaming. Se-dol has since retired from professional Go, admitting to South Korea’s Yonhap news agency, “Even if I become the number one, there is an entity that cannot be defeated.”
Applications in machine learning have also been used to predict, divert and prepare for climate-related disasters and transform green projects with self-adjusting thermostats and distributed energy grids. Crusading efforts in healthcare have used AI data models to predict gene structures of several congenital diseases hitherto untraceable.
Growing popularity of Machine Learning in daily life especially in everyday automated functions such as banking, monetary transaction, personalised digital media, social networking, home security, logistics and e-commerce have made it a global market that is expected to expand at 42.08% CAGR during 2018–2024.
Reports listed on researchandmarkets.com, a Dublin-based market intelligence provider reveals that among the various AI technologies being implemented in India, the highest investments are in the field of machine learning applications. According to the firm, investments in artificial intelligence in India, which stood at Rs 773.64 billion in 2017, is expected to expand at a CAGR of 33.49% during the 2018-2023 period.
“The reason behind such high anticipated investments is the growing popularity of AI technologies, especially ML applications, across business functions like finance, accounting, general management, IT, operations, and administration,” it noted. That apart, sectors like advertising, digital marketing and social media use ML applications can simulate campaigns and predict outcomes by tracking sentiment analysis and user behaviour through clicks, comments, views and shares.
Boston-based AI Research and Advisory, Emerj, in its 2019 overview of the AI landscape in India observed that in 2017, Bangalore was one of their largest sources of job applicants, and their single biggest city in terms of readers, overtaking both London and NYC. The spate of growing interest in AI and ML applications has been further accentuated by increasing government and corporate focus in these sectors.
DigitalIndia, the government’s flagship programme to further e-governance services through multichannel platforms aims to link nodal agencies operating under Railways, Agriculture, Industries, Finance and Advanced Computing to roll out seamless data and technology services for the country’s non-urban population.
NITI Aayog or the National Institution for Transforming India, signed a Statement of Intent (SoI) with IBM in 2018 to develop Precision Agriculture using AI across 10 districts in the first phase. It also plans to leverage use of AI to cover societal needs in healthcare, education and building of smart cities, ensuring smart mobility and transportation in the country.
AI’s varied applications have brought several skillsets into peak demand, a few of them being AI engineers, Business Intelligence Developers and AI programmers. To make programming language as intelligent as the human brain, several languages have been leading choices for engineers.
Python, R, Java, Scala and Rust are some of the key trending programming languages dominating the AI user interface sphere. Python is the most popular language used to measure sentiment analysis while Java is likely to be used to spot malware attack or cyber breach. R offers statistical analysis programming while Scala tracks real-time parallelised analytics. Rust, a preferred option for most programmers ensures speed and memory safety among everything else.
