How to Pakistan slowly in machine learning

KARACHI: What do people do to improve accuracy and speed of work? They acquire knowledge that their brain uses to react, calculate and perform a task. What do computers do to make them more efficient or accurate? They use a method called Machine Learning (ML). As the world evolves and companies try to increase their production more efficiently, they are turning to the use of artificial intelligence (AI). Also advancing in IT, Pakistan is also trying to stay with the world in AI and ML with the help of brilliant minds. Still, most of them work for foreign companies due to limited resources in Pakistan. Artificial intelligence is everywhere today, but there was a time when the entire field was not considered valuable. After initial advances and much hype in the mid-to-late 1950s and 1960s, breakthroughs stalled and fell short of expectations. There simply wasn't enough computing power to realize this potential. The operation of such a system was also prohibitively expensive. As a result, both interest and funds dried up. Then, increased research funding and an expanding set of algorithmic tools revived the effort in the 1980s. But it didn't take long for the decades-long AI winter to return. Then came two major changes that directly enabled AI as we know it today. AI efforts have shifted from rule-based systems to ML techniques that can learn using data without external programming. At the same time, the World Wide Web became ubiquitous in the hands of billions of people around the world, leading to an explosion of the data and data sharing that ML relies on. AI (AI) is the ability of computers or computer-controlled robots to perform tasks that require human intelligence and judgment. Siri and Google Translate are examples of AI use cases and use an AI system based on the learning process of human neural networks. Many say that AI will improve the quality of our daily lives by being more efficient than humans at basic and complex tasks, making life easier, safer and more efficient. Others argue that AI threatens people's privacy, classifies people in a way that exacerbates racism, displaces employees and increases unemployment. Machine learning ML is a subcategory of artificial intelligence that allows software applications to predict outcomes more accurately without being explicitly programmed. ML algorithms use historical data as input to predict new output values. We have made significant progress over the last ten years. Although AI and ML are often used interchangeably, they have fundamental differences. AI is an umbrella term for a set of technologies that enable computers to learn and behave like humans. In short, AI makes the computer smart; however, ML is responsible for how computers become intelligent. Unlike traditional programming, a hand written program that takes input data, runs it on a computer, and produces output, in ML or augmented analytics, the input data and outputs are fed to an algorithm to create the program. This leads to meaningful insights that can be used to predict future outcomes. ML algorithms use statistics to find patterns in vast amounts of data, including images, numbers, and words. If data can be stored in digital form, it can be fed into ML algorithms to solve specific problems. Engineer ML Abdul Raheem told The Express Tribune that the first method came out of his pure statistics in the 1950s. "They solved formal mathematical problems by looking for patterns in numbers, evaluating the proximity of data points, and calculating vector directions. Today, half of the Internet works on these algorithms. If you see a list of articles you want to read next, or your bank blocks your card at a gas station in the middle of nowhere , that's probably one of those little guys' jobs." he said. "Big tech companies are big fans of neural networks. Obviously, 2% accuracy means $2 billion in revenue for them, but it doesn't make sense when they're small. I've heard stories of a team that spent a year working on a new recommendation algorithm for e-commerce sites before we realized that 99% of traffic came from search engines. Their algorithm was useless and most users didn't even open the main page," Raheem said, adding that the sole purpose of ML is to predict results based on incoming data. If the task is not represented this way, it wasn't an ML problem to begin with.