Gainingam Phaomei
Mr. Gainingam is a Project Manager in TurnoutNow, a data analytics firm that deals with Beacon technologies. The company employs Machine Learning and AI to make sense of the millions of beacons data points that are collected during Events – to generate actionable insights for business. He is also a social worker and works for the welfare of the North East community in Chandigarh.
Machine Learning (ML) / Artificial Intelligence (AI) is gaining much popularity and is very much in the media spotlight. It is the subject of discussion by many speakers around the world. ML/AI in today’s world are driving the corporates business system. We should not eschew this subject anymore. The time is ripe wherein we ought to be familiar with these terms, and it’s implementation and future implications.
What exactly is ML/AI?
Machine learning is an application of artificial intelligence that provides systems the ability to learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
The ideas behind machine learning are that the computer/machine can be trained to automate tasks that would be exhaustive or impossible for a human being. This is made possible by feeding millions of available data to the machine learning system so that it could master the art of accurate prediction. In the beginning, the machine can make a mistake. Over time it learns enough to make proper decisions and perfect itself.
Your personal Assistant Siri or Google uses ML. Weather predictions for the next week come using ML. Win Predictor in a sports tournament uses ML.
What is Artificial intelligence (AI)?
Artificial intelligence (AI) is the ability of a computer program or a machine to think and learn. It is also a field of study which tries to make computers “smart.” They work on their own without being encoded with commands.
Machine Learning is a current application of AI based around the idea that we should be able to give machines access to data and let them learn for themselves.
Some examples of ML/AI implementation
E-commerce:
ML/AI capability is used to recommend other products based on one’s purchase history. If you have bought a 5-inch Android phone with 2 GB RAM for 5000 rupees, the next time you log in to Amazon, you’ll be recommended other phones with similar specs or prices. It may even recommend you to buy a popular headphone, screen guard, or phone cover that goes well with your last purchased phone. How does Amazon know how to recommend these products? ML/AI is the reason behind all these.
Advertisements:
Google advertisements are based on machine learning. Google records what activity you performed on the internet, including the products you’ve viewed or the sites you’ve visited. This information is fed into an ML/AI module, and then it generates relevant recommendations and advertisements matching closely to your interests. Viewing or clicking the advertisement generate revenue for Google.
Self-driving Car:
Have you heard about Google car? This Google car is full of lasers on the roof, which instructs where exactly it is in the surrounding area. It has radar in the front, which informs the car of the speed and motion of all the other vehicles nearby. It also uses all of these data to figure out not only how to drive the car but also to figure out and predict what potential drivers around the car are going to do. What’s impressive is that the car is processing almost a gigabyte a second of data.
Welcome to the world of driverless cars. Very soon, we would not need a human taxi driver to take us to our destination.
Healthcare:
In 2019 recently, Google unveiled a new AI, which it believes can predict if and when a patient is going to die more reliably than doctors. The algorithm can quickly access a patient’s medical records for essential data, which could indicate the likelihood of their survival.
Google also recently announced its new research – an AI model that can predict lung cancer accurately and boost survival rates, with a 94.4 percent success rate.
Today AI is used to design evidence-based treatment plans for cancer patients, instantly analyze results from medical tests to escalate to the appropriate specialist immediately, and conduct scientific research for drug discovery.
Here are some other areas where ML/AI is implemented
Marketing
Extensive use of AI is done in marketing thanks to abundant access to data. Before the age of mass data, researchers develop advanced mathematical tools like Bayesian analysis to estimate the value of a customer. With the boom of data, marketing department relies on AI to optimize the customer relationship and marketing campaign.
Finance Industry
Machine learning is growing in popularity in the finance industry. Banks are mainly using ML to find patterns inside the data to prevent fraud.
Automation
Using Machine learning and AI, industries and factories could use machines or robots to autonomously carry out the duties without the need for any human intervention.
Honda’s Asimo, the world’s most advanced humanoid robot can function entirely autonomously. This robot is a beautiful illustration of Machine Learning and AI implementation for automation in a robot. Just look up for “Asimo” in Google, and you’ll find lots of details about this masterpiece.
How does Machine learning work?
Machine Learning involves learning from data. The greater the volume of data, the higher the chance of success in prediction or recommendations. The machine learns in a way similar to the human being. Humans learn from experience. The more we know, the more easily we can predict. By analogy, when we face an unknown situation, the likelihood of success is lower than the known situation. Machines are trained in the same way. To make an accurate prediction, the machine looks at examples. When we give the machine a similar example, it can figure out the outcome. However, like a human, if it was fed a previously unseen example, the machine has difficulties in predicting. Machines can also predict the future, as long as the future doesn’t look too different from the past.
If you are still struggling to grasp the concept of Machine Learning/Artificial Intelligence, here’s a quick logical reasoning quiz as an illustration:
- 2 – 4
- 4 – 16
- 6 – 36
- 8 – ?
What’s the answer? I am sure the answer is 8 x 8 = 64? How did you come to 64?
That’s precisely the kind of behavior that we are trying to teach machines. We are trying to teach machines to “Learn from Experience.” Machine Learning algorithms enable the computers to learn from data, and even improve themselves, without being explicitly programmed.
Why ML/AI matters?
AI will shape our future more powerfully than any other innovation this century. Anyone who does not understand it will soon find themselves feeling left behind, waking up in a world full of technology that feels more and more like magic.
AlphaGo (a computer program) defeated one of the best human players Professional Go player Lee Sedol at Go – an extraordinary achievement in a game dominated by humans for two decades after machines first conquered chess.
In March 2017, OpenAI created agents that invented their own language to cooperate and more effectively achieve their goal. Soon after, Facebook reportedly successfully training agents to negotiate and even lie. On August 11, 2017, OpenAI reached yet another incredible milestone by defeating the world’s top professionals Dendi (human) in 1v1 matches of the online multiplayer game Dota 2.
What qualifies as “artificial intelligence”?
The specific standard for technology that qualifies as “AI” is a bit fuzzy, and interpretations change over time. The AI label tends to describe machines doing tasks traditionally in the domain of humans.
Deep Blue a chess-playing computer developed by IBM defeated world chess champion Garry Kasparov in 1997.
The ongoing effort in AI is to allow machines and software systems to perform an intellectual task that human being can – including learning, planning, and decision-making under uncertainty, communicating in natural language, making jokes, manipulating people, trading stocks, or reprogramming itself.
A recent report by the Future of Humanity Institute surveyed a panel of AI researchers on timelines for AGI, and found that “researchers believe there is a 50% chance of AI outperforming humans in all tasks in 45 years”.
Machine learning is at the core of our journey towards artificial general intelligence, and in the meantime, it will change every industry and have a massive impact on our day-to-day lives.
Jobs associated with Machine Learning and AI
According to a US Department of statistics report, demand for data scientists and data engineers is projected to grow around 40 percent by 2020. In the US alone, the department estimates a shortage of about 150,000 people with data science skills, as of now.
What is Data Science?
Data Science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Data science is the same concept as data mining and big data.
Huge Demand in India
There is a massive demand for skilled professionals in India. Companies are mainly hunting job profiles such as Data Scientist, Data Analyst, Big Data Engineer, Statistician. Not only they are handsomely paid, but a career in analytics has much more to promise.
To become a data scientist, your areas of expertise should include either computer science, information technology, math, or statistics. Experience and fluency in skill sets such as R, Python, Java, Hadoop, and SQL/NoSQL Databases are preferred.
Companies that hire data scientists
Companies have recognized the immense business value which can be delivered using data. Google, Amazon, Facebook, Baidu are just some of the companies which have made investments in data products.
