What does the future behold for Machine Learners?
When thinking of AI, machine learning, smart computers, we tend to imagine something very contemporary, something that has appeared very recently. But is it? Let’s find out where did it come from and where does the future of Machine Learning stand.
As we understand it today, our world has two sides to its face: the real world and the virtual. In the real world, we keep learning and acquiring things with every passing day and continue growing and improving our abilities by considering the success of the past decisions of our life. Now, if this same process is automated using computers, the virtual world is what we call ‘Machine Learning’. Ever wondered how does Netflix recommends your shows? How does Google have all the answers? How does Swiggy make your left day go right? How does Amazon recommend the products you must be interested in? The answer to all of this is ML.
What is Machine Learning?
To know the future of it, we need to know what is it on the primary.
In very generic terms Machine learning is a branch of Artificial intelligence (AI) that circumvents around building applications that possess the ability to learn from given data and work on their accuracy without being programmed to do as so. An algorithm essentially is a progression of statistical processing steps, where, in machine learning these same algorithms undergo training to identify patterns amidst myriad data presented in front of them to further make decisions and predictions based on that. Consequentially it could be derived that the quality of the algorithm would be directly proportionate to the accuracy of decisions or processed results.
The very fact that machine learning as we know it today traces its roots back in1940s might sound a bit surprising but its inception institutionalized during that period with a very important book written on human cognition and since then this field has progressed substantially especially in recent times with the advent of widespread internet services coupled with the availability and development of technology itself.
The History Behind:
“Machine learning” as a term transcends its inception back in the year 1952. Arthur Samuel, a pioneer in the field of computer gaming and artificial intelligence developed a computer program to play the age-old game checkers in the 50s itself.
However, the program at that point had insufficient memory available so to tackle this, he launched some sort of search algorithm called alpha-beta pruning which enabled his design to incorporate a scoring function using the position of the pieces on the checkers board which attempted to calculate the probability of each side's success. This presumption by the algorithm was formulated by utilizing a minimax strategy which advanced into a minimax algorithm. Machine learning gradually emerged as an independent field in itself constituting a plethora of researchers and technicians shifting its objective from extensive Artificial intelligence training to practical problem-solving. Consequently assuming a shift of trajectory from already inherited AI approaches to methods and tactics instrumental in probability theories as well as statistics. The success of Machine learning as we know it kicked in the 1990s before which the field was engaged primarily with neural networks, however, with the advent of the internet and with a huge influx of digital data enabling a smooth transaction and procurement of the ML services.
The Future of Machine Learning
Now, that we roughly have an idea of what machine learning is, where do you think its future stands? Well, the future definitely gets its inspiration from the past. At the moment the speech recognition technique is practiced by undergoing the training process done by utilizing a different neural network deep learning technique known as Long short term memory or LTSM. This network possesses the ability to learn tasks that require memory of events that happened thousands of discrete steps before, which has a major role to play in speech recognition techniques. Google’s X Lab developed an ML algorithm capable of browsing cat videos all by itself in the year 2012 and soon after this in the year 2014, Facebook developed Deepface, which is basically an algorithm with the ability to recognize faces from photographs with almost equal accuracy as humans.
Stanford University defined machine learning as:
“the science of getting computers to act without being explicitly
programmed.”
ML is now responsible for the most advancements in technology today. These days, all the youngsters have been a lot into OTT services like Netflix, Amazon Prime, Hotstar, and whatnot. It is all ML, that when we search for shows, they recommend shows related to our interests. Even, if we put the data aside, we can see it prospering in front of our eyes. Doctors use MRI or different imaging analysis systems and it has proved very helpful in detecting several diseases, tumors, which human eyes couldn’t have spotted.
The machine learning market is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a compound annual growth rate of 44.1% during the forecast period.
If the survey says the market can grow by almost 7 times in a span of just six years, who’s to say it wouldn’t double or triple by the next decade?
Increased Adoption of Quantum Computing
“The world is running out of computing capacity. Moore’s law is kinda running out of steam … we need quantum computing to create all of these rich experiences we talk about, all of this artificial intelligence”, said Satya Nadella, Microsoft CEO
Quantum computing is essentially the exploitation of collective properties of quantum states which after being entangled with Machine learning incites faster data processing, leading to an enhancement in analysis and meaningful extraction of insights from provided datasets. The capital market under the realm of technology has been regularly attempting to harness the power of quantum computing to generate effective techniques including companies like IBM and Google etc. IBM’s Melbourne 16 quantum computer or Google’s sycamore, to be precise. It would not be incorrect in any way to predict that these decisions of the market can be deciding the future of ML
Conclusion
At the end of the day, AI/ML is an invention of human minds. The complete automation of various tasks is possible today just because of human imagination. We still have a long way to go to create an intelligent system that can overpower us. Although Machine Learning is the significant future of the technical world, it also needs constant regulation and observation so that the negative effects do not overpower.
But until then, as big data keeps getting bigger, as computing becomes more powerful and affordable, and as data scientists keep developing more capable algorithms, machine learning will drive greater and greater efficiency in our personal and work lives.
