Machine Learning (ML) algorithms can learn from data and improve themselves. In a way, that learning process is akin to the way the humans learn from daily experience and improve their own skill sets. Artificial Intelligence (AI) facilitates “emulation of human thinking in actions. “So, together AI and ML are capable of delivering smart robots who can learn from daily experience and keep refining their abilities. Businesses typically have about 65 to 70 percent of their programming tasks that the staff workers conduct by themselves.
When all these tasks become automated through Machine Learning powered Artificial Intelligence, about three quarters of the employed manpower may potentially lose their jobs, or their jobs will have to be restructured in different ways. Smart machines will take the place of efficient human workers, and conduct the daily tasks better, quicker, and more accurately – or so says the ML and AI experts.
To begin, let us get the terminology cleared up a bit. The term Artificial Intelligence refers to a technology that makes machines or robots mimic human behavior or human thinking through computer controls. The term Machine Learning is a subfield of AI that teaches machines to learn from data or experience and improve themselves without any “further” human intervention. The term Deep Learning (DL) is a specialized subfield of Machine Learning that can enable software systems to self-train for the performance of particular tasks. Thus AI is the parent technology with ML as a child, and DL probably as a grandchild. Machine Learning is essentially an intermediary between Artificial Intelligence and Deep Learning.
Digital assistants, whether in sales, telemedicine or classrooms are good examples of Machine Learning powered AI, where programmed intelligence guides the digital assistants to make quick and accurate decisions based on available data, so that these assistants can guide human consumers to make the right decisions. The digital assistants continuously improve their algorithms based on new data, and this capability for self-improvements makes ML an indispensable tool to AI solutions. Without ML, AI solutions were static, and they rarely went beyond programmed thinking. Now, with the incredible self-teaching power of Machine Learning algorithms, Artificial Intelligence solutions have become formidable enough to challenge humans in all business roles. The Wired magazine article, Machine Learning Cognitive Systems Next Evolution in Enterprise Intelligence reinforces the common belief that the self training capability of ML will set the stage for many AI applications in future.
Recent Machine Learning Powered Breakthroughs in AI Research
The recent breakthroughs in AI, whether in image, speech, or translations have all been possible because of tremendous advancements in Deep Learning in the last several years. As DL has succeeded in showing its mettle, one cannot deny the role of DL in larger AI applications. The self-learning or self-training capabilities of both ML and DL have revolutionized Artificial Intelligence. The sudden burst of activities in the realm of AI, thanks to ML and DL, has enabled AI startups to capture and analyze data at historic high volumes, along with enormous increases in funding for numerous companies. The second quarter of 2016 witnessed 121 funding rounds for such startups, compared to only 21 funding rounds in the equivalent quarter of 2011. Now, the five stalwarts in AI research – Amazon, Google, Facebook, IBM, and Microsoft – have formed a Partnership on AI to uplift public awareness on the subject and conduct research. The biggest reason that capital funding has poured in is the demonstrated ability of Machine Learning powered AI applications.
Deep Learning However Deep is Still a Child of Machine Learning
In a KDNugget article titled Artificial Intelligence is Dead Long Live Deep Learning, the author uses the analogy of a car to describe AI. He says that if Artificial Intelligence is a car, then Machine Learning is its engine, and Deep Learning is a specific type of engine. The author also predicts that in the next few years, many enterprises will take advantage of ML and DL in entirely new ways. That certainly speaks for the important role of ML and DL in Artificial Intelligence.
According to Fortune magazine’s article titled Why Deep Learning is Suddenly Changing Your World, the birth of interactive systems like Apple’s Siri, Microsoft’s Cortana, or Amazon’s Alexa has established a genre of talking interfaces. Moreover Google DeepMind’s AlphaGo is the fruit of a happy union of Deep Learning and reinforcement learning, which succeeded in unseating the human Champion of the Chinese Game Go. This achievement has been termed a landmark in the evolution of AI technologies. This spectacular AI feat would not been possible 10 years ago without the remarkable advancements in Deep Learning or Machine Learning. AlphaGo is the direct proof of concept that systems can learn from observations or experience. Reportedly while training, AlphaGo played one million Go games against itself. What’s the Difference between AI, Ml, and Deep Learning? claims that the reason that AlphaGo is a striking example of the combined power of AI, ML, and DL. So yes, Deep Learning can do amazing things but only with the help of supremely powerful Machine Learning algorithms. Without the algorithmic power of ML, even the best AI applications will not execute.
Machine Learning Promises a Future for AI
Deep Learning has blessed AI applications with granular task enablement, whether in driverless cars, in digital healthcare, or in movie recommendations. The article titled How Machine Learning and AI Impact Our Lives and Plan predicts that by 2025, AI and ML together will displace a third of all human workforce. This article further states that a number of challenges facing AI have been solved by borrowing ideas from Machine Learning. ML enriches AI applications by its data-powered intelligence. From these discussions, one can safely conclude that currently ML and DL are the driving forces behind the market success of AI applications.
Since 2015, the data explosion has been more deftly handled by Machine Learning techniques, especially the avalanche of unstructured data like images, text, transactions, and else. Artificial Intelligence applications would never have been able to tackle the Big Data so well, had it not been for the Machine Learning powered algorithms. MarketsandMarkets, has projected that the global AI market will reach $5 billion by 2020. This report claims that the increased use of Machine Learning in advertising, media, and finance sectors, and the market demand for AI across application are the key drivers of the Artificial Intelligence market.
In The Race is on to Control Artificial Intelligence and Technology’s Future, one can view that the technology giants are struggling hard to gain the Number One position in AI research. Why? The implicit indication in this race is whoever wins will dominate the next information age. IDC has predicted that by 2020, 60% of all Machine Learning applications will run on an IBM, Microsoft, Google, or Amazon platform. Thus, one can conclude that in order to dominate the world, the Artificial Intelligence champions will have to depend on Machine Learning techniques and solutions.