Machine Learning and Artificial Intelligence

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by Ravindra Savaram
Last modified: February 7th 2017

Machine Learning and Artificial Intelligence

Accelerated technological advances in data and analytics are redefining the business space, supercharging performance, and fuelling the emergence of fresh innovations and new business models.

As technology continues to evolve, it brings in new waves of advancements in not so familiar areas like analytics, robotics, artificial intelligence, and in particular, machine learning. The new technologies, combined together, are making significant inroads into businesses, economy, and the society at large. 

Machine Learning and Artificial Intelligence are two trending buzzwords in the tech-town today. These terms are often used interchangeably. However, they are actually not the same. Since the general perception is that they are the same, creates a great deal of complexity and confusion.

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So to understand the difference better, it would be really interesting to take a deeper look into the history of artificial intelligence and machine learning. 

The Era of Intelligence

Artificial Intelligence (AI), though it sounds like a new term, has a long history that dates back to several decades. The concept has been a topic of research ever since Aristotle introduced syllogism, a system of formal and mechanical thought process. 

However, the birth of today's AI started in the 1940s and 50s, when intellectuals from the field of engineering, mathematics, political science,  economics, and psychology ushered in the idea of creating an artificial brain. This paved the way for the establishment of Artificial Intelligence as an academic discipline in the year 1956.

In 1950, Alan Turing, an English computer scientist, developed the  Turing Test, which was basically a test of a machine's ability, compared to that of a human being. Turing Test was the first serious approach in the field of artificial intelligence. 

Later, as the scope broadened, the goals of artificial intelligence boiled down to a number of subsets, which include -  

1. Knowledge representation
2. Deduction, Reasoning, and Problem Solving
3. Planning and scheduling
4. Natural language processing (NLP) 
5. Computer vision 
6. Creativity
7. Strong AI
8. Machine learning

As mentioned above, machine learning can be considered as a subset of the whole idea called artificial intelligence. While artificial intelligence incorporates many areas, machine learning is more about locating invariants and patterns in big data. 

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Machine learning enables self-learning algorithms to help machines learn from big clusters of data sets. The self-learning process allows the machine to perform a series of actions that lead to a predefined outcome.   

Machine learning algorithms can identify patterns in existing data, recognize related patterns in future data, to make more informed, data-driven predictions. These algorithms, based on heuristic data,  develop behaviors that can adapt to new circumstances, and learn from new experiences, to perform a variety of tasks. 

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The most familiar example of machine learning would be Google search. We have all noticed that when we misspell a search query, for instance, we type in 'macine' instead of 'machine', we are immediately prompted with a message - ‘Did you mean machine?’ This is accomplished by Google's machine learning algorithms.

Apart from correcting spellings, Google's machine learning algorithms recognize and remember what you had searched for before, so that it can provide you with better suggestions in future. 

In addition to Google algorithms, rankings like news feed and ads, recommendation engines such as social media, shopping, and Netflix movie, knowledge navigators like Siri can also be reckoned as other real-world applications of machine learning. 

Machine Learning and Artificial Intelligence in Modern Business

When South Korean Master Lee Se-dol was defeated in the board game 'Go', by Google DeepMind’s AlphaGo program, all corporate eyeballs started turning towards machine learning. It became fairly evident that both artificial intelligence and machine learning are positioned to become the most disruptive technologies in the business landscape, which have a lot to offer in terms of further innovations. 

Today, machine learning algorithms are being used to develop more advanced solutions to achieve breakthroughs in complicated areas like risk analysis and fraud prevention, as well as in many other fields like analyzing data, gaining customer insights, and building strong customer engagements. 

Previously, machine learning and artificial intelligence were synonymous with Big Data analytics, but now, with the advent of innovations like voice/face recognition solutions, knowledge navigators, smart home appliances, and autonomous, self-driving cars, these technologies have become a part of our everyday lives.

The Game Has Just Begun

Artificial intelligence, and in particular machine learning, are game-changers, promising to take us to the next-level. These disruptive technologies will change the rules of the game, breathing in creative insights that will help develop next-generation business models. 

Recent studies reveal that investments in artificial intelligence reached phenomenal heights in 2016, with tech giants like Google and Baidu spending close to $20B to $30B on AI. 90 percent of these investments were in R&D, and 10 percent in new acquisitions. U.S based companies emerged on top with 66 percent of the total AI investments, while China seized the second place with 17 percent.

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