An understanding of the AI/ML ecosystem
Bonjour to the new “cool big” Artificial Intelligence Era, an era where a robot helps the woman with the dishes while handling the man a beer. An era where 2 words like Okay Google, Hi Alexa, Hi Siri or Hi Bixby makes your life easy and hassle free. An era, when Netflix or Amazon suggests movies or books for you, Artificial intelligence is becoming a common place, from your smart phones and your Amazon account to the driverless cars that will soon grace public roads in Australia. Some argue that it’s magic, while other consider it to be job stealing robots. Well, the ultimate truth is that the pursuit of AI technologies and Machine Learning is rapidly becoming a fixture of our daily lives.
Its origins reach back over 60 years. The term was coined in 1956 at a Dartmouth Workshop titled “Dartmouth Summer Research Project on Artificial Intelligence”for the first time and many contributions have been made by a vast array of practitioners and computer scientists since then. even before people have been trying to replicate machines which can replicate human nature even intelligence. However all these years it was very slow moving, It has not generally been smooth cruising, we have had periods where we have hit harsh patches and AI has gone into hibernation. We call it the “winter of AI”. The reason it was called winter of AI because computing power was not sufficient. Today, that seems like a daunting task for most technologists as the space have been evolving incredibly fast.
At times, the difference between AI, machine learning, and deep learning is a bit fuzzy. Mathematically speaking, deep learning is a subset of machine learning, and machine learning is a subset of Artificial Intelligence. With this understanding in mind we can define Artificial Intelligence as the branch of computer science that focuses on building machines capable of mimicking or simulating intelligent behavior. The ascent of machine learning was a move in thinking, of how machines could learn without being explicitly programmed from existing information/data, and adjust or change their behavior and make predictions or take autonomous actions like “This one is a dog, this one is a cat.”However, Deep learning is the new kid in the school and a subfield of Machine Learning that tries to work with the algorithms inspired by how the human brain functions .Deep learning is used to help to go through new industrial challenges such as computer vision for driverless vehicles, speech recognition, and natural language processing for human voice interfaces. For example, Google uses deep learning in its voice recognition algorithms.
Implicitly, the term AI is suggesting mimicking Human Intelligence. But could this really happen? Maybe the current iteration of AI is not as effective as to swoop away Human Intelligence from the loop. However, it can definitely solve much bigger swath of problems with its advanced algorithms, Powerful and cost efficient computing power, elasticity (the ability of a computer system to adapt to different workloads by spinning up and shutting resources automatically) and Big Data Espousal.
When AI came into the light for the first time, many practitioners coded everything from scratch in a wide variety of languages. Initially languages such as Prolog, Lisp were popular. Later on, Java and C++ became relevant. Lately, the de facto AI languages have become R and Python.
In the corporate world, Software companies, researchers, industry firms, and business companies are paying closer attention to use artificial intelligence in many innovative areas like autonomous vehicles, image recognition, language translation, and natural language processing for analytics. Domain wise, BFSI (Fraud Detection) healthcare (advanced and fast diagnostics), Automobile (Self Driving Cars), F&B , Fashion (Online platforms), etc. is exploiting AI in the best way they can while demographic wise the US is ruling AI followed by Europe and Asia Pacific.
Some of the key players of the Artificial Intelligence market include Apple Inc., Bloomberg, Coursera,Facebook, Fingenius Limited, General Vision, Inc., Google Inc., IBM Corporation, Inbenta Technologies,Inc., Intel Corporation, Microsoft Corporation, Numenta, Inc., Nvidia Corporation, Qualcomm,Sentient Technologies Holdings Ltd. and Tesla Motors. Facebook and Google use machine learning to analyze users, click patterns and deliver personalized content and ads. Others are swinging to machine learning to comprehend everything from consumer buying and spending patterns to real estate and housing rental markets.
Putting it all together, AI and ML has moved past the “magic” or “science fiction” and has taken a place in our core lives. We are using it without even realizing. The foundation on which AI has rested its pillar is made up of vast, rich data sources that are making deep learning a reality. So, wait for it as the wild ride has just begun!
Source: Deep Learning on Medium