The Difference between Artificial Intelligence, Machine Learning and Deep Learning

Today, read any tech article or news and you will be fired with the terms “Artificial Intelligence”, “Machine Learning” and “Deep Learning”. The biggest corporate giants Google, IBM, Facebook, Microsoft, and Amazon are voraciously acquiring Artificial Intelligence startups and companies. In just 3 months of 2017, 34 acquisitions were made. Forrester in the new report, “Prediction 2017: Artificial Intelligence Will Drive the Insights Revolution”, predicts a 300% increase in investment in Artificial Intelligence from 2016 to 2017. The report further proceeds to say that “insight-driven businesses will steal $ 1.2 trillion per annum from their less-informed peer by 2020.” Therefore, it is a worthy investment to disambiguate between the terms Artificial Intelligence, Machine Learning, and Deep Learning and explore the 20 best platforms.

Artificial Intelligence (AI)

Artificial Intelligence, or simply AI, is the broad umbrella term describing computer systems attempting to mimic human-like intelligence. John McCarthy, who coined the term in 1956, defines it as “the science and engineering of making intelligent machines.” Today, the areas of AI technology are primarily Robotics, Machine Learning, Machine Vision and Natural Language Processing. But the key benefit of AI in business is Predictive Analytics. Using AI algorithms, enterprises can grow exponentially to gain a significant competitive advantage over their peers. Venture Scanner reveals the exponential growth of funding in AI startups, with 2016 seeing over $ 2.5 Billion.

Machine Learning (ML)

The subfield of AI called Machine Learning (ML) focuses on developing algorithms that can help computer systems learn automatically, without being explicitly programmed. To accomplish this task, a wide range of algorithms have been developed such as Linear Regression, Logistic Regression, Support Vector Machines (SVM), K-Means, Decision Trees, Random Forests, Naive Bayes, PCA and lastly, Artificial Neural Networks (ANN). Venture Scanner reveals that over $ 3.5 billion has been invested in ML applications with over 400 companies investing in the field.

Deep Learning (DL)

But today, the new buzzword ruling the market is Deep Learning (DL), and this technique was born out of ANNs. Not only is it insanely popular, but it is slowly wiping out all other techniques of ML. Deep Learning uses multi-layered neural nets and learns by crunching a large amount of data. Though the core idea was presented in the ’60s, it is only today with the availability of data and powerful Graphical Processing Units (GPUs) that it proved successful. The recent accomplishments of DL were in the field of Machine Vision, Machine Translation, Speech Recognition, Automated Game Playing and Self Driving Vehicles.

20 Best Platforms for AI

Currently, instead of selling their AI software directly, the tech giants are developing platforms to implement Machine Learning and Deep Learning, which will allow customers to build mobile applications on them. And therefore, even smaller companies can develop state-of-the-art software using AI without getting lost in the nitty-gritty of the algorithmic technicalities. Here are 20 of the most popular platforms and APIs:

1. AlchemyAPI
2. Amazon Machine Learning
3. API.ai
4. AT&T Speech
5. BigML
6. Caffe
7. CNTK
8. Deeplearning4j
9. DiffBot
10. DMTK
11. Google Cloud Prediction API
12. IBM Watson
13. Infosys Mana
14. KAI
15. Mahout
16. Microsoft Azure Machine Learning
17. OpenCyc
18. PredictionIO
19. TensorFlow
20. Wit

Today in the world, AI is the biggest game-changer which is causing technology to advance faster than ever before. With the huge availability of data and open-source platforms, it is extremely easy for enterprises to implement AI. Therefore, if you fail to plunge into AI today, you might just find yourself out of business in a few years.