Artificial intelligence is like machine learning. But they are completely different and some concepts related to it has to be cleared.

1. Artificial intelligence refers to a broad set of methods, algorithms and technologies that makes the software 'smart' that seem human-like to an outside observer.

2. Machine learning has some elementary engineering sensibility whereas artificial intelligence is more correlated to automation and sophisticated data handling.

3. Machine learning covers multiple technology, whereas AI is a process which only refers wide variety of algorithms and methodologies that enable software improvement.

4. Machine learning is more into deeper analysis, segmentation and networking in contrast to standalone operation of artificial intelligence

5. Cognitive understanding is more prominent in artificial intelligence because analytics and forecasting play a clinical role. So, accuracy in automation required cognitive learning.

To read, follow: www.cio.com/article/3040600/five-things-you-need-to-know-about-ai-cognitive-and-neural-and-deep-oh-my.html

While you sit reading this article, if you stretch your arms, you would have electronic help all around you. A smartphone which could pay your bills, plan your schedule and tell you it’s time for a meeting or showing you the nearest food joints when you are hungry. These interruptions imply that we are surviving on advanced, analytics driven machine intelligence. All this said, for machine intelligence to be more powerful, we should be ready to accept a higher level of intrusion. For example, a patient detected with a heart disease, his device could suggest him to take a nap or hit the gym, or could wake him up when he is feeling anxious or stressed. Read more at: http://www.forbes.com/sites/teradata/2015/07/16/why-machine-learning-is-the-next-penicillin/

Data science techniques are becoming increasingly popular these days to improve business outcomes. Hadoop, already showcased the broader use of big data technologies and their impact on businesses. In this new age, the limits of machine learning are constantly being tested as innovators are trying new techniques that decrease human intervention as much as possible. Companies are ready to work with the data they have in Hadoop. Penetration of SQL on Hadoop has been a great help as they have created an environment that has made data accessible to downstream apps and learning algorithms. Machine intelligence is catching up in all spheres, data science is becoming a new trend with data scientists coming in demand. To know more, please follow:

http://www.dataversity.net/predictions-for-data-science-over-the-coming-years/

Self-learning machines need data to learn. So one possible way to get the data that is being generated from all the devices being used across the world is IoT-Internet of Things. Devices communicate with each other and are interconnected through various networks. This could help advance many fields especially medical field. But a standard protocol has to be developed for devices to communicate with each other. It was proposed that the protocol should take care of safety of humans, security of IT infrastructure.

To read more, follow: http://www.v3.co.uk/v3-uk/analysis/2410409/big-data-and-iot-powered-self-learning-machines-wont-take-over-the-world