Advertisement is one of the major ways through which businesses can attract customers. A lot of money and time is invested in order to create ads. However, these days a helping hand has come for rescue and is successfully able to attract customers by presenting customized ads. Machine Learning Algorithms, Artificial Intelligence and Deep Learning have come into play. With the help of these technologies, customized ads can be created based on the current searches done by customer. For example, you recently searched for “affordable mobile phones”. These learning algorithms tracks it down and soon starts displaying mobile phones ads presented by various companies. Other than that, Data Mining also plays an important role in this. Among various data that is available on world wide web, data mining algorithms browser and stores valuable data. 

Read more about this at https://www.techworm.net/2018/06/how-machine-learning-algorithms-help-businesses-target-their-ads.html

Artificial Intelligence is spreading its wings and is coming into rescue in various fields. One such field which comes into rescue for humans is the health care sector. Combination of these two fields can bring great advancement in health care sector. Artificial Intelligence and Machine learning have already come into action in medicine. Following are the top 4 applications:

1. Diagnosing Diseases: Not all diseases can easily be rectified. This could be time consuming and expensive. Here, various Deep Learning algorithms prove to be a solution. This focus on automatic diagnosis, making diagnosis much cheaper and accessible. 
2. Developing Drugs Faster: Drug development is a time taking and a tedious task. It involves analytics and various rounds of testing. AI has already aced in speeding up the process.
3. Personalizing Treatment: Same medical procedure can not be carried out on every patient. Choosing the course of treatment can be a difficult and a great responsibility. Machine Learning can automate this task. It can help in designing the right treatment plan.
4. Improving Gene Editing: This is a technique that relies on targeting and editing specific location on the DNA. A careful selection needs to be made. Machine Learning models have successfully been able to predict target and effects successfully.

To read more at https://towardsdatascience.com/artificial-intelligence-in-medicine-1fd2748a9f87

Recently a team of researchers developed an AI based system that can write codes for the programmers and it can also predict the solutions for them. The system uses neural sketch learning method of deep learning to recognize patterns in millions of codes written in Java. Also, the system itself is trained by using millions of Java programs. Currently, it can be used to evaluate undocumented APIs that can be quite difficult for any programmer to use. This is the starting and we can visualize the upcoming future of how AI can help programmers in writing codes, moreover solving the problems which are quite difficult to solve by humans or may take a long time. 

Slowly and steadily we are moving towards digital transformation and its has become one of the important tasks for businesses. With the evolving technology, businesses are also growing and evolving. Here are the top 12 tech trends that we need to look for in year 2018 :

1. Computer Vision
2. Deep Learning
3. Natural Language Generation
4. Businesswide Networking Fabric
5. Distributed Ledger Technology
6. Edge Computing
7. Quantum Computing
8. Serverless Computing
9. Augmented, Virtual and Mixed Reality
10. Digital Twin
11. Additive Manufacturing
12. Nanotechnology

It is believed that to stay in the competition of business, one should welcome new forms of technology and implement them helping business to grow. This will bring the needed digital transformation and help in decision making and planning strategies. 

Read more at https://www.techrepublic.com/article/here-are-the-12-tech-trends-that-will-dominate-business-in-2018/

Artificial Intelligence, Machine Learning and Deep Learning are relatively newer technologies invading the fields of information technology, business etc. Though developers are walking towards this era, currently the number of experts is relatively less. The company often makes mistakes by starting up with the technologies instead of focusing on business needs. They often make mistakes by assigning out of domain work to some. For e.g. Hiring data scientists and asking them to build something interested from given database. Rather than a team must be formed of product managers, data engineers, data scientist and DevOps engineers.A team of four will be a kick start to improve our process and giving better results. Now everybody has an opportunity to improve the models, optimise the deployment and scale the business. 

Talking about ML, many projects fail due to complex structures. This could occur because of working on wrong problem, to having wrong data, failing to build a model or failing to deploy it correctly. Read more at: https://medium.com/@guyernest/the-flywheel-of-machine-learning-systems-50aa6d992382

Artificial Intelligence, Machine Learning and Deep Learning are now the most exploring topics for any techie. In spite of enough differentiation between these terms, they are often used interchangeably. To put an end to this confusion one could say that ML and DL are nothing but cousins of AI. 

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Many applications of AI are being seen and used today. From voice-powered personal assistants like Siri and Alexa to self-driving cars and many more are applications of AI.

On the other hand, Machine learning is an artificial intelligence (AI) that is discipline geared toward the technological development of human knowledge. Machine learning allows computers to handle new situations via analysis, self-training, observation and experience.

Whereas, deep learning is a subset of machine learning which is a collection of algorithms used in ML to build and train neutral networks and act as decision making nodes.

So, though AL, ML and DL are interrelated but in this vast field of technology they all stand on their own and using them interchangeably would not be justice.

Read more at: https://towardsdatascience.com/cousins-of-artificial-intelligence-dda4edc27b55

Deep Learning, the subset of AI which helps in Image Recognition, Speech Recognition and much more. Now, it may also be useful in analyzing images of the galaxy and help us find out how they were evolving. Recently, a group of researchers, trained a deep learning program using galaxy simulations that helped in analyzing the images of the galaxy from the Hubble Space Telescope. Read more at: 

https://www.sciencedaily.com/releases/2018/04/180423125115.htm

The latest version of Deep Learning Application (Bayou) will help humans in programming. With the help of just a little information and some keywords, Bayou can almost predict the programmers brain. The researchers have used method of Neural Sketch Learning to train its Neural Network.  It can easily take over forums like Stack-Overflow because of its instant reply to programming problems. Read more at: 

https://www.futurity.org/artificial-intelligence-bayou-coding-1740702/

Quantum computing is the area of study focused on developing computer technology based on the principles of quantum theory, which explains the nature and behavior of energy and matter on the quantum level. In Quantum it can’t be clearly predicted which key element of the technology has entered commercialization and resulted in a massive change. Commercial applications like Temporal Defense Systems (TDS), Westpac, Commonwealth, and Telstra, and QuantumX are being adopted in Quantum computers by Lockheed Martin. Quantum computers can be used for simulation, optimization and sampling. Therefore, the most important action for data science is plotting how Quantum will disrupt the way we approach deep learning and artificial intelligence. Read more at: http://www.datasciencecentral.com/profiles/blogs/quantum-computing-and-deep-learning-how-soon-how-fast

 2016 has been the year of deep learning, some big breakthrough were achieved in 2016 by Google and DeepMind.Some of the most significant achievements are as follow :

 AlphaGo triumphs Go showdown : AlphaGo the google’s AI for the game Go to everyone’s surprise was able to beat Go champion Lee Sedol.

 Bots kicking our butts in StarCraft : DeepMind AI bots were able to outperform some of the top rated StarCraft II players.

 DIY deep learning for Tic Tac Toe : AlphaToe a AI bot was able to outperform most of the people that played with it.

 Google’s Multilingual Neural Machine Translation : Google was able to make a model which is capable of translating text b/w languages, reaching a new milestone in linguistics and NLP.

 Hence , in a nutshell , 2016 was the year for Deep Learning and a lot of unachievable milestone were conquered during the annual year.

 To know more you can read the full article by Precy Kwan at http://www.datasciencecentral.com/profiles/blogs/year-in-review-deep-learning-2016

Computer systems are quite similar to living organisms. The living part of the system is not so much the hardware or even the software it is the data. We seek to build computer systems based on ourselves and take this as models. Non computer science based behavior patterns and structures provides a language for discussing the patterns, behaviors and structures of systems. An artificial neural network is inspired by the biological nervous systems, such as the brain. It is composed of a large number of highly interconnected neurons to solve specific problems. An Artificial Neural Network or ANN is configured for pattern recognition or data classification. For more read the article written by Bruce Robbins (CEO, Xcipi) : http://www.smartdatacollective.com/bruce-robbins/397616/data-alive-through-deep-learning-and-intelligence