According to authors,Machine-Learning, a subset of Artificial Intelligence (AI), is applied by firms in evaluating  financial decisions like assessing credit worthiness and eligibility criteria for an insurance policy, fraud prevention, by tracking fraud indicative transactions, thereby reducing the fraud rate, risk management, tasks of compliance, natural language processing, where this system can shift through thousands of commercial- loan contracts in seconds  and trading, where it is used both to crunch market data and to select and trade portfolios of securities. In the quantitative front, machine-learning algorithm , helped in making investment decisions, by picking up acquisitions before announcement, managing the client money and moving beyond the boundaries of conventional trading strategy. Read more at http://www.economist.com/news/finance-and-economics/21722685-fields-trading-credit-assessment-fraud-prevention-machine-learning?zid=291&ah=906e69ad01d2ee51960100b7fa502595

AI or Artificial Intelligence is the hottest sector in India with lots of innovation across sectors. With China is leaping ahead in the AI race on many levels (research papers, R&D, investment and even policies) India is only now waking up to the benefits of AI. If India wants to participate in the AI revolution, then it needs a policy that brings together Indian academicians, researchers, labs, private players and investors on the same platform. Read more at: http://analyticsindiamag.com/india-stand-ai-race-vis-vis-china-us-rest-world/

The growing potential of big data should incorporate analytics into its strategy to make better and faster decisions. Businesses can use data and analytics to make better decisions by using granular data. New machine learning has a lot of applications and benefits. Systems enabled by machines can provide better benefits. The technologies could generate productivity gains and an improved quality of life, but they carry the risks associated. Industries are getting greatly influenced by data and analytics, and the effects will become more pronounced when it reaches the masses. Organizations that can harness these capabilities will be able to create significant value and differentiate themselves, whereas other people might not be happy with this. Read more at: http://blogs.economictimes.indiatimes.com/et-citings/managing-analytics/

The three critical steps involved in deployment of machine learning algorithm and exposing it to real world are :

Define a goal based on a metric : Decide if you want human level intelligence or an acceptable one as this decision will affect time and engineering cost of your system. Also define a metric to measure performance of your model.

Build the system : Build a minimum viable system without worrying much about accuracy. Then build an incremental strategy to improve your system by solving problems you face in each iteration.

Refine the system with more data : Initial metric values are not the indicators of real life, your data and users might change , so regularly monitor the system performance. Update it with new data and fine tune the model accordingly.

Read more at : http://www.erogol.com/short-guide-deploy-machine-learning/

Sometimes even the Machine learning algorithms behave so dumb that an image recognition model can be confused by generating an adversarial instance, i.e. by changing few pixels by either taking derivative of model output or exploiting genetic algorithms. Adversarial instances lie in low probability regions which is in contrast with limited instances of high probability regions from which the model was trained. A possible approach to solve this problem is ensemble training - To let multiple models back each other. As we look forward to developing more artificial intelligent systems it would become common to encounter such problems.

You can read more at: http://www.erogol.com/ensembling-against-adversarial-instances/

 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

Machine Learning is the backbone of today’s insights on customer, products, costs and revenues which learns from the data provided to its algorithms. And hence algorithms are the next most important thing in data science after data.
Hence , the question which algorithm to use ? Some of the most used algorithms and their use cases are as follow :

1) Decision Trees - It’s output is easy to understand and can be used for Investment decision ,Customer churn ,Banks loan defaulters,etc.

2) Logistic Regression - It’s a powerful way of modeling a binomial outcome with one or more explanatory variables and can be used for Predicting the Customer Churn, Credit Scoring & Fraud Detection, Measuring the effectiveness of marketing campaigns, etc. ,

3) Support Vector Machines - It’s a supervised machine learning technique that is widely used in pattern recognition and classification problems and can be used for detecting persons with common diseases such as diabetes, hand-written character recognition, text categorization, etc. ,

4)Random Forest: It’s an ensemble of decision trees and can solve both regression and classification problems with large data sets and used in applications such as Predict patients for high risks, Predict parts failures in manufacturing, Predict loan defaulters, etc.

Hence based on your need and size of your dataset , you can use the algorithm that is best for your application or problem.
You can read the full article by Sandeep Raut at http://www.datasciencecentral.com/profiles/blogs/want-to-know-how-to-choose-machine-learning-algorithm

It has been evident that a great customer experience is one of the signs of a healthy business model. Machine Learning and Data Analytics are playing a fundamental role in building consumer intelligence systems. It is important to capture data and there is no single magic source to collect data. Telecoms are making billions by selling data. You need to ensure that the data is relevant to business. Once you have the right data, you are ready to model, design and engineer and deploy your 360-degree customer view platform and achieve the enhance customer experience for your organization.

You can read more at: http://www.datasciencecentral.com/m/blogpost?id=6448529%3ABlogPost%3A508502

Whenever we talk about some advanced applications, we can see that we have moved way too ahead in the field of Artificial Intelligence (AI). Machine Learning (ML), a branch of AI, is a major factor to turn applications into intelligent ones. There are companies which are building such ML/AI technologies and others are incorporating ML/AI technologies in their applications and services to make it smarter and to provide customer better and easy facilities. But for the automated applications to provide better customer experiences, we need to have human beings in the loop. Although we have achieved great success in building many ML/AI applications, but we are still in the early stages of the journey. For more details, check the given link: https://techcrunch.com/2016/07/06/key-trends-in-machine-learning-and-ai/

Twitter hires ex-apple worker, Alessandro Sabatelli who is an interface designer and joined as the Director of VR and AR. He is also the founder of Ixomoxi. He will be working on the machine learning and AI team-Corex. Sabatelli will also work on Magic Pony, a technology that enhances video and image quality on twitter. By combing Machine learning and augmented reality together, Twitter can develop snapshot features like Face Swap etc. Also, augmented reality can combined with geolocation and geotagging tool. Read more at:  http://tech.firstpost.com/news-analysis/twitter-hires-ex-apple-designer-as-director-of-vr-and-ar-322924.html?utm_source=top_stories 

In the digital era where an individual is afraid of losing jobs not to recession, but two robots and machine learning, it becomes important for us to learn about the extent to which machines can replace human beings. In the near future where automation might delete few occupations entirely, it is expected to affect almost all occupations to some degree. Knowledge about technical feasibility is a prerequisite for automation of the activity. The second major factor that is always the reason for concern is cost. The cost of developing such automation system needs to be taken care of. Physical activities are most susceptible to automation. The retail industry is another place where automation would do wonders. However, financial sector lies in the middle range of automation. People managing and developing things can still relax a bit as automation in this field has very low technical potential. The focus should be on automating sectors with high technical feasibility and replacement of workers from these sectors to opportunities where automation is yet to do wonders. Read more at: http://www.mckinsey.com/business-functions/business-technology/our-insights/where-machines-could-replace-humans-and-where-they-cant-yet

Twitter will now be shaking hands with a machine-learning startup that specializes in working with images, to deliver better video and picture content to expand its Machine Learning and AI parts. According to the sources, nearly $150 million is invested in this machine learning startup. A team of engineers will help Twitter by letting the users explore new experiences and share them. Read more at:  http://economictimes.indiatimes.com/small-biz/startups/twitter-allows-users-to-share-140-second-videos/articleshow/52848116.cms

Satisfying the customers mean retailers needs to be more accurate on the business context, needs more granularity in terms of data and insights, and the ability to respond closer to or in real time. This is where big data technologies provide practical solutions that deliver performance and economies at scale. Understanding customer behaviour and its impact on shopping decisions is a relatively underutilized science, therefore only a combination of big data, machine learning and predictive & prescriptive analytics can tap into the real potential of how organizations understand and respond effectively to customer needs today. Read more at: http://www.computerworld.in/interview/big-data-machine-learning-and-analytics-can-help-us-understand-customer-needs-seema-agarwal-manthan

Machine learning is a type of Artificial Intelligence that provides a computer with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach them to grow and change when exposed to new data. Artificial Intelligence is supposed to be the core of Big Data, producing exponential growth in the volume of data used for scientific research. Out of all two most popular machine learning techniques are, supervised and unsupervised learning. Nowadays, learning algorithms such as Bayesian networks and support vector machines is being used more extensively in daily commercial systems. To learn more read at: http://www.dataversity.net/machine-learning-now/

Machine learning goes a stage past big data analytics, where machines utilize calculations to adjust and gain from past encounters. In the client experience domain, machine learning permits new information about client knowledge to be enhanced as new information is added to the models. Examples of innovative machine learning applications in use today, i.e. tracking and maintaining customer profile data, hospitals use machine learning models to incorporate factors. Machine learning stages are presently broadly accessible which give demonstrated instruments to help the standard engineer group assemble information rich applications. For more read: http://www.hpcwire.com/solution_content/using-machine-learning-enhance-customer-experience/

Nowadays, we hear about data science technologies like machine learning and data modelling. But, now new cloud services for machine learning is gaining importance and more do-it-yourself (DIY) tools will emerge for sectors like financial services, healthcare or retail, predictive sales or marketing. Read more at: http://www.business2community.com/b2b-marketing/diy-predictive-modeling-pitfalls-opportunities-01527315#PHERr4OuiDd4QyeJ.97

Nowadays, computers solve crimes, drive cars, cure sickness and accurately predict political races. But the problem is, it's not enough to just store, access and process data. Machine learning and artificial intelligence algorithms are divided into two factors: huge quantities of timely and accurate data. There are four typical methods to be used to acquire data. They are -1. Required self-declared data,2. Self-declared or observed data 3. Volunteered self-declared data, 4. Crowdsourced observed data. For more read the article link written by David Elkington: http://techcrunch.com/2016/03/01/getting-predictive-about-politics-and-everything-else/

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

The intelligence exhibited by machines is called artificial intelligence (AI). To a layman this field is full of mystery. But AI is embedded is many things that often go unnoticed. Organizations are using AI to make the consumer experience better. So, you are no longer cluttered with irrelevant ads. The recommendations on the social media platforms are the result of machine learning and artificial intelligence models. There are smart entertainment apps that learn from your behavior and choose what is best suited for you. To read more follow:   http://insidebigdata.com/2016/02/12/why-consumers-need-to-stop-fearing-artificial-intelligence/

Every human being is interested to know their chances of getting sick in the future. This is where artificial intelligence scientists steps in. With the combination of machine learning, natural language processing and text analytics scientists are working to find out some latest concepts and technologies in biomedicine. Each term is then given a score, which, based on the technology's analysis, identified its predicted rate of incline. So based on that, few predictions have been made for 2016.  To know more, follow: http://www.science20.com/news_articles/ai_may_tell_us_whats_going_to_be_big_in_science_this_year-165373