Medicare gets easier with 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

Predictive Analytic supported with contextual Integration is the secret of success

Contextual Integration refers to identifying meaningful relationships between different information types. This gives a multi-dimensional view of the data rather than a single access point. The best approach is to analyze these volumes of data from different perspectives. The traditional way is to follow a fragmented approach. The web teams, marketing and sales team will look at the different statistics offered by data. This lengthens the time to take decisions and also introduces inaccuracy. The need is to look at data from many angles to create a multi- dimensional profile of the customer. Then predictive analytics can assess and lead to intelligent messaging. Machine Learning is also helping to improve these predictive analytics algorithms by checking it on the real time data. Read more about it in the article written by Dominik Dahlem (Senior Data Scientist at Boxever) at: http://data-informed.com/contextual-integration-secret-weapon-predictive-analytics/

Prevent System outage with Machine Learning

Failures in the functioning of equipment are inevitable in any kind of industry. The repair and recovery time often leads to big financial losses each year. But we have machine learning and predictive analytics as a solution. The machine learning models are trained to learn the ideal functioning of the machinery. Then this functioning is compared with how the machines are working at present. So if even a minor change occurs somewhere, it doesn't go unnoticed. Then, with the help of predictive analytics the loss that can take place in the near future is predicted. This adds to the huge advantage of the firms. Learn more about this in the article written by Mike Reed   (manager of analytical services for Avantis PRiSM software) at: http://www.intelligentutility.com/article/16/02/saving-money-and-man-hours-machine-learning

Machine Learning gets better with "human in the loop"

Machine Learning is getting easier and accessible because of the computing power becoming affordable. Moreover, big enterprises are making their algorithm open source. This is because data is the food. More data an algorithm gets, the better it becomes. But from step 1, making algorithms, feeding data in humans play a significant role. Sometimes there are outliers which the algorithms cannot interpret. Here human intervention is necessary. They manually check such pieces. But when these are fed into algorithms, they make them robust by identifying outliers. Thus, human intervention is both necessary for accuracy and training. Read more at: http://insidebigdata.com/2016/01/11/human-in-the-loop-is-the-future-of-machine-learning/

What 2016 holds for Machine Learning?

The evolution of Machine Learning (ML) is affected by the approach of the tech giants towards it. Open Source Platforms and the data sources also have an important impact on the ML models. Tech giants have realized the importance of ML, and this is becoming the new normal for them. They are now focusing on providing ML models as a Service. These are built for the common usage, not just for the data scientists. Most of the softwares being used for ML are open sources, thus affecting the market of other softwares making sources. Tools like Apache Spark are going to dominate the market. Read more about it on: http://www.infoworld.com/article/3017251/data-science/what-machine-learning-will-gain-in-2016.html

A Series Of Tech Predictions

We've been thinking about the Internet of Things all wrong. According to various predictions by various companies, there were various statements specifying volume and amount of money, number of connections. These are just numbers, Numbers, more numbers. If we believe in the predictions, there is no way that current analytical solutions can manage that level of information. In the immediate future artificial intelligence capabilities are required. Which means all companies who have an analytics platform play will have to invest in A.I. research, acquire and finally emerge with solutions based on methods beyond machine learning. Or risk being left behind. If this sounds vaguely familiar, it's because right now all efforts are pointing towards machine learning and algorithms as the goal for analytics. To read more visit on: http://www.forbes.com/sites/theopriestley/2015/12/08/a-series-of-unfortunate-tech-predictions-artificial-intelligence-and-iot-are-inseparable/#39f25ec8523a1253d985523a

Cybersecurity Risk to Machine Learning Algorithms

Cybersecurity is a very important and is becoming one of the biggest worries for companies. According to various surveys, companies are investing a lot of money in cyber security and training their employees in it. It is estimated that till 2020 investment in the cybersecurity will be around $170 billion. In today’s world as the data is rising, so the risks on it are also increasing. The pattern classification systems that machine-learning algorithm rely on themselves exhibit vulnerabilities that can be exploited by hackers. As we know machine learning algorithms train themselves with the training data set so it may be manipulated according to the needs as hacker wants. For example, Search-engine-optimization algorithm was trained and manipulate website content to boost results in the search ranking or senders of junk emails try to fool spam-filtering algorithm. Even the results of the public election can also be affected by 20% or more as it is found that the order in which candidates appear in search results can have significant impact on perception. To read more about Cybersecurity risks, follow the article by Dr. Kira Radinsky (CTO and Co-founder of SalesPredict) at: http://blog.kiraradinsky.com/author/kiraradinsky/

Things that AI can do better than we do

When we talk about Artificial intelligence, we always come across the question,' will there ever be possible that machine replaces humans and preforming better than us?' The answer is partially 'yes', as at least in many things machines are performing as unchallenged champions of creativity and intelligence. Areas where artificial intelligence already performing better than humans are. 

#Search the web quicker. Machine learning AI helps in web engine optimization through understanding the meaning of words and phrases, and can therefore guess what should be in the page ranking in never seen before searches. 

#Work in deadly environments. Robots can survive in conditions where humans can’t like deep space, the oceans penthouse, or inside a radioactive reactor. 

#Get a PhD quickly. Few critics of AI argue that machines could never be creative, or curious, or discover anything of significance, but team at Tufts have proved the naysayers wrong. 

#Deliver a correct medical diagnosis. #Translate in many languages. 

Read more at: http://www.huffingtonpost.com/george-zarkadakis/5-things-ai-can-do-better_b_8906570.html?utm_hp_ref=technology&ir=Technology

Changing Life with Machine learning and artificial intelligence

Why to go far? If we see in our recent past artificial intelligence and machine learning were very exciting and dream topics among engineers and developers. But now machine learning has emerged as the ideal branch of big data and working as oxygen to concepts like artificial intelligence. Year 2015, was a year of massive market shifts. Machine learning (ML) and its superset artificial intelligence (AI) where computer receives new information and learn without supervision have played very important and revolutionary role for the shift. Still Machine Learning has much more in the store. Year 2016 is going be a big year for machine learning. Usually, computers have been used to enhance the ability to carry out tasks. Users see this with features like autocomplete and spell check. In the upcoming year these leaps are likely to be made on three fronts: natural language processing, personalization, and security.  To know more about machine learning follow the article written by Motti Nisani(author) at: - http://www.geektime.com/2015/12/27/2015s-big-leap-into-machine-learning/

The most common data science skills

As the field of Data Science is growing, the confusion regarding the skills needed to be a data scientist is also increasing. Most of us think data science skills range from computer science and statistics, to machine learning and strong communication. But, the top data science skills list includes data analysis at the top, followed by others like R, Python and machine learning. As per recruiter lists, R, Python, SQL, SAS and Hadoop are appreciated. To know more about data science skills, follow the article written by Daniel Levine (Content Marketer for RJMetrics) at: http://www.smartdatacollective.com/daniellevine/366486/top-20-data-science-skills