According to authors, AI and Machine Learning algorithms, introduces us to an era of intelligent conversational environments , by  bringing about a paradigm shift in technologies and strategies ,thereby collaborating the rate of progress in business and process for automation, thus making an enterprise more smarter ,flexible and conversational. AI mainly focuses on making support systems more efficient across a multitude of applications, processes and business domains, bringing about intelligent collaboration capabilities to the emerging Conversational Workspace platforms, improving workflows, automating repetitive tasks and initiating automated interactions via voice assistance ,thereby, helping an organization to take better decisions and grow. Read more at https://aitrends.com/speech-recognition/idg-contributor-network-the-growing-impact-of-artificial-intelligence-on-workplace-collaboration/

In recent times, innovation of machine learning and artificial intelligence has changed the paradigm of the tech world. Some trends prominent in the development of machine learning are: (i). Big companies are developing machine learning based artificial intelligence systems and it will continue to grow, (ii). Along with data, with the development of machine learning, algorithm economy is expected to develop more, which will distinguish small business from the large ones, (iii). Interaction between machine and humans is expected to increase. In conclusion, machine learning and artificial intelligence is expected to grow but humans can’t be completely replaced right now. Read more at: http://www.datasciencecentral.com/profiles/blogs/trends-shaping-machine-learning-in-2017

Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.

Machine Learning algorithms are classified as –
1) Supervised Machine Learning Algorithms
2) Unsupervised Machine Learning Algorithms
3) Reinforcement Machine Learning Algorithms

Top 10 Machine Learning Algorithms --

1)    Naïve Bayes Classifier Algorithm
2)    K Means Clustering Algorithm
3)    Support Vector Machine Algorithm
4)    Apriori Algorithm
5)    Linear Regression
6)    Logistic Regression
7)    Artificial Neural Networks
8)    Random Forests
9)    Decision Trees
10)  Nearest Neighbours

To know more: https://www.dezyre.com/article/top-10-machine-learning-algorithms/202

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/

If we talk about the recent past, machine learning was only used by a few who understood the algorithms and had access to very huge amount of data on which to employ it. But with the evolution of big data technology becoming a commodity and algorithms easier to use, machine learning has moved out of the hands of the few to the hands of the citizen developers and regular users. Four key steps taken in 2015 for the development of the machine learning are:-

# learning became easier to use.

# Everyone and their brother released a machine learning library or toolkit. 

#Big data to feed machine learning also became cheaper and easier.

#The label “machine learning” was applied to way too many items.

Read more at: http://www.infoworld.com/article/3017250/application-development/4-great-leaps-machine-learning-made-in-2015.html

Earlier we have seen that CRM required manual input to fill in notes from sales activities and collect information about prospect behavior. But, nowadays, this is done with the help of technology and machine learning is one such technology. Machine learning is helping sales teams to fill in the notes. But, first we must know what machine learning is. It refers to a computerized data analysis where algorithms learn from new information and quickly decide what the next best action is. Web search results, credit scoring and email spam filtering are powered by machine learning algorithms nowadays. Such technologies are extending to CRM also. To know more, follow: http://it.toolbox.com/blogs/insidecrm/machine-learning-your-way-to-better-crm-69781