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Top 5 languages to learn for ML

The power of machine learning is growing exponentially. Almost no industry domain is remaining untouched with the wonders and powers of machine learning.  Machine learning is just an application of artificial intelligence whose algorithms helps to analyze the historic experience without being explicitly programmed to predict the future affairs. Before jumping into the world of machine learning, it’s important to know which languages are being used to analyze the data and predict the future. Here are those 5 languages which are being using for machine learning: 

1. Python

2. R Programming

3. LISP

4. Prolog

5. javaScript

why and how are these languages being used for machine learning? For detailed information,

https://www.analyticsinsight.net/top-5-machine-learning-programming-languages-you-should-master/

 

 

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Data management over cloud

Although cloud storage has benefited the businesses in intelligence enterprises but still it shouldn’t be trusted blindly. Cloud storage techniques have helped digital data storage and the changes are remarkable.

The 2 challenges that are connected with cloud storage, cloud lock-in and management complexity are:

• Cloud lock-in: Failure to transfer data from one cloud storage facility to other.

• Management complexity: The inability to perform proper management of available storage environments.

Following are the solutions to these problems that may be seen as unsolvable.

1. Gateway device: It behaves as an agent between the on-premise storage and the cloud storage.

2. Hybrid cloud: With the help of hybrid cloud, cloud storage acts as an extension to on-premise data storage.

3. Multicolored controller: It permits the data to be seen at the same time.

For more information go to,

https://www.analyticsinsight.net/how-to-solve-the-challenges-of-data-management-on-cloud/

 

 

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Economic productivity and effects of Artificial Intelligence

The economic productivity of the country should rise up if there are improvements in technology and artificial intelligence but this is not the case. When the industry employs AI then this lead to more leisure on people’s part and making them useless. According to a survey it has been found that there is a rise in inequality in wages and hence AI needs to be implemented. There should be greater productivity gains but it is dipping. The reason could be slow commercialization. Though it displaces labour but it creates jobs in other sectors. Automation does not replace jobs but it does a part of the jobs.

For more information visit:

https://analyticsindiamag.com/why-has-the-economic-productivity-not-picked-up-despite-the-advancements-in-ai/ 

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How revolution in cloud computing pushes surprising business growth?

Nowadays businesses have opened new gates for certain innovations using cloud computing technology with a proper and selective approach. Most of the IT executives are now entirely focused on how to make cloud a way to achieve their business goals and their entire focus is on cost optimization instead of cost management. In terms of transformation, the cloud has been a central to many organizations. Big data technology has allowed storing and recapturing of the vast amount of information. Regardless of any sector, most of the organizations have transferred all their data to the cloud. But still, most of the executive’s are reluctant to adopt cloud due to security concerns.

Businesses can achieve maximum growth only by accessing, controlling and analyzing all flaws present in the cloud network. That is why multi cloud is more preferred.

Due to digital transformation most of the companies have enforced cloud services to become more profitable.

For more information, go to:

https://www.analyticsinsight.net/how-innovation-in-cloud-computing-drives-exceptional-business-growth/

 

 

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The correct outlook to unite your organization with cloud computing

Nowadays for every organization it has became essential to be associated with cloud as a platform, infrastructure and service. Cloud computing is a very helpful tool as it can be used to create new revenue opportunities for the organization. This era of cloud computing has expanded the efficiency of the computing by reinforcing memory, processing, bandwidth and storage. If you haven’t dispersed your organization to the cloud yet, these can be the right footsteps to follow:

Step 1: create an assessment

Step 2: choose a right cloud environment for your business

Step 3: decide your cloud architecture

Step 4: choose the right cloud computing provider

Step 5: make a strategy for risk mitigation

Step 6: make a plan for mitigation  

Step 7: execute your computing plans

Step 8: examine the implementation

Need a better understanding of these steps?  visit:

https://www.analyticsinsight.net/the-right-approach-to-integrating-cloud-computing-into-your-organization/

 

 

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Revolution in the world of manufacturing with the merge of machine learning and 3D printing

Of course we have achieved 3D printing, but somehow we are still not able to produce a metal object which is capable of replacing the real world articles. Now implementing machine learning with 3D printing we have the capability to have real world objects replaced by objects produced by 3D printers. In the world of manufacturing researchers are planning to produce self correcting and repairing machines. There can be multiple approaches to have self-correcting machines. What are they? 

For more information, visit:

https://www.analyticsinsight.net/the-confluence-of-machine-learning-and-3d-printing-will-revolutionize-manufacturing/

 

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How AI can be used to forecast severe brain disorders?

Whenever it comes to complications, we all know human brain is the most unpredictable and complicated organ of the human body. Any brain injury leads to damage of millions of cells due to lack of oxygen in the body. Such damages require immediate attention of the doctors. But somehow, making out and analyzing those reports results to the latency which more often comes out as life threatening news for the patient.   

So, how AI can contribute its role here? For increasing the efficiency of the workflow some AI algorithms has been applied to the machine which are now capable of detecting the abnormalities requiring urgent attention of the doctor.

Want to know more about how actually AI and deep learning is applied to radiology? 

Go to:

https://www.analyticsinsight.net/how-artificial-intelligence-predicts-life-threatening-brain-disorders/

 

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Autism Diagnosis using Artificial Intelligence

Autism being a spectrum or developmental disorder characterized by lack of social skills and repetitive behavior is being diagnosed using several methods in order to identify the onset of this disease. One such method is used by Autism Diagnostic Observation Schedule which inspects videotapes on the basis of an assessment between an examiner and a child for understanding the child’s behavior. According to a paper published in Science Translational Machine by researchers from the University of North Carolina at Chapel Hill and Washington University School of Medicine, doctors could precisely forecast which child might develop autism before hitting 24 months and with a 96 percent accuracy rate. A fully cross-validated machine learning was developed which used the scans of the 6-month-old infants. 59 high risk brain scans were taken over 230 regions and the whole brain was mapped creating matrices of functional connectivity from each child’s MRI data.  The algorithm further analyzed the brain scans of the 6-month-old infants and it properly predicted 9 out of 11 infants had the symptoms of autism at 24 months, with a sensitivity of 81.8%. Such data-driven approach is a good indicator of predictive measure that suggests that AI and machine learning could someday possibly recognize diseases with accurateness and extend treatments for the mass and maybe halt the headway of the disorders themselves.

Read more at: https://www.analyticsinsight.net/artificial-intelligence-machine-learning-can-be-used-to-predict-autism-in-children/

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Big data in Google’s Multilingual Semantic Indexing

Google has been dominating the search engine industry over the years, though it has been frequently criticized of not providing search results in non-English languages. To cater to the problem, it has resorted to semantic indexing thereby becoming proficient at providing multilingual search results. The spectrum of search contents have been widening with time thus hinting at an expanding and trending macro environment. The search engines use algorithms which are solely based on Artificial Intelligence which would be rather simpler with limited pre-defined inputs. In its quest to understand the true meaning of different search queries, the algorithms are required to understand the contextual meaning behind various pairs of words which is attributable to deep learning. Despite capturing 70% of the search engine market globally, certain discrepancies arise due to regulatory policies. However, according to Shout Agency, the core problem is not the structure of algorithms as Google can make educated assumptions indexing any language but discrepancies in search results persist. The crux of the matter entirely stems from the fact that Google has had limited opportunities to conduct deep learning in some language than others. A potential risk is involved due to smaller user base and fewer Google employees that can understand the language enough to determine the worth of the content which lowers the chance of Google to conduct manual penalties for content. This could lead to greater pervasiveness of spun content throwing away algorithms dependent on deep learning.

Read more at:  https://www.smartdatacollective.com/google-search-algorithms-use-big-data-multilingual-latent-semantic-indexing/

 

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Artificial Intelligence lends to Agile Machine Learning

Of late, Agile methodologies have been taking root in data science boosting complex collaborations between data scientists and other developers. Agile can be easily ported over to Machine Learning and Artificial Intelligence domains due to its feedback-heavy, iterative nature and given that incessant improvement is an innate part of AI. Such methodologies are characterized by fast feedback loops and short development sprints. Agile projects, in distinction to old-school waterfall approaches, involve error correction and cyclical stakeholder input and primarily focuses on short term goals rather than the long-term view. AI researchers should think of research as an iterative, evolving process to remain receptive and adaptive as per Agile’s basic tenets. To ensure that projects do not grind to a halt, maintaining a buffer of solutions for implementation is a priority as data scientists work on multiple projects, each taking months to complete. The iterative nature of Agile well captures experimentation as a core part of AI and ML projects. Agile maximizes value throughout the development process.

Read More at: http://www.dataversity.net/case-agile-machine-learning/

 

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Artificial Intelligence makes way for intelligent healthcare.

Allowing various components of technology to come together seamlessly, Artificial Intelligence (AI) has been taking over the technology arena for some time. The pressing budget of UK’s National Health Services (NHS) has led to clinics being short-staffed and overworked. In the healthcare industry, AI uses algorithms to be tantamount to human cognition to analyze composite medical data. Institutions such as Massachusetts General Hospital, The Mayo Clinic, NHS have been applying AI programs to processes such as drug development, personalized medicine, treatment protocol development and patient monitoring. Use of AI may introduce certain type of risks such as algorithm bias, DNR implications and machine morality issues, though, research on the use of AI aims to validate its efficacy in improving patient results prior to its broader adoption. AI enables evaluation of current health of patients, access to more up-to-date research through NLP and identifies warning signs earlier in the care process thereby reducing risks of medical issues having long-term implications. Requiring a vast amount of data is indispensable for AI solutions to provide powerful insights. However, the obstacles faced in the use of AI must be overcome before a true change can take place.

Read More at: https://www.smartdatacollective.com/artificial-intelligence-healthcare-changing-industry/

 

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Big Data in Website Analytics

Organic traffic is used for visitors landing on the website as a result of organic search results. Recently, companies have been witnessing a drop in organic traffic. Prioritizing quantitative analysis over qualitative business assessment would lead a company on the road to success. Companies can manage to hit their KPIs by understanding current view generation trends with the help of close data-driven analysis of site traffic. A continually changing SEO rules have contributed to declining organic site traffic for many businesses transforming an advantage into a source of punishment for manipulative guest posting. Visibility and ranking are conflicting priorities with data being the determining factor of increasing or decreasing organic site activity. Social media data and visibility are among the other major factors contributing to website traffic. Despite searches not directing to the social pages, the best ranked sites are attributed with more social interactions. Target sites having the greatest degree of visibility are the matter of concern for ranking algorithms. Using big data in general and unique views coupled with first visits in particular, gives an organic traffic boost to businesses. Referral marketing boosts reputation and is effective at generating new businesses

Read more at: https://www.smartdatacollective.com/wheres-the-traffic-increasing-organic-leads-with-big-data/

 

 

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Cryptocurrencies: to be trusted or not.

Data awareness is a sensitive topic within the crypto community and fears regarding crypto will be disseminated by new efforts that educate people about data privacy and security.

The Information Commissioner’s Office (ICO) has started conducting a number of data awareness campaigns with the aim of building trust in cryptocurrencies. Data security breaches and privacy violations are the two most concerning issues underlying the faith in crypto market. Data privacy laws and EU’s Global Data Protection Requirement (GDPR) work to encourage people to the usage of cryptos and re-establish consumer confidence thus restoring faith in digitalization. Under the new data guidelines in the era of GDPR, ICOs are working to ensure compliance among the users of cryptocurrencies. Smaller businesses, despite having smaller budget, less brand equity and subject to draconian fines,  too garner trust and meet compliance targets. ICO Data Awareness efforts inevitably benefits the crypto industry.

Read More at: https://www.smartdatacollective.com/ico-data-awareness-campaigns-create-more-trust-cryptocurrency/

 

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Big Data in Big Insurance

Insurance companies, over the decades, have been overwhelmingly dependent on credit scores for judging customers’ credit worthiness. Analysis of credit scores are in practice long before big data acquired a firm foot in the consumer analytics industry. However, they have often been criticized of being biased against the most credit-worthy individual. Not neglecting the obvious imperfection of these credit score based actuarial algorithm models, to make nuanced decisions regarding the credit risks of their customers, insurers have resorted to using big data. There may be certain variables incorporated in credit scoring algorithms that overstate customer dependability. A person with a good credit score, even if he faces a couple of repayment defaults due to sudden financial breakdown, would have his current credit score unaltered. Several other reasons have made insurers skeptical of using credit rating in the era of big data. With the help of big data Insurers now recognize that credit based insurance policies have increased the risk of unjust racial profiling. Limitations and fallacies of credit-scoring are being continually exposed by analytics modeling compelling insurance actuaries to upend existing policies and have greater reliance on data-intensive approach.

Read More at: https://smartdatacollective.com/big-data-causing-insurance-actuaries-move-away-using-credit-scores/

 

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Personalization through Targeting

The sprouting digital age has created both opportunities and challenges when it comes to businesses with customers. On one hand, the outburst of new channels has opened up amazing new ways to involve the audiences. On the other hand, it challenges the business enterprises to cut the noise out and stand apart to build a more sophisticated, intuitive and personalized relationship with their customers. In this regard they have gone one step further to personalize information such as name, title, organization, purchase history etc. and utilizes interactive and real-time data to create highly appropriate communication that is relevant to the user. This is what is called the hyper-personalization. Such an action of creating messages that target and connect with a specific subset of the overall consumer audience leads to companies’ willfully abandoning broad reaching marketing messages and creating different campaigns for different groups of people. This matter revolves around the question “what people want” and it’s predicted that the recent decade will see e-commerce companies connecting their brand through hyper personalization. Some of the renowned brands like Starbucks, Amazon and Spotify have adopted hyper-personalization, where AI and machine learning analyze numerous factors to power their recommendation engine. 

Read more at: https://monk.webengage.com/hyper-personalization-marketing-future/

 

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Machine Perception: An era of Smart Robotics

Smart Robotics is becoming an evolving field in the area of artificial intelligence. Modern robots have become far more intelligent and adaptable in a continually evolving environment than their predecessors which is attributable to machine perception plays an indispensable role in their development when used in conjunction with more sophisticated machine learning steps.

Data scientists and AI engineers must overcome certain challenges to improve the future of robotics. According to Rewired, there are ways to improve machine perception to fortify smart robots. Treating machine perception algorithms as a passive system coupled with poorly thought on assumptions were the grave mistakes made by programmers. As a remedial procedure, learning as a proactive, multi-sensory process in being borne in mind.  Dependence on antiquated sensory systems have been replaced by new sensory systems to process inputs. According to Russell graves of CoSMoS Laboratory, improved positioning and quality of data forms the building blocks of modern machine perception. 

Read More at: https://www.smartdatacollective.com/vital-role-machine-perception-modern-smart-robotics/

 

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Multilingual Notebook

Although data science, artificial intelligence, or Web 2.0. are the new buzzword, Jupyter Notebooks are revolutionizing in the sense that it is an open-source web project, a piece of software that allows data scientists to create and share documents that contain live code, equations, visualizations and explanatory text. Originally called IPython Notebook, it’s built to write and share code and text, within the domain of a web page. Even though Jupyter has its roots in Python (it evolved from IPython Notebooks), it is now multi-lingual. In fact the name itself comes from 3 languages: Ju(lia)+Py(thon)+(e)R and  Jupyter kernels now extends to 80 more languages. They are very convenient in the senses that have become a standard for data analysis and scientific research, allows us to publish our narratives in many formats, from live online notebooks to slide decks; and it simplifies the problem of distributing employed software to coworkers and associates.

Read more at: https://www.oreilly.com/ideas/what-is-jupyter

 

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Overcoming Data Silos: The corporate’s challenge

Predictive Analytics, Artificial Intelligence, bots, data science – the waves of advances in data science keep on coming. Access to old data and not skill base or technology, turns out to be the biggest obstacle for powerful analysis insight which requires a tedious data preparation. Data Silos are something of a buzzword, a demon lurking in the enterprise which makes it prohibitively costly to extract data and makes company initiatives nearly impossible. Silos lead on to limited information, redundant data and interdepartmental inefficiencies. To make the data streamlined, accessible and impactful to the organization’s bottom-line, the development of silos must be mitigated in a progressive and pragmatic approach. Things aren’t as beautifully simple as the buzzword “data lake” might conjure. A combination of various methods including use of the right software, encouraging proactive communication, blurring departmental descriptions and roles coupled with the goal of integration at the background would lead to an integrated platform thus overcoming the problem of data silos. Focus on Wide Data Analytics and not only big data, stands indispensible to achieve a future state of mature analytical competency, however, silos aren’t entirely evil in the context of data management.

Read More at: https://smartdatacollective.com/how-to-eliminate-silos-in-company-wide-data-analytics/

 

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Deep Learning: A blessing or a curse for the tech industry

Deep Learning Networks (DNNs) are some of the most powerful deep learning algorithms constructed from multiple layers of linear and non-linear processing units. Neural Networks interpret sensory data through machine perception, labeling and clustering raw input.

The advent of synthetic data will overturn the competitive advantages of machine learning that powerful tech companies get by amassing visual data sets of images and videos. Synthetic data is computer generated data that burlesque real data; in other words data that is created by a computer. Many initial startups face the “cold start” problem where lack of quality labels make it difficult to train computer algorithms. To resolve this problem, data simulators, which are highly flexible and versatile, are being used to generate contextually relevant data in order to train algorithms. However since, big companies exponentially expand their initiatives to gather relevant data, they do not face the same challenge.

Data simulation will bring parity between big technology companies and startups. The major challenge facing the startups is to leverage the best visual data with correct labels to train computers accurately so that they can compete against functionaries with inherent data advantage.

Read More at: https://tcrn.ch/2KfIraQ

 

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Mobile Business Intelligence (BI); The Mobile Revolution

With the advent of smart phones, mobile BI generated attention which enabled distribution of critical business metrices, KPIs and data to remote workers. retailers, sales, marketers and small business owners keep a beat on the pulse of their business responsibilities using BI applications on mobiles. 

 In days of Symbian devices, accessing data on mobiles was cumbersome. Nowadays, mobile BI applications are accomplished either by accessing the application on mobile browser or a innate application designed for a specific mobile OS. Mobile BI is rapidly transmuting spaces in the software industry. Independent researches divulge high expectation for the growth of mobile BI. Mobile BI is one part of the BI puzzle. Given that BI is about making gainful decisions analyzing the right data, mobile BI enables access to the data by all including the remote employees. Mobile access to BI data enables a ‘game-like’ experience thus allowing businesses to remain nimble and intelligent.

Read More at: https://business2community.com/business-intelligence/is-your-bi-tool-designed-for-mobile-how-to-tell-why-it-matters-01266138

 

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