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/

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/

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/

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/

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/

Budding businesses often uses personalization processes that relies on accumulating customer data from third-party sources but that era is coming to a close. CRM systems can be significant sources of vision that empowers and enable companies to see their data from all angles but at the same time can be difficult to use and maintain. Few tips to convert CRM systems from potentially confusing resource into a powerful weapon are as follows:-

·         Tell the software what to streamline-

CRM automation is better than the manual processes in the sense that it can remove the obstacles involving tedious and time consuming works, by importing data, making smarter schedules and compiling related data.

·         Learn to spot at-risk accounts-

CRM software is the ideal instrument to track customer engagement as it allows employees to help clients when things look bad. For an example, it can set up the flags for different triggers, such as repeat complaints. The software can alert sales representatives when customers set off one of the triggers.

·         Use Robots to make CRM adoption-

Virtual assistants such as chatbots, powered by artificial intelligence, can network with CRM systems to set appointments, add client information to files, and locate information without forcing users to sieve through data manually. 

 Utilized efficiently and correctly, a CRM system can transform from an equipped essential into a competitive advantage.

Read more at: https://www.entrepreneur.com/article/312994

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/

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/

Global connectivity throughout many dimensions has provided an opportunity to make momentous developments in financial and technological inclusion. While blockchain and bitcoins were on top of the headlines in 2017, social and mobile payments have fundamentally changed the financial markets. New electronic trading venues are now being introduced to fixed income trading. ‘FinTech’ refers to these processes, products or business model and new applications in the financial services industry that are provided as an end-to-end process via the Internet and are composed of one or more complementary financial services. At Renaissance we utilize the available resources with the new innovation and technology in order to compete with the traditional financial institutions and intermediaries in the distribution of financial services. The key services provided are of distribution channels, transparency and accountability, financial capability, expanding rural capability and so on. Fintech will also likely to become greener in 2018 in the sense that there will be now a financial incentive for investments into quantum computing which has the potential to greatly reduce the estimated amount of electricity consumed by all of the current computers processing bitcoin. This is possible with the help of carbon footprint of the cryptocurrencies. 

Read more at: http://fortune.com/2017/12/26/4-technology-trends-2018/

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/

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/

This is an era where all of us are tech savvy and are connected to each other through social platforms linked through smart devices. The new generation expects real time response to their queries with personalized feelings and suggestions from the diverse brands they trust and engage with. Business enterprises are now resorting to chatbots or virtual agents that can manage consumers’ queries without any delay. A Chatbot or the “Chatter Robot” is a platform that imitates human conversations including text or spoken language by using artificial intelligence techniques such as Natural Language Processing (NLP), image, audio and video processing. It’s ability to network using a simple interface and engage with customers in a more natural and friendly manner has given itself a cutting edge across dimensions. Although chatbots and apps are intermingled with each other, they are different in the sense that the interactions with the bot take place sequentially (as a conversation), and the bot is used inside a chat app. Moreover chatbots have an identity of its own which is separate from its interaction with the user. 

Read more at : https://www.happiestminds.com/Insights/chatbots/

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

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

IoT and artificial intelligence are two of the buzzwords recently. According to IDC, spending of IoT is expected to exceed $ 1.4 trillion by 2021. And more the IoT means more the data and as the amount of data increases it becomes difficult to manage and analyze the data. Here, AI acts as a reliever. AI can help find patterns and analyze those huge stacks of data. So, overall, AI and IoT are considered common friends that have enough potential to build a bright smart future of upcoming technological world.

Read more at : https://www.informationweek.com/big-data/ai-machine-learning/ai-and-iot-techs-new-bffs/a/d-id/1332108

Enterprise level organizations should try to incorporate blockchain into their system. At the same time they should not just get blockchain just for the trend, but it should be done with a strong sight towards companies’ scope. Blockchain can benefit these enterprises by simplifying the third party transactions and overseas value transfers. Blockchain can also provide incredible advancement to analytics systems which will inflate the data-input quality and insight outputs. 

With many companies still stuck to take advantage of data, analytics has to be number one question because this is a key stage to implement AI successfully. There is a sequence of evolution in analytics, starting from descriptive to prescriptive. 

Nowadays, organizations tend to skip traditional analytics and shift into AI. Many enterprises use the descriptive analytics, applying BI techniques: combine all your data to get a quick review on what’s going on in the company. 

Without the insight that analytics brings, it will be hard to assess the outcome of any artificial intelligence system. Analytics keeps AI transparent, responsible and may help increase the productivity of AI systems. Unproductive information control leads to the unnecessary operational costs. AI analytics helps to find out cost savings and prepares a report with the help of main ROI metrics to keep up productive decision making.

From food to clothing, to real estate, and to everything; now AI and machine learning is everywhere. They can recognize foods, value of real estates, things in your images and many more. They are really good at whatever it does. There have been varieties in application of AI and it will be interesting to integrate all those stuffs into one general intelligence.

CES 2018 showed the present scenario of the robot industry. The best of the companies presented the best of their futuristic gadgets, mostly smart home integration. The current progress is ready steady and mesmerizing but we are still about a decade behind having a real human like butler, or in better words a real human like robot friend sitting next to us. 

Read more at: https://www.popsci.com/personal-robot-butlers-ces-2018

Classification of data is very important in many organizations. They can be used to make decisions. But the task of classification can be very tedious. Now imagine a machine doing this job. Classification using machine learning is with the help of supervised learning approach and algorithms. Machine learns from the data input given to it and with the help of this learning, it classifies new observation.

For example, we want to check number of male and female members in an organization. Here we can train our machine to do this classification. 

Classification using machine learning is one of the trending technologies being used in various fields. It has many applications in many domains other than IT.

Various algorithms can be used to implement classification. There are two types of learners in classification – 
Lazy Learners - which simply store the training data and wait until a testing data appears. They classify the data based on most related data.
Eager Learners – that construct a classification model based on given training data.

Different classification algorithms are – Decision Tree, Naive Bayes, Artificial Neural Networks, K-nearest neighbor.

Read more about them and various evolution methods at https://towardsdatascience.com/machine-learning-classifiers-a5cc4e1b0623