Technewsky: Big Data

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    • Monday, 23 July 2018

    Key Predictions for AI, Big Data, and Analytics in 2018

    03:50:00 0
    Key Predictions for AI, Big Data, and Analytics in 2018

    Latest research report, expectation in 2018: The important for AI is over, believe that in 2018 enterprises will mostly jump beyond the marketing to identify that AI needs hard work- planning, building and controlling it right.

    Some new agencies also announcement and improvements: Better mankind and machine association due to improved interfaces; boosting business intelligence and optimize service by moving reference to the cloud new AI portability assisting the redesign of investigation and data management plays and activities and working the quickly of the insights-as-a-service market.



    As an output 2/3 enterprise predict to involve AI over the next 12 months, up from only accept 40% in 2016 when to increase 11% more 51% in 2017. Still AI ratio to increase in future.

    25% businesses will accessory point and click counter with informal parameters.

    Professional data using natural language and providing desire output in real time will become useful features of logical applications.

    20% of business will build AI to create decisions and offer real time command.

    AI will recommend what to provide consumers, suggest conditions to put delivers and order employees on what to say and do – in real time.

    The huge global analysis answering at business with more than huge data of unstructured information or data has two time since 2016. However, because classic pattern text analysis technologies are so difficult, only 32% of organizations have successfully optimized text data and even less are fetching other unstructured resources. This is about to modify as deep learning has created optimizing this kind of data more error free and scan.

    33% of businesses will put their data lakes off life manage.

    Not under stable connection to change the enterprise outcomes, in past acceptable will pull the collecting plug on their data lakes to show if they fill for themselves.

    50% of business will accept a cloud-first technique for big data performance.

    We have holding a public cloud first rules in 2018 for data, big data and analytics, as they look for more access over costs and more portability than on premise software can output.

    66% of business owner will build perception centers of quality as a stuff for organizational adjustment.

    With companies delivering the voice the consumer into every business support in a cooperative way, 56% of business already report developing customer perception centers of excellence rather them centric or mostly contributed models to fulfill this.

    The mostly of primary data optimizer will move from national defense to crime.


    Business related will execute opportunities to innovate with data, either through analytics embedded in internal business procedure or through latest data embedded technologies and services. In 2018, more than 50% of will report the Managing director up from 34% in 2016 and 40% in 2017.

    Data expert will become the latest new job title.

    13% of data-oriented hiring job on online hiring sites are for software engineers, versus less than 1% for data scientists, consider the trend of big data enterprise becoming goal complicate and the require to offer broader support to the business investigator.

    The insights-as-a-service trend will double as insight followers boost traction.
    66% of business already outsource between 11% and 75% of their BI apps.



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    Wednesday, 11 April 2018

    Why Artificial Intelligence Must Need Startup for a Business?

    08:21:00 0
    Why Artificial Intelligence Must Need Startup for a Business?

    AI may be providing the business network by storm, but crucial decision parson are still trying to   aware the revolutionizing new technology. Here are 5 main things every business decision marking want to know right now.

    Data available before AI:  Understand the difference between training and inference.

    Training an AI structure can be likened to a child learning a language.  Most children learn language from innumerable hours of attending to their parents and everyone else around them talk. They devour massive numbers of “data” over time and regularly learn the language.  Working on the time, the inputs, and the child’s attempt and aptitude, certain language proficiencies are achieved.

    Like a child, the AI model requires an objective. Then, it is built by uncovering the model to training data that reinforces the objective. With the right kind of data and volume of data, (likely “Big Data”), an enough amount of time and processing power, a definite level of proficiency (accuracy) can be achieved. Only then, like a child who learned how to speak and understand a particular language can they then utilize this knowledge to act and play.  Then the AI procedure can be used as segment of a business portability or process.



    Once trained, an AI model and the work pattern that it powers, can then be used in production, i.e. leveraged as part of a new or top product or service (i.e. think Alexa or Siri), a better consumer communication (think chatbots), or a latest or better way of doing business (think internet of things and predictive maintenance). This is called “inference”. There are numerous pre-trained models and systems that can be leveraged, acquired, or purchased. And if you can looking a model that significantly meets your requires, the secondary phase is already completed. So, if you buy a self-driving car, that car is always using “inference” to create driving decisions and take plans. But, someone else instruct that AI system to do that.

    The issue for a business is that their partitions may available from combining data from outside sources with data which is enjoy by the business itself. In include, the objective connected with the business value that the business can create may be various from that of a pre-trained system. There is active research around “transfer learning”, i.e. applying the training performed in one scenario to a various scenario. However, anyhow of whether move learning can be applied, a business is simply creating unique products and services, customer communications, and ways of doing business, and own unique data. So, “training” is wanted before “inference” and it all debut with data.

    AI is about trusted Relationships, not Functional Relationships, Executives want to comprehend Accuracy to generate business decisions around AI

    As the story goes, when Newton discussed an apple falling from a tree, he built a functional relationship between force, mass, and acceleration due to gravity. With this relationship, many things can be “resulted” from that method. AI does not run that way. In this portfolio, with AI, numbers or millions of different kind of things would be observed falling from different types of things and from different heights. Depend on “seeing” all that haven (being trained) the AI system could then make forecast (inference) about how other things will down. The AI system is numeric data. Individually, it will get some things right and some things wrong. The question that subscribe is how “accurate” is the AI system in making that forecast?

    In business systems, the more crucial question is how accurate does the AI system want to be? In the current state of AI, it seems, that with more and more data and calculating power, training and AI system can become more accurate. But, more and more data and computing power, and more functional skills to “wrangle” that data together at expensive price. So, a business expert needs to aware that not only does the objective need to be clearly tested, and the data needed to train to that objective identified, the mandatory accuracy needs to be defined.

    For quick a business creating a fun system depend on AI (i.e. identify dogs and cats) may not want to be that exact. However, a system used by consider to control a sign in a dog should be more accurate. A self-driving car that can calculate the difference between a dog and or some other spaces in the roadmap, requires to be extremely accurate. The accuracy wanted for an individual business system straight relates to data, training time, effort and computing power needed and so is a critical part of business cases.

    The calculation is not direct rocket science, the exact is. Business executives want to understand that AI success is more and more about the AI atmosphere used to innovate.

    As announced above, AI needs to learn (be trained). There is a lot of research going on around learning from smaller data sets. This is because successful AI today offers required accuracies by training on very large data-sets. And until this research bears fruit, successful AI training takes lots and lots of data and to train in reasonable time periods and takes significant compute resources.

    Today, AI bottlenecks trend to goal on absence of skilled “Data Scientists”, huge amount of students are choosing courses around Machine Learning (the data science part of AI) and even more are considering the MOOCs around AI like the not paid version for course fast.ai or Google’s free impact course on Machine Learning. There is also NVIDIA's Deep Learning Institute which offers online courses that provide you manage to a completely configured GPU-accelerated workstation in the cloud, complete with software tools, neural networks, and datasets.

    Finally, more key tools are coming out to make AI models, like Google’s TensorFlow and a lot of task is being put into AI platforms to automate much of the “data mugging” and “knob turning” that Data Scientists want to function. So, AI is becoming less about Rocket Science and more achievable and possible for more and more businesses. The clue that are not moving away right now are required for data and the want for framework to train on that data. So, business expert need to understand how they will have this Big Data/AI infrastructure or platform to grip so that AI innovation is realized within their business.

    The future is uncertain, but AI use case opportunities for business are understand today. Business chief need to understand the AI opening and issues individual to their business without being vague.

    There are a lot of creepy doomsday outline corresponding with talk of robots and Skynet around AI. This talk guides to Executives being distracted about the very interesting competition and awesome opportunities AI presents today.

    First, about the “scary” future. Today, the sizeable AI Machine Learning “Deep Neural Nets” have tens of millions of sensory cell. The latest research on a human brain says we have about eighty six billion neurons with one hundred and fifty trillion connections. AI systems are just not there yet. In twenty or fifty years, that may modify.  A lot of analysis also think the current state of the way AI is taught will reach a brick wall. I.e. the research postulated that the way we are doing AI today is not the way the smart works. So not only are we at orders of magnitude less in scale and having challenging at that scale, the brain probably does things much various and better than the way we currently train AI networks.

    For business executives forwarding to improve consumer interactions, supply new method of doing business and delivering new products and services, AI is believed to be a must have. Today, there are real opportunities and problems for which business executives can leverage AI.  AI is getting better and better at computer vision (i.e. used for self-driving cars), natural language processing (i.e. for language translation or processing unstructured text), and speech recognition (i.e. think Siri or Alexa). Business want to innovate and AI provides that innovation opportunity that they or their competitors will leverage. They need to understand what they can detect, classify, segment, predict, or recommend that will allow them to create new ways of doing business, new customer interactions, and new or improved products and services.

    In addition, they need to understand the real risks about using AI today. That risk is centered on the quality of training data. Is that data faulty, biased, have errors or have other problems? A model develop with some data will then be mismatched, partisan, error prone, etc… Business Executives want to put training data very seriously. If they are leveraging pre-trained figures, how do they know what that model was trained on? If they are training AI, is the data they are using appropriate? It is one thing to amaze a dog or a cat because of bad training data, but an entirely various thing to treat a customer inappropriately because of improper data. Proper data access must be applied for all AI initiatives.

    If AI is to put Machine Learning and data to use for business benefit, Business Executives want to be directly and strongly involved.

    Right now, too often, whether it is because of time, lack of mastery, or not trusting that AI is consistent to their business, most executives are not strongly involved in AI genius within their business. They “outsource” this project to technical teams. Business Executives authority are perfectly around the success of their business. AI is not about expert research, it is about affecting data and machine learning to drive business success. Without business leadership, AI success in business will only be random and limited. Active and indeed proactive involvement of business leadership is critical. And that is why business executives want to understand the 5 things covered in this blog about AI right now.
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    Tuesday, 2 January 2018

    In 2018 - Big Data & Artificial Intelligence Impact of Sciences Sector

    03:03:00 0
    In 2018 - Big Data & Artificial Intelligence Impact of Sciences Sector
    Most of various IT industries with financial services have had a long track record of updating information and applying analytics to development consumer relationships and building new services, life sciences companies have only in current years start to fully embrace and grip upon the opportunities to manage and apply their data in a methodical way to a range of drug development and patient care problems

    As real science companies’ start to strongly mature their use of data, remarkable progress is starting made in the efficiency of software development and the standard of insights produced at the research stage. However, given the increasing size of learning about human resource and another processes, the opening to deploy data for even larger boost also continue to increase.



    Here are 5 major ways we see Big Data and AI impacting the Life Sciences in 2018:

    1. As both President Trump and Alex Azar, his applicant to execute the Department of Health and Human Services, have made clear, we can await the environment in the US to be progressively hostile to high drug prices.  It will, therefore, be require for life science companies to defend their research forecasts and their benefits margins by utilizing robust data that clearly demonstrate the value of their products.


    The opportunity to adequately coordinate data from the real world (e.g. medical insurance claims), genomic research and clinical behinds will offer real sciences companies to unlock answers to a host of standard  questions such as the true success of treatments which can then be used to defend pricing location in this increasingly tough market environment.

    2. Improving the speed and quality of bi-directional learning between the patient and the cure discovery procedure has been a basic strategy for life sciences companies in the last few years.  However, their ability to do this definitely has been hobble by poor data access and data quality issues.  As best practice in data strategy (including governance and architecture) advance to move through the industry we can expect the value unlocked by such translational medicine to accelerate.

    3. As risks and inability continue to dog many real science supply chains world, the employment of new technologies such as blockchain allows the likely to thoroughly advance levels of control and quality amount whilst at the same time reducing overall costs for infrastructure.

    4. As new branches of science deepen our knowledge of genomics and the broader implications of epigenomics, opportunities for utilizing AI to gain previously impenetrable insights are emerging over the horizon.  Although still very much at the research stage, indications are that these techniques will increasingly impact fields such as oncology.

    5. With all the different fields of study opening up beyond genomics and epigenetics (proteomics, metabolomics, transcriptomics, et al), it’s important to remember the wise words of Prof John Quackenbush of the Dana-Farber Cancer Institute, “At the end of the day, the most important ‘omics of them all is econ-omics.”  Accessing and analyzing the right data to deliver sustainable business value remains the central purpose for life sciences firms.

    We have known for a long time how a single drug can impact parts of the population in various ways.  With hospital Electronic Medical Records (EMRs) offering an progressively completing view of each patient, the ways in which, quiet issues notwithstanding, researchers can boost insights from this data into how their therapies are operating at a more granular level will be increasingly important to the conduct of life sciences companies as they fine tune the delivery and costing of medication to where it is most efficient for patient require and the corporate bottom line.  Opportunities for beneficial learning will accelerate further as EMRs and clinical trial technologies become increasingly integrated as we move beyond 2018.


    Whatever the coming year holds, one thing is beyond doubt:  Exciting new ways to create value and improve patient care await those firms willing to exploit the data tools and techniques that are now emerging.
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