My inbox is flagged with "1" unread message, Even when I empty my inbox folder, this bold "1" stays next to my folder, new as it contains a new message.

I've tried to "empty" it, to "clean" it, to "mark all as read" it. Nothing works.

EDIT: Works with Outlook 2016 as well

DISCUSSION

• What exactly is the definition of the term "Data Science"?
• • One possible definition is that "data science is a multifaceted discipline, which encompasses machine learning and other analytic processes, statistics and related branches of mathematics, increasingly borrows from high performance scientific computing, all in order to ultimately extract insight from data and use this new-found information to tell stories."
    At a high level, "data science is the study of the generalizable extraction of knowledge from data." It's a combination of multiple disciplines that have been around for decades.
    Data Science Association's Professional Code of Conduct states that a "Data Scientist means a professional who uses scientific methods to liberate and create meaning from raw data".
  • What's a typical Data Science process?
  • 
  • • In science, one starts with a hypothesis, conducts experiments and makes observations to either prove or disprove the hypothesis. In Data Science, the scientific process is similar except that the use of data and algorithms becomes central to the process.
      The process starts with an interesting question, often aligned to business goals. Available data is then cleaned and filtered. This may also involve collecting new data as relevant to the question. Data is analyzed to discover patterns and outliers. A model is built and validated, often using machine learning algorithms. Model is often refined in an iterative manner. The final step is to communicate the results. The results may inspire the data scientist to ask and investigate further questions.
    • What's a typical Data Science pipeline?
    • 
    • • The data science pipeline may be treated as that part of the data science process that deals specifically with data. It starts with the gathering of raw data, processing it, analyzing it via algorithms and finally visualizing the results. Thus, the pipeline basically transforms data into useful insights.
        An important aspect of this pipeline is data engineering. It can be broken down into three steps (not standardized):
        • Data Wrangling: Raw data is cast to a form suitable for analysis. This could involve combining multiple datasets, removing inconsistencies, converting datasets to a common format, etc.
        • Data Cleansing: Real-world data is messy with missing values, bad delimiters or inconsistent records. Cleansing ensures and even repairs data to syntactic and semantic correctness. Data points could be dropped if they cannot be repaired.
        • Data Preparation: This makes the data suitable as input to algorithms. This may involve range normalization, conversion of categorical data to numerical values, etc.
        When building ML models, the typical approach is to partition available data into training and testing datasets. The former is used for learning and the latter is used for validation.
      • Could you give some examples of questions that data science answers?
        Here are a few examples: Will this tire fail in the next 1000 miles? Is this bank transaction normal? What will be my sales for the next quarter? What sort of customers are not coming back to my store? Which printer models are failing the same way? As a self-driving car, should I now slow down or brake completely?
        In the real world, data science has been successfully used by LinkedIn to increase growth in user connections. Google uses it in a number of products. GE uses it to optimize service contracts. Netflix uses it to improve movie recommendation. Kaplan uses it to uncover effective learning strategies. All these are a result of data scientists asking the right questions.
      • How is a data scientist different from a data analyst/architect/engineer?
      • 
      • • A data scientist is multidisciplinary in terms of skills and expertise. She may embody some of the other related roles:
          • Data Analyst: Collects relevant data, visualizes data with various tools and tries to find patterns and insights. Knows basic statistics. Has business/domain knowledge. Probably doesn't deal with big data.
          • Data Architect: Architects a system to manage big data. Often this role is embodied within a data engineer since tools and technologies overlap. This role becomes redundant with MLaaS (Machine Learning as a Service).
          • Data Engineer: Develops and manages infrastructure that deals with big data. Well versed with tools such as Hadoop, NoSQL and MapReduce. Sets up data pipelines.
          Where a data scientist stands out is in her use of ML algorithms, which requires both statistics and computational skills. A data scientist augments these skills with her ability to deal with large datasets and domain knowledge.
        • Should a data scientist start with a problem statement or explore available data?
          If you're new to data science, without much domain knowledge, defining a problem statement can be difficult. In such a case, you could start with exploratory analysis. This can then guide you towards asking the right questions.
          Given enough data, exploration is likely to yield patterns and correlations. These could even occur due to measurement errors or data processing artifacts. But are these findings relevant? Asking the right questions and defining a problem statement will give better focus. Some even claim that lack of a problem definition could lead to disaster because you don't know what you're looking for.
        • What skills must a data scientist have?
        • 
        • • Data scientists are required to be multidisciplinary with knowledge and expertise in statistics, programming, application domain, analytics and communication. However, unicorns who have all of these are rare.
            Often specializing in a couple of areas with exposure to others is desirable.Companies don't rely on a single all-knowing data scientist; they form a data science team. A data science team may include the Chief Data Officer, business analyst, data analyst, data scientist, data architect, data engineer and application engineer. Some of these may be combined in a single person. For example, a single person may fulfil the roles of data architect and data engineer.
            Anyone with strong data and computational abilities can do well as a data scientist. An essential skill is to turn unstructured data into a form suitable for analysis. This is not something that a traditional quantitative analyst can do.
            Technical skills must be complemented with business acumen, creativity and reasoning. This will help a data scientist ask relevant questions, assess the suitability of available data and present results the right way.
          • Should a data scientist learn cloud computing?
            Data science workflows typically happen on a local computer. There are however scenarios where cloud computing makes sense. The dataset could be too large for local memory; or local computational capability is insufficient for the problem; or the workflow's output feeds into a larger production environment.
            When dealing with large datasets, a data scientist needs to get familiar with cloud technologies, platforms and tools. This may include storage, running database queries and managing Apache Spark clusters.
          • As a beginner, what should be my learning path to become a data scientist?
            One approach is to be practical and hands-on from the outset. Pick a topic in which you're passionate and curious. Research available datasets. Tweet and discuss so that you get clarity. Start coding. Explore. Analyze. Build data pipelines for large datasets. Communicate your results. Repeat this with other datasets and build a public portfolio. Along the way, pick up all the skills you need.
            You may instead prefer a more formal approach. You can learn the basics of languages such as R and Python. Follow this with additional packages/libraries particular to data science: (R) dplyr, ggplot2; (Python) NumPy, Pandas, matplotlib. Get introduced to statistics. From this foundation, start your journey into Machine Learning. To relate these to business goals, some recommend the book Data Science for Business by Provost and Fawcett. But you should put all this knowledge into practice by taking up projects with datasets and problems that interest you.
            At the University of Wisconsin, statistics is covered first before programming. To become inter-disciplinary, you may choose to learn aspects of data engineering (data warehousing, Big Data) and ethics.
          • Could you give some tips for a budding data scientist?
            The following tips might help:
            • Data science is about answering scientific questions with the help of data. Don't focus just on the aspect of handling data, dataset size or the tools.
            • Understand the business, its products, customers and strategies. This will help you ask the right questions. Have constant interaction with business counterparts. Communicate with them in a language they can understand.
            • Consider alternative approaches before selecting one that suits the problem. Likewise, select a suitable metric. Sometimes derived metrics may yield better prediction compared to available metrics.
            • Understand the pros and cons of various ML algorithms before selecting one for your problem.
            • Find a compromise between speed and perfection. On-time delivery should be preferred over extreme accuracy.
            • Useful data is more important than lots of data. Use multiple data sources to better understand data and its discrepancies.
            • Be connected with the data science community, be it via blogs, meetups, conferences or hackathons.
            • Practice with open datasets. Learn from the solutions of others.

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Each of these technologies have immense potential and are rightly getting all the attention. However, if we take these technologies together, we are taking about an explosive multiplier effect. I can already see a potent trinity of IoT, Blockchain and AI taking shape in coming years.

My earlier article on Business Models for IoT got several comments from the readers about security, trust and smart decisions. A few comments were also about the true differentiation of IoT from other enterprise-wide large IT implementations. I had written the the IoT business model article two years back when the Blockchain technology was still in infancy (to be honest, I did not have much idea about it then). Now, I can clearly see the benefits of using Blockchain technology along with IoT platform.

What is Blockchain? Blockchain is a digitized, decentralized ledger of all transactions. The transactions are replicated across multiple computers and linked to each other to make any tempering with records virtually impossible. This immutable way of managing records eliminate the need for any central entity managing the transactions. Blockchain should not be confused with the bitcoins. Blockchain is the platform while bitcoin is just one of the many applications that uses Blockchain platform just the way internet is to email. Click here quick tutorial on Blockchain.

The usage of IoT with Blockchain is already gaining momentum. IBM’s Watson IoT platform integrates well with Blockchain (backed by Hyperledger Fabric) effectively combining the two technologies. Similarly, Azure IoT also has good integration with Ethereum, Corda and Hyperledger Fabric. Below are some of the examples of using Blockchain in conjunction with IoT :

The Blockchain technology is still in infancy with a few drawbacks that would take some time to resolve but the technology is here is to stay. The biggest drawback is the speed of transactions. Currently, transactions throughput in Blockchains is very limited. As a result, the costs are still high and the full benefit of removing the middleman is yet to be realized. Efforts are underway to increase the transactions throughput.

In earlier times, codes were written and machines would perform as per the software code. The machines brought productivity gains by making processes efficient but failed to do things that they were not coded for. Humans know more than what they can train others and for this very reason machines could never compete with humans in the past. However, now with artificial intelligence and deep learning, it is possible to train machines by making them experience different situations. The algorithms get better with time. This is a revolution and can be effectively used in IoT. IoT means a lot of data. With help of AI, IoT platforms can get the ability to recognize meaningful patterns buried in mountains of data and take decisions difficult even for humans.

IoT platforms are already using machine language which is a sub set of Artificial Intelligence. With full fledged Artificial Intelligence, IoT platforms would be akin to being on steroids. To achieve the full benefit of IoT, not only the speed of analysis is important but also the accuracy of analysis. The decisions made by IoT platforms would get better over time as the AI platform learns with experience.

Predictive maintenance is an area of interest to many manufacturers. The endeavor is always to cut down on the downtime, increase machine availability without increasing expenditure on maintenance. SK Innovation, a leading South Korean oil refiner, is using artificial intelligence to predict the failure of connected compressors. AI based systems have helped save Google 40% of data centers cooling costs by predicting the temperature and pressure thereby limiting the energy usage. All major IoT platforms like Azure IoT, AWS IoT, etc. now have machine learning for predictive capabilities already incorporated but going forward the deep learning capabilities would become more commonplace.

Below is the visual representation of how the three technologies work in tandem with each other. The diagram is a little simplistic as the processes with not happen in parallel all the time and there would be overlaps.

Artificial Intelligence gets layered over the IoT platform while the data from external sources flows through the Blockchain platform. Even the data exchange within the IoT network can happen over Blockchain to ensure traceability and recording of all transactions. Multiple IoT networks can exchange data while the power of AI will get exponentially enhanced with more data. The trinity of these technologies will not only help increase efficiency but also help businesses deliver better customer service.

The trinity is not without its drawbacks. Using the three technologies together would lead to higher complexity while the privacy issues would continue to haunt a number of business and consumer applications. There would be challenges around compatibility due to lack of protocols and standards. Some of the applications are likely to charter in untested territories and may face legal or lack of legal challenges.

IoT, AI and Blockchain can complement each other well and can potentially remove some of the drawbacks of these technologies when implemented in isolation. The idea of these technologies working in tandem is not new but in my opinion the convergence would happen faster than predicted. We need to stretch our minds to fully envision the impact of these three technologies taken together. The technologies have long been driven by the brilliance of technology folks. Now is the time for visionary business people to wake up to the potential of this trinity and start to look at creative ways of using them for appropriate solutions. The limitation is not going to be the technology solutions but the imagination of the business leaders.