The Mirage and Metamorphosis of Data and AI

I have just taken a break to rejuvenate my creative juices. It was a time to reflect and innovate. We are often so busy in our day to day lives we don't stop and reflect. I spent my time reading and catching up on bleeding edge technology. I am always fascinated to see what is coming next, what problems researchers are trying to address and how Data and AI could be utilised to benefit industry and the world around us.

The role I enjoy the most is as a Data and AI philosopher providing thought leadership. We are at an exciting time in history to witness and contribute to the mirage and metamorphosis of Data and AI. My explorations find exciting challenges in diversity and Data and AI at the centre of most things we want to achieve. Research is increasingly needed in industry to achieve business success due to the increasing complexity within industry and the world around us. We need to move away from agile for certain tasks to enable complexity to be understood and use systems thinking techniques.  My findings on the future mirage and metamorphosis of Data and AI are a complex interconnected world around Data and AI and mastering that complexity is the key to success. 

  

References
The data and AI market landscape 2019: The next wave of hybrid emerges
https://www.zdnet.com/article/the-data-and-ai-market-landscape-2019-the-next-wave-of-hybrid-emerges/
Part I: A Turbulent Year: The 2019 Data & AI Landscape
https://mattturck.com/data2019/
Part II: Major Trends in the 2019 Data & AI Landscape
https://mattturck.com/2019trends/
Navigating AI hype in search of success, Oliver Pickup (Sunday Times 12 May 2019)
The real big-data problem and why only machine learning can fix it
https://siliconangle.com/2019/08/09/real-big-data-problem-machine-learning-can-fix-mitcdoiq-startupoftheweek/
Big Data is just Data
https://buckwoody.wordpress.com/2019/08/26/big-data-is-just-data/
Maximising the AI opportunity
https://info.microsoft.com/rs/157-GQE-382/images/UK-DIGTRNS-CNTNT-content-MGC0003240.pdf
The Data Ethics Framework principles
https://www.gov.uk/government/publications/data-ethics-framework/data-ethics-framework

Microsoft ML for Apache Spark

Microsoft Research announce a new version Microsoft ML for Apache Spark, an open-source and distributed ML and microservice library. v0.18 brings Vowpal Wabbit on Spark, Speech to Text & more!

Microsoft Machine Learning for Apache Spark (MMLSpark) is an ecosystem of enhancements that expand the Apache Spark distributed computing library to tackle problems in Deep Learning. It enables sending streaming data to Power BI.
Website: http://aka.ms/spark Paper: http://aka.ms/spark-paper

Global AI Nights 2019

The Global AI Night is a free evening event organized in London by community people, who are passionate about Artificial Intelligence on the Microsoft Azure. It is at the The Microsoft Reactor in London on Thursday September 5, 2019 5:45 PM – 10:00 PM Register here.

Database Trends Awards

Database Trends and applications have names have listed the best relational database and best big data platform.






Best relational database: SQL Server
"According to Craig S. Mullins, president & principal consultant, Mullins Consulting, Inc, relational continues to dominate: IDC forecasts that relational DBs will still account for more than 80% of the total operational database market through 2022, and Gartner forecasts that through 2020, relational technology will continue to be used for at least 70% of new applications and projects."

Best big data platform: Cloudera Enterprise Data Cloud

"To leverage the immense power of their data, organizations need a solid strategy that incorporates everything from security to data governance to the right big data technologies. Enabling both on-prem and cloud deployments—or a hybrid strategy—big data platforms today support data warehouses, data lakes, data science, engineering, machine learning, myriad database management systems, and much more.  And while Hadoop is a key element of big data platforms today, there are also many other open source components, support capabilities, and advanced features that round out a big data platform to give data-driven companies the big data capabilities they need"

Discover Datasets

There are many thousands of data repositories around the world and to make it easy to access this data Google have launched a Dataset Search service.

https://toolbox.google.com/datasetsearch

This aimed to be a companion of sorts to Google Scholar, the company’s popular search engine for academic studies and reports.

Read more about the service here.

The Big Data Problem

The article The real big-data problem and why only machine learning can fix it and video from the MIT CDO conference, Cambridge, MA contains an interesting discussion on why ETL and MDM don't scale and why placing a schema later doesn't deliver usable data. The key is using machine learning to classify and prep data.

Linking Data Science Activities to Business Initiatives

This hypothesis development canvas can help with thinking like a data scientist.