Passionately curious about Data, Databases and Systems Complexity. Data is ubiquitous, the database universe is dichotomous (structured and unstructured), expanding and complex. Find my Database Research at SQLToolkit.co.uk . Microsoft Data Platform MVP

"The important thing is not to stop questioning. Curiosity has its own reason for existing" Einstein

Friday 6 October 2017

Machina Summit.AI

I attended IPExpo Europe 4-5 October in ExCel in London with the specific attendance at the Machina Summit.AI.

The opening keynote was by Professor Brian Cox OBE on ‘Where IT & Physics Collide’.  The talk interlinked big data, quantum mechanics and quantum computing. The whistle top tour mentioned the Sloan Digital Sky Survey, which are the most detailed three-dimensional maps of the universe; general relativity; history of space and time; the theory of cosmology; and quantum mechanics ending with quantum theory and predicting the distribution of galaxies. This was an amazing talk and gave a glimpse of the interconnected future.

This was followed by Brad Anderson, Corporate Vice President of Microsoft on ‘Business as usual in a digital war zone’. We live in turbulent times with a 300% increase in user account attacks this year, 96% of malware is automated polymorphic which costs business $15 million. Attacks happen in increasing waves and old defences never stand up against these attacks. In this intelligent war you need an intelligent graph. He introduced the Microsoft Azure Active Directory service as the new control plane. There is the need to eliminate false positives, classify email and guarantee data never leaves the browser and be able to use a real time evaluation engine.

A few other talks covered the practice of monitoring with machine data. There are 2 types of monitoring, transitional IT and the new data driven IT. For the latter there is the need to rethink and improve how IT operates using machine learning to be proactive. Organizational silos and increasing quality are things that need to be broken down to be able to address the velocity data in a more agile way to produce actionable insights.

Conrad Wolfram, Strategic Director, Wolfram Research talked about ‘Enterprise computation: the next frontier in AI and data science’ Todays data challenge is about accessibility of data, personalisation of data and providing insightful answers. Data Science is multi paradigm and machine learning does not have all the answers. Computation is required for everyone with smart automation and computational thinking is needed for everyone. Data science needs to be personalised, multifaceted but unified.

The day 2 keynote was given by Stuart Russell, Professor of Electrical Engineering and Computer Science, University California Berkeley on ‘Human-Compatible AI’. He discussed what is coming soon. Basic language understanding with web-scale question answering and intelligent assistants for health, education, finances and life (not chatbots!!). Robots for unstructured tasks (home, construction, agriculture) and new tools for economics, management and scientific research. He discussed the premise that eventually AI systems will make better* decisions than humans. Well *taking into account more information and looking further into the future. He argued that for the case of super intelligent AI, that you can’t switch off the machine and AI will never succeed.

Other sessions discussed the journey of chaos and how everything fails all the time. To address this there is the need to consider that every journey begins with a single step. There is the inevitable question to consider skills versus knowledge and that is practice.

Microsoft talked about their 'AI and Analytics in the Enterprise'. There is now a need to look at more than the rear view mirror, to see what happened. There is a convergence of cloud, data and AI. With that Microsoft have created an AI platform that is fast and agile, with AI built in and enterprise proven for on-premises to edge to create insights. The evolution of the data state takes into account increasing data volumes, new data sources and types and open source languages. There are 3 stages between the heterogeneous sources and providing apps and insights.
  • Ingest – data orchestration and monitoring
  • Store – Data Lake and storages
  • Machine learning – preparations and train ( Hadoop / spark / SQL and ML) then model and serve (on-prem, Cloud, IoT).

In summary the 2 day conference provided great insight into many new technical areas and raised thought provoking questions about the future of data and AI. 

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