By Aldis Ērglis, Intelligent Machines Riga


Large countries like USA and China officially announced that they have AI (Artificial Intelligence) strategy, some countries like the United Arab Emirates have even state minister of AI ( Recently France president Emmanuel Macron in a large interview for Wired ( also announced that France will have state strategy for AI. Macron stated that focus areas in AI for France are Health Care and Mobility (self-driving cars and workforce mobility). He also speaks about open data and even open algorithms for political and regulatory processes in the country.


It seems that AI field is changing from previous technological development dominance by Google, Microsoft, Facebook, and a wide range of other companies to practical use cases in business and even in politics and governance.


I expected that first we will hear widely about AI strategy for almost every company but there are some AI strategies on country level already.


Of course, my question is about small countries, like Latvia with less than 3 million people and tens of similar size countries over the world. Are we too small for own AI development and need to wait for developments in larger countries and use what they give us? Or should we come with an initiative to create European Union AI strategy for all EU members? In France example, we see that large countries will go forward by themselves.


To answer to question about AI strategy for the small country, we need to step back and think about what is AI strategy? According to Michael Porter strategy is a way how we make a focus on what we spend resources and how we differentiate – create unique proposition. AI Strategy is how we spend resources in the field of AI development to make a unique proposition for people and business. Therefore, a small country must have their own AI Strategy because resources are very limited and secondly it will be very hard to differentiate by using large country developments without local specifics.


State priorities in AI field could be education or transportation or health care or state services or something else. The small country probably can’t focus on more than one field. Therefore, it must be selected carefully and well linked to country development strategy.


The state must define AI strategy to start creating state AI ecosystem linked with scientific organizations and businesses locally and internationally. To be an equal partner on global AI field small countries need to offer something very unique (unique developments, unique experience), very specific niche solution and well developed what usually means an investment of resources, real-life approbation, and communication. Therefore, the decision must be prepared carefully.


Latvia could be a leader in AI solutions in the fields of forest management, ecological agriculture, export development, e-government, and others if starts to invest in it right now and try to implement solutions in real life acquiring experience what will be equally exchanged with experience of other countries in future.


Visit the RIGA COMM 2018 Machine Learning Practical Application Conference on 11 October to find out more.

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By Aldis Ērglis, Intelligent Machines Riga


We are already experiencing an impact of Machine Learning approach in business. We see it in Google search, on Amazon or Netflix recommendations. It means companies with large data amounts can feed learning algorithms with right data and get amazing results in decision making or decision support field.


Machines will learn faster and more accurately than humans for sure, and we are not talking about smart Robocop, AI like machines but even simple statistical algorithms, like described in Daniel Kahneman, HBR article Noise – Even more, most of algorithms and models are available and most are for free, like more than 12000 R packages available on the internet.


So, technically all companies can start using Machine Learning to create additional products and services, add intelligence to existing products and services or reduce costs by automation even in decision-making field but mostly in decision preparation for decision makers.


Why cannot all companies do that? Because Machine Learning needs a lot of good quality data to learn right things. Few companies are already collecting or let say mining data from their processes, customers, employees, and systems for Machine Learning purpose. And believe or now for good ML different data is needed. For example, to analyse and learn from processes you need the collect duration of operations instead of date and time of state. That is different data than ERP currently collecting because the purpose of data is different. So, it means to implement Machine Learning we need to rethink all data we are collecting and start collect data for ML purpose. Sources of data will be behind your organization boundaries, additionally to data inside the organization you need to collect data from the internet about your customer – using Competitive Intelligence approach and so on.


Is data a new Oil? Forbes argues in the recent article ( that data is not new Oil. Maybe comparing very precisely it is not. But I see some analogies that will help business leaders to think about data in the better way. To run the economy as we use a lot of raw materials, oil is needed, and during years we found how to discover, get, and use oil. The same idea with data, you can’t use any data you have, you need to discover, build up mining process, control quality, distribute it and use for the right purpose. Not all oil we can get from earth is usable so the data even more.


I see that organizations will be under pressure to change their mind because of competitive advantage of using ML. So, companies need to start with thinking how ML can help achieve business strategy and create ML strategy. ML strategy will show in what areas, processes you need to collect good data. And even if you are not doing any ML, find out and start collecting data, it will take time and will not be possible to do it overnight. So, collecting right data is your competitive advantage.


Visit the RIGA COMM 2018 Machine Learning Practical Application Conference on 11 October to find out more.

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