What does Europe need to be relevant in AI?
It’s not about having enough computer programmers in Europe capable of producing new AI algorithms. What we need are knowledgeable experts from non-computer fields and open data. Oh, and caution.
AI will certainly help create new products and services, increase productivity, and improve competitiveness, no doubt about that. The question most relevant to the old continent is - what needs to be done so we have fruitful AI solutions “Made in Europe”?
Those who think we just need to educate a new generation of AI programmers may be mistaken. With AI experts being in such a high demand and most research being done in the US and China, it’s hard to keep the brightest programmers from fleeing there.
But sure, we do need to educate new experts on innovative IT. If today a company of, let’s say a hundred employees, has two or three “classic” IT guys, it will need as many experts on big data and AI tomorrow. But Europe’s opportunity lies elsewhere.
It’s not about the programmers
First of all, more than hardcore AI geeks we need experts from other, non-technical fields, like farmers, with the ability to crunch data. We all know the success of AI algorithms depends on the quality of input data. It must be up-to-date, unbiased, complete, and, above all - relevant. And what data is relevant to a given problem can only be determined by an expert in that particular field.
The best presentation on AI I have ever seen, was by a shy sales marketer, who started off by admitting it was his first time at an IT conference. He presented an AI program for forecasting inventory at a large grocery store chain. The solution didn’t require anything from the world of “classic IT” - no server, database, or classic programming. He found all the required tools in the cloud. However, when he initially used the out-of-box AI algorithms, the predictions were slightly worse than those made by store managers.
The key was to supplement AI algorithms with specific knowledge of what factors influence the sale of particular groceries. The sale of some foods depends on local demographics, another on the purchasing power, while another only depend on the weather, he explained. Years of experience in the field gave him the knowledge, and when he applied it, the AI model predicted better than humans.
So what Europe needs are similar experts in various fields ranging from agriculture to energy, from construction to, say sports, who can supplement their existing computer literacy with an ability to work with data, understand machine learning, and choose and train the right model. A relatively short training course could be sufficient for that.
The second thing we urgently need is the data itself
If you have not yet been persuaded by the need for open data by the improved transparency and efficiency of public services and the economic potential of all the new products that can be created, there is a new, and perhaps the strongest argument in favour of making as much data as possible publicly available - the AI.
Data is so essential to AI that if we don't have open data, we won't even have AI. We will have as much AI, as we will have data. It’s that simple.
There is no better example than weather data. Many European public weather services still don’t publish their reports as open data.
NASA in the United States once also asked money for photos from the Hubble telescope, but the court then decided that photographs taken by a device funded by taxpayers should be made available to them for free.
If a progressive French winemaker, for instance, decides to make an automated AI watering system, he will not only need his knowledge of vineyard irrigation, but also very detailed weather forecasting, in a raw data stream via an API. This is because an inaccurate weather forecast fed to the AI algorithm could lead to a decision to water the plants before rain, when over-irrigation of a vineyard is much worse than drought.
You see the French farmer has a great advantage over an American or Chinese programmer in developing this application to a specific French vineyard by having the knowledge about, for instance, the soil permeability of a particular location. But he needs the data. A similar situation could be in a wide range of other fields.
Open data initiatives should therefore gain a new momentum. Various public (and private) institutions are now buying all kinds of new sensors that measure temperature, car traffic, air quality, etc. All that data should be made public!
Finally - all this will be useless if we don’t realise:
It’s also possible to get burned by AI
Toronto was recently targeted by a protest that united angry activists, frightened citizens, and outraged politicians. What were they all afraid of? Sensors, data, AI - all pillars of the intended construction of a “smart city” built by Google, who also planned to use data from toilet flushing in people’s homes.
So the third thing countries have to do to distinguish themselves in their ability to harvest AI potential is to learn to use it in a way so as not to harm themselves, and to align progress with public opinion.
AI brings new challenges such as unintentional discrimination due to unfair decisions, or the way that AI algorithms make decisions is non-transparent. So that’s why scholars from the humanities, such as psychologists, philosophers, sociologists, lawyers or political scientists, should now explore the interface between AI and society. Many big questions remain unanswered and some, perhaps even bigger, we have not even been asked yet.
How can we use AI to make decisions about people so that this process can be explained, transparent, and fair?
How do we recognise when AI has unwanted prejudice in making decisions?
When can we leave AI alone to make decisions and when should humans supervise it?
It is also important to carefully consider whether people should be given the opportunity to “opt-out” of being “handled” by AI algorithms, and be treated solely by a human. For instance it is great that Europe has banks that allow customers to open accounts over a video-call via biometric scanning, but perhaps it’s equally important these banks keep the option of opening an account at a bricks and mortar branch with a human clerk.
We have to learn to answer these questions ourselves, here in Europe. Many AI algorithms come to us from abroad and could be trained on samples that don’t work well here, or could make decisions, which are not aligned with our values. Just like a movie that is rated PG-13 in the U.S. can be rated “R” in Europe, it could be similar with AI tools.
We will have to learn all this to use AI responsibly. We can then offer this knowledge to others, perhaps monetize it, and even in this way be a relevant player in the world of AI. Perhaps over time “what it does to us” will be equally or even more important as “how does it do it”.
And if we do not learn to identify and continually address these challenges, there is a risk that the public will oppose the deployment of AI, as in the Toronto example, which would be a pity.
It is in the interest of human progress to combine technologies with civil society. It is therefore necessary to constantly watch their impact on society, and ensure that these technologies are deployed in an ethical manner so that all people benefit from them. Only then will the desired progress arrive.
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