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The Artificial Intelligence Industry and Global Challenges

Whoever controls the strongest artificial intelligences controls the world.

In this article I examine the global artificial intelligence industry and in this context consider the aspects of politics, data, economy, start-ups, financing, research and infrastructure.

Although there are now plans to break Google's monopoly (USA and Europe), for example by imposing European fines on Google and Facebook, politics is lagging behind the market by over a decade.

As far as AI is concerned, for the first time in recent history I have observed a multitude of initiatives, strategies and actions by dozens of governments around the world - with very different goals and approaches.

From China's point of view, artificial intelligence is an important tool for strong foreign policy, military dominance, economic success and for controlling one's own population.

Although the USA has not yet found a red line under President Trump, the state has been promoting the research and implementation of AIs for decades through its countless secret services and ministries.

In Israel, there is a close network of universities, access to the Asian and American capital markets, close cooperation with the military and the government.

In addition, Canada has a clear AI strategy, research, investment and implementation have been promoted for years.

Also worth mentioning are Japan, Korea and India, which have good prerequisites for playing a relevant role in the AI industry in the coming years.

reading reference at this point is the report of national strategies of artificial intelligence of the Konrad Adenauer Foundation (Part 1 and Part 2).

While politics provides the framework conditions for research, financing, education, data, promotion and regulation, in the medium term AIs must be developed by companies and brought onto the market.

There are two types of companies: Those that develop and sell AI as a core product and those that use AI to complement their value chain.

On the one hand, AIs can replace existing business models, and on the other hand, they can be integrated into countless company-internal processes: Accounting, controlling, production, marketing, sales, administration, personnel management and recruiting.

By the way, this is the primary driver of applied artificial intelligence: reduce costs and maximize profits.

For this reason, marketing leaders (Google, Facebook), software companies (Salesforce, Microsoft) and e-commerce retailers (Zalando, Amazon) have been heavily involved in AI for years.

Morgan have already recruited thousands of employees with a focus on machine learning and data science.

GDPR/DSVGO may indeed have the good intention to create a European data internal market, but currently form an enormous location disadvantage for Europe.

Personal discussions with clinics and doctors showed me that the health industry no longer shares any data.

This literally costs human lives, because this obstacle is detrimental to health research and life-prolonging algorithms.

These young companies are often more courageous, faster and more flexible in developing new products than established companies.

Backed by the capital of venture capital funds and business angels, start-ups take high risks in the expectation of extraordinary success.

It secures financing (from seed to IPO), gains talent, grows, develops products for which customers pay, scales and becomes a corporation.

Today, hundreds of thousands of narrow artificial intelligence applications are based on the research results of the last 30 years, after we reached the critical volume of computing power and data availability in 2012.

MIT alone is investing 1 billion dollars in the training of new AI degree programmes by 2020.

Leading minds conduct research for corporations with more data and financial resources: Richard Socher (Salesforce), Yann LeCun (Facebook), Andrew Ng (until 2017 Baidu) or Demis Hassabis (Google).

But even these institutes do not manage to play in the first league in the global competition for talent, data and capital.

But it is precisely research that will be decisive in the coming decades when it comes to the question of who will develop the first general artificial intelligences.

While China will focus strongly on 5G, which is critical for real-time AI applications and the networked industry, Europe will not play a leading role in this technology issue either.

If you want to know more about the current state of AI, I recommend the State-of-AI-Report 2019 and my presentation of the Rise of AI 2019 as video.

While Europe is still considering whether to compete at all, China, the US, Israel, the UK and Canada are already competing for data, markets and talent.

Our working population lacks retraining opportunities that also meet the needs of the growing digital industry.

Results either disappear into the drawer, or the IP transfer is in bureaucratic terms a horror, especially for young companies and spin-offs.

Companies still have to build products, founders have to start start-ups, VCs have to finance these start-ups and researchers have to do research.

And it must be a matter of course for politicians to take care of the education of pupils, students and qualified further education in general.

AIs are increasingly influencing the media, industry, education, security, military and financial markets.

For example, the Chinese company Squirrel AI helps millions of students to develop individual learning content that matches their learning pace and skills.

Authorities must therefore recruit the necessary specialist staff, develop concepts and put them into practice.

Artificial intelligence can prevent - or reinforce - prejudice, racism, corruption and sexism.

Therefore, every nation, every state system and every ethnic group must begin to discuss their own AI ethics frameworks.

This also requires AI ethics frameworks in companies that are based on social values and also reflect the corporate culture in the code.

I estimate that 50% of today's human activities are carried out by machines in 20 years because they are cheaper and faster.

It is therefore important that we think about how we can retrain those people who perform tasks today that will be performed by machines tomorrow.

And let us finally pay better the people who take over important social activities such as bringing up our children, teaching pupils and caring for us in old age.

Either way, we need a radical new social model, because the earth does not need 10 billion philosophers, artists, entrepreneurs or programmers.

A few chunks of this wealth will then fall to the administrative class (lawyers, bankers, entrepreneurs, investors) and hardly anything remains for the remaining 99% of the population.

We are not just talking about money here, but about access to the machine code and thus to the control of the global economy, the military and information.

And what if the majority of people lose their current importance for the system, namely to work and consume?

Does the idea of the Club of Rome then become real and we reduce ourselves (involuntarily) to 500 million people?

Department of Energy Announces $15 Million for Development of Artificial Intelligence and Machine Learning Tools

Today, the U.S. Department of Energy’s (DOE’s) Advanced Research Projects Agency-Energy (ARPA-E) announced $15 million in funding for 23 projects to accelerate the incorporation of machine learning and artificial intelligence into the energy technology and product design processes as part of the Design Intelligence Fostering Formidable Energy Reduction (and) Enabling Novel Totally Impactful Advanced Technology Enhancements (DIFFERENTIATE) program.

The selected projects then conceptualized machine learning and artificial intelligence-based solutions to help engineers execute and solve these problems in a manner that dramatically accelerates the pace of energy innovation.

Additionally, the tool is expected to leverage a number of machine learning techniques—including decision trees, supervised learning and reinforcement learning—and is likely to reduce the cost and time required to develop new ultra-efficient power-converter designs.

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