Hysteria around the future of artificial intelligence (AI) captured the world. There is no shortage of sensational news about how AI can
cure diseases ,
accelerate innovation, and
improve human
creativity . If you read the headlines of the media, you can decide that you already live in the future, in which the AI penetrated into all aspects of society.
And although it cannot be denied that the AI has opened a
rich set of promising opportunities for us , it also led to the emergence of thinking, which can be described as faith in the omnipotence of AI. According to this philosophy, if there is enough data, machine learning algorithms
can solve all the problems of mankind .
But this idea has a big problem. It does not support the progress of AI, but on the contrary, it puts at stake the value of machine intelligence, disregarding important safety principles and setting people on unrealistic expectations about the capabilities of AI.
Faith in the omnipotence of AI
In just a few years, the faith of the omnipotence of AI snuck from the conversations of the technological evangelists of Silicon Valley into the minds of government representatives and lawmakers around the world. The pendulum has swung from the anti-utopian view of
AI that is
destroying mankind to the utopian belief in the
coming of our algorithmic savior .
We are already seeing how governments provide support to national AI development programs and compete in the technological and rhetorical
arms race in order to gain an advantage in the booming machine learning sector (MO). For example, the British government
promised to invest £ 300 million in AI research to become the leader in this field. Fascinated by the transformative potential of AI, the French president
Emmanuel Macron decided to
turn France into an international center of AI . The Chinese government is expanding its AI capabilities with a
state plan to create a Chinese AI industry in the amount of $ 150 billion by 2030. Faith in the omnipotence of AI is gaining momentum and is not going to give up.
Neural networks - easier said than done
While many political statements extol the transformative effects of the impending "
AI revolution, " they usually underestimate the complexity of implementing advanced MO systems in the real world.
One of the most promising varieties of AI technology is
neural networks . This form of machine learning is based on an exemplary imitation of the neural structure of the human brain, but on a much smaller scale. Many AI-based products use neural networks to extract patterns and rules from large amounts of data. But many politicians do not understand that simply by adding a neural network to the problem, we will not necessarily immediately get its solution. So, by adding a neural network to democracy, we will not make it instantly less discriminated, more honest or personalized.
Challenging data bureaucracy
AI systems need a huge amount of data to work, but in the public sector there is usually no
suitable data infrastructure to support advanced MO systems. Most of the data is stored in offline archives. A small number of existing digitized data sources are drowning in bureaucracy. The data is most often spread across different government departments, each of which requires a special permit for access. Among other things, the public sector usually lacks talents equipped with the necessary technical capabilities to fully reap the
benefits of AI .
For these reasons,
sensationalism associated with AI gets a lot of criticism. Stuart Russell, a professor of computer science at Berkeley, has long been preaching a more realistic approach, concentrating on the
simplest, everyday applications of AI, instead of a hypothetical world capture by super-intelligent robots. Similarly, a professor of robotics from MIT,
Rodney Brooks, writes that "almost all innovations in robotics and AI take much, much more time to actually implement than both specialists in this field and all others realize."
One of the many problems of implementing MO systems is that AI is extremely
susceptible to attacks . This means that a malicious AI can attack another AI in order to force it to give out incorrect predictions or act in a certain way. Many
researchers have warned that it is impossible to immediately roll out AI, without preparing the appropriate
standards for security and protective mechanisms . But until now, the topic of AI security is not getting enough attention.
Machine learning is not magic
If we want to reap the benefits of AI and minimize potential risks, we need to start thinking about how we can meaningfully apply MOs to specific areas of government, business, and society. And this means that we need to start discussing the
ethics of AI and the
distrust of many people to the MO.
Most importantly, we need to understand the limitations of AI and those moments in which people still have to take control of themselves. Instead of drawing an unrealistic picture of the capabilities of AI, you need to take a step back and
separate the real technological capabilities of AI from magic.
For a long time,
Facebook believed that problems like the spread of misinformation and hate speech can be algorithmically recognized and stopped. But under pressure from lawmakers, the company quickly promised to replace its algorithms with an
army of 10,000 human reviewers .
In medicine, they also recognize that AI cannot be considered the solution to all problems. The "
IBM Watson for Oncology " program was an AI that was supposed to help doctors fight cancer. And although it was designed to give the best advice, experts find
it difficult to trust the machine . As a result, the program was
closed in most hospitals, where its trial launches took place.
Similar problems arise in the legislative field, when
algorithms were used in US courts for sentencing. The algorithms calculated the values of risks and gave
recommendations on sentences to the judges. But it was found that the system increases structural racial discrimination, after which it was abandoned.
These examples show that there are no AI-based solutions for everything. Using AI for the sake of AI itself is not always productive or useful. Not every problem is best solved by applying machine intelligence to it. This is the most important lesson for all those who intend to increase investments in government programs for the development of AI: all solutions have their own price, and not everything that can be automated can be automated.