Does Artificial Intelligence benefit the Common Good?

The invention of the steam engine triggered revolutionary societal changes. Machines replaced physical work. They produced goods cheaper, faster and more effectively. In the end everyone profited from that through more prosperity. A similar development is becoming apparent today: in many areas artificial intelligence is replacing processes which require human thinking. Will we also all benefit from this?

Choosing the right AI model

The popularity of AI (Artificial Intelligence) systems is growing in leaps and bounds. Businesses around the world are starting to understand how these systems can benefit them, but AI is not yet at the stage where you can simply take it out of the box and expect it to perform. AI systems require training, which is where AI models come into the equation.An “AI model” is an algorithm that is taught using data and input from a human expert. The model is evaluated on its accuracy by comparing the results it provides against the decisions made by the human expert. This often requires the algorithm to review a significant amount of data. In addition, by accessing data from multiple sources, the model is better able to find patterns which it can use to further streamline its analysis. The model is attempting to replicate the decision process of a team of experts with access to the same data. Understanding the intricate process of AI training is crucial for companies aiming to deploy efficient AI models.

Artificial Intelligence in Germany

Artificial intelligence is changing the world. There is no doubt about it. Self-learning algorithms are now being developed all over the world and an increasing number of economies are focusing on promoting AI development at a domestic level. Machine learning benefits mankind all over the world – individual countries profit from it. But what is the current status of artificial intelligence in Germany? Can German AI development compete on an international level? And what about state subsidies?

Types and Importance of AI Training Data

1997 was a pivotal year in the world of Artificial Intelligence (AI) as it was the first time a machine managed to defeat a world champion in chess. Deep Blue was an IBM Supercomputer and, after losing to Garry Kasparov 4-2 in 1996, had learned and improved and came back to defeat him in 1997 after a hard-fought battle.

Machine learning (ML) and AI use complicated algorithms to learn and process information. These algorithms mimic the human brain, and just like a human child learns, they can also be taught through data and experience.

What are the benefits of artificial intelligence (AI) for the environment? – Seven astonishing insights

Will AI-driven cars save the roads from a total gridlock? Can artificial intelligence help live a more resource-efficient life? Will artificial intelligence make the supply of energy easy to achieve? We provide insight into seven AI and the environment questions.

Cognitive Computing – Hype or Progress?

First, there was artificial intelligence, then the terms machine learning and deep learning followed. And now there is cognitive computing. What’s so special about this new concept? Is it just a new buzzword from the IT scene that can be exploited for marketing purposes? Or is it a new approach that brings machines one step closer to human thinking?

Is Google Misogynistic?

Is Google misogynistic? The ranking for certain keywords certainly provides grounds for this suspicion. However, the phenomenon of unequal treatment isn’t really the fault of the algorithms but is a fundamental problem with the German language. And those who want to can ensure for themselves that the results list is free from discrimination.

Text classification – areas of application on the Internet

There are billions of websites with countless texts on the Internet. This makes it difficult to keep track of them. Text classification is a method that provides an overall view and structures the offer. Which application areas are there for text classifications in the World Wide Web?

Realistic training data for machine learning

Data are the foundation for training algorithms. The more realistic the data, the better the results. This is because artificial intelligence is based on precise and reliable information for training its algorithms. This is obvious but it is often overlooked. The training data are realistic when they reflect the data that the AI system gathers in real operation. Unrealistic data sets prevent machine learning and lead to expensive false interpretations.

A Snapshot of AI, Machine Learning, and Deep Learning

Real-world examples, prominent use cases, and AI’s (artificial intelligence) potential

The industrialized world is full of machines that outdo humans in strength and speed. Cranes lift steel beams to towering heights, car engines send passengers flying down the road at impossible speeds, and tree shredders pulverize entire pine trunks in a snap. Such inventions replicate and vastly outperform humans at tasks requiring physical exertion. They possess “artificial brawn”.

What then is artificial intelligence?

At a first pass, we can think of AI as a machine (i.e., a computer) replicating human cognitive tasks. A calculator, for example, embodies basic AI to do a job for which humans must use their brains: math. While definitions of “artificial intelligence” abound, Peter Norvig and Stuart Russell, forefathers of the discipline, explain it well when talking about “rational agents”. A rational agent, they say, “acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome”. Most explorations into AI fit the “rational agent” description in some way. An exemplary case of AI application in developing rational agents is in the realm of face recognition software, where training data plays a critical role. For insight on how training data influences the effectiveness of such technology, you might find this case study on training data for face recognition software highly informative.