If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Falkner, Andreas (Siemens AG Austria) | Friedrich, Gerhard (University of Klagenfurt) | Haselböck, Alois (Siemens AG Austria) | Schenner, Gottfried (Siemens AG Austria) | Schreiner, Herwig (Siemens AG Austria)
The development of problem solvers for configuration tasks is one of the most successful and mature application areas of artificial intelligence. The provision of tailored products, services, and systems requires efficient engineering and design processes where configurators play a crucial role. For more than 25 years the application of constraint-based methods has proven to be a key technology in order to realize configurators at Siemens. This article summarizes the main aspects and insights we have gained looking back over this period.
ASP has been applied fruitfully to a wide range of areas in AI and in other fields, both in academia and in industry, thanks to the expressive representation languages of ASP and the continuous improvement of ASP solvers. We present some of these ASP applications, in particular, in knowledge representation and reasoning, robotics, bioinformatics and computational biology as well as some industrial applications. We discuss the challenges addressed by ASP in these applications and emphasize the strengths of ASP as a useful AI paradigm.
The idea is ridiculously simple (perhaps why it is effective?): I don't understand the claim "Remember all the narratives we told about how depth learns hierarchical representations, and higher level representations -- those higher level representations don't seem to matter so much after all.". The net has over 100 layers!?! It seems like if you just dropped layers, the net could learn an unrealistic generative model.
It's been more than 20 years since IBM's Deep Blue won its first match against world chess champion Garry Kasparov, marking the first time an artificial intelligence machine defeated a reigning champion. The buzz surrounding AlphaGo's decisive victory has less to do with the win, and more with how the machine outsmarted Sedol. Given the staggering complexity of the game, with a near-infinite number of possible moves, the machine could not rely on memorizing every possible move to decide its next play. The buzz surrounding AlphaGo's decisive victory has less to do with the win, and more with how the machine outsmarted Sedol.
Let's look at a very simple problem of network representation. The relationship "between" is fundamentally among three things – a center object and one object on either side. But what happens when we have a set of objects (Figure 3) and try to represent their relationships with this graph-based approach (Figure 4). In future articles, I'll describe some other facts that don't have a hack in graphs, can't be represented with simple networks, and require enhancements to the formalism.
This guy is juggling a lot of problems right now. Rubik's cube wizard, who goes by rubocubo, showed off a mind-boggling display of hand-eye coordination and quick-thinking. The impressive display of flexibility and problem-solving makes us wonder how many hours of practice this entire trick required. It probably took one whole summer ... cubed.
Our new results, based on computer modelling, link evolutionary processes to the principles of learning and intelligent problem solving – without involving any higher powers. If past selection has shaped these building blocks well, it can make solving new problems look easy – easy enough to solve with incremental improvement. In other words, gene networks evolve like neural networks learn. While connections in neural networks change in the direction that maximises rewards, natural selection changes genetic connections in the direction that increases fitness.
Many people head for the city to make their fortune, and now it seems that living in urban areas can give your prospects a boost - if you're a bullfinch at least. Researchers have found that birds that live in the city are not only better at problem-solving tasks, they also have better immunity than their rural counterparts. This is the first study to find clear cognitive differences in birds from different environments. Researchers have found that birds that live in the city are not only better at problem-solving tasks, they also have better immunity than their rural counterparts. This is the first study to find clear cognitive differences in birds from different environments.