Artificial intelligence (AI) is a relatively young branch of science that stirs the imagination of many. Even movie directors from hollywood are not exceptions. Development in AI area is very fast and there is no indication that this will change soon. I still remember my first contact with learning devices. This happend at the end of the last millennium when I realized neural networks (NN). They have immediately attracted my attention, because such devices were not known till then.
NN are made along the lines of mammalian brain. During the learning NN extract the essence from the data. After the learning we can ask NN questions. It gives us the right answers even to questions that during learning did not participate. NN learns autonomously and therefore may exceed the teacher's (poeple's) knowledge. Here are some important achievements of artificial intelligence:
- A couple of years ago the computer Deep Blue became the world champion of chess.
- In the year 2011 a computer beat the two best human competitors on the US show Jeopardy.
- Google already have a personal vehicle, which drives automatically on normal public roads and follows all traffic regulations.
- Honda's famous robot ASIMO
- Converting speech into written word and speech translation in a foreign language just experiencing a scientific breakthrough. After decades of effort machines have recorded and translated speech almost as good as the people.
And now a few words about my research work in the field of AI. I made my first research on the field of speech recognition. This is a complicated problem. I tried to find out, which NN would be suitable for the recognition of different speech voices (phonemes). I built a suitable NN and tought it to recognize syllables with the accuracy of about 95%.
My second research was on the field of robotics. I built a NN that moves the legs of a six-legged, ant-like robot. The robot can successfully overcome uneven ground and small steps.
One of the biggest problems on which I stumbled upon during the use of NN, was problematic learning. Like humans NN have problems with learning. The most known process of learning for the NN is ‘‘Backpropagation’’. This learning process has some disadvantages. I dug into that and found a couple of ideas for improvement:
- The second improvement is called ‘‘Border Pairs Method’’ (BPM). This improvement first eliminates unimportant learning data. During the learning process, it finds the optimal construction of the NN and it doesn’t come to overfitting. After the learning, the NN answers correct all learning questions.
In essence, we could say, that the BPM method isn’t learning to memorize, but learning to understand. On top of that, the BPM method qualitatively allows us removing of noise in the learning data, finding features and clustering. The BPM method combines the good qualities of the SVM method and Backpropagation.
In July 2014 a book entitled Advances in Machine Learning Research (Nova Publishers, New York) was published. It describes the mentioned algorithms (Bipropagation and BPM).
I’m looking forward to your response here or in our Artificial Intelligence community on the Google+.