On Water, Memory and Learning

ORIGINAL: Chemoton § Vitorino Ramos' research notebook

29 June, 2012

Video – Water has Memory (from Oasis HD, Canada; link): just a liquid or much more? Many researchers are convinced that water is capable of “memory” by storing information and retrieving it. The possible applications are innumerable: limitless retention and storage capacity and the key to discovering the origins of life on our planet. Research into water is just beginning.

Water capable of processing information as well as a huge possible “container” for data media, that is something remarkable. This theory was first proposed by the late French immunologist Jacques Benveniste, in a controversial article published in 1988 in Nature, as a way of explaining how homeopathy works (link). Benveniste’s theory has continued to be championed by some and disputed by others. The video clip above, from the Oasis HD Channel, shows some fascinating recent experiments with water “memory” from the Aerospace Institute of the University of Stuttgart in Germany. The results with the different types of flowers immersed in water are particularly evocative.

This line of research also remembers me back of an old and quite interesting paper by a colleague, Chrisantha Fernando. Together with Sampsa Sojakka, both have proved that waves produced on the surface of water can be used as the medium for a Wolfgang Maass’ “Liquid State Machine” (link) that pre-processes inputs so allowing a simple perceptron to solve the XOR problem and undertake speech recognition. Amazingly, Water achieves this “for free”, and does so without the time-consuming computation required by realistic neural models. What follows is the abstract of their paper entitled “Pattern Recognition in a Bucket“, as well a PDF link onto it:

Figure – Typical wave patterns for the XOR task. Top-Left: [0 1] (right motor on), Top-Right: [1 0] (left motor on), Bottom-Left: [1 1] (both motors on), Bottom-Right: [0 0] (still water). Sobel filtered and thresholded images on right. (from Fig. 3. in in Chrisantha Fernando and Sampsa Sojakka, “Pattern Recognition in a Bucket“, ECAL proc., European Conference on Artificial Life, 2003.

[...] Abstract. This paper demonstrates that the waves produced on the surface of water can be used as the medium for a “Liquid State Machine” that pre-processes inputs so allowing a simple perceptron to solve the XOR problem and undertake speech recognition. Interference between waves allows non-linear parallel computation upon simultaneous sensory inputs. Temporal patterns of stimulation are converted to spatial patterns of water waves upon which a linear discrimination can be made. Whereas Wolfgang Maass’ Liquid State Machine requires fine tuning of the spiking neural network parameters, water has inherent self-organising properties such as strong local interactions, time-dependent spread of activation to distant areas, inherent stability to a wide variety of inputs, and high complexity. Water achieves this “for free”, and does so without the time-consuming computation required by realistic neural models. An analogy is made between water molecules and neurons in a recurrent neural network. [...] in Chrisantha Fernando and Sampsa Sojakka, “Pattern Recognition in a Bucket“, ECAL proc., European Conference on Artificial Life, 2003. [PDF link]