Competition between ferro-retrieval and anti-ferro orders in a Hopfield-like network model for plant intelligence

We introduce a simple cellular-network model to explain the capacity of the plants as memory devices. Following earlier observations (Bose \cite{Bose} and others), we regard the plant as a network in which each of the elements (plant cells) are connected via negative (inhibitory) interactions. To investigate the performance of the network, we construct a model following that of Hopfield, whose energy function possesses both Hebbian spin glass and anti-ferromagnetic terms. With the assistance of the replica method, we find that the memory state of the network decreases enormously due to the effect of the anti-ferromagnetic order induced by the inhibitory connections. We conclude that the ability of the plant as a memory device is rather weak.Comment: To be pulished in Physica A (Proc. STATPHYS-KOLKATA V), 9 pages, 6 eps fig

Biological efficiency in processing information

Signal transduction, or signal-processing capability, is a fundamental property of nature that manifests universally across systems of different scales -- from quantum behaviour to the biological. This includes the detection of environmental cues, particularly relevant to behaviours of both quantum systems and green plants, where there is neither an agent purposely transmitting the signal nor a purposefully built communication channel. To characterise the dynamical behaviours of such systems driven by signal detection followed by transduction, and thus to predict future statistics, it suffices to model the flow of information. This, in turn, provides estimates for the quantity of information processed by the system. The efficiency of biological computation can then be inferred by measuring energy consumption and subsequent heat production.Comment: 13 page

The role of the therapeutic alliance in internet-delivered cognitive-behaviour therapy

My research tested the importance of the client–practitioner relationship to therapeutic outcomes, utilising an Internet-based treatment for panic disorder. Results indicate that, while the relationship remains important when shared online, it matters less than in face-to-face settings. This calls into question conventional models of what makes psychological treatments effective

Redefining “Natural” in Agriculture

Can organic farming and genetically engineered crops coexist in an agriculture of the future? Tony Trewavas reviews the new bookTomorrow's Table

Learning, Social Intelligence and the Turing Test - why an "out-of-the-box" Turing Machine will not pass the Turing Test

The Turing Test (TT) checks for human intelligence, rather than any putative general intelligence. It involves repeated interaction requiring learning in the form of adaption to the human conversation partner. It is a macro-level post-hoc test in contrast to the definition of a Turing Machine (TM), which is a prior micro-level definition. This raises the question of whether learning is just another computational process, i.e. can be implemented as a TM. Here we argue that learning or adaption is fundamentally different from computation, though it does involve processes that can be seen as computations. To illustrate this difference we compare (a) designing a TM and (b) learning a TM, defining them for the purpose of the argument. We show that there is a well-defined sequence of problems which are not effectively designable but are learnable, in the form of the bounded halting problem. Some characteristics of human intelligence are reviewed including it's: interactive nature, learning abilities, imitative tendencies, linguistic ability and context-dependency. A story that explains some of these is the Social Intelligence Hypothesis. If this is broadly correct, this points to the necessity of a considerable period of acculturation (social learning in context) if an artificial intelligence is to pass the TT. Whilst it is always possible to 'compile' the results of learning into a TM, this would not be a designed TM and would not be able to continually adapt (pass future TTs). We conclude three things, namely that: a purely "designed" TM will never pass the TT; that there is no such thing as a general intelligence since it necessary involves learning; and that learning/adaption and computation should be clearly distinguished.Comment: 10 pages, invited talk at Turing Centenary Conference CiE 2012, special session on "The Turing Test and Thinking Machines