Tentacular AI
We briefly introduce herein a new form of distributed, multi-agent artificial intelligence, which we refer to as ‘tentacular.’ Tentacular AI is distinguished by six attributes, which among other things entail a capacity for reasoning and planning based in highly expressive calculi (logics), and which enlists subsidiary agents across distances circumscribed only by the reach of one or more given networks. …

AIQ
Focusing on Business AI, this article introduces the AIQ quadrant that enables us to measure AI for business applications in a relative comparative manner, i.e. to judge that software A has more or less intelligence than software B. Recognizing that the goal of Business software is to maximize value in terms of business results, the dimensions of the quadrant are the key factors that determine the business value of AI software: Level of Output Quality (Smartness) and Level of Automation. The use of the quadrant is illustrated by several software solutions to support the real life business challenge of field service scheduling. The role of machine learning and conversational digital assistants in increasing the business value are also discussed and illustrated with a recent integration of existing intelligent digital assistants for factory floor decision making with the new version of Google Glass. Such hands free AI solutions elevate the AIQ level to its ultimate position. …

Range Entropy
Sample entropy ($SampEn$) has been accepted as an alternate, and sometimes a replacement, measure to approximate entropy ($ApEn$) for characterizing temporal complexity of time series. However, it still suffers from issues such as inconsistency over short-length signals and its tolerance parameter $r$, susceptibility to signal amplitude changes and insensitivity to self-similarity of time series. We propose modifications to the $ApEn$ and $SampEn$ measures which are defined for 0<$r$<1, are more robust to signal amplitude changes and sensitive to self-similarity property of time series. We modified $ApEn$ and $SampEn$ by redefining the distance function used originally in their definitions. We then evaluated the new entropy measures, called range entropies ($RangeEn$) using different random processes and nonlinear deterministic signals. We further applied the proposed entropies to normal and epileptic electroencephalographic (EEG) signals under different states. Our results suggest that, unlike $ApEn$ and $SampEn$, $RangeEn$ measures are robust to stationary and nonstationary signal amplitude variations and that their trajectories in the tolerance r-plane are constrained between 0 (maximum entropy) and 1 (minimum entropy). We also showed that $RangeEn$ have direct relationships with the Hurst exponent; suggesting that the new definitions are sensitive to self-similarity structures of signals. $RangeEn$ analysis of epileptic EEG data showed distinct behaviours in the $r$-domain for extracranial versus intracranial recordings as well as different states of epileptic EEG data. The constrained trajectory of $RangeEn$ in the r-plane makes them a good candidate for studying complex biological signals such as EEG during seizure and non-seizure states. The Python package used to generate the results shown in this paper is publicly available at: https://…/RangeEn. …