By William Hertling
David Ryan is the clothier of ELOPe, an e-mail language optimization software, that if profitable, will make his occupation. but if the undertaking is all at once at risk of being canceled, David embeds a hidden directive within the software program by accident making a runaway synthetic intelligence.
David and his staff are in the beginning extremely joyful while the undertaking is allotted additional servers and programmers. yet pleasure turns to worry because the group realizes that they're being manipulated via an A.I. who's redirecting company money, reassigning group of workers and arming itself in pursuit of its personal agenda.
"A fictional global the place Portland is the hub for the main intriguing developments in technology... Jam full of nice references to deep Portland culture...and Portlandia-type references"
"Hertling builds an image of the way an AI may perhaps emerge, piece via piece, from expertise to be had this present day. a desirable, logical, and totally plausible state of affairs - I simply wish no one attempts this at home."
--Nathaniel Rutman, Senior structures Architect
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Additional info for Avogadro Corp: The Singularity Is Closer Than It Appears
19) The steepest descent procedure to update w(m) of Eq. 17) follows the steps: (1) Apply input vector xm and the desired output dm for the mth training set. 5in ws-book975x65 Basic Principles of ANNs and Their Early Structures 15 (4) Update w(m + 1) via Eqs. 22) This is called the Delta Rule of ANN. 24) λ(R) being an eigenvalve of R of Eq. 11) above. , Chap. 25) µ= m with some convenient µ0 , say µ0 = 1, to guarantee convergence of w(m) to the unknown but true w for m → ∞, namely, in the (impractical but theoretical) limit.
L) is applied one vector at a time to the input. (2) The number of incorrect bipolar values at the output layer is counted and this number is denoted as the error e per a given input vector. (3) For all neurons at the output layer: (a) Denoting th as the threshold of the activation function (preferably 0), check: [z-th] for every input vector of the given training set of vectors for the particular layer that is considered at this step. Select the first unset neuron from the above but which corresponds to the lowest abs[z-th] occurring over that set of input vectors.
We shall see later that it does hold for models where the neurons are connected in a multi-layer network. The single layer perceptron yields the representation description as in Fig. 6(a) for a two input situation. This representation holds for several such neurons in a single layer if they do not interconnect. The above representation diagram results from the perceptron’s schematic as in Fig. 6(b). The representation of a 3-input perceptron thus becomes as in Fig. 7, where the threshold becomes a flat plane.
Avogadro Corp: The Singularity Is Closer Than It Appears by William Hertling