![]() We want to go through your deck if you’re building a great business, we want to speak with you if you’re a serious entrepreneur. They give investors the opportunity to cut through the noise fast enough so they can focus on the deals that require deeper analysis and insight. But they’ve consistently borne fruit in time, resources, and attention saved. ![]() These are the signals that are often difficult to explain, let alone justify, because they sound insultingly shallow. Instead, I’m referring to the Other signals, the signals that almost always denote a lack of seriousness, forethought, or even-temperament. ![]() I’m acutely aware of the negative impact that having a signal-heavy approach may have when it comes to assessing deals (read: the incongruous lack of VC-backed women and minorities), which can often lead to groupthink, cookie-cutter investing, or an unconscious bias towards or against certain types of founders. I am not referring to the patterns associated with a given industry, business model, or entrepreneur. You can catch Part One here.)īefore I begin, I think it’s helpful to define what I mean by ‘universal signals’. k-restricted nondeterministic signal machine is a nondeterministic signal machine which accepts an input iff produces a special accepting signal, which have at most two nondeterministic rule for each collision, and at most k collisions before any acceptance.(This is Part Two of a Three Part Series: “The Two Most Important Words in Venture Capital: Pattern Recognition”. We show that for a specific class of nondeterministic signal machines, called k-restricted nondeterministic signal machine, there is a deterministic signal machine computing the same result as the nondeterministic one, on any given initial configuration. In this paper, we introduce the concept of non-deterministic signal machine, which may contain more than one defined rule for each set of colliding signals. Originally signal machine is defined by its rule as a deterministic machine. Rules of signal machine specify what happens after a collision, or in other words, specify out-coming signals for each set of colliding signals. Signals are moving in space freely until a collision. A signal machine starts from an initial configuration which is a set of moving signals. A signal machine is defined as a set of meta-signals and a set of rules. It is possible to build fractals and to go one step further on to use their partial generation to solve, e.g., quantified SAT in “constant space and time”.Ī signal machine is an abstract geometrical model for computation, proposed as an extension to the one-dimensional cellular automata, in which discrete time and space of cellular automata is replaced with continuous time and space in signal machine. Not only are these machines capable of classical computation but moreover, using the continuous nature of space and time they can also perform hyper-computation and analog computation. The other half concentrates on signal machines: line segments are extended when they meet, they are replaced by others. The first half of the chapter presents three models of computation based on geometric concepts-namely: ruler and compass, local constrains and emergence of polyhedra and piece-wise constant derivative. The encountered time scales are discrete or hybrid (continuous evolution between discrete transitions). Various understandings of computing are encountered in such a setting allowing classical (Turing, discrete) computations as well as, for some, hyper and analog computations thanks to the continuity of space. This chapter presents what kind of computation can be carried out using an Euclidean space-as input, memory, output.-with dedicated primitives.
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