There's a belief that whatever it is I'm looking for is out there, but I have a really difficult time finding it. Search algorithms alone are falling short in being able to provide real context around information.
I design genetic algorithms, neural network and artificial intelligence systems.
Heuristic is an algorithm in a clown suit. It’s less predictable, it’s more fun, and it comes without a 30-day, money-back guarantee.
You cannot invent an algorithm that is as good at recommending books as a good bookseller.
We at Google have made tremendous advances in understanding language. Our knowledge graph has been fundamental to that. The new algorithm that we launched today called Hummingbird has been a great leap forward.
These algorithms, which I'll call public relevance algorithms, are-by the very same mathematical procedures-producing and certifying knowledge. The algorithmic assessment of information, then, represents a particular knowledge logic, one built on specific presumptions about what knowledge is and how one should identify its most relevant components. That we are now turning to algorithms to identify what we need to know is as momentous as having relied on credentialed experts, the scientific method, common sense, or the word of God.
Trees and bones are constantly reforming themselves along lines of stress. This algorithm has been put into a software program that's now being used to make bridges lightweight, to make building beams lightweight.
The classes of problems which are respectively known and not known to have good algorithms are of great theoretical interest. [...] I conjecture that there is no good algorithm for the traveling salesman problem. My reasons are the same as for any mathematical conjecture: (1) It is a legitimate mathematical possibility, and (2) I do not know.
I remember that mathematicians were telling me in the 1960s that they would recognize computer science as a mature discipline when it had 1,000 deep algorithms. I think we've probably reached 500.
Genetic algorithms (GAs) are defined as search procedures based on the mechanics of natural selection and genetics, and we think we know what innovation is - at least in some sort of qualitative way - but what does one have to do with the other?
Capablanca's phenomenal move-searching algorithm in those early years, when he possessed a wonderful ability for calculating variations very rapidly, made him invincible.
A metaphysical tour de force of untethered meaning and involuting interlocking contrapuntal rhythms, 'The Clock' is more than a movie or even a work of art. It is so strange and other-ish that it becomes a stream-of-consciousness algorithm unto itself - something almost inhuman.
The emphasis on mathematical methods seems to be shifted more towards combinatorics and set theory - and away from the algorithm of differential equations which dominates mathematical physics.
Mathematics is not about numbers, equations, computations, or algorithms: it is about understanding.
It is raining DNA outside. On the bank of the Oxford canal at the bottom of my garden is a large willow tree, and it is pumping downy seeds into the air. ... spreading DNA whose coded characters spell out specific instructions for building willow trees that will shed a new generation of downy seeds. ... It is raining instructions out there; it's raining programs; it's raining tree-growing, fluff-spreading, algorithms. That is not a metaphor, it is the plain truth. It couldn't be any plainer if it were raining floppy discs.
In popular books and articles, information technology writer Carr has worried over the ways that algorithms like those employed by Google are reshaping the ways we think.
The neural network is this kind of technology that is not an algorithm, it is a network that has weights on it, and you can adjust the weights so that it learns. You teach it through trials.
Obviously the more transparency we have as auditors, the more we can get, but the main goal is to understand important characteristics about a black box algorithm without necessarily having to understand every single granular detail of the algorithm.
My particular focus at the moment is on the development of genetic algorithms and neural networks that work together to create computer architectural systems.
I'm less interested in uniqueness than in goodness. I see so many concerts where the program notes are more interesting than the music. I remember talking to one composer who went through the most complicated mathematical algorithm to generate some material from scratch. It took weeks and weeks, and he came up with a C major chord. For me, honesty is more interesting than originality.
What geographic profiling does is it takes a look at the locations of a connected series of incidents - say murders in a serial murder case or robberies in a serial bank robber case - and it spatially analyzes the point pattern of incidents, and creates a probability surface from those, working from the basis of an algorithm that says people offend close to where they live, but not too close.
Love was not specified in the design of your brain; it is merely an endearing algorithm that freeloads on the leftover processing cycles.
No one knows what the right algorithm is, but it gives us hope that if we can discover some crude approximation of whatever this algorithm is and implement it on a computer, that can help us make a lot of progress.
We have a lot of argument about laws but none of it solves the problem. Let's examine what happened, why did we miss the Tsarnaev brothers, why did we miss the San Bernardino couple? It wasn't because we had stopped collected metadata it was because, I think, as someone who comes from the technology world, we were using the wrong algorithms.
I did once leave one of [my kid] watching something on YouTube, something completely innocuous, and I went out of the room and the algorithm kept playing the next thing and the next thing and somehow worked its way around to showing him the trailer for John Carpenter's The Thing - at which point I walked back in. He wasn't happy.
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