Sitting around amongst a group of peers, amidst conversation one may often reference a scene/experience/moment, sifting through the day we see this synopsis of events, a cluster of correlation that drives our interrelated collaborative instincts to pull from depths we often do not fathom. Understanding this occurrence, or the moments in which our brain seeks assistance as it is hindered only by the pressure we apply, we turn to searches.
An example of such would be to seek a composition, a layered musical element from an experience, emotion, occurrence — in which we correlate from something we have seen/heard/felt and back to something we have experienced. Music is especially qualified in theses regions as it captivates such so eloquently. The score, measures, notes aligned with actions in so many moments wherein which words lack weight.
An example of such is:
Which curiously dances through many scenes of mind, time, and place. As we stare into its depth, the occurrence is more than fiction, but how our minds carry the notes that evolve at each breath.
This piece alone “In the Hall of the Mountain King”, shares its own proclivity with angst and venture, as it tumbles through tradition as a “leitmotif”, stringing along with a cadence of its own accord.
As many these occurrences may provide the connection of this, music is important to note, as often stemming from sound, these memories initially correlate. We can see laterals in less complex terms for movies providing soundtracks, understanding, in which these overtures afford us the experience even without the visual, or draw us back to the emotion.
We see attributions in engaging kinesthetic with the device where these experiences have a propensity to occur in correlations between Starbucks music in-app experiences, with the ability to save or view playlists as a noted piece in which to align experiences. Something the Egyptians may have even understood in crafting the pyramids in ‘The Band of Peace’, with their harmonious inlays.
Outside of the musical phenomena alone, the collaboration of motion and imagery create a visual venture of its own delight. The conduction of relation can be found when we are emotively driven by experiences outside ourselves. Why in which we connect with shows/films/documentaries often relies on the arrangement of such pieces to correlate back to our own. The archetype established by genre is less a box and more so a relative conundrum.
We can turn to neurosciences to begin to understand how the connections of sounds, motion/lighting/storytelling, play into our brainwave entrainment and more so synchronization. Phenomena that connect what is being projected, into our minds, and developing a neurological rapport, if you will.
Upon experience, we understand that we have instinctual signals for anticipation, or even how our minds begin to devolve repetitive sound and convert it to a new level of noise for a deeper focus. From neuroscientist Uri Hasson, we can apply a concept of high order relation, entrainment, and common ground to approximate our suspension of disbelief or even our interwoven sense of experience from a film in which we know only to be a written creation.
These correlative perspectives are yielded upon the ability of the storyteller, in this sense, protagonists to convey such a message in which aligns with our understanding. Much as there are many flavors of everything, there are as well speech patterns, physical presentations, and subtle paradoxes in language that lead us to such differentiated preferences. In the matter of dialog we can collectively understand that these experiences are an inference of such, and in an organized fashion, the validity of stories are as well.
“The sum of all of us together, coupled, is greater than our paths”. — Uri Hasson
Where the understanding of sound, motion, speech, dialog, and storytelling lead us is to how artificial intelligence uses these contexts to approximate the aforementioned sifts and algorithmically provide a path to the foreseen favorable returns. Much as the leitmotif, carries its own conjecture, so do the inputs in which are gathered through our interactions and presented parameters.
Encoding the human mind, much as machine training is derived from an understanding of these contributions, rules of inference govern how in which we contract means from a subject aligning the relative inputs with the sought output. The challenge, from what I understand, lies not in deriving the understanding in which we seek, more so the most efficient path to that means.
As we are searching/sifting/gathering information our needs are balanced with the semantics at hand, understanding our interaction, action, and inputs are something I have elaborated on previously, though applies to this manner of constructing algorithms/paths/training for the worker (machines) to compute.
When we first open a freshly minted personal computer, when we are first admitted into existence, or when a neural network is first established, all of these constructs, yes humans as well, have a basic barring set forth, sparing you a Kantian analysis//or conflicting tenants, we/they exist with a basic governance. AS we develop as part of the onset, as you build paths through interactions, logins, and saves, and as the nets gather input from training, all of the occurrences become subject of their subjected experiences. It is imperative to understand this, just as is the soliloquies that paint these reveries into existence.
When devising the approach to GAN’s for the outcome of not only measuring, interpreting, training but creating a video, seeking to understand the “high order relations” in which these networks can decipher can increase their cleverness and viability. The methods in which training these competing entities, generator, and discriminator, through patterns of input is continuously developing. Some scrape the internet for a library of content, others contend on self-training specific images through layering, and in continuous effort services like Keras look to solve for more of these entities with a more defined existing pattern. The data in which is sought is becoming the real estate of the future, as many a reference to this has become humorous anecdotes through tech culture, it will begin to become more apparent to all, just click your way through a multi-image captcha and join the ride.
Much as Hasson mentions, the future, and present of these experiences as it extends to solving, improving, and creating new patterns for networks, will evolve from connectivity. The moments in which you reach to search will evolve past derivatives and into a beyond, of inference that is evolving by the minute.
Connecting facial structures, juxtaposing images, and sifting through mounds of information is becoming increasingly more contextual. The application of such is one means, in which I have been working to solve as a component of video analysis, considering such impacts as all of the above into which means those patterns will unfold.
Though past an initial search, obvious structures may be already connected, as mentioned leitmotif of “In the Hall of the Mountain King”, carries its weight as a classification in memorable scenes, so if looking, you will find based on structure, repetition, and relative inputs in which you seek, thanks to the exceptional work of the artificial research and input patterns that already exist.
Admittedly, such an experience led me to ponder, in which understanding that many occurrences are not so concise or relatively aligned, but more so devised through iterative steps, multiple queries, and counter logic that can be over and over events, discouraging. Simplifying the solution to finding information via a sliver of query may seem elusive, but there is much to be understood from the interactions of GAN’s and their contrasting approach to training.
As the logic that enables these networks to train themselves becomes even stronger the proliferation of connection will grow instep, devising this path, in its most efficient form, is only evolving with more contribution and research. The imperative to consider, in this setting, is not only the solution, but the neurologically connected steps in which take us there for the broadest form of populous, and that begins, in my opinion, with understanding the fundamental contributions in which align these elements natively.
The exploration in these networks is growing, as the forms in which to solve them. There are various considerations for approaches, patterns, and algorithms for understanding these paths in machine learning, that only continue to solidify the practice.
In computer vision, an evolutionary component of these means, AVA is adding data that applies human interaction to align perception of humans to machines for understanding, reference, and contrast.
Announcing AVA: A Finely Labeled Video Dataset for Human Action Understanding
Teaching machines to understand human actions in videos is a fundamental research problem in Computer Vision, essential…
The shifts in demand for computation of the networks is leading to research in GPUPU and developments in companies such as Nvidia and AMD, to support the research and application of machine learning. Associative memory is a larger contribution to CNN’s can be influenced to such shifts.
Many of the existing contributions in computer vision exist through CNN’s though training these networks within themselves, or unsupervised becomes an increasingly sought function as the evolution of Deep Convolutional Generative Adversarial Networks unfolds.
Connecting the attributions of these neural models, we can begin to understand how to leverage contributions from Convolutional Neural Networks (CNN)’s and General Adversarial Networks (GAN)’s to provide efficient means in both generating and solving with supervised and unsupervised models.
In tandem the resources from APIs such as Keras, labeled data, and neural research will continue to shift the technologies in which we use, relating the evolution of such whys/hows/and what's to become more form factor in the human sense of the experience. Imagining closer proximity between thought and action is in a true sense a goal of connecting the two marvelous neural paths of human and machine.
Interesting enough, the original sense of the approach to convolutional neural networks or ConvNets is derived from that of animal oculars, and relative to our neural paths above, through its identification process of deciphering and separating layers. Such in which we apply logic to diversifying the approaches to developing frameworks around neural networks, we also adhere to the practices in which are set in our initial perceptions of the world.
To learn more about the approaches to Convolutional Neural Networks you can visit this blog, to discover more about Generative Adversarial Networks, you can view the paper that outlines the framework, and understand more on computer vision and deep learning relations you can visit this blog and its mentioned research outlets.
Edvard Grieg: “In the Hall of the Mountain King” from Peer Gynt / Neeme Järvi (-), “In the Hall of the Mountain King” as a leitmotif (-), Harmonious Inlays in ‘The Band of Peace’ pyramid (-), This is your brain on communication TED Talk from Uri Husson (-), Rules of inference (-), AVA: Video Dataset (-), Effective neural network acceleration on GPGPU (-), Deep Convolutional Generative Adversarial Networks (-), Explanation of Convolutional Neural Networks (-), Generative Adversarial Networks (-), Computer Vision and Deep Learning (-)