Artificially Intelligent Micro-Computing With Raspberry Pi & TensorFlow Lite
If you have found yourself saddened by your Tamagotchi pet, wearing thin the tracks on your hit clips, or even mortified at the evolution of board games to video channels, there is hope for the future of all physical and digital collaboration.
From the evolution of games in the ’80s with the launch of Atari to the technology behind the oven, yes, the oven, there is a development of such mechanics in which unto these days has seemed rather obtuse. Far beyond the means of playing, making, or using any of these challenging devices, like the oven, there is hope as well. What we come to understand about computing is that life has evolved the hums, sweeps, and beeps that once filled rooms continue to move towards evolution in which we can fit in our hands. Imagining such, we can consider blowing out cartridges a primitive act.
If you like myself have stared at something, wondering, if it could be better in the sense of its approach, solution, or experience, raise your hand. Yes, it seems a few of many find at least one plausible entry in this journal, of would solve. I imagine these such instances, visually, as those that rig rubber bands or tape around lego formations or, even better wooden constructs that in which yield temporary salivation. Far from computing matters, I imagine such as the early entrepreneur selling shower curtain rings or traveling around with their handcrafted devices, though, we now look to those wide-eyed creators to inform us on the solutions of the future.
What we find at the end of such ventures, of those proclivities enough, is something in which we find rather brilliant. Much as a kite with a key, it takes lightning to strike twice for our sometimes significance to actually occur. In the matter, these inventions iterate over time to involved the structures of probability and meet somewhere up a margin of means. What we relish through these outcomes is connectivity to devising the improbable, defying the metrics we apply to ourselves, and building upon the new ground in which we scorched only minutes before.
Redefine Tangibility in Machine Learning
An act in which we create something that before a moment ago did not exist was said to be untrue, and frequently is not aligned with our current vision. As solutions and concepts are masqueraded across screens we once doubted, these matriculations continue to collect. Where this leads, is imagining how each small step, fragments of technologies, become leaps in applicatory means.
Knowing in which these pieces impact, where, and visualizing their occurrence is the practical sense of intelligence. Looking, at our journals of solve, flipping through pages, and finding the moment in which you can pinpoint a new step in a variance of cognition, becomes this graceful bound.
As mentioned, many of the applications of today were merely subject of intrigue once before, straining our minds to see that in which we could only imagine, driving it into fruition, with more than just thought, but some elbow grease. I suppose, in the technical sense, you may want to wipe down and stay grounded for these ventures, though seeing as these means apply across a stream of practicality and deep machine probability, it might get a little messy.
TensorFlow Neural Network Modeling
The evolution of TensorFlow and its increasing viability for productive machine learning practices only continues to scale or shrink, depending on how you consider such. The evolution of TensorFlow mobile, which already has positive attributions for lightweight processing on mobile devices, will enable even more form factor creations to dive into the powers of machine intelligence.
When we align the sense of machine intelligence through micro means, the real-world applications and benefits continue to factor, in this sense, where it can fit, how much less it takes, energy and computationally, the more that can be solved and connected through these interactions.
If, for a moment, we align the technologies of machine intelligence, neural networks, natural language processing, and IoT devices as the means of known, and apply the adapted contributions of shrinking components through means such as raspberry pi, the Arduino open-source kits, or the Intel NUC, we can continue our journey to correlate how in which these devices will not only improve our experience but also the understanding of the connected world/people/families and empower more and more to find means in which the evolution of this technology will increase their digital correspondence and argue in a way betterment of usage.
We can pear into the existing solutions of diverse creation through this means in which Massimo Banzi discusses, with the developments in the open-source community with and around the Arduino.
As he states we can understand the unfolding era of inventors and creators, whom take these emerging technologies and apply them, as being part of the “makers-movement”, something in which many have been waiting for a name. We can understand more about this shift from Limor ‘Ladyada’ Fried whom Massimo refers to as a hero of this movement, along with that of open-source hardware.
In the interview, Limor compares the maker movement itself to the homebrew computer club/s that generated much of the technology we use today, in well put terms…
“Whatever makers are doing for fun now, is what everybody is going to be doing in 10 to 15 years.” — Limor ‘Ladyada’ Fried
The empowerment of these concepts is that we can make impacts in the moments ahead without asking permission from companies to innovate, taking our own demand as an initiative in practical application. Separate from the contraptions mentioned early, these frameworks apply across boards that are manufactured from even more dynamic approaches. With the schematics available, users/people/humans can create their own means in which to fit these architectures or retrofit current models across a spectrum of experiences. In this sense, Massimo mentions that “hardware becomes a piece of culture”, a way in which the availability of intellect, and practical application of computation, empowers society to solve for individual needs.
Form-Factor Machine Learning Hardware
In the existing community of hardware development and machine learning tandem, there have been initial steps to validate the use of sensors input across these devices to incorporate the understands of human interaction. As an accelerometer that gathers data from your phone, may come into play, there are existing examples of how such could play out and be built on to empower our understanding of these experiences.
At Arduino Day 2016 David Mellis points to research as part of these sensory inputs, how we can begin to define interactions, view them, and interpret the data for better understanding. It is important to note his mention of available algorithms and tools to a user across these hardware components that connect and interpret the inputs as well as clarify their input through the pattern.
These are the challenges many faces when approaching this technology, outside of these applications in particular, but has a growing awareness in which to empower individuals to understand the computation behind such creations. Along the path of the makers-movement, it is important to plot these solutions as steps to a broader approach/understanding/equity of information in solving needs in which can become independently developed solutions for many.
Tool-kits alike are becoming prominent sources for achieving these applications and streamlining the application of machine-based intelligence across platforms/devices/solutions. When we look at the application of such in a use case-based approach, it allows us to adapt the thought and apply it to other solutions.
Many of the present examples to scale of say the raspberry pi with TensorFlow exist through image classification, capturing the data input and processing it in the cloud, validating a/few inputs/s through a trained neural network. These practices rely on Convolutional Neural Networks or CNN’s that we discussed in a previous outlook referencing neural video. The classification component of these sensors works in tandem with this framework to fit the feed-forward approach of machine articulation.
As we look towards integration we need to understand the various frameworks as their application, their viability in practice, and the approach in which reveals approximate patterns in solving. Comparatively many of these frameworks are working towards cross-application, as the needs are specifically sought for various solutions.
Recurrent Model Training Insights
You may be wondering in which means I will approach, now ‘classic’ technologies, board games, or ovens, ah ovens. This is where the application of these aforementioned micro-micro-computers if you will come into scope. In many a sense, these technologies are being tested and researched to power many things from autonomous vehicles to drones, as their form factor feeds into viability that we are still seeking to apply on many scales. The inference of my three examples as such are means in which we discover conditional logic that is relative to how a human thinks. As automation and IoT devices develop, improving the machine intelligence, or launching it past our own, means first discovering what, then how, and in which means.
Visualize the situation in which we first interact with a human, open a board game, or turn on the oven to cook/bake. In all of these settings, we have an initial experience that correlates how in which we base our next move. There are a standing set of principles we learn as we progress, but the first moment in which we initiate that neural pathway, we begin to map how each unfolds. Inherently, our brain then takes inputs to create persisting information in which we develop better practices from each interaction. We can all consider instances in which we have embarrassed ourselves socially, overlooked an advantage, or burned our pending, wonderful creations. All of these means applied, fulfill a syntax that allows our brain to reference upon the next interaction.
Combining Convolutional Neural Network (CNN)’s and Recurrent Neural Networks (RNN)’s, machines can begin to process labeled data to understand patterns in action. This as part of its connection to IoT sensors and cameras can correlate these visual patterns to begin to understand our intention and emotion.
In tandem, the framework and (RNN’s) apply a Long Short Term Memory (LTSM) approach in historically layering data — in which we are not only training the recognition but storing it as a model for persisting reference. As does the RNN add logic to machine intelligence perception in high-level means, but can additionally add attention.
In the interactions mentioned, of relational rapport, game theory, and cooking/baking, we can now build a correlation between not only our initial interactions, storing and building from them, but developing a correspondence of, in a sense empathy. In the near future, this may mean that your oven might just save an optimal time, encourage you to choose a preset input, or an interactive game may stop to explain where you could have gone, all based on your reaction. The idea of attention in machine intelligence empowers these inputs in the sense that it will allow for further collaboration, devising intent, and filtering/gating the selected inputs in which are chosen through the matters of the RNN’s focus.
The impact of RNN’s can not only help refine inputs, but help develop and train new models for existing neural networks, and their accuracy through reinforcement training. Much as experiences develop our interactions over time, the correlation of RNN’s application applies a similar model to development as well.
Using Machine Learning to Explore Neural Network Architecture
At Google, we have successfully applied deep learning models to many applications, from image recognition to speech…
Aligning these neural paths to our own, helps us further understanding how in which we can collaboratively develop both senses, in a means, to refining how we understand ourselves reflectively, and how to develop machines with human-like thought.
Connecting Intelligent Machines Through Networks
As we come to understand the current applications of these frameworks, their increasing depths, and valuable interactions we can build upon which to begin plotting the integration of hardware, IoT devices, and virtual interfaces that connect them.
We have collectively begun understanding these sensations in the means of a virtual assistant, as we learn to connect these correlations, we can imagine such as the new interface. Beyond that of the current solutions, which are quite helpful, as applications, there is a new frontier upon us. Aligning, computer vision, machine intelligence, deep learning, and an array of IoT devices we can see the shift from simple interactions and inputs to a convergence of definitive means. A voice in our ear such as in “Her”, an interactive life assistant, that connects our home, to our devices, to our car, and continuously monitors all of such interactions to employ assistance where sensed.
As the inputs to these interactions become increasingly connected and simplified through means such as natural language processing of text or even voice, the connectivity of machine intelligence will begin to learn and develop around our choices and interactions, enhancing or iterating for many of which in which we seek in our journals of solve. Take, for example, this concept of interaction, as an iterative step closer to the future, an example from an article called “Voices of the Future” on bots, in this example, in reference to Alexa’s increased efficiency in response and what in a sense, that feature focus proves.
“ The possibility to talk to a device using no screen and without unnecessary pauses as if it were a person proved to be a critically necessary feature for a successful interaction experience.” — Alex Galert
Each step is a leap closer to unifying intelligence's, in many ways these much anticipated and unfolding occurrences are exciting, allowing for more innovation and thought, through removing many an unnecessary action throughout the day. With the shift and development of these platforms, the collaboration of devices and people, society will begin to enter a new era of interaction, the means in which we think, solve, and contribute grow evolvingly closer.
Understanding these contributions, the correlation of the human mind, to a computer, and how these occurrences have been dreamt, written, and designed far before their initial conceptions are only a hint at how optimistic we can look to these solutions to continue to improve the lives of the world. With the increase of open source platforms, technologies, and devised solutions, these applications in thought and action are continuing to unite and solve for everyone.
We must continue to pursue curiosity, test theory through new mediums, and apply thought to the nature of the experiences we create. As the refinement of these technologies occurs, it is our duty to venture into them to define new occurrences and build from their opportunity a better experience for all.
Computing History Anecdote (-), TensorFlow Lite Announcement (-), “How Arduino is open-sourcing imagination” TED Talk from Massimo Banzi(-), Meet the Makers: Limor Fried (-), “Makers and Machine Learning” (-), “Machine Learning for the Maker Community” by David Mellis (-), raspberry pi with TensorFlow examples by Rikki Endsley (-), Convolutional Neural Networks (-), “Two-Stream RNN/CNN for action recognition in 3D Videos” from Patrick van der Smagt(-), Long Term Short Term memory networks by Christopher Olah (-), “Attention and Augmented Recurrent Neural Networks” by Chris Olah and Shan Carter(-), “Using Machine Learning to Explore Neural Network Architecture” by Quoc Lee and Barrett Zoph(-), “Voices of the Future” by Alex Galert(-)
Thanks for Reading, Keep Minimizing!
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