Machine intelligence is a developing factor in an enumerating amount of roles, studies and solutions alike. The exchange of data as we mentioned is a forming factor around driving the contributions for learned methodologies. We have encountered visual recognition of human behavior models through video, measured analysis of natural language processioning through conversational experiences, and weighted countermeasures in security applications for defensive ordinances.
Analyzing these forms, we begin to understand the projection of sorts in which these machine vectors become intelligent matrixes of sorting behavioral knowledge into predictive models. As we discover the processing power of definable metrics in which become outputs from these onsets, we then begin to understand how in which they can be used to predict actions, along a sociological curve. Knowing that these algorithms are becoming increasingly available, and open-sourced through APIs or even developing marketplaces, we can see how approachable these solutions can become for settings in behavioral psychology.
These applications can provide assistance in countermeasures for militaristic operations, medical assistance inaccuracy, or as above stated projecting and defining patterns in the human condition in which can plot this data to begin to predict deficits/choices/trends in a forecasting model.
We have mentioned forecasting models previously to gather human activity recognition using data from smartphones in a time series function. These models reign true to the approach of many forecasting solutions and can be scaled or shifted to that of large data sectors in aviation, gated sensors across IoT devices, and in the future increasing interaction with beacons across consumer markets. Relative to these models/frameworks/functions we will need to understand how the development of structured data enhances our approach at defining these forecasts.
For a moment consider this concept as an elusive proposition such as the mortgage collapse of 2008, the film, “The Big Short” comes to mind, whenever I imagine AIs application in this space.
Christian Bale as Dr. Michael Burry, who played an intricate part in estimating the unstable housing market in 2005, three years before its collapse, why? Because looking at the data, understanding the functions at play, the banks nature of structuring the bonds, and gathering a deficit in the ABX, he saw that eventually, it would unravel. It is brilliant in a forecasting sense, certainly, though imagine we had the technology to not only predict these futures, but audit, and mitigate them — this is why data is becoming/is gold, AI is the sift, and IoT is the pickaxe, and we are heading towards our own future of knowing, each swing.
Outside of push notifications, our behaviors through geofenced spaces and interactions with IoT sensors, devices, and frameworks will begin to map our preference, from where we are looking to what we are doing, in stores, our workspaces, and digital experiences — across timelines structured for such analysis.
These can be positioned as a beneficial factor for both ends, in the sense that consumers/users/humans will have a more interwoven experience with the solutions they choose, as well as the shifts in these settings supply/demand/needs will be plotted as well. Knowing this, it is proper to approach what we know now in these solutions and begin to map how the frameworks of the next iteration will solve, through layers, functions, and matrix in neural networks.
The inputs from devices (assistive voice actions, beacons, vehicle adapters, and cameras) added into the existing info structure of web interactions, mobiles, security, and widespread sensors will render new datasets for the future. Once structured, labeled, and processed, will appear as they do now to traders, fund managers, and financial leaders in their niche sectors, though, the trained models of the machine intelligent iterations, will not only be able to process the data based on time series functions but plot and forecast the additive contributions to have a high-level vision across an array of possible futures.
The above example is just one measure of data, based on the use form of these devices, and how they lead to a further understanding of human interaction as a whole.
Take this example as a scope of how we can approach understanding considerations from a financial API in estimating stock futures, considering both seasonal and time-sensitive vectors:
A simple deep learning model for stock price prediction using TensorFlow
For a recent hackathon that we did at STATWORX, some of our team members scraped minutely S&P 500 data from the Google…
This timewise model uses the prepared and structured data to illustrate how the architecture of feed-forward networks and backpropagation of the hidden layers can create an increasingly accurate model of functions across the matrices to relegate definitive points of interest in the market shifts. As the available quantified data grows in weight, in well, the qualitative factors and frameworks. This is mentioned in the process of training and forecasting, for scaling the data to fit larger curves in which aid in the predictability of the formulas that factor the calculations across the network, establish the biases, and unfold unto the output.
It is also interesting to mention the (MLP) Multilayer Perceptron use in this format, this follows another interesting path to constructing the framework. Based on the dataset, the solution involved, and how these models fold into the neural network, we can see that these paths are non-linear in the sense, that they calculate a new weight each step, to improve the gestation of data through the hidden layers and refine it before finding the output layer. This form is layered to calculate deviations in the estimating weight layers and return to the next with an exchange of the factor for the next.
If you imagine this as a group of people measuring the analysis, each one would factor the solution based on their given focus (weight), function the activation (vector), plot their bias (graph/matrix/hyperparameter) and then pass their findings to the next. Once each has gone through, the group then passes the information back to the person before, and again, before, to refactor based on the calculated biases — this begins the evaluation phase for tuning the vectors to prioritize the functions for increased accuracy in projection. The more data we have to build the initial model with, and the more people we have to factor and pass, the further the forecast is in a sense of accuracy.
Through the matters of discovering these means we have encountered a series of solutions to comprehensive problems, align the lines of forecasting, at the means in which we mention through richer data, we will need to combine the many processes to begin connecting the networks as more intricate and deeper structures.
We have seen (CNN)s or ConvNets process images and text to classify understanding in computer vision and for sentiment analysis. Pooling layers and Bayes’ theorem apply to these calculations as they begin to inference experience from the data, through means of training, and develop predictive analytical skills. Mentioning the inputs from imagery and natural language processing from out IoT devices, we can use this form to begin to capture and understand the actions in which develop through the human form. Learning the matters in which align with the relative field of measure, say (water) consumption/futures/exchange and then begin to plot the interactions in which occur across the network to continuously process this means to the next branch.
Once the inputs are processed we can feed the informed data through a non-linear machine in which can concatenate the data and begin to understand patterns across its structure. Imagine the aforementioned group of people as a team, which then passes their results and individual weighted biases to the next team. This team is perceiving the patterns in their nature as they hand their factors through to the next, this model evokes that of the LTSM, where each individual becomes a gated version of the previous model and refines the pattern through the process.
This example explains well how this framework of RNN could be aligned as the iterative step or paralleled for solving patterns recognized by the initial layers, with applied gates to decipher further evaluations.
In past posts, I've described how Recurrent Neural Networks (RNNs) can be used to learn patterns in sequences of inputs…
Once these layers devise functions post-evaluation, the LTSM network can be run concurrently or post-process to the CNN in developing recognition and sorting information. For example, say our model is based on known factors of environmental impact, like hurricanes, if we know there is a propensity of such a situation or a global awareness of other impacts, we can offset our model to run data with known factors in these scenarios, while also forecasting an exemplary time to focus investment/allocation/inventories, allowing for gains based on implemented decision models.
The LTSM framework takes these known variables as counters and measures them along with or after the calculations to create a precision graph of consumption based on behavioral inferences. Outside these occurrences we can apply the same disaster model with others to juxtapose known consumer data and impending forces, in which we can use both networks as a CNN-RNN(LTSM) model to weigh and value the proximation, evolving to a forecast of known patterns.
In this example, we can see how the estimative property of this model is used to weight probability in multi-labeled contexts and embedding them into data for classification.
Through this process we gain higher-level focus of contributing factors to images, strengthen the techniques in which can apply computer vision, and understand the collaboration between network unification in developing probability properties to solve at scale.
With this model as well increase the machine intelligent ability of attention, which we have mentioned before, as the visual proponent of focus. Through the training, connective measures, and deep learning principles of the machine we begin to establish a sense of learned focus that the machine will begin to develop through bias.
As in “The Big Short”, Dr. Michael Burry’s focus on the specific metrics in which allowed him to see the faults in the market foundation came through a deep analytical understanding, the matters in which we construct and train these networks can work to attune the attention of the machine to perceive the data in which they themselves practice — fostering an environment of reflection, as well as constructing frameworks for individuals focuses of perception. The cognitive biases in which we construct or learn will become increasing factors in the meta focuses of the machines in which we create, building in these frameworks, comes from not only an understanding of the technology but that of self.
“The Big Short” [film] (-), “The Big Short” [GIF] (-), Voice Assistant (-), Beacons and Dongles (-), Vehicle Adapters (-), IoT Cameras (-), Voice Assistant Actions Graph (-), Deep-Learning Model for Stock Prediction (-), Bayes’ theorem (-), LSTMs (-), CNN-RNN: A Unified Framework for Multi-label Image Classification (-)