It is a curious thought to often consider the future of how artificial intelligence will develop in emerging markets, developing worlds, and technologies helping to solve. There are many concepts revolving how in which these technologies will take form, impact jobs, and/or create opportunity in new sectors. Along with the existing shifts in economic roles, artificial intelligence is currently positioned and being applied in many FinTech, MedTech, and transportation applications, from neural nets, and the shifts in the layers of service at being.
What we come to understand is that there is many an appreciation for the human-based approaches to all of these broad solutions, and often the mention of such to the populous can be something of a domino of indifference. Rather, of course, what AI developers/researchers/funders would rather see is how these shifts are creating different opportunities and providing a larger role for humans in the process.
Though as it may seem, replacement of humans in certain areas of commerce, staffing, or any of the roles we imagine today could shift the availability for work. I purpose a perspective that many see, as in it elevating our means to help others, support a developing world, and enhance the jobs we see many finding favorable roles in today. Though machines may begin to automate many of the interactions we anticipate from humans today, we have to view some of them, on the bases that as a civilization, we have moved past. This is the means of filling certain roles could be viewed as damaging, or an opportunity as well to see new frontiers for how we approach the jobs that those being filled by autonomy create.
Additionally, from these markets emerging and developing, we can inference the understanding of the impact on how we can develop artificial and machine intelligence improvements in every setting. As the developments of online transactions continue, with the increased use of applications on mobile, and data storage on cloud servers — there is an ever-growing need for refining and combating fraud, breaches, and attacks. Keeping information safe, while speeding up the interactions, and offering new services in the exchange is how the modern operations of services will develop, in this means, machine intelligence will have an increasing hand.
Pattern recognition is a large factor in the benefits of utilizing machine intelligence, the ability, to interpret, store, and decipher mass amounts of information from an unbiased approach will afford these networks a large role in the developing future. In all of these three senses, benefits in security and plotting analysis forecasts, neural networks can work to learn and develop around processing data in a beneficial manner. With a focus on Artificial Neural Networks (ANN)s, in this space, there is an increased parallel in how these networks train to solve and come to conclusion autonomously.
The benefit of forming these networks comes from again, mirroring that of how our own systems work, referencing the immune system as a structure for defense as a presence within the body, we can begin to understand how training and aligning Artificial Immune Systems, collaborate to deflect that of cyber attacks, through pattern recognition, data processing, and deductive reasoning abilities. Often as we have seen, it is in this manner in which we must consider, thinking ourselves to develop the machine intelligence in which performs as such, amplified by its abilities to process and filter data.
We can reference this research as a presentation of these concepts, how we approach the initial understanding that individual neurons work collaboratively trained on their own, or mirror biases to perform as functional cells in delivering the processed data. These constructions defer in the sense of our previous applications, as the vectors set for these gradients are formed often individually through their own vectors, which can be aligned in means of matrices in TensorFlow.
To apply such an understanding, we can look first to identify the weight of each neuron:
Neural Networks 1: The neuron
In the next two posts, I plan to introduce the classification algorithm called an Artificial Neural Network (ANN). As…
For each node we set a distribution, in which the input vectors are measured and returned, to calculate the accuracy of the node, aligned with the data set in which we are using to train. Through a process of, well, regression, we attune the node to the data we are measuring to refine the accuracy of the distribution, across the node.
A kernel can be allotted to sort these definitions in a more dynamic setting, or hyperplane, instead of adjusting for each gradient, rather, adding uniformity to the acceptance of the neuron, and allowing it to tune itself.
To layer these distributions and understand the value of the network itself:
Neural Networks 2: Evaluation
In last week's post, I introduced the Artificial Neural Network (ANN) algorithm by explaining how a single neuron in a…
As the inputs of the neuron return a distribution to the next, layering affords the network the ability to decipher additional information. The platitude of many minds/brains/heads is better than one comes to mind in this sense, as if each time you passed the word through the game of telephone, these utterances allow for clarification for the next, and so on. Making decisions across the neuron allows the next to consider the solved components, the network, in this sense transcribes the defined evaluation, based on the parameters set in the gradient and then instructs the next.
In this case, we can acutely align each set of parameters to a known set of data and process the inputs to a determinate value. This is a considered principle as a solution and becomes that for APIs in which have refined specific intentions and goals based around predefined variables.
It is important to consider these measures in the sense that unique solutions may require additional development practices to approach solutions in a more impactful/defined/intricate manner. As there are many a discussion about the idea of white boxing AI, it is important to understand that with a general solution, especially in such industries, there can also become many general intrusion, in a sense.
Forecasting is something we often seek in many realms, applying a keen understanding of many variable sums to align a predictive model around behavior/patterns/decisions — all in which can often come down to time-based models of understanding. The data in which we inherently move in such a pattern, previously mentioned, as the concept of social elasticity, then becomes the model in which we align with these neural networks to begin predicting such occurrences.
Anything from purchasing behavior to airline travel, or stock fluctuation, will begin to be sorted by intelligent machines, fair to mention, the algorithm/thought/consideration behind such is the fulcrum of complexity, though understanding the technology, allows us to move towards developing such.
Time series classification with Tensorflow
Time-series data arise in many fields including finance, signal processing, speech recognition and medicine. A standard…
In this example, we see two models for analyzing the data, one for image classification as the CNN with pooling layers, and the other the LTSM model of RNN for sentiment analysis in the text. The Convolutional Network works to classify images while the Recurrent Network uses hyperparameters in the LTSM cell to transcribe language, both devising meaning through training to identify human activity, referred to as (HAR) Human activity recognition. This sort of deep learning approach juxtaposes the initial mention of neural networks. Aligning the frameworks with more context for solving the data, having a topical understanding of the input segment, can then be translated with fewer steps to develop and refine, without shifting the gradients of each neuron.
These similar approaches can not only be used for filtering text inputs from data such as social networks or conversational interfaces, or measuring image classifications from the likes and entries in such as well, but preventive measures outside of human activity, in the form of patient screening, on a broader, more accessible, scale.
Accuracy of a Deep Learning Algorithm for Detection of Diabetic Retinopathy
Question How does the performance of an automated deep learning algorithm compare with manual grading by…
In this study, detection algorithms are built through layers of transposed datasets to reference each other for classification accuracy, and then provide grading of inputs through CNNs to identify positive data in which can then be learned by the machines as the weighted vectors we considered before. From this research, we can understand that these deep learning paths, can be trained from large datasets to create algorithms through learned neural paths in which provide high accuracy in output return, without the need for initial preprocessing and measuring of the vector data to refine the curves individually.
These advancements not only provide more global outreach in medical analysis with an adaptive approach to the tangibility but a sense of opportunity for trained entities to be developed by more individuals, basing knowledge on applicatory means instead of heavy mathematical analysis of data.
In this study we see a similar use of deep convolutional nets to learn images and begin classifying from positive data:
Skin cancer classification with deep learning
Deep learning matches the performance of dermatologists at skin cancer classification.
In both of these studies DCNNs are leveraged to classify and identify imagery from patients with known symptoms, rather images of the symptoms, and then used to teach the network in which these means appear as then transcribe input data over the neuron layers to deliver outputs in the sense of diagnostics. In these two specific scenarios, the set diagnostics achieved accuracy in which was higher than or comparable to that of the physicians in the field. Note, that these networks were trained, and adjusted by the means of labeled classification from these physicians, but then could be adapted to spread the knowledge such by adapting applications to classify imagery from mobiles. The tangibility of medical analysis may allow more patients to find instruments for pre-evaluation or preventative measures in becoming overwhelmed by such ailments.
In these intricate layers of estimator neural calculations, we can understand how a neuron develops its bias, how the network coordinates a process of understanding, and how through deep learning and layer, begins to develop an intelligence that can solve for many. These practices begin to approach a need for collaboration of frameworks across the spectrum, as we move towards autonomous patient care solutions, the need for classification of inputs will begin to spread across languages, dialects, and image sets alike.
With a focus on collecting the positive data for reference and the help of professionals in the field, to label, and attune them, we can begin to build new frameworks for combinative transcribing in which input devices and sensors will converge to not only enhance the understanding of the collective data, but the human experience as well. Studies around combining these networks and approaching the pooling of unification through them will allow for more adaptive measures in providing for a wider base of individuals/means/measures.
What we can understand from these examples, from the research in the process, and from interacting with these networks, is that more people everywhere will gain increased countermeasures through the develop and implementation of these technologies. As the forms in which we can provide these solutions grows towards asynchronous computing, we will see more experiences like the mentioned becoming probable and tangible to a wider array of users alike.
Applications of Artificial Intelligence Techniques to Combating Cyber Crimes (-), Neural Networks: The Neuron (-), Neural Networks: Evaluation (-), Time Series Classification with TensorFlow (-), Accuracy of a Deep Learning Algorithm for Detection of Diabetic Retinopathy (-), Skin Cancer Classification with Deep Learning (-)