“It transforms the specific relations between the consenter and consentee about a clearly defined action—we can consent to sexual relations, borrowing a car, surgery, and the use of personal information. Without consent, the same acts become sexual assault, theft, battery, and an invasion of privacy,” say the authors of “AI and the Ethics of Automating Consent.”
Modern technology advances so quickly, it runs headlong into ethical dilemmas such as consent without the means to adjudicate them in a timely manner.
When it comes to our online activity, artificial intelligence systems that collect, process, and generate our personal data intensify many ongoing problems with consent, such as giving us adequate notice, choice, and options to withdraw from sharing data.
The unpredictable and unforeseen use of data by AI systems has come as a surprise to many in the tech industry.
The authors investigate whether these problems impact morally transformative consent in AI systems.
They argue that while supplementing consent with further mechanization, digitization, and intelligence-either through proffering notification on behalf of the consentee or choosing and communicating consent by the consenter-may improve take-it-or-leave-it notice and choice consent regimes, the goal for AI consent should be one of partnership development between parties, built on responsive design and continual consent.
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
As one of the saying states that learning is far ahead apart from topics and theories from books and articles. One of such learning threads is observation, and in Machine Learning the process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.
Machine learning algorithms are often categorized as supervised or unsupervised. Supervised algorithms require a data scientist or data analyst with machine learning skills to provide both input and desired output, in addition to furnishing feedback about the accuracy of predictions during algorithm training. Data scientists determine which variables, or features, the model should analyze and use to develop predictions. Once training is complete, the algorithm will apply what was learned to new data.
Unsupervised algorithms do not need to be trained with desired outcome data. Instead, they use an iterative approach called deep learning to review data and arrive at conclusions. Unsupervised learning algorithms -- also called neural networks -- are used for more complex processing tasks than supervised learning systems, including image recognition, speech-to-text and natural language generation. These neural networks work by combing through millions of examples of training data and automatically identifying often subtle correlations between many variables. Once trained, the algorithm can use its bank of associations to interpret new data. These algorithms have only become feasible in the age of big data, as they require massive amounts of training data.
Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised.
Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design and board game programs, where they have produced results comparable to and in some cases superior to human experts.
Deep learning models are vaguely inspired by information processing and communication patterns in biological nervous systems yet have various differences from the structural and functional properties of biological brains (especially human brain), which make them incompatible with neuroscience evidences.
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
In a word, accuracy. Deep learning achieves recognition accuracy at higher levels than ever before. This helps consumer electronics meet user expectations, and it is crucial for safety-critical applications like driverless cars. Recent advances in deep learning have improved to the point where deep learning outperforms humans in some tasks like classifying objects in images.
Deep-learning software attempts to mimic the activity in layers of neurons in the neocortex, the wrinkly 80 percent of the brain where thinking occurs. The software learns, in a very real sense, to recognize patterns in digital representations of sounds, images, and other data.
The basic idea—that software can simulate the neocortex’s large array of neurons in an artificial “neural network”—is decades old, and it has led to as many disappointments as breakthroughs. But because of improvements in mathematical formulas and increasingly powerful computers, computer scientists can now model many more layers of virtual neurons than ever before.
Deep learning is a specialized form of machine learning. A machine learning workflow starts with relevant features being manually extracted from images. The features are then used to create a model that categorizes the objects in the image. With a deep learning workflow, relevant features are automatically extracted from images. In addition, deep learning performs “end-to-end learning” – where a network is given raw data and a task to perform, such as classification, and it learns how to do this automatically.
Another key difference is deep learning algorithms scale with data, whereas shallow learning converges. Shallow learning refers to machine learning methods that plateau at a certain level of performance when you add more examples and training data to the network.
A key advantage of deep learning networks is that they often continue to improve as the size of your data increases.
In machine learning, you manually choose features and a classifier to sort images. With deep learning, feature extraction and modeling steps are automatic.
We live in a world of numbers. Whether it is memory size, CPU performance, payroll, taxes, weather, rainfall, sales, profit, loss, height, weight, population, encryption, power, the list goes on. We live in a world of numbers. As software Developers, we use numbers all the time. Our software takes them as input, manipulates and processes them, uses them internally, and delivers them as output. Numbers are fine when you need to know a particular value that answers a question, but there are so many of them they can be overwhelming. Sometimes it's not enough to know the numbers, we need to compare them, to look at trends, patterns, and relationships. Sometimes we want to use them to tell a story, and the best way to do that is through visualization. They say, "A picture is worth a thousand words." Well, a picture can be worth considerably more than a thousand numbers. At some point in their career almost every Developer is called upon to visualize data. It may be a simple chart or a full-featured dashboard. Delivering on even a simple request can be remarkably complex. How much data is there? Is it on the server or client? How does it need to be processed for display? Where is the display rendered and is it going to be a Bitmap, HTML 5, Scalable Vector Graphics, or some other output format? What kind of clients should you support? And what are the consequences of that choice? Fundamentals of Data Visualization, the things that every Developer must know.
Data Scientists, Graphic Designers, and You
There are three main roles that come into play in the creation of any Visualization. Let's call the first role the Data Scientist Role. The Data Scientist is the person who understands the source data, what it means, and how it should be processed and rendered in order to tell a particular story. Data Scientists are skilled in math and statistics, and how to select the correct type of chart for the data and the story. Let's call the next role the Graphic Designer Role. The Graphic Designer knows how to make the chart look appealing. Graphic Designers understand vision, human perception, and a bit of psychology, and can ensure that when people see a chart, they'll interpret it correctly and understand the story that you're trying to tell. Finally, there's the Developer role.
You can't visualize data without data. And you can't really talk about numeric data without just a little bit of math.
Now just to be perfectly clear, the concept of Business Intelligence is not an oxymoron, at least not always. Business Intelligence is, in fact, one of the most common applications for data visualization. This is, at least in part, because businesses have the most money available to pay for visualizations. It is also, in part, because businesses tend to accumulate large amounts of data and frequently have no idea what to do with it. They know that there is value in the data somewhere and hope that visualizing the data can provide some insight to help the business become more successful. There are a number of terms you will frequently hear when working on Business Intelligence, such as, Reporting, Analytics, Data mining, Process mining, Business performance management, Benchmarking, Competitive Analysis, Big data, Data warehousing, and so on, but as a Developer, the visualizations that you'll be asked to develop for Business Intelligence will most likely address three distinct questions. To introduce them, we can take advantage of a patent lawsuit between Apple and Samsung. Though not a big fan of patent lawsuits, in this case it had the curious result of publicly revealing Apple's sales of iPhones and iPod Touch for the years 2007-2012. The first question that Business Intelligence tries to address is, What's Going On? Executives are hired to manage the business. Mostly they work at a fairly high-end strategic level. They lack the time and often the skills to dig deeply into the vast amounts of available data. They are looking for reports that concisely present data and visualizations that are representative of the overall operation of the business. Let's look at the results for iPod Touch Sales for the entire period. This is a great example of a report. It answers a simple question. How many iPod Touch units did I sell in each period and how much Revenue did they bring in? Think about where this data came from. There is no doubt that Apple's internal databases contain data on each and every sale. They know when it was, how much it was for, and what distribution channel was used. A different report might list sales by geography or what percentage went through Apple Stores or through Amazon.com To obtain the report we have, someone must have performed queries against the underlying database, filtering by date and product type and aggregating sales from all available sources. Using reports to answer specific questions on the operation of a business is perhaps the most common Business Intelligence task you will face, and it is one of the easiest.