Essay On Adversarial Attacks On Machine Learning Algorithms

One of the main features that ensure the protection and just outcomes for all people is the consistency of the adversarial system. This means that each case will have the same features with the examination of evidence and witnesses as well as the ability for the

Understanding the way A.I. works is crucial to understanding A.I. goals. There are several traits that separate Artificial Intelligence from regular machines. One such trait is an A.I.’s ability to “think” through Neural Networks, which are networks made of simulated neurons and neuron layers designed to process and evaluate data. The simulated neurons are individual receptors designed to process and evaluate inputs. Following their evaluation, the neurons send an output to another simulated neuron in the next neuron layer. These neuron layers are layers of simulated neurons grouped by what type of input they receive and output they produce, that scales in complexity (Knight). MIT tech review senior editor Will Kight provided the example of

Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world. So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task [12]. Deep learning is another Machine Learning (ML) algorithm. Deep learning is essentially a set of techniques that help you to parameterize deep neural network structures, neural networks with many, many layers and parameters. Deep Learning breaks down tasks in ways that makes all kinds of machine assists seem possible, even likely. The confusion matrix, in Figure 8 shows that the accuracy of this model is (90.80) with weighted average precision (91.37) greater than recall (91.11) and F1-score (91.24). From the above results, it appears that Deep Learning classifier achieve higher accuracy, precision, recall, and F1-score.

I have started working on a research project using machine learning to build an intrusion detection mechanism. To build up on this background, I am also interested in the projects of implementing machine learning algorithms with an application toward the biological sciences and machine learning from multi-modal data. Working on designing machine learning algorithms with Dr. Kevin Chen would be an enlightening experience to build up my ability with the burgeoning field of machine learning. Machine learning from multi-modal data would also be useful as physical security is also a crucial component of cyber-security as well. I hope to contribute to designing the data sets and machine learning algorithms that can handle the new sensory data available to computers.

With the development of technology, both for ethical and unethical practices, it is a consistent battle with firms protecting their own AIS, and hackers finding the ability to hack into information to receive an advantage in committing a fraud. There are many cases in which a firm(s) were victims of the act committed by the hackers. The main three cases discussed here is: the Department of Veterans Affairs (May 2006), Sony’s PlayStation network (April 2011), and Target’s AIS hack.

Taking this into consideration, we should aim to develop an effective Machine learning application using available Artificial Intelligence paradigms like voice recognition, image processing, natural language processing and knowledge representation. This application will be integrated with Google’s IoT devices like Google Home, Google Android Phones, smart watches and wearables, which are widely available and in use. These devices will continuously monitor and collect data from its surroundings like the android phone can collect voice calls, text messages, and image data available, Google Home can collect noises around it and location, while wearable devices can collect personal data like heartbeat rate, curiosity and anxiety levels. Since we will be dealing with sensitive data, we can incorporate a feature that these devices will collect data only when needed, or activated with a command like “Ok Google” or “Help Google”. The data from these devices will send to our application over the network, where we can analyze and detect. After which the least we could do is to call an emergency hotline and report the location, phone number and address for a possible domestic violence case

The human dependence on technology has been increasing rapidly because humans are trying to incorporate technology in every aspect of life. The increasing dependence on technology in today’s world has led to an increased level of vulnerability of the private information of the people. Most of the technology being used today is open to attack and can be hacked. According to CFO Magazine Global Business Outlook Survey, 80 percent of the U.S. companies have been hacked successfully [PHYS ORG, website]. Thus, cyber-security is a major concern in today’s world. One of the major causes of vulnerabilities found in cyber-security is the acts done by people either performed deliberately or accidentally.

Artificial intelligence (AI) is thought of as an abstract concept confined within the borders of sci-fi movies, when in reality, it is a complex and thriving field that has already found its way into humans’ daily lives. Many people scoff and murmur at the sight of a new, time-saving app or a self-driving car, while others are excited to keep pushing technology to its limits, or even past humans’ limits. However, the question that computer scientists find themselves asking is, “How smart is too smart?”

The theories around AI and machine learning brings new dangers. Specifically, machine learning frameworks frequently have low " interpretability," implying that people experience problems making sense of how the structures achieved

As technology has advanced, information systems have become an integral part of every day life. In fact, there are not too many public or private actions that can take part in today’s society that do not include some type of information system at some level or another. While information systems make our lives easier in most respects, our dependency upon them has become increasingly capitalized upon by persons with malicious intent. Therefore, security within the information systems realm has introduced a number of new devices and software to help combat the unfortunate results of unauthorized network access, identity theft, and the like – one of which

A task in security is to create cryptographic primitives based on hard AI problems. that are computationally intractable. The discrete logarithm problem is to the Diffie-Hellman key exchange, the Digital Signature Algorithm, the elliptic curve cryptography .Using hard (AI) Artificial Intelligence problems for security ,is an exciting new paradigm. By using this paradigm, the most notable primitive invented is Captcha. which distinguishes human users from computers by presenting a challenge, beyondthe capability of computers. But it is easy for humans.

To further elaborate on how it actually works; in machine learning instead of programming of the computer to solve a problem, the programmer actually writes a series of rigid codes to make the computer lean to solve a problem from various examples. As you know that computers can solve complex problems like predicting the pattern of movement of galaxies and so on but it can’t perform easy tasks like identifying objects like a tree or a house, although now a days there are a couple of search engines and applications that are able to do that like Google Reverse Image Search and Apple Images, but they still fail when the image is overshadowed by some other image. So machine learning is basically making the computer think in a way how humans would in this

First of all, we have encountered one major problem that is how to interpret a neural network given its black box characteristics. We really wanted to try ourselves giving interpretation to our results so that we dug into the existing literature and found out a very interesting research paper written by Garson in 1991. In « Illuminating the black box: a randomization approach for understanding variable contributions in artificial neural networks », Olden and al. describes Garson’s algorithm very concisely so that we were able to create a user-defined function on Python that replicates the method. The interpretation of the method is provided below.

Deep Learning (or deep structured learning, or hierarchical learning or deep machine learning) is a branch of Machine Learning which is based on a set of algorithms that attempts to model high level abstractions in data by using a deep graph with multiple processing layers which are composed of multiple non-linear and linear transformations.

Deep architectures are algorithms that apply successive non-linear transformations to data, in an attempt to discover higher and higher level representations of this data [1]. The most common example is an Artificial Neural Network (ANN) with multiple hidden layers. While deep learning is an old concept, learning algorithms on deep architectures were slow and thus impractical [2]. Discovery of new training methods and improved processing power in recent years have allowed for state-of-the-art empirical results in computer vision and speech recognition. Most famously, a group of researchers from Stanford and Google trained a deep network on millions of unlabeled images, which was then able to detect human faces, bodies, and cats [3]. Deep