If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
How many of you think that e-commerce is come of age? A friend of mine was telling me how she finds browsing through 80,000 dresses online to find that one perfect dress for a date the most tedious thing to do. And rather prefers to go to one store with 20 limited and options and pick one dress that is the best from among them. The dilemma of choosing from thousands, lakhs and crores of products to find that one particular things has put online shopping at the risk of saturation. In this world of fast entry and exit, continuous updating of technology is a must in any sphere and for e-commerce the ship will soon sink if it doesn't change its current strategy and include disruptive technology as a part of its growth plan.
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, autonomous locomotion and board game programs, where they have produced results comparable to and in some cases superior to human experts. With massive amounts of computational power, machines can now recognize objects and translate speech in real time. 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, which make them incompatible with neuroscience evidences.
The majority of the deep learning applications that we see in the community are usually geared towards fields like marketing, sales, finance, etc. We hardly ever read articles or find resources about deep learning being used to protect these products, and the business, from malware and hacker attacks. While the big technology companies like Google, Facebook, Microsoft, and Salesforce have already embedded deep learning into their products, the cybersecurity industry is still playing catch up. It's a challenging field but one that needs our full attention. In this article, we briefly introduce Deep Learning (DL) along with a few existing Information Security (hereby referred to as InfoSec) applications it enables. We then deep dive into the interesting problem of anonymous tor traffic detection and also present a DL-based solution to detect TOR traffic.
I have been asked by quite a few people on how to start Machine Learning and Deep Learning. Here, I have curated a list of resources which I used and the path I took when I first learnt Machine Learning. I will keep on updating this article as I find more helpful resources. This will teach you the ropes of Machine Learning and will brush up your Linear Algebra skill a little bit. Make sure you do all the assignments and after you have completed the course, you will get a hold of Machine Learning concepts such as; Linear Regression, Logistics Regression, SVM, Neural Networks and K-means clustering.
TensorFlow, the open source software library developed by the Google Brain team, is a framework for building deep learning neural networks. It is also considered one of the best ways to build deep learning models by machine learning practitioners across the globe. In deep learning models, which rely on a lot of data and computing resources, TensorFlow is used significantly. Given its flexible architecture for easy deployment on various platforms such as CPUs, GPUs and TPUs, TensorFlow remains one of the favourite libraries to get into ML. Its huge popularity also means that tech enthusiasts are on a constant lookout to learn more and work more with this library.
This post is part 1 of a series. Part 2 is an opinionated introduction to AutoML and neural architecture search, and Part 3 will look at Google's AutoML in particular. There are frequent media headlines about both the scarcity of machine learning talent (see here, here, and here) and about the promises of companies claiming their products automate machine learning and eliminate the need for ML expertise altogether (see here, here, and here). In his keynote at the TensorFlow DevSummit, Google's head of AI Jeff Dean estimated that there are tens of millions of organizations that have electronic data that could be used for machine learning but lack the necessary expertise and skills. I follow these issues closely since my work at fast.ai focuses on enabling more people to use machine learning and on making it easier to use.
Aki Fujimura, chief executive of D2S, sat down with Semiconductor Engineering to discuss Moore's Law and photomask technology. Fujimura also explained how artificial intelligence and machine learning are impacting the IC industry. What follows are excerpts of that conversation. SE: For some time, you've said we need more compute power. So we need faster chips at advanced nodes, but cost and complexity are skyrocketing. Fujimura: Moore's Law is definitely slowing down, but I'm confident there will be continued innovation everywhere to keep it going for a while. There's a lot that every discipline of the eco-system is working on to make incremental and breakthrough improvements.
Machine Learning training bootcamp is a 3-day specialized training course that covers the essentials of machine learning, a shape and utilization of man-made reasoning (AI). Machine learning computerizes the information investigation process by empowering PCs, machines and IoT to learn and adjust through experience connected to particular undertakings without unequivocal programming. Learning Objectives: Learn about Artificial Intelligence and Machine Learning List similarities and differences between AI, Machine Learning and Data Mining Learn how Artificial Intelligence uses data to offer solutions to existing problems Explore how Machine Learning goes beyond AI to offer data necessary for a machine to learn, adapt and optimize / Clarify how Data Mining can serve as foundation for AI and machine learning to use existing information to highlight patterns List the various applications of machine learning and related algorithms Learn how to classify the types of learning such as supervised and unsupervised learning Implement supervised learning techniques such as linear and logistic regression Use unsupervised learning algorithms including deep learning, clustering and recommender systems (RS) used to help users find new items or services, such as books, music, transportation, people and jobs based on information about the user or the recommended item Learn about classification data and Machine Learning models Select the best algorithms applied to Machine Learning Make accurate predictions and analysis to effectively solve potential problems List Machine Learning concepts, principles, algorithms, tools and applications Learn the concepts and operation of support neural networks, vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means and clustering Comprehend the theoretical concepts and how they relate to the practical aspects of machine learning / Be able to model a wide variety of robust machine learning algorithms including deep learning, clustering and recommendation systems Course Agenda and Topics: The Basics of Machine Learning Machine Learning Techniques, Tools and Algorithms Data and Data Science Review of Terminology and Principles Applied Artificial Intelligence (AI) and Machine Learning Popular Machine Learning Methods Learning Applied to Machine Learning Principal component Analysis Principles of Supervised Machine Learning Algorithms Principles of Unsupervised Machine Learning Regression Applied to Machines Learning Principles of Neural Networks Large Scale Machine Learning Introduction to Deep Learning Applying Machine Learning Overview of Algorithms Overview of Tools and Processes Request More Information .
If we can protect videos, audio and photos with digital watermarking, why not AI models? This is the question my colleagues and I asked ourselves as we looked to develop a technique to assure developers that their hard work in building AI, such as deep learning models, can be protected. You may be thinking, "Protected from what?" Well, for example, what if your AI model is stolen or misused for nefarious purposes, such as offering a plagiarized service built on stolen model? This is an concern, particularly for AI leaders such as IBM. Earlier this month we presented our research at the AsiaCCS '18 conference in Incheon, Republic of Korea, and we are proud to say that our comprehensive evaluation technique to address this challenge was demonstrated to be highly effective and robust.