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) …
Scientists at Harvard have copied their large, insect-inspired robot HAMR (Harvard Ambulatory Microrobot) into a smaller form factor. The new robot, HAMR-JR, is the size of a penny, measuring 2.25 centimetres across. The robot is capable of quick movement, able to travel 14 times the length of his body in a single second, which makes it one of the smallest and fastest robots currently made, according to Harvard. While it might be the size of a penny, it is significantly lighter, weighing only 0.3 grams. The ability to keep the familiar design, but change the scale of the robot, means that it can be used in a variety of purposes, including surgeries or large-scale industry because of its ability to carry heavy payloads. The method of miniaturising the robot was surprisingly straightforward: researchers simply shrunk the 2D sheet design of the robot, as well as its circulatory, to a more minute scale.
This past week, we witnessed wrenching debates over speech--involving protesters on the street, our Twitterer-in-chief, and aspiring New York Times op-ed writers. Some of the best tools we have to inspire and contextualize social movements are books and film, and in the next week, we will host conversations with some of the most interesting leaders in the book industry and Hollywood. We hope you'll join us: After a man is injured in a forklift accident, he takes on a lucrative offer to "raise" a robot. After a jarring first impression (imagine a toddler in the body of a massive robot), the relationship makes the protagonist rethink much of his life. In the response essay, John Frank Weaver, author of Robots Are People Too warns about the manipulative capabilities of all-too-human robots: "A company that records all your interactions raising a child--the stress, the exhaustion, the jubilation, the love--has a treasure trove of information about what makes you tick as a person, even when the child is a robot."
Frank Herbert's classic science fiction novel Dune, first published in 1965, is still extremely influential. "I was worried," Kressel says in Episode 417 of the Geek's Guide to the Galaxy podcast. "I was like, 'Am I going to read this and not like it now? Have I outgrown this book?' It was the exact opposite. I love it even more."
Neural Architecture Search has become a focus of the Machine Learning community. Techniques span Bayesian optimization with Gaussian priors, evolutionary learning, reinforcement learning based on policy gradient, Q-learning, and Monte-Carlo tree search. In this paper, we present a reinforcement learning algorithm based on policy gradient that uses an attention-based autoregressive model to design the policy network. We demonstrate how performance can be further improved by training an ensemble of policy networks with shared parameters, each network conditioned on a different autoregressive factorization order. On the NASBench-101 search space, it outperforms most algorithms in the literature, including random search.
Hands-on of Machine Learning in Cybersecurity Supervised and unsupervised machine learning models for cybersecurity Description Machine learning is disrupting cybersecurity to a greater extent than almost any other industry. Many problems in cyber security are well suited to the application of machine learning as they often involve some form of anomaly detection on very large volumes of data. This course deals the most found issues in cybersecurity such as malware, anomalies detection, SQL injection, credit card fraud, bots, spams and phishing. All these problems are covered in case studies.
Gain a Strong Understanding of TensorFlow - Google's Cutting-Edge Deep Learning Framework Build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow Set Yourself Apart with Hands-on Deep and Machine Learning Experience Grasp the Mathematics Behind Deep Learning Algorithms Understand Backpropagation, Stochastic Gradient Descent, Batching, Momentum, and Learning Rate Schedules Know the Ins and Outs of Underfitting, Overfitting, Training, Validation, Testing, Early Stopping, and Initialization Competently Carry Out Pre-Processing, Standardization, Normalization, and One-Hot Encoding Description Gain a Strong Understanding of TensorFlow - Google's Cutting-Edge Deep Learning Framework Build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow Set Yourself Apart with Hands-on Deep and Machine Learning Experience Grasp the Mathematics Behind Deep Learning Algorithms Understand Backpropagation, Stochastic Gradient Descent, Batching, Momentum, and Learning Rate Schedules Know the Ins and Outs of Underfitting, Overfitting, Training, Validation, Testing, Early Stopping, and Initialization Competently Carry Out Pre-Processing, Standardization, Normalization, and One-Hot Encoding Data scientists, machine learning engineers, and AI researchers all have their own skillsets. But what is that one special thing they have in common? They are all masters of deep learning. We often hear about AI, or self-driving cars, or the'algorithmic magic' at Google, Facebook, and Amazon. But it is not magic - it is deep learning. And more specifically, it is usually deep neural networks – the one algorithm to rule them all.
The book is about quickly entering the world of creating machine learning models in R. The theory is kept to minimum and there are examples for each of the major algorithms for classification, clustering, features engineering and association rules. The book is a compilation of the leaflets the authors give to their students during the practice labs, in the courses of Pattern Recognition and Data Mining, in the Electrical and Computer Engineering Department of the Aristotle University of Thessaloniki.
We invited three industry expert speakers using AI to battle climate change. During the hour long webinar, Anita Faul, Data Scientist at the British Antarctic Survey, Lauren Kuntz, CEO and Co-Founder of Gaiascope and Topher White, CEO and Founder of Rainforest Connections walked us through their business use applications of AI to fight the change in climate. Anita started her talk with an explanation of the Thwaites Glacier, otherwise know as the'Doomsday Glacier'. This glacier is responsible for 4% of all sea level increase - if it were to melt completely, sea levels would rise by half a meter in total (hence the name). Therefore, Anita's objective at the Antarctic Survey is to identify icebergs efficiently and reliably in Synthetics Aperture Radar (SAR) satellite images to estimate ice loss.
Musk was responding to a massive feature story published in the MIT Technology Review about OpenAI, the AI research lab founded in part by Elon Musk, alongside others. The lab operates with the mission of developing safe and ethical AI that'll be good for the world. But MIT Tech's reporting tells of how Open AI went from being a transparent organization to a relatively opaque one (hence Musk's preceding Tweet about OpenAI needing to "be more open"). Musk's ability to self-aggrandize or self-flagellate is usually surprising in equal measure, but never shocking: Industries often argue for their own regulation as a way to keep government regulators off their backs. Though credit where it's due: Musk has been, as in the case of when he argued in favor of regulating autonomous weapons, more substantially -- and more effectively -- vocal than most when it comes to regulating AI. Whether or not this will have any substantial effects on other companies (statements from CEOs, regulatory commission efforts, etc) let alone Tesla or OpenAI will be nothing if not a compelling plot to watch.