Seemingly, one of the most controversial things about Tesla cars is its Autopilot feature, a driver-assist feature that helps drivers navigate and pilot their vehicle. Oddly, while news of exciting Autopilot features comes out regularly, general information about exactly what Autopilot is, what the options are, and what it can and cannot do seem to be few and far between. I have tried to collect and answer the biggest questions about Autopilot below to help prospective buyers know what the system is and is not, as well as to inform journalists about the system in case they find themselves trying to cover a news story regarding the system. When the next questionable news story comes out, please feel free to link this article for anyone wondering about the system. Please note that all of the below information refers to Tesla vehicles containing Autopilot 2.0 hardware or higher in them (vehicles built since October of 2016). Although, the majority of the information will apply to all Tesla vehicles that are Autopilot enabled.
Advances in Unmanned Aerial Vehicles (UAVs), also known as drones, offer unprecedented opportunities to boost a wide array of large-scale Internet of Things (IoT) applications. Nevertheless, UAV platforms still face important limitations mainly related to autonomy and weight that impact their remote sensing capabilities when capturing and processing the data required for developing autonomous and robust real-time obstacle detection and avoidance systems. In this regard, Deep Learning (DL) techniques have arisen as a promising alternative for improving real-time obstacle detection and collision avoidance for highly autonomous UAVs. This article reviews the most recent developments on DL Unmanned Aerial Systems (UASs) and provides a detailed explanation on the main DL techniques. Moreover, the latest DL-UAV communication architectures are studied and their most common hardware is analyzed.
This post expands on the NAACL 2019 tutorial on Transfer Learning in NLP. The tutorial was organized by Matthew Peters, Swabha Swayamdipta, Thomas Wolf, and me. In this post, I highlight key insights and takeaways and provide updates based on recent work. The slides, a Colaboratory notebook, and code of the tutorial are available online. For an overview of what transfer learning is, have a look at this blog post. Transfer learning is a means to extract knowledge from a source setting and apply it to a different target setting. In the span of little more than a year, transfer learning in the form of pretrained language models has become ubiquitous in NLP and has contributed to the state of the art on a wide range of tasks.
This post expands on the NAACL 2019 tutorial on Transfer Learning in NLP. The tutorial was organized by Matthew Peters, Swabha Swayamdipta, Thomas Wolf, and me. In this post, I highlight key insights and takeaways and provide updates based on recent work. The slides, a Colaboratory notebook, and code of the tutorial are available online. For an overview of what transfer learning is, have a look at this blog post. In the span of little more than a year, transfer learning in the form of pretrained language models has become ubiquitous in NLP and has contributed to the state of the art on a wide range of tasks.
PLEASE NOTE THE NEW ADDRESS OF MORSE BARNES-BROWN & PENDLETON at 480 Totten Pond Road. Artificial intelligence technologies are threatening to take over many decision-making tasks humans perform at work and in personal life. AI systems are already making critical decisions in areas previously thought to be the exclusive domain of humans: driving cars, reviewing job applications, underwriting loans, and even endeavoring to create patentable innovation and recommending sentencing in the criminal justice system. What does this rapid and seemingly unstoppable development in artificial intelligence mean for the legal profession? In his talk, Joe Barkai will provide an overview of key AI technologies.
Researchers from University of Pennsylvania, Northwestern University, University of Maryland, Columbia University, and Emory University published a new article in the Journal of Marketing that provides an overview of automated textual analysis and describes how it can be harnessed to generate marketing insights. The study, forthcoming in the January issue of the Journal of Marketing, is titled "Uniting the Tribes: Using Text for Marketing Insights" and authored by Jonah Berger, Ashlee Humphreys, Wendy Moe, Oded Netzer, and David Schweidel. Online reviews, customer service calls, press releases, news articles, marketing communications, and other interactions create a wealth of textual data companies can analyze to optimize services and develop new products. By some estimates, 80-95% of all business data is unstructured, with most of that being text. This text has the potential to provide critical insights about its producers, including individuals' identities, their relationships, their goals, and how they display key attitudes and behaviors.
Historically, we carried out content moderation using third party vendors, but with the increasing volume of the images (and text content) we started to automate as much of this work as possible with the help of machine learning models. In the next few sections, we will provide an overview of our modeling framework, data collection, and evaluation frameworks. One challenge we faced when we started this project was the lack of enough labeled data with granular categories for user generated content. In the past, Expedia teams labeled content using crowd-sourcing, but in many cases we found that images had only been labeled as approved or rejected without specifying the reason. This meant we lacked the training data to inform models why an image was rejected (an image can be rejected because it had low quality, or because it contains identifiable children, or for many other reasons).
Recent years have seen a rising interest in developing AI algorithms for real world big data domains ranging from autonomous cars to personalized assistants. At the core of these algorithms are architectures that combine deep neural networks, for approximating the underlying multidimensional state-spaces, with reinforcement learning, for controlling agents that learn to operate in said state-spaces towards achieving a given objective. The talk will first outline notable past and future efforts in deep reinforcement learning as well as identify fundamental problems that this technology has been struggling to overcome. Towards mitigating these problems (and open up an alternative path to general artificial intelligence), I will then summarize a brain computing model of intelligence, rooted in the latest findings in neuroscience. The talk will conclude with an overview of the recent research efforts in the field of multi-agent systems, to provide the future teams of humans and agents with the necessary tools that allow them to safely co-exist.
Artificial intelligence is a trending technology from quite a few years now. You must have heard a lot about it in tech news and blogs. There are various predictions about the future of Artificial intelligence but have you ever been keen to about its initial stages? In contemporary times, AI along with its subsets machine learning and deep learning are ruling the innovations in the software industry market. In fact, the magic of AI is such that 41 percent of consumers are expecting that their life will change with AI in the future.
The sooner fraud detection occurs the better as the likelihood of further losses is lower, potential recoveries are higher, and security issues can be addressed more rapidly. Catching fraud in an early stage, though, is more difficult than detecting it later, and requires specific techniques. Packed with numerous real-world examples, Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques authoritatively shows you how to put historical data to work against fraud. Authors Bart Baesens, Véronique Van Vlasselaer, and Wouter Verbeke expertly discuss the use of unsupervised learning, supervised learning, and social network learning using techniques across a wide variety of fraud applications, such as insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, and tax evasion. This book provides the essential guidance you need to examine fraud patterns from historical data in order to detect fraud early in the process.