Existing computational models to understand hate speech typically frame the problem as a simple classification task, bypassing the understanding of hate symbols (e.g., 14 words, kigy) and their secret connotations. In this paper, we propose a novel task of deciphering hate symbols. To do this, we leverage the Urban Dictionary and collected a new, symbol-rich Twitter corpus of hate speech. We investigate neural network latent context models for deciphering hate symbols. More specifically, we study Sequence-to-Sequence models and show how they are able to crack the ciphers based on context. Furthermore, we propose a novel Variational Decipher and show how it can generalize better to unseen hate symbols in a more challenging testing setting.

A crucial challenge to efficient and robust motion planning for autonomous vehicles is understanding the intentions of the surrounding agents. Ignoring the intentions of the other agents in dynamic environments can lead to risky or over-conservative plans. In this work, we model the motion planning problem as a partially observable Markov decision process (POMDP) and propose an online system that combines an intent recognition algorithm and a POMDP solver to generate risk-bounded plans for the ego vehicle navigating with a number of dynamic agent vehicles. The intent recognition algorithm predicts the probabilistic hybrid motion states of each agent vehicle over a finite horizon using Bayesian filtering and a library of pre-learned maneuver motion models. We update the POMDP model with the intent recognition results in real time and solve it using a heuristic search algorithm which produces policies with upper-bound guarantees on the probability of near colliding with other dynamic agents. We demonstrate that our system is able to generate better motion plans in terms of efficiency and safety in a number of challenging environments including unprotected intersection left turns and lane changes as compared to the baseline methods.

Niccolo Mejia covers AI applications across industries at Emerj. He holds a bachelor's degree in Writing, Literature, and Publishing from Emerson College. In this article, we explore the applications of AI software within the automotive industry from production and manufacturing to insurance and transportation. We will discuss the equipment involved in collecting and analyzing data along with the potential value they offer to manufacturers, shared mobility companies, insurers, and drivers. We begin our overview of AI in the automotive industry with how machine vision technology could improve the robots that car manufacturers use to build vehicles and maintain quality control.

A series of short documentaries on the future and implications of AI and robotics made by Dr Beth Singler, produced and directed by DragonLight Films, and with funding from the Faraday Institute, the Leverhulme Centre for the Future of Intelligence, the Economic and Social Research Council, and ARM, are available through the Cambridge University's Youtube Channel:

FDA Commissioner Scott Gottlieb is making the most of his final week at the agency. In the month that has passed since Gottlieb rattled the medical device industry with news of his impending resignation, the commissioner has issued 18 public statements pertaining to nearly all corners of the agency's realm, from food, tobacco, and cosmetics to drugs and devices. Friday is Gottlieb's last day on the job. On Tuesday, Gottlieb said the agency will consider a new regulatory framework for reviewing medical devices that use advanced artificial intelligence algorithms. AI has been making headlines in medtech for a while now, and this is certainly not the first time FDA has turned its attention to how AI-based medical devices should be regulated.

Please click the subscribe button at the top of this article to have articles in our DeepTech series delivered directly to you each week. Researchers at Iowa State University designed an AI system to create personalized prosthetic aortic heart valves. These customized valves can restore normal blood flow for people with aortic valvular disease. Over 90,000 people in the US have valve replacement surgery every year. Traditional drug discovery is a very long and expensive process involving many tests to determine the safety and efficacy of each new drug candidate.

One of the terms that we seem to be hearing about a lot these days is AI (Artificial intelligence). The revenues gained from the use of AI in technology is massive, and this is evident from how little things like even the smartphone apps are changing. These advancements show us the type and scale of technology we can look forward to in the future. In regards to AI in marketing, it's evident that it's currently at an infant stage, and hence we're just beginning to see artificial intelligence being used in content marketing. Artificial intelligence is intelligence demonstrated by machines which in contrast is to mimic the natural intelligence displayed by humans.

Turing Award winners (from left to right) Yoshua Bengio, Yann LeCun, and Geoffrey Hinton at the ReWork Deep Learning Summit, Montreal, October 2017. AI "Sputnik moment" (say it in Chinese*) is at hand China is overtaking the US not just in the sheer volume of AI research papers submitted and published, but also in the production of high-impact papers as measured by the top 50%, top 10%, and top 1% most-cited papers. "By projecting current trends, we see that China is likely to have more top-10% papers by 2020 and more top-1% papers by 2025" (Allen Institute for Artificial Intelligence). Cisco attributes the decline to their increased confidence that "migrating to the cloud will improve protection efforts, while apparently decreasing reliance on less proven technologies such as artificial intelligence" (Cisco). Nearly 90% of IT leaders see their use of AI/ML increasing in the future and 41% look for technology that is powered by AI, a top factor in their purchasing decisions.

"I was really surprised how primitive information technology is in the hospitals," says Regina Barzilay, a professor at the Massachusetts Institute of Technology who is working on improving mammography with artificial intelligence. "I was really surprised how primitive information technology is in the hospitals," says Regina Barzilay, a professor at the Massachusetts Institute of Technology who is working on improving mammography with artificial intelligence. Regina Barzilay teaches one of the most popular computer science classes at the Massachusetts Institute of Technology. And in her research -- at least until five years ago -- she looked at how a computer could use machine learning to read and decipher obscure ancient texts. "This is clearly of no practical use," she says with a laugh.

What makes basil so good? Machine learning has been used to create basil plants that are extra-delicious. While we sadly cannot report firsthand on the herb's taste, the effort reflects a broader trend that involves using data science and machine learning to improve agriculture. The researchers behind the AI-optimized basil used machine learning to determine the growing conditions that would maximize the concentration of the volatile compounds responsible for basil's flavor. The study appears in the journal PLOS One today.