Regardless of how artificial intelligence (AI) is defined, there is little doubt that this resource can be of great value, especially in big data applications. Undoubtedly, AI is fast becoming a major technological tool for prescriptive analytics, the step beyond predictive analytics that helps us determine how to implement and/or optimize optimal decisions. In business applications, it can assess future risks, quantify probabilities and in so doing, give us insights how to improve market penetration, customer satisfaction, security analysis, trade execution, fraud detection and prevention, while proving indispensable in land and air traffic control, national security and defence. There are also a host of healthcare applications such as patient-specific treatments for diseases and illnesses. Recently, the popular concept of "Singularity" was perceived by computer scientists.

The idea of shifting decision-making to algorithms and artificial intelligence is a compelling one: It can, so the theory goes, remove the inconsistencies and prejudices of human decision-making, and make a fairer world. Life-changing decisions are inevitably affected by our human biases and frailties. One Israeli study, for instance, found that, at the beginning of the day, judges granted parole in two-thirds of cases, granted close to zero just before the lunch break and then granted more again after lunch, once the judges had eaten and taken a break. So if we can't even tackle bad decision-making because we're hungry, how do we even begin to tackle deeper-seated biases? Shifting away from a world so affected by human fallibility therefore seems compelling, but it comes with huge dangers that could entrench existing divides and failures.

Tokyo, Japan and Princeton, NJ, U.S.A., May 15, 2018 - NEC Corporation (NEC; TSE: 6701) announced today successful transmission tests that took place over a commercial subsea cable measuring more than 10,000 kilometers using artificial intelligence (AI) and probabilistic shaping at a modulation of 64QAM. NEC, in a joint research publication with Google, has demonstrated for the first time that the FASTER open subsea cable can be upgraded to a spectral efficiency of 6 bits per second per hertz (b/s/Hz) in an 11,000km segment. This represents a capacity of more than 26 terabits per second (Tb/s) in the C-band, which is over 2½ times the capacity originally planned for the cable, for no additional wet plant capital expenditure. In doing so, the authors set a spectral efficiency-distance product record of 66,102 b/s/Hz in a field trial performed together with live traffic neighboring channels. The team achieved this result using near-Shannon probabilistic-shaping at a modulation of 64QAM, and for the first time on a live cable, artificial intelligence (AI) was used to analyze data for the purpose of nonlinearity compensation (NLC).

Beyonce is at the top of her game, and diet has a lot to do with it. Recently, Beyoncé gave her first major public performance since giving birth to twins. Forty-four days before her Coachella performance, she announced via Instagram that she was going vegan ahead of the show. She also changed the link on her Instagram bio (apparently for the first time) to her new vegan meal planner so others could eat like her as she prepared for the event. And it seems that her decision to go vegan paid off.

Recurrent and convolutional neural networks comprise two distinct families of models that have proven to be useful for encoding natural language utterances. In this paper we present SoPa, a new model that aims to bridge these two approaches. SoPa combines neural representation learning with weighted finite-state automata (WFSAs) to learn a soft version of traditional surface patterns. We show that SoPa is an extension of a one-layer CNN, and that such CNNs are equivalent to a restricted version of SoPa, and accordingly, to a restricted form of WFSA. Empirically, on three text classification tasks, SoPa is comparable or better than both a BiLSTM (RNN) baseline and a CNN baseline, and is particularly useful in small data settings.

Tesla co-founder Elon Musk said on Twitter that it was "super messed up" that an incident involving his company was receiving significant attention, while thousands of auto accidents a year are regularly ignored.

I am doing a video analysis A.I. project for a client, and I want to share with you a super fun idea that popped into my mind tonight while I was waiting for stuff to render. I really don't have time to be writing this, but I feel like I have to. Some ideas are just too awesome to not stay up all night programming. Let's get an A.I. system to outline people in a music video. There have been some great object detection articles on Towards Data Science, and so I don't want to post duplicates of the same cool stuff.

Tristram Buckley says the benches in the Forbidden Journey ride gave him "shaken adult syndrome." It's safe to say Tristram Buckley wasn't swept up in the magic of the Wizarding World of Harry Potter. Buckley, a former visitor to the Universal Studios Hollywood location of the Potter-themed park, claims in a new lawsuit that one of the rides -- Harry Potter and the Forbidden Journey -- left him in pain after causing severe injury to his spine, TMZ reported Monday. According to the suit, Buckley took a seat on the ride's Enchanted Bench, which is suspended from a mechanical arm that moves along a track. The seats also pivot and sway to give riders the sensation of flying through the scenarios presented on a wrap-around screen.

Subscribe to receive updates on Industry 4.0 The Fourth Industrial Revolution is upon us. The first three were based, respectively, on mechanization, mass production, and computing/automation; Industry 4.0 is all about the marriage of physical and digital technologies. Just as with the previous revolutions, Industry 4.0 is disrupting and redefining industries. This time, however, the revolution is progressing with unprecedented speed, driven by smart, connected technologies that are developing at an exponential rate.1 These technology innovations--including cloud computing and platform technologies, big data and analytics, mobile solutions, social and collaborative systems, Internet of Things (IoT) technology, and artificial intelligence (AI)--are fueling and accelerating a new era of digital business transformation. They're reshaping how organizations work, innovate, and create products--and enabling completely new kinds of products and services.2 They're spurring businesses to invent new business models and reimagine how they deliver value to their customers and markets. More broadly, industry boundaries are expanding and blurring, and relationships with business partners are being redefined. Yet too many organizations remain unprepared for the new revolution. A recent Deloitte Industry 4.0 study of C-level executives around the world indicates that, across all industries, only 14 percent of CXOs are "highly confident" that their organizations are ready to harness the changes associated with the new era.3

I am trying to perform multiclass text classification (for 24 classes) on a set documents, but I have a very small dataset currently (1200 total examples). The data collection process is a bit tedious in my case, hence the small dataset size. The best result I have achieved till now is 58% accuracy with an SVM model and a single layer CNN model. Is there any other approach I can try other than collecting more data? I have tried oversampling the training set, but it didn't seem to improve the performance.