A BSTRACT Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations, longer training times, and unexpected model degradation. To address these problems, we present two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT (Devlin et al., 2019). Comprehensive empirical evidence shows that our proposed methods lead to models that scale much better compared to the original BERT. We also use a self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream tasks with multi-sentence inputs. As a result, our best model establishes new state-of-the-art results on the GLUE, RACE, and SQuAD benchmarks while having fewer parameters compared to BERT -large. The code and the pretrained models are available at https://github.com/ Many nontrivial NLP tasks, including those that have limited training data, have greatly benefited from these pre-trained models. One of the most compelling signs of these breakthroughs is the evolution of machine performance on a reading comprehension task designed for middle and highschool English exams in China, the RACE test (Lai et al., 2017): the paper that originally describes the task and formulates the modeling challenge reports then state-of-the-art machine accuracy at 44. 1%; the latest published result reports their model performance at 83. 2% (Liu et al., 2019); the work we present here pushes it even higher to 89 .4%, a stunning 45 .3% Evidence from these improvements reveals that a large network is of crucial importance for achieving state-of-the-art performance (Devlin et al., 2019; Radford et al., 2019). It has become common practice to pre-train large models and distill them down to smaller ones (Sun et al., 2019; Turc et al., 2019) for real applications.

Artificial Intelligence is changing banking, health, business, and the military. But so far, it has been slow to go big in K-12 education, said Scott Garrigan, a professor at Lehigh University at a session at the International Society for Technology in Education's annual conference here. But that is likely to change in the coming years, he said. No sector will be untouched by AI. It will produce changes as big as the automobile," Garrigan said. "We have no idea what's going to happen as AI rolls out massively.

The landscape of jobs will likely be dramatically transformed by AI in the coming years, and while some jobs will go by the wayside, other jobs will be created. It isn't clear yet how the nature of job automation will impact the economy, whether or not more jobs will be created than displaced, but it is obvious that those who work in the positions created by AI will need training to be effective at them. Displaced workers are going to need the training to work in the new AI-related job fields, but how can these workers be trained quickly enough to remain competitive in the workplace? The answer could be more AI, which could help personalize education and training. Bryan Talebi is the founder and CEO of the startup Ahura AI, which aims to use AI to make online education programs more efficient, targeting them at the specific individuals using them.

Data Science, Machine Learning or the field of Artificial Intelligence is exploding with ever-expanding knowledge areas, numerous new breakthroughs and mind-boggling advancements in innovation. It is becoming extremely difficult to manage time to keep up with the change no matter where one stands now, expert or novice. Whatever category you are in, you can easily find 1000 hours in a year to learn more to achieve one of the above goals. These 1000 hours do not count as your on the job learning or training that you are getting in office. This is exclusively your extra learning activities beyond your office business as usual hours.

We will discuss some of the best machine learning certifications which you can obtain to show off your skills or achieve a good job as a machine learning expert. It is one of the most highly-rated and premium courses of Eduonix for learning Machine Learning. It includes 45 lectures with over 13 hrs of video content and 12 exclusive Machine Learning projects. With this online tutorial, you will be able to build real-world machine learning projects which are highly demanded in the industry. It won't teach you ML from the beginning but with the prior knowledge of programming languages like Python and others, you will create some cool AI & ML projects like- And there is a reason why I said it a little gem.

Waste Management – With AI working in tandem with IoT, it becomes easier for city authorities to remotely monitor waste levels. Additionally, Artificial intelligence in smart cities can help optimize waste management by providing urban planners and authorities with operational and route optimization analytics. Road Traffic – Perhaps the greatest challenges facing many urban areas today is street traffic. A noteworthy goal of Artificial intelligence in smart cities is to enable commuters to get starting with one part of the city then onto the next securely and as fast as could be expected under the circumstances. To accomplish this, urban areas are going to the utilization of IoT and AI-empower traffic arrangement solution.

On today's episode of the podcast, I got to chat with software engineer Jackson Bates who lives and works in Melbourne, Australia. Jackson used to be a high school English teacher, but gradually taught himself to code and landed a pretty sweet gig as a React dev, partly by chance. Today he works part time as a developer, part time as a stay at home dad, and volunteers his time with various open source projects. Jackson grew up in England, and studied English in school. Although going into education seemed a logical choice, he dabbled in other fields - like working at a prison cafeteria - for a while before landing a teaching job.

Lately, Artificial Intelligence (AI) is one of the largest studied fields. There are many applications for it, but education may be one of the most promising. It's no secret that students change. The students today are much different than the students of ten or twenty years ago, but education has stayed relatively the same. There have been a few updates in the curriculum to include the uses of computers and the internet, but besides that, not much has changed.

Abu Dhabi: Taking another bold step in the world of artificial intelligence (AI), Abu Dhabi on Wednesday announced the opening of the world's first dedicated AI university – Mohammad Bin Zayed University of Artificial Intelligence (MBZUAI). Located in Masdar City with the latest state-of-the-art facilities and equipment, the university will offer both masters (two years) and PhD programmes (four years) for local and international graduate students across three main specialised fields – machine learning, computer vision and natural language processing – as the UAE looks to equip the next generation of students with the latest expertise in the field of AI. Official applications for the university are open from this month, with registrations taking place in August of next year. The first batch of classes will start in September 2020. How are we going to produce the right number of people with the right mindset [and] the right knowledge ... That is what this university is about -- providing that person power over 5 to 10 to 20 years.

Algorithms are grading student essays across the country. So can artificial intelligence really teach us to write better? Todd Feathers, who wrote about AI essay grading for Motherboard, called up every state in the country and found that at least 21 states use some form of automated scoring. "The algorithms are prone to a couple of flaws. One is that they can be fooled by any kind of nonsense gibberish sophisticated words. It looks good from afar but it doesn't actually mean anything. And the other problem is that some of the algorithms have been proven by the testing vendors themselves to be biased against people from certain language backgrounds."