Understand and implement word2vec Understand the CBOW method in word2vec Understand the skip-gram method in word2vec Understand the negative sampling optimization in word2vec Understand and implement GloVe using gradient descent and alternating least squares Use recurrent neural networks for parts-of-speech tagging Use recurrent neural networks for named entity recognition Understand and implement recursive neural networks for sentiment analysis Understand and implement recursive neural tensor networks for sentiment analysis Install Numpy, Matplotlib, Sci-Kit Learn, Theano, and TensorFlow (should be extremely easy by now) Understand backpropagation and gradient descent, be able to derive and code the equations on your own Code a recurrent neural network from basic primitives in Theano (or Tensorflow), especially the scan function Code a feedforward neural network in Theano (or Tensorflow) Helpful to have experience with tree algorithms In this course we are going to look at advanced NLP. Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices. These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words. In this course I'm going to show you how to do even more awesome things. We'll learn not just 1, but 4 new architectures in this course.

Understand and implement word2vec Understand the CBOW method in word2vec Understand the skip-gram method in word2vec Understand the negative sampling optimization in word2vec Understand and implement GloVe using gradient descent and alternating least squares Use recurrent neural networks for parts-of-speech tagging Use recurrent neural networks for named entity recognition Understand and implement recursive neural networks for sentiment analysis Understand and implement recursive neural tensor networks for sentiment analysis Install Numpy, Matplotlib, Sci-Kit Learn, Theano, and TensorFlow (should be extremely easy by now) Understand backpropagation and gradient descent, be able to derive and code the equations on your own Code a recurrent neural network from basic primitives in Theano (or Tensorflow), especially the scan function Code a feedforward neural network in Theano (or Tensorflow) Helpful to have experience with tree algorithms In this course we are going to look at advanced NLP. Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices. These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words. In this course I'm going to show you how to do even more awesome things. We'll learn not just 1, but 4 new architectures in this course.

The recent surveys, studies, forecasts and other quantitative assessments of the health and progress of AI provided new numbers regarding business leaders' assessment of China as a global AI leader, the current worldwide ranking of China's AI-related entrepreneurial and research activities, plans for AI adoption by U.S. enterprises and expectations regarding its impact on jobs, and the use of AI in face recognition, physical security monitoring, cashierless retail, categorizing open-ended survey responses, and detecting plant diseases and atrial fibrillation. A doctor examines a magnetic resonance image on a computer screen during the CHAIN Cup at the China National Convention Center in Beijing, June 30, 2018. A computer running artificial intelligence software defeated two teams of human doctors in accurately recognizing maladies in magnetic resonance images in a contest that was billed as the world's first competition in neuroimaging between AI and human experts. The U.S. Department of Homeland Security estimates face recognition will scrutinize 97% of outbound airline passengers by 2023 [The Economist] More than 4.5 million websites use reCAPTCHA and the system collects hundreds of millions of daily solves or more than 100 person-years of labor every day; Google/reCAPTCHA has extracted to date over $7 billion of free labor [hcaptcha] The Bureau of Labor Statistics' injury and illness database is built upon text-based descriptions of work-related injuries and illnesses it receives from workplaces across the country each year; categorizing the description into actionable data used to be done manually, but this year, the BLS has done 80% of that automatically using deep neural networks [governmentCIO] The AI market worldwide is estimated to grow by $75.54 billion from 2019 to 2023 [Technavio] The AI market worldwide is estimated to reach $202.57 Data is eating the world quote of the week: "The market for data labeling passed $500 million in 2018 and it will reach $1.2 billion by 2023, according to the research firm Cognilytica. This kind of work, the study showed, accounted for 80 percent of the time spent building A.I. technology"--The New York Times AI is "mimicking the brain" quote of the week: "Computer vision… is nothing like the human sort"--The Economist Robots are eating the world quote of the week: "A human can certainly move a part faster than a cobot [collaborative robot]. However, it does not take coffee breaks and continues to work for several hours after we have already gone home"--Pekka Myller, Ket-Met Robots are eating the world quote of the 19th century: "[A Linotype] could work like six men and do everything but drink, swear, and go out on strike"--Mark Twain

The first autonomous vehicles were built in the 1980s and since then, major companies and research organizations have started developing prototype autonomous vehicles: General Motors, Bosch, Nissan, Audi, Volvo, Oxford University, Google, and more. Recent car models are equipped with cruise control features that follow road markings and automatically adjust speed to maintain a proper distance between vehicles in the same lane. An autonomous vehicle must be competent in a wide range of machine learning processes before it can drive safely. Multi-step AI systems allow autonomous vehicles to process image and video data in real time and safely coordinate with other vehicles on the road. We as humans have no problem recognizing other vehicles, pedestrians, trees, road signs, and other objects when we're driving.

Andreessen Horowitz, the venture capital firm, is backing Palmer Luckey's virtual wall start-up at a $1 billion-plus, unicorn valuation. This is the same Palmer Luckey who left Facebook after it was revealed that he was backing right-wing internet trolls and anti-Hillary Clinton meme factories in 2016. Mr. Luckey's surveillance start-up, Anduril, has gone where many Silicon Valley tech workers have refused to go. The start-up, which is also backed by the Palantir co-founders Joe Lonsdale and Peter Thiel, is testing digital cameras and artificial intelligence technology that tracks people crossing the southern border. Tech workers have made clear in recent months that they want no part of similar military and surveillance projects.

As made clear by our principles for Trust and Transparency, IBM has always understood that we need to lead by demonstrating responsible adoption and use of new technologies that we develop and bring to the world. That is why we are a big supporter of the EU's work to develop Ethics Guidelines for Trustworthy AI, now nearing completion. The guidelines will cement Europe's position as a global pioneer for artificial intelligence that is developed and deployed responsibly and ethically. I have the privilege of being a member of the High Level Expert Group on AI selected by the European Commission and mandated to develop ethics, policy and investment recommendations for AI. Work is ongoing in all three areas.

"Part of what we want is to educate the writing community in a really open way. So we have set this up to find and incubate new writing talent and get them up to speed with what we do." To qualify for The Writers' Room, a track record of screen credits was preferred but not essential.

The AI revolution has arrived. And although the technology is still in its infancy, it promises to radically transform the global economy--impacting human lives, culture, and politics in ways that we can scarcely imagine. A recent article by Forbes Technology Council member Christian Pedersen argued that artificial intelligence will create new opportunities for data scientists, researchers, analysts, and other highly educated technical specialists even as the more easily-automated job functions of low skill workers "fall to the wayside." Pedersen's argument is correct on both counts, but the AI economy is also dependent upon one more ingredient: subject-matter expertise. Participation in the burgeoning AI economy doesn't require an advanced degree in data science or fluency in the latest programming languages.

H3 Dynamics has partnered with Curitiba-based EPH Engineering in Brazil, a firm that specializes in hydropower design, dam inspections and safety plans, to launch a turnkey dam inspection solution that combines AI-enabled damage assessment and HYCOPTER fuel cell drones capable of flying 3.5 hours at a time. With over 5,000 dams submitted to the Brazilian Dam Safety Plan, and two recent collapse incidents causing more than 300 deaths and major environmental damage, Brazilian authorities have tightened inspection and upkeep requirements in the country. "Many accident reports show that problems were not detected by instrumentation but by visual observation. Drones can help, but due to the large dimensions of these structures we need much longer flight times." Some of the dams are so large that they would require months of battery-powered drone flights to fully scan their surfaces.

This Brisbane AI startup claims to be able to predict ad creative success on various platforms with 82.4% certainty. Junction AI, artificial intelligence start-up coming out of Brisbane and Austin, Texas claims to have developed a proprietary machine learning platform that takes the guesswork out of selecting creative and copy for Google Ads, social media ads and web promotions. Ostensibly, marketers will be able to drag and drop their images and text onto the Junction AI dashboard and receive a percentage score of the probability that their promotion will convert a certain target audience. The startup says marketers, agencies and business owners can expect to predict the success of digital advertising with a confidence interval of 82.4%. The platform is currently in pre-release testing and proof trials with 12 agencies and brands in the US and Canada, with tests in the Australian market to begin soon.