Bias – both human and data-based – is the biggest ethical challenge facing the development and adoption of artificial intelligence, according to a panel of world-leading AI luminaries. Speaking at last week's Dreamforce conference, Salesforce chief scientist and adjunct professor of Stanford's computer science department, Dr Richard Socher, said the rapid development of AI will inevitably impact more and more people's lives, raising significant ethical concerns. "These algorithms can change elections for the worse, or spread misinformation," he told attendees. "In some benign natural language processing classification algorithms, for example, you may want to maximise the number of clicks, and find something with a terminator image has more clicks so you put more of those pictures in articles." But it is the bias coming through existing datasets being used to train AI algorithms that arguably presents the biggest ethical problem facing industries.

There are two major approaches for structured classification. One is the probabilistic gradient-based methods such as conditional random fields (CRF), which has high accuracy but with drawbacks: slow training, and no support of search-based optimization (which is important in many cases). The other one is the search-based learning methods such as perceptrons and margin infused relaxed algorithm (MIRA), which have fast training but also with drawbacks: low accuracy, no probabilistic information, and non-convergence in real-world tasks. We propose a novel and "shockingly easy" solution, a search-based probabilistic online learning method, to address most of those issues. This method searches the output candidates, derives probabilities, and conduct efficient online learning. We show that this method is with fast training, support search-based optimization, very easy to implement, with top accuracy, with probabilities, and with theoretical guarantees of convergence. Experiments on well-known tasks show that our method has better accuracy than CRF and almost as fast training speed as perceptron and MIRA. Results also show that SAPO can easily beat the state-of-the-art systems on those highly-competitive tasks, achieving record-breaking accuracies. The codes can be found at https://github.com/lancopku

Advances in of Natural Language Processing and Machine Learning are broadening the scope of what technology can do in people's everyday lives, and because of this, there is an unprecedented number of people developing a curiosity in the fields. And with the availability of educational content online, it has never been easier to go from curiosity to proficiency. We gathered some of our favorite resources together so you will have a jumping off point into studying these fields on your own. Some of the resources here are suitable for absolute beginners in either Natural Language Processing or Machine Learning, and others are suitable for those with an understanding of one who wish to learn more about the other. The resources on this post are 12 of the best, not the 12 best, and as such should be taken as suggestions on where to start learning without spending a cent, nothing more!

A lot has happened since Part III. While the last couple of articles went in-depth into exactly I was learning, this one will be a little different. Rather than break it down week by week, I'll cover the major milestones. I graduated from the Udacity Deep Learning Nanodegree (DLND) in August last year. Thinking about how I emailed the support team asking what the refund policy was before starting the course makes me laugh.

The Applied Data Science: Machine Learning program will give you hands-on experience in one of the hottest areas of data science. You will learn tools for predictive modeling and analytics, harnessing the power of neural networks and deep learning techniques across a variety of types of data sets. Each of the four courses in this program will let you demonstrate your newly-acquired skills through a course project. ECTS credits will be awarded to learners who successfully complete all four courses and course projects as well as a final capstone project. These course details are subject to change; please refer to the program outline at the time of registration.

There are a lot of serious topics covered in Altered Carbon, a new science fiction series from Netflix. It delves into misogynistic power structures and the nature of identity. It touches on just how much of our morality is driven by the fact that we die and what might happen if death suddenly stopped being an endpoint and, instead, became a minor stopgap in an ultimately immortal life. But that is not what I'm here to talk to you about. Because while watching Altered Carbon -- even the stuff I didn't like all that much -- my primary critical reaction was, "This is so RAD!!!!" Imagine me sitting in the back of eighth-grade study hall, filling my notebook with scrawled images from this show (that my parents don't know I've seen, because if they did, my Netflix consumption would be seriously questioned), occasionally clicking over my four-color pen to red to write the word "rad" in all caps in the margins.

There's no doubt about it - Data Science is big news right now. We see it on the news every day, the increasing number of news stories about Big Data, the Internet of Things, Deep Learning, Artificial Intelligence, smart cars, smart cities, smart politicians. OK, maybe I went a bit too far with that last one... There's also a great appetite for learning about Data Science too. Every month I get an email from Udemy telling me which courses are their best sellers. The list isn't about Data Science, but there are always plenty of Data Science courses right up there at the top of the list.

The main goal of this reading is to understand enough statistical methodology to be able to leverage the machine learning algorithms in Python's scikit-learn library and then apply this knowledge to solve a classic machine learning problem. The first stop of our journey will take us through a brief history of machine learning. Then we will dive into different algorithms. On our final stop, we will use what we learned to solve the Titanic Survival Rate Prediction Problem. With that noted, let's dive in! As soon as you venture into this field, you realize that machine learning is less romantic than you may think. Initially, I was full of hopes that after I learned more I would be able to construct my own Jarvis AI, which would spend all day coding software and making money for me, so I could spend whole days outdoors reading books, driving a motorcycle, and enjoying a reckless lifestyle while my personal Jarvis makes my pockets deeper. However, I soon realized that the foundation of machine learning algorithms is statistics, which I personally find dull and uninteresting. Fortunately, it did turn out that "dull" statistics have some very fascinating applications. You will soon discover that to get to those fascinating applications, you need to understand statistics very well. One of the goals of machine learning algorithms is to find statistical dependencies in supplied data.

A new national survey commissioned by Northeastern University and conducted by Gallup finds most U.S. adults have an overall positive view of artificial intelligence, but believe they are ill-prepared to deal with AI's expected impact on the global digital economy. The survey comes on the heels of numerous international studies forecasting significant job loss resulting from AI. Overall, 22 percent of Americans with a bachelor's degree or higher level of education say their college or university studies prepared them well or very well to work with AI. Moreover, only 18 percent are extremely confident they could secure the education needed to obtain a comparable job should they lose their current position to advances in new technology. "The answer to greater artificial intelligence is greater human intelligence," said Northeastern President Joseph E. Aoun. "The AI revolution is an opportunity for us to reimagine higher education--to transform both what and how we teach. If colleges and universities can adapt and modernize, we can ensure that tomorrow's learners will be robot-proof."

The reality is that it's neither one. The fear is that automation is sweeping all before it, gobbling up jobs; displacing millions of workers and leaving them unemployed and, worse, unemployable; and exacerbating the income gap. It's reviled by many as a greater threat than jobs shipped overseas, even prompting some to suggest taxing robots to slow their spread. The counterview is that automation is not replacing jobs nearly fast enough. We don't have enough workers to do the jobs available now, and this will get worse as demographic trends play out.