Over the past six months, Kent Walker, SVP of global affairs at Google explained, Google has encouraged teams throughout the company to "consider how and whether our AI Principles affect their projects." A new training aimed at both technical and non-technical employees hopes to address the multifaceted ethical issues that arise in their work. It's based on the "Ethics in Technology Practice" developed at Santa Clara University and further tailored for the AI Principles. In a number larger than a hundred, employees from around the globe have tried the course, with Google hoping to make it more accessible in the future.

Though only 23 minutes on the high-speed rail from Shenzhen North to West Kowloon, the journey from the mainland to Hong Kong seems to transport me back half a century. The concrete jungle of my childhood memories hasn't changed one bit. Time seems trapped in the amber of this city of seven million, while the Shenzhen Bay area that I departed has already arrived at the future ahead of schedule. My classmate from a decade earlier, Dr. Ng Lok Tin of the Hong Kong University of Science and Technology, waits for me at the station exit. As though to highlight the discombobulation of modern China, he greets me in Cantonese though he's a native of Shanghai; I, Hong Kong born, on the other hand, speak to him in Modern Standard Mandarin. "Leung Wah Kiu, what's this really about?" he asks me. "A few days ago, two plainclothes officers approached me to ask if Professor Lau had been in touch and for the contact info of his relatives and friends in Hong Kong." "I thought he had been placed in ...

The coming year brings the cleanup and a return to the optimism that technology is really good for the world. By that of course we mean finally playing the much hyped, much delayed Harry Potter augmented-reality game. Also eagerly awaited: 5G arriving in cities, Apple's iOS getting a facelift and Disney launching its own streaming service. We're predicting a year of fulfilled promises--including a possibly forced change in companies' data-collection practices. Here's our annual roundup of the tech that will affect us in the year ahead.

Engineering allowed us to push the limits of human capabilities. We used our understanding of nature and utilized that to serve our purposes. Be it a high performant mechanical machinery or an encoded silicon chip. Computers have been by far one of the most intricate utilization of nature's forces put to help humans in pushing their limits of capabilities i.e. many tasks which can be performed by computers can never be performed that quickly and efficiently by a human or a set of humans. As Steve Jobs would say, computers are like a bicycle for our minds.

What's the best platform for hosting your code, collaborating with team members, and also acts as an online resume to showcase your coding skills? Ask any data scientist, and they'll point you towards GitHub. It has been a truly revolutionary platform in recent years and has changed the landscape of how we host and even do coding. It acts as a learning tool as well. How, you ask? I'll give you a hint – open source! The best part about these releases is that the researchers behind the code also provide pretrained models so folks like you and I don't have to waste time building difficult models from scratch. Additionally, we regularly see the top trending repositories aimed towards coders and developers – this includes resources like cheatsheets, video links, e-books, research paper links, among other things. No matter which level you are at in your professional career (beginner, established or advanced), you will always find something new to learn on GitHub. Natural Language Processing (NLP) was easily the most talked about domain within the community with the likes of ULMFiT and BERT being open-sourced.

In a time where global newsrooms are becoming smaller due to enhanced technological advancement, robotics journalism has emerged as a threat to the fourth estate. Artificial Intelligence has introduced a new paradigm in present-day journalism and newsrooms across the globe are facing fears of staff cuts. Automated journalism has already made its way into newsrooms with automated news writing and distribution, without human supervision already a reality. 'Jia Jia'was the first humanoid robot journalist created by developers from the University of Science and Technology in China's Anhui province in April. She hit headlines when she reported for the country's news agency Xinhua and conducted a live interview with an editor of a popular tech magazine.

Blockchain and cryptocurrencies are gaining ground in the technological world, and so is artificial intelligence.

It's the transformational technology that will rest at the heart of virtually every company's daily operations in the future. Artificial Intelligence – commonly referred to as AI – is changing the way that markets and workforces function across every sector. It's based on the premise that automated systems can learn from data, identify patterns and make decisions with minimal human intervention. And – for one Manchester entrepreneur – this is where the problems with AI potentially arise. Reuben Singh, founder of Salford-headquartered alldayPA, remains convinced that companies overlook the importance of "human contact" – and specifically the emotional intelligence (EI) they bring to this dynamic – at their peril.

The video showed the woman in a pink off-the-shoulder top, sitting on a bed, smiling a convincing smile. But it had been seamlessly grafted, without her knowledge or consent, onto someone else's body: a young pornography actress, just beginning to disrobe for the start of a graphic sex scene. A crowd of unknown users had been passing it around online. She felt nauseous and mortified: What if her co-workers saw it? Would it change how they thought of her? Would they believe it was a fake?

The determinantal point process (DPP) is an elegant probabilistic model of repulsion with applications in various machine learning tasks including summarization and search. However, the maximum a posteriori (MAP) inference for DPP which plays an important role in many applications is NP-hard, and even the popular greedy algorithm can still be too computationally expensive to be used in large-scale real-time scenarios. To overcome the computational challenge, in this paper, we propose a novel algorithm to greatly accelerate the greedy MAP inference for DPP. In addition, our algorithm also adapts to scenarios where the repulsion is only required among nearby few items in the result sequence. We apply the proposed algorithm to generate relevant and diverse recommendations. Experimental results show that our proposed algorithm is significantly faster than state-of-the-art competitors, and provides a better relevance-diversity trade-off on several public datasets, which is also confirmed in an online A/B test.