In China, face recognition is transforming many aspects of daily life. Employees at e-commerce giant Alibaba in Shenzhen can show their faces to enter their office building instead of swiping ID cards. A train station in western Beijing matches passengers' tickets to their government-issued IDs by scanning their faces. If their face matches their ID card photo, the system deems their tickets valid and the station gate will open. The subway system in Hangzhou, a city about 125 miles southwest of Shanghai, employs surveillance cameras capable of recognizing faces to spot suspected criminals.

Impressive image captioning results are achieved in domains with plenty of training image and sentence pairs (e.g., MSCOCO). However, transferring to a target domain with significant domain shifts but no paired training data (referred to as cross-domain image captioning) remains largely unexplored. We propose a novel adversarial training procedure to leverage unpaired data in the target domain. Two critic networks are introduced to guide the captioner, namely domain critic and multi-modal critic. The domain critic assesses whether the generated sentences are indistinguishable from sentences in the target domain. The multi-modal critic assesses whether an image and its generated sentence are a valid pair. During training, the critics and captioner act as adversaries -- captioner aims to generate indistinguishable sentences, whereas critics aim at distinguishing them. The assessment improves the captioner through policy gradient updates. During inference, we further propose a novel critic-based planning method to select high-quality sentences without additional supervision (e.g., tags). To evaluate, we use MSCOCO as the source domain and four other datasets (CUB-200-2011, Oxford-102, TGIF, and Flickr30k) as the target domains. Our method consistently performs well on all datasets. In particular, on CUB-200-2011, we achieve 21.8% CIDEr-D improvement after adaptation. Utilizing critics during inference further gives another 4.5% boost.

To paraphrase Arthur Schopenhauer, genius is seeing what everyone else sees and thinking what no one else has thought. Put another way, genius is breaking down the usual silos that isolate ideas and knowledge into specific fields and purviews. Thanks to work about to be presented by researchers at Carnegie Mellon University's School of Computer Science and the Hebrew University of Jerusalem, it may soon apply to AI. The researchers have just given computers the capacity to mine patent databases and other research records in order to repurpose old ideas to solve new problems. To do it, they had to devise a method to teach computers to make analogies.

The explosive growth of structured and unstructured data, along with the availability of new technologies such as cloud computing and machine learning algorithms, have made the expanded use of artificial intelligence (AI) in banking possible. According to Goldman Sachs, AI will enable $34 billion to $43 billion in annual "cost savings and new revenue opportunities" within the financial services sector by 2025. With more data accessible than ever before, banks are actively working on opportunities to better integrate machine learning into their businesses. However, this does not have to mean a less personalized experience for customers. Rather, it is crucial for customer loyalty that, even with decreasing face-to-face interactions, customized interactions are the norm.

Last night, OpenAI's Dota 2 bot beat the world's most celebrated professional players in one-on-one battles, showing just how advanced these machine learning systems are getting. The bot beat Danil "Dendi" Ishutin rather easily at The International, one of the biggest eSports events in the world, and remains undefeated against the world's top Dota 2 players. Elon Musk's OpenAI trained the bot by simply copying the AI and letting the two play each other for weeks on end. "We've coached it to learn just from playing against itself," said OpenAI researcher Jakub Pachoki. "So we didn't hard-code in any strategy, we didn't have it learn from human experts, just from the very beginning, it just keeps playing against a copy of itself. It starts from complete randomness and then it makes very small improvements, and eventually it's just pro level."

Andrew Ng is leaving Baidu after three years as it chief scientist in what is expected to be a huge setback for the Chinese tech group's artificial intelligence efforts. The departure is the second instance of a high-profile foreign hire leaving a Chinese company this year. Ng announced on Wednesday that he will be leaving the firm in April via a post on Medium. The departure could prove a significant blow to Baidu which is increasingly focused on AI. Earlier this year CEO Robin Li described the technology as the search giant's "key strategic focus for the next decade". The firm also scrapped its medical department to focus further on AI last month.

In the future, we will be competing against medically-enhanced workers who can work longer and harder than us. Artificial intelligence will make it easier to monitor our every move in the office. This may sound like science fiction, but it's a likely reality, according to a new report by professional services firm PricewaterhouseCoopers. The report, which drew upon a team of science researchers and a survey of more than 10,000 workers based in China, Germany, India, the U.K., and the U.S., predicts that rapid advances in technology, resource scarcity, and population demographics are among the key forces that would radically shape the future of work by 2030. According to PwC, these forces will result in four potential futures: one where "humans come first," one where "innovation rules," one where "companies care," and one where "corporate is king."

While many studies explore the distant future of AI, this article will focus on the exciting developments that we can see taking place in the online economy right now, and what we can expect to see in the next five years. AI is already changing how our work is done; reinforcing the role of people as drivers of business growth, while also improving efficiency through automation. Let's explore how AI is not only contributing to the growth of the online industry but also how it drastically improves the online shopping experience of customers. Check out a previous article on the economics of augmented reality for more reading on recent tech developments. Before we look at the here and now, let's consider where this path of AI may eventually lead us to.

The rivalry between banks and FinTechs has, at times, been tense if not downright combative. Enter APIs to help the two sides coexist more peacefully. API solutions are doing more than helping two different types of financial institutions find ways to collaborate. By integrating artificial intelligence and machine learning capabilities, companies are relying on API solutions to fight cybersecurity threats and help businesses determine the trustworthiness of a potential trading partner before entering a risky arrangement. Because these solutions that can help reduce the risk of conducting digital commerce, investors are taking notice and writing the big checks with several AI and machine learning solutions securing millions in recent fundraising rounds.

His stark warning came at a time when the US and North Korea remain on heightened alert amid spiraling tensions on the Korean Peninsula. Earlier this week, both sides degenerated to open threats, demonstrating readiness to use coercive force if provoked to do so. Whereas the US said it may rely on strategic bombers to hit North Korean targets, the Asian nation's military announced that a plan of striking the American airbase in Guam will be ready soon. Adding fuel to the crisis, President Donald Trump said the US military assets are "locked and loaded" in case if Pyongyang misbehaves. The heated exchange – coupled by saber-rattling – has revived the threat of war on the Korean Peninsula, with many speculating on its impact on global affairs.