In a remarkable step to render human journalism obsolete, state news outlet Xinhua teamed up with search engine Sogou to create the world's first female AI news anchor. According to reports, the anchor, Xin Xiaomeng will make her first appearance in March, during the upcoming two sessions political meeting. As far as applying artificial intelligence in journalism goes, China is pushing the boundaries further than ever. Just four months ago, Xinhua debuted the first male AI News anchor during China's world internet conference in Wuzhen. But, that was just the beginning.

Recent developments are poised to move AI to center stage, leading to revolutions across businesses around the globe. However, challenges remain, and an uncertain future makes it difficult to predict exactly where and how AI will be most effective. Protiviti and ESI ThoughtLab recently conducted a survey of 300 senior executives across the globe to uncover just how AI is being implemented and what to expect in the coming years. According to the survey, just 16 percent of businesses report gaining significant value from AI. Over the next two years, however, this number is expected to more than triple, potentially leading to a majority of businesses relying on AI.

Qualcomm's interest in robots is definitely more than a passing fancy. It just unveiled RB3, its first all-encompassing platform for robotics. The part is based on last year's Snapdragon 845 system-on-a-chip but is "highly optimized" for drones and other automatons, including AI and sensor processing, support for private LTE networks and tight security. Companies can customize it to their heart's content if they're not happy with the off-the-shelf hardware, and choose an OS like Linux or Robot Operating System. You can expect to see RB3-based bots sometime in 2019, with big names like LG and South Korea's Naver on the short list alongside the likes of Anki, JD and OrionStar.

The quality of machine translation produced by state-of-the-art models is already quite high and often requires only minor corrections from professional human translators. This is especially true for high-resource language pairs like English-German and English-French. So, the main focus of recent research studies in machine translation was on improving system performance for low-resource language pairs, where we have access to large monolingual corpora in each language but do not have sufficiently large parallel corpora. Facebook AI researchers seem to lead in this research area and have introduced several interesting solutions for low-resource machine translation during the last year. This includes augmenting the training data with back-translation, learning joint multilingual sentence representations, as well as extending BERT to a cross-lingual setting.

By 2025, augmented analytics will have a far-reaching impact, especially on the US economy. This shortage along with the higher hiring cost will demand for the use of citizen data science. It will permit businessman to leverage data without having earlier experience or expertise. "Through 2020, the number of citizen data scientists will grow five times faster than the number of expert data scientists. Organizations can use citizen data scientists to fill the data science and machine learning talent gap caused by the shortage and high cost of data scientists," said an official from renowned organization.

Artificial Intelligence (AI) was all the rage in the 1980s. Specifically, companies invested heavily to build expert systems – AI applications that captured the knowledge of acknowledged human experts and made it available to solve narrowly defined types of problems. Thus, expert systems were created to configure complex computer systems and to detect likely credit card fraud. This earlier round of AI was triggered by a series of successful academic expert applications created at Stanford University. Dendral analyzed mass spectra data and identified organic molecules – something that, previously, only a few chemists could do. Another expert systems was called Mycin, and it analyzed potential of meningitis infections. In a series of tests, it was shown that Mycin could analyze meningitis as well as human meningitis experts, and it even did slightly better, since it never overlooked possible drug incompatibility issues. The expert systems developed in the Eighties all followed the general approach followed by Dendral and Mycin.

During the AI boom of the 1980s, the field also enjoyed a great deal of hype and rapid investment. Rather than considering the value of individual startups' ideas, investors were looking for interesting technologies to fund. This is why most of the first generation of AI companies have already disappeared. Companies like Symbolics, Intellicorp, and Gensym -- AI companies founded in the '80s -- have all transformed or gone defunct. And here we are again, nearly 40 years later, facing the same issues.

When people think of Artificial Intelligence (AI), the major image that pops up in their heads is that of a robot gliding around and giving mechanical replies. There are many forms of AI but humanoid robots are one of the most popular forms. They have been depicted in several Hollywood movies and if you are a fan of science fiction, you might have come across a few humanoids. One of the earliest forms of humanoids was created in 1495 by Leonardo Da Vinci. It was an armor suit and it could perform a lot of human functions such as sitting, standing and walking.

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