Former US Treasury secretary Larry Summers recently took exception to current US Treasury Secretary Steve Mnuchin's views on artificial intelligence (AI) and related topics. The difference between the two seems to be, more than anything else, a matter of priorities and emphasis. Mnuchin takes a narrow approach. He thinks that the problem of particular technologies called "artificial intelligence taking over American jobs" lies "far in the future." And he seems to question the high stock-market valuations for "unicorns" – companies valued at or above US$1 billion that have no record of producing revenues that would justify their supposed worth and no clear plan to do so.

Will there be a time in the not-so-distant future when people won't need to learn a second language -- instead relying on machine translation powered by artificial intelligence to interpret real-time conversations? This is a key question, and a fear, for many businesses in the language industry. But panelists at a recent symposium on how the "internet of things" will affect media outlets and education, co-hosted by English language school chain Aeon Corp. and The Japan Times, agreed that AI and other technologies will never be able to completely replace humans. For those who want to study a second language, the goal is not simply to become fluent in the language, said Aeon President Yoshikazu Miyake. "It's to become confident enough to enjoy communication in a foreign language," Miyake said.

You might have probably heard about the artificial intelligence being developed by some big researchers around the world. The current period of era is also about creating technology that not only process faster, but also works efficiently just like the human brain. The innovation in such technologies has given rise to cognitive computing, which nothing but another miracle innovative development by a human brain to let the machine learn just like human being. Recently, IBM with its new cognitive system called as IBM Watson have entered into the segment of artificial learning to make system that is capable of learning and understanding knowledge to interact with human in a more natural way. The cognitive computing is a self-learning technology platform that uses data mining and pattern recognition to simulate itself in a way that human brain works.

In a book that's become the darling of many a Silicon Valley billionaire -- Sapiens: A Brief History of Humankind -- the historian Yuval Harari paints a picture of humanity's inexorable march towards ever greater forms of collectivization. From the tribal clans of pre-history, people gathered to create city-states, then nations, and finally empires. While certain recent political trends, namely Brexit and the nativism of Donald Trump would seem to belie this trend, now another luminary of academia has added his voice to the chorus calling for stronger forms of world government. Far from citing some ancient historical trends though, Stephen Hawking points to artificial intelligence as a defining reason for needing stronger forms of globally enforced cooperation. It's facile to dismiss Stephen Hawking as another scientist poking his nose into problems more germane to politics than physics.

Artificial intelligence is a hot topic in theoretical and applied research, as well as in general discussions about the profound impact it may have on individuals, industries and economies. The new Centre for Artificial Intelligence at UTS (UTS: CAI) will focus on the theoretical foundations and advanced technologies that will create intelligent machines with greater capacity for perception, learning and reasoning. "Establishing this centre gives us the opportunity to explore beyond core technology and into the impact of our discoveries. This includes the ethics of artificial intelligence, such as interrogating the way it will impact the future of work; and moral decisions we will need to explore around developments such as driverless vehicles," Deputy Vice-Chancellor Research Professor Glenn Wightwick told guests at the launch event. NSW Chief Scientist Professor Mary O'Kane pointed to Australia's well-established research into AI, which included her own experience gaining a PhD in the 1970s at the NSW Institute of Technology, now UTS.

Global chip maker Intel on Tuesday announced a string of initiatives to boost the usage of Artificial Intelligence (AI) in diverse sectors by collaborating with partners and customers across the country. "Our developer education programme will educate 15,000 scientists, developers, analysts and engineers on AI technologies, including Deep Learning and Machine Learning in India," said Intel South Asia Managing Director Praksh Mallya here. AI is a software programme that makes computers and machines think intelligently and faster with more predictability than a human mind. AI is also the main workload in data centres which operate in line with the Moore's Law of computing power doubling every year. By 2020, the industry expects more servers to process data analytics than other workloads and analytics predictors will be built into every application.

A Chinese artificial intelligence engineer has given up on the search for love and "married" a robot he built himself. Zheng Jiajia, 31, decided to commit after failing to find a human spouse, his friend told Qianjiang Evening News. Zheng had also become tired of the constant nagging from his family and pressure to get married, so he turned to a robot he built late last year and named Yingying. After two months of "dating", he donned a black suit to "marry" her at a ceremony attended by his mother and friends at the weekend in the eastern city of Hangzhou. While not officially recognised by the authorities, the union had all the trappings of a typical Chinese wedding, with Yingying's head covered with a red cloth in accordance with local tradition.

In September 2010, a three-person AI startup called DeepMind Technologies launched in London, with the goal of "solving intelligence." Four years later, Google acquired the company for $500 million. And by 2016, it had achieved a major victory in AI: Mastering the complex game of Go. This story represents the fantasy of many AI researchers, eager to launch their own ventures in the AI startup space. But the field has become saturated, and the terms "AI," "deep learning," and "machine learning" are often overhyped and misunderstood.

Recall Sunday, April 10, 2016. Jordan Spieth had just teed off in the final group of a sure-to-be exciting day at Augusta National. The Irishman drew his ball to the center of the 16th green, where it curled left and toward the hole. It fell in the cup for an ace and created the first roar in what seemed like days. Lowry punched both fists high in the air, spanked Patrick Reed's open hand and gave his best Kirk Gibson impression, much to the delight of fans in the stands, Verne Lundquist in the CBS booth and, behind some glass doors hundreds of yards away, International Business Machines (better known as IBM).

Being able to predict whether a song can be a hit has impor- tant applications in the music industry. Although it is true that the popularity of a song can be greatly affected by exter- nal factors such as social and commercial influences, to which degree audio features computed from musical signals (whom we regard as internal factors) can predict song popularity is an interesting research question on its own. Motivated by the recent success of deep learning techniques, we attempt to ex- tend previous work on hit song prediction by jointly learning the audio features and prediction models using deep learning. Specifically, we experiment with a convolutional neural net- work model that takes the primitive mel-spectrogram as the input for feature learning, a more advanced JYnet model that uses an external song dataset for supervised pre-training and auto-tagging, and the combination of these two models. We also consider the inception model to characterize audio infor- mation in different scales. Our experiments suggest that deep structures are indeed more accurate than shallow structures in predicting the popularity of either Chinese or Western Pop songs in Taiwan. We also use the tags predicted by JYnet to gain insights into the result of different models.