Quantum computing and artificial intelligence are both hyped ridiculously. But it seems a combination of the two may indeed combine to open up new possibilities. In a research paper published today in the journal Nature, researchers from IBM and MIT show how an IBM quantum computer can accelerate a specific type of machine-learning task called feature matching. The team says that future quantum computers should allow machine learning to hit new levels of complexity. As first imagined decades ago, quantum computers were seen as a different way to compute information.

NIMS, the University of Tokyo, Niigata University and RIKEN have jointly designed a multilayered metamaterial that realizes ultra-narrowband wavelength-selective thermal emission by combining the machine learning (Bayesian optimization) and thermal emission properties calculations (electromagnetic calculation). The joint team then experimentally fabricated the designed metamaterial and verified the performance. These results may facilitate the development of highly efficient energy devices. Thermal radiation, a phenomenon that an object emits heat as electromagnetic waves, is potentially applicable to a variety of energy devices, such as wavelength-selective heaters, infrared sensors and thermophotovoltaic generators. Highly efficient thermal emitters need to exhibit emission spectrum with narrow bands in practically usable wavelength range..

At first, Vaisagh Viswanathan, CTO at impress.ai, will talk about the challenges of hiring a tech team that works in a different country and timezone. And how he manages to use AI to help him scale the team while making sure major product milestones are met. Thereafter, Gaurang Torvekar, Co-founder and CEO of Indorse, a Skills Assessment Platform, will speak about his perspectives on hiring the right tech talent. He will also be sharing his experiences on how Indorse scaled its tech team beyond Singapore and all around the world. We want to dedicate more time to discussion and sharing among the community.

What did you think of the last commercial you watched? Would you buy the product? You might not remember or know for certain how you felt, but increasingly, machines do. New artificial intelligence technologies are learning and recognizing human emotions, and using that knowledge to improve everything from marketing campaigns to health care. These technologies are referred to as "emotion AI." Emotion AI is a subset of artificial intelligence (the broad term for machines replicating the way humans think) that measures, understands, simulates, and reacts to human emotions.

The 11th meeting of Forum for Information Retrieval Evaluation 2019 will be held in Kolkata, India. Started in 2008 with the aim of building a South Asian counterpart for TREC, CLEF and NTCIR, FIRE has since evolved continuously to meet the new challenges in multilingual information access. It has expanded to include new domains like plagiarism detection, legal information access, mixed script information retrieval and spoken document retrieval to name a few. Continuing the trend started in 2015, the FIRE will consist of a peer-reviewed conference track along with evaluation tasks. We invite full and short papers from information retrieval, natural language processing, and related domains.

Talk Abstract: In spite of great success of deep learning a question remains to what extent the computational properties of deep neural networks (DNNs) are similar to those of the human brain. The particularly non-biological aspect of deep learning is the supervised training process with the backpropagation algorithm, which requires massive amounts of labeled data, and a non-local learning rule for changing the synapse strengths. In this talk I will describes a learning algorithm that does not suffer from these two problems. It learns the weights of the lower layer of neural networks in a completely unsupervised fashion. The entire algorithm utilizes local learning rules which have conceptual biological plausibility.

Artificial intelligence is no longer just in the realm of science fiction. While we have been long promised AI that will enhance our day-to-day lives, the fact is that it is already here and doing just that. Though AI capabilities like that depicted film, television, and video games is still quite a ways off, the AI that does exist improves the world around us nevertheless. AI has been a major driving force in business innovation over the last few years, and while the layman may never know it, AI has already affected their life in some way or another. One way that artificial intelligence and machine learning has an enormous impact on society is through the influence of the credit card industry.

Not to be outdone, Panasonic Corp.-- also a major Olympic sponsor -- showed off its "power assist suit." When worn, the suit offers support to the back and hip area and allows for heavy objects to be lifted with less effort. Panasonic said 20 of the suits will be used at the Olympics and could help guests with their luggage and with other lifting chores.

H2O WORLD SAN FRANCISCO – H2O.ai, the open source leader in AI and ML, today announced new and innovative capabilities for its data science and machine learning platforms, H2O, AutoML and H2O Driverless AI, to address the critical scalability and performance needs of all organizations. As part of these new capabilities, and to further the company's mission to democratize AI, H2O.ai has added several new algorithms that address common use cases that customers need today. In addition, H2O Driverless AI is a winner of InfoWorld's 2019 Technology of the Year for the second year in a row. The award honors and recognizes the best in software development, cloud computing, big data analytics, and machine learning tools. This year's judging panel recognized H2O Driverless AI for outpacing all other vendors with "automated simplicity" of its algorithms that do the heavy lifting of feature engineering, model selection, training and optimization – enabling even non-AI experts to uncover hidden patterns using both supervised and unsupervised machine learning.

At the world's top computer-vision conference last June, Google and Apple sponsored an academic contest that challenged algorithms to make sense of images from twin cameras collected under varied conditions, such as sunny and poor weather. Artificial intelligence software proficient at that task could help the US tech giants with money-making projects such as autonomous cars or augmented reality. But the winner was an institution with very different interests and allegiances: China's National University of Defense Technology, a top military academy of the People's Liberation Army. That anecdote helps illustrate China's broad ambitions in AI and recent prominence on the field's frontiers. In 2017 the country's government announced a new artificial intelligence strategy that aims to rival the US in the crucial technology by 2020.