This week in AI, chipmaker Esperanto raised $58M. Samsung makes moves with its digital assistant. China unveils an AI news anchor. Here's what went down in artificial intelligence this week. Become a CB Insights customer.

NEW DELHI: China's state press agency has unveiled a virtual newsreader designed to deliver headlines 24 hours a day. Xinhua's "artificial intelligence news anchor" is a lifelike digitised reporter which can read out text by mimicking the image and voice of a real human presenter. The agency claims the virtual presenter -- a realistic looking man, sharply dressed in a suit -- "can read texts as naturally as a professional news anchor". China's state press agency has unveiled a virtual newsreader designed to deliver headlines 24 hours a day. Xinhua's "artificial intelligence news anchor" is a lifelike digitised reporter which can read out text by mimicking the image and voice of a real human presenter.

Browse 64 Market Data Tables and 37 Figures spread through 133 Pages and in-depth TOC on "Humanoid Robot Market - Global Forecast to 2023" The humanoid robot market for software is expected to grow at a higher CAGR during the forecast period. As the technological advancement will lead to the growing complexity in terms of features such as AI and autonomous operations, the value of the software part in the robot will grow faster than hardware as software will assist the complex functionalities to process efficiently and accurately. The biped motion type captured a larger share of the overall humanoid robot market in 2016. The actual human-like appearance can be realized in humanoids only when the robot is capable of walking on feet like humans; owing to this, a majority of the humanoid robot manufacturers are focusing on their designs to make biped robots. The Americas accounted for the largest share of the overall humanoid robot market in 2016.

We say a lot with our hands. We spread them wide to indicate size, stab the air for emphasis and reach out to draw people in. Waving our hands about when we speak makes us appear less robotic – and that's true for robots too.

The business community and the corporate sector should understand that Artificial Intelligence (AI) and data science are new, rapidly developing fields offering novel business opportunities. Collaborating with the IT industry and start-ups will yield rich dividends for both, Chairman, Sri Lanka Association for Software and Services Companies (SLASSCOM), Jeevan Gnanam said. SLASSCOM organised the first-ever AI Asia Summit in Colombo last week which exclusively discussed the topics of Data Science and Machine Learning with many acclaimed international and local speakers outlining the latest developments in AI across application, research and adoption. Sri Lanka's IT/BPM industry for the past two decades has predominantly focussed on two key areas for export earnings; that is software development and finance and accounting. Over the years the industry has grown positively with some of the major global partners, making Sri Lanka one of the key destinations for IT/BPM business.

A New York Times, Wall Street Journal, and USA Today Bestseller! Kai-Fu Lee named a Wired Icon, as part of Wired Magazine's 25th Anniversary Feature Publishers Weekly Fall 2018 Top 10 in Business & Economics Featured in the New York Times, the Wall Street Journal, the Washington Post, Wired, Financial Times, Bloomberg Businessweek, Business Insider, Forbes, and more. "After thirty years of pioneering work in artificial intelligence at Google China, Microsoft, Apple and other companies, Lee says he's figured out the blueprint for humans to thrive in the coming decade of massive technological disruption: 'Let us choose to let machines be machines, and let humans be humans.'"--Forbes "Kai-Fu Lee believes China will be the next tech-innovation superpower and in his new (and first) book, AI Superpowers: China, Silicon Valley, and the New World Order, he explains why. Taiwan-born Lee is perfectly positioned for the task."--New Times "AI Superpowers: China, Silicon Valley, and the New World Order, by Kai-Fu Lee, about the ways that artificial intelligence is reshaping the world and the economic upheaval new technology will generate. We need to start thinking now about how to address these gigantic changes."--Senator

Five car-following models were calibrated, validated and cross-compared. The intelligent driver model performed best among the evaluated models. Considerable behavioral differences between different drivers were found. Calibrated model parameters may not be numerically equivalent with observed ones.

The paper proposes an on-line monitoring framework for continuous real-time safety/security in learning-based control systems (specifically application to a unmanned ground vehicle). We monitor validity of mappings from sensor inputs to actuator commands, controller-focused anomaly detection (CFAM), and from actuator commands to sensor inputs, system-focused anomaly detection (SFAM). CFAM is an image conditioned energy based generative adversarial network (EBGAN) in which the energy based discriminator distinguishes between proper and anomalous actuator commands. SFAM is based on an action condition video prediction framework to detect anomalies between predicted and observed temporal evolution of sensor data. We demonstrate the effectiveness of the approach on our autonomous ground vehicle for indoor environments and on Udacity dataset for outdoor environments.

Uncertainty estimation in large deep-learning models is a computationally challenging task, where it is difficult to form even a Gaussian approximation to the posterior distribution. In such situations, existing methods usually resort to a diagonal approximation of the covariance matrix despite, the fact that these matrices are known to give poor uncertainty estimates. To address this issue, we propose a new stochastic, low-rank, approximate natural-gradient (SLANG) method for variational inference in large, deep models. Our method estimates a "diagonal plus low-rank" structure based solely on back-propagated gradients of the network log-likelihood. This requires strictly less gradient computations than methods that compute the gradient of the whole variational objective. Empirical evaluations on standard benchmarks confirm that SLANG enables faster and more accurate estimation of uncertainty than mean-field methods, and performs comparably to state-of-the-art methods.

Speech emotion recognition is an important aspect of human-computer interaction. Prior works propose various transfer learning approaches to deal with limited samples in speech emotion recognition. However, they require labeled data for the source task, which cost much effort to collect them. To solve this problem, we focus on the unsupervised task, predictive coding. Nearly unlimited data for most domains can be utilized. In this paper, we utilize the multi-layer Transformer model for the predictive coding, followed with transfer learning approaches to share knowledge of the pre-trained predictive model for speech emotion recognition. We conduct experiments on IEMOCAP, and experimental results reveal the advantages of the proposed method. Our method reaches 65.03% in the weighted accuracy, which also outperforms some currently advanced approaches.