Japan's first robot bartender has begun serving up drinks in a Tokyo pub in a test that could usher in a wave of automation in restaurants and shops struggling to hire staff in an aging society. The repurposed industrial robot serves drinks in its own corner of a pub run by restaurant chain Yoronotaki. A face on an attached tablet computer smiles as it chats about the weather while preparing orders. The robot, made by QBIT Robotics, can pour a beer in 40 seconds and mix a cocktail in a minute. It uses four cameras to monitor customers to analyze their expressions with artificial intelligence software.

Determining whether an AI system is maintaining fairness in its predictions requires an understanding of models' short- and long-term effects, which might be informed by disparities in error metrics on a number of static data sets. In some cases, it's necessary to consider the context in which the AI system operates in addition to the aforementioned error metrics, which is why Google researchers developed ML-fairness-gym, a set of components for evaluating algorithmic fairness in simulated social environments. ML-fairness-gym -- which was published in open source on Github this week –is designed to be used to research the long-term effects of automated systems by simulating decision-making using OpenAI's Gym framework. AI-controlled agents interact with digital environments in a loop, and at each step, an agent chooses an action that affects the environment's state. The environment then reveals an observation that the agent uses to inform its next actions, so that the environment models the system and dynamics of a problem and the observations serve as data.

DeepMind Technologies, a Google subsidiary and Artificial Intelligence (AI) firm, disclosed that it will adopt Blockchain technology and make use of Distributed Ledger Technology (DLT).This move will help the company secure patient data more efficiently. DeepMind creates algorithms designed for applications, gaming protocols and stimulation. It earned fame for developing a machine-learning program that can be capable of playing video games. Likewise, DeepMind developed the so-called "Neural Turing Machine" that copies short-term memory of human beings. It signed a five-year contract with Royal Free London NHS Trust recently so it can apply the technology to healthcare. The problem is this accord created some hullabaloo as it allegedly affected confidentiality of patient data.

Accurate and efficient models for rainfall runoff (RR) simulations are crucial for flood risk management. Most rainfall models in use today are process-driven; i.e. they solve either simplified empirical formulas or some variation of the St. Venant (shallow water) equations. With the development of machine-learning techniques, we may now be able to emulate rainfall models using, for example, neural networks. In this study, a data-driven RR model using a sequence-to-sequence Long-short-Term-Memory (LSTM) network was constructed. The model was tested for a watershed in Houston, TX, known for severe flood events. The LSTM network's capability in learning long-term dependencies between the input and output of the network allowed modeling RR with high resolution in time (15 minutes). Using 10-years precipitation from 153 rainfall gages and river channel discharge data (more than 5.3 million data points), and by designing several numerical tests the developed model performance in predicting river discharge was tested. The model results were also compared with the output of a process-driven model Gridded Surface Subsurface Hydrologic Analysis (GSSHA). Moreover, physical consistency of the LSTM model was explored. The model results showed that the LSTM model was able to efficiently predict discharge and achieve good model performance. When compared to GSSHA, the data-driven model was more efficient and robust in terms of prediction and calibration. Interestingly, the performance of the LSTM model improved (test Nash-Sutcliffe model efficiency from 0.666 to 0.942) when a selected subset of rainfall gages based on the model performance, were used as input instead of all rainfall gages.

Linking job seekers with relevant jobs requires matching based on not only skills, but also personality types. Although the Holland Code also known as RIASEC has frequently been used to group people by their suitability for six different categories of occupations, the RIASEC category labels of individual jobs are often not found in job posts. This is attributed to significant manual efforts required for assigning job posts with RIASEC labels. To cope with assigning massive number of jobs with RIASEC labels, we propose JPLink, a machine learning approach using the text content in job titles and job descriptions. JPLink exploits domain knowledge available in an occupation-specific knowledge base known as O*NET to improve feature representation of job posts. To incorporate relative ranking of RIASEC labels of each job, JPLink proposes a listwise loss function inspired by learning to rank. Both our quantitative and qualitative evaluations show that JPLink outperforms conventional baselines. We conduct an error analysis on JPLink's predictions to show that it can uncover label errors in existing job posts.

With the advent of huges volumes of data produced in the form of fast streams, real-time machine learning has become a challenge of relevance emerging in a plethora of real-world applications. Processing such fast streams often demands high memory and processing resources. In addition, they can be affected by non-stationary phenomena (concept drift), by which learning methods have to detect changes in the distribution of streaming data, and adapt to these evolving conditions. A lack of efficient and scalable solutions is particularly noted in real-time scenarios where computing resources are severely constrained, as it occurs in networks of small, numerous, interconnected processing units (such as the so-called Smart Dust, Utility Fog, or Swarm Robotics paradigms). In this work we propose LUNAR, a streamified version of cellular automata devised to successfully meet the aforementioned requirements. It is able to act as a real incremental learner while adapting to drifting conditions. Extensive simulations with synthetic and real data will provide evidence of its competitive behavior in terms of classification performance when compared to long-established and successful online learning methods.

A Navy X-47B drone is launched off the nuclear powered aircraft carrier USS George H. W. Bush off the coast of Virginia, Tuesday, May 14, 2013. It was the Navy's first test flight of the unmanned aircraft off a carrier. A Navy X-47B drone is launched off the nuclear powered aircraft carrier USS George H. W. Bush off the coast of Virginia, Tuesday, May 14, 2013. It was the Navy's first test flight of the unmanned aircraft off a carrier. Dr. Robert J. Marks, Director of the Walter Bradley Center for Natural and Artificial Intelligence, joins the Nick Digilio Show to make the case for killer robots. As warfare advances, Dr. Marks argues that the best way to shorten and prevent war is to develop more robotics for the battlefield.

Killer robots may remain a dystopian vision of the future for now, but another military deployment of AI could be sooner to arrive on the battlefield. Known as the Aided Threat Recognition from Mobile Cooperative and Autonomous Sensors (ATR-MCAS), the system is being developed by the US Army to transform how the military plans and conducts operations. It's comprised of a network of air and ground vehicles equipped with sensors that identify potential threats and autonomously notify soldiers. The information collected would then be analysed by an AI-enabled decision support agent that can recommend responses -- such as which threats to prioritize. The system was developed by the Army's Artificial Intelligence Task Force (AITF), which was activated last year to improve the Army's connections with the broader AI community.

Clearview AI CEO Hoan Ton-That tells CBS correspondent Errol Barnett that the First Amendment allows his company to scrape the internet for people's photos. Google and YouTube have sent a cease-and-desist letter to Clearview AI, the facial recognition company that has been scraping billions of photos off the internet and using it to help more than 600 police departments identify people within seconds. That follows a similar action by Twitter, which sent Clearview AI a cease-and-desist letter for its data scraping in January. The letter from Google-owned YouTube was first seen by CBS News. (Note: CBS News and CNET share the same parent company, ViacomCBS.) The CEO of Clearview AI, a controversial and secretive facial recognition startup, is defending his company's massive database of searchable faces, saying in an interview on CBS This Morning Wednesday that it's his First Amendment right to collect public photos.

The document, which was prepared by chief economic adviser Krishnamurthy Subramanian and tabled by finance minister Nirmala Sitharaman in Parliament on Friday, said the banking sector must scale up in tandem with the size of the Indian economy to support growth and development. The growth and efficiency of state-owned lenders, which account for over two-thirds of the banking space, is imperative for India to become a $5-trillion economy in the next five years, survey added. However, inefficient public sector banks can severely handicap the country's ability to make use of the unique available opportunities, and this could impact growth. "The state of the banking sector in India, therefore, needs urgent attention," it said. The survey said banks should also introduce employee stock ownership (ESOP) scheme and link it to the performance of employees. "Ownership by motivated, capable employees across all levels in the organization could give such employees tangible financial rewards for value enhancement, align their incentives with what is beneficial to the public sector banks, and create a mindset of enterprise ownership for employees," it added.