IBM yesterday announced that several new European clients have selected IBM Watson Internet of Things (IoT) technologies. New contracts signed with Spanish electricity grid operator Red Eléctrica de España, Italian elderly care provider Cooperativa Sole, Dutch telecommunications operator Tele2 and Israeli manufacturer of smart air conditioning Electra Group are examples of IBM's commitment to transforming business and improving operations with the power of Artificial Intelligence (AI)-enabled, IBM Cloud-based Internet of Things (IoT) technologies. Red Eléctrica de España (http://www.ree.es/en), the sole transmission agent and operator of the national electricity system in Spain has selected IBM Watson IoT technologies as part of its Intelligent Asset Management initiative project. The electricity system operator's objective is to plan and optimize the maintenance and replacement activities of their strategic transmission assets (like substations and transmission lines) with the support of the IBM Watson IoT platform. With an IoT analytics solution that is designed specifically to help Energy and Utilities improve asset maintenance and utilization, a team from IBM Global Business Services (GBS) has begun implementation of the platform and is working in unison with REE to develop asset degradation models and design optimization plans specific to the electricity system operator's requirements.

BIG BLUE IBM has used its Watson artificial intelligence (AI) tech to develop a new algorithm for multi-face tracking. The system uses AI to track multiple individuals across scenes, despite changing camera angles, lighting, and appearances. Collaborating with Professor Ying Hung of the Department of Statistics and Biostatistics in Rutgers University, IBM Watson researcher Chung-Ching Lin led a team of scientists to develop the technology, using a method to spot different individuals in a video sequence. The system is also able to recognise if people leave and then re-enter the video, even if they look very different. To create this innovation in AI, Lin explained that the team first made'tracklets' for the people present in the source material.

IBM Watson Health and medical imaging contrast agent company Guerbet have entered a strategic partnership to develop artificial intelligence (AI) software to support liver cancer diagnostics and care by utilizing CT and MRI technology. The collaboration will have Guerbet and IBM Watson Health co-develop clinical decision support solutions including Watson Imaging Care Advisor for Liver, a diagnostic support tool that will utilize AI to automate the detection, staging, tracking, monitoring, therapy prediction and response of primary and second liver cancer for clinicians, according to a Guerbet press release published July 10. "Imaging is a critical area of healthcare where we believe artificial intelligence can be used to expand the physician's view so they can be more informed in their diagnostic and treatment decisions for their patients," said Anne Le Grand, vice president of imaging at IBM Watson Health.

Engineers recently laid off from IBM Watson Health, the division rooted in artificial intelligence, say the company's mission to make AI profitable is failing, according to IEEE Spectrum. IBM cut dozens of Watson Health employees--primarily from its three acquired companies Phytel, Explorys and Truven--at the end of May. The company is severely disorganized, which led to redundancies and internal competition, the former employees said. Now they are speaking out about IBM's issues with its AI. They allege the problems at Phytel stem from IBM's inability to make Watson profitable.

The CMO Performance Report 2018 report, from digital marketing challenger agency, QueryClick, surveyed over 150 Chief Marketing Officers for UK consumer brands with a revenue of over £150 million and an e-commerce offering, found that 66% of CMOs for large retail brands have plans to invest in machine learning to enhance their digital marketing strategies within the next 12 months. The survey also found that over half (53%) of retailers will invest in voice search technology within the next 12 months. By 2019, the voice recognition market is predicted[1] to be a $601 million industry, and its value is set to accelerate even further as research predicts[2] 50% of all searches will be voice searches by 2020. Overall, 75% of CMOs said their brand will change its SEO strategy to ensure it appears in voice-led search results. Of those, 43% said they would do this within the next 12 months.

IBM Watson burst onto the world stage in 2011 when it participated in the trivia-based game show Jeopardy!. The supercomputer beat out two former champions to claim a victory for "artificial intelligence". Since then, Watson has embarked on a number of challenges across a variety of domains, from identifying the best cancer treatments to improving weather forecasting. For its latest endeavor, Watson is looking to improve the quality of life for individuals with autism and other cognitive disorders. Autism refers to a group of complex disorders of brain development characterized by difficulties in social interaction, verbal and nonverbal communication and possible repetitive behaviors.

As my Masters is coming to an end, I wanted to work on an interesting NLP project where I can use all the techniques(not exactly) I have learned at USF. With the help of my professors and discussions with the batch mates, I decided to build a question-answering model from scratch. I am using the Stanford Question Answering Dataset (SQuAD). The problem is pretty famous with all the big companies trying to jump up at the leaderboard and using advanced techniques like attention based RNN models to get the best accuracy. All the GitHub repositories that I found related to SQuAD by other people have also used RNNs. However, my goal is not to reach the state of the art accuracy but to learn different NLP concepts, implement them and explore more solutions.

About Jen Booton Jen is a senior writer at SportTechie covering the many ways technology is disrupting sports. On any given day she may cover a wide variety of stories ranging from the newest virtual reality training tools for the NFL, the rise of eSports leagues and the infiltration of drones in extreme sports. Prior to joining SportTechie, Jen was a technology reporter at MarketWatch, where she covered major Silicon Valley companies, such as Apple, Amazon, Google and Facebook. Jen is a licensed skydiver who jumps out of planes, helicopters and hot air balloons for fun in her spare time. She's a former NCAA cross country athlete and currently lives in Hoboken, New Jersey.

Two years after originally announcing it, Medtronic and IBM Watson have launched their joint platform the Sugar.IQ, a digital diabetes assistant. "It is designed for people who are currently using Guardian Connect; so made for people on multiple daily injections. It is a personal assistant a little bit like Alexa or Siri," Huzefa Neemuchwala, global head of digital health solutions and AI at Medtronic, said in a Facebook live informational session. "It is an intelligent assistant that keeps track of all of your information and has all of your information in one place. Then through Watson technology we use this information to power insights so we can better manage your diabetes so that you can spend more time in range."

Many question answering systems over knowledge graphs rely on entity and relation linking components in order to connect the natural language input to the underlying knowledge graph. Traditionally, entity linking and relation linking have been performed either as dependent sequential tasks or as independent parallel tasks. In this paper, we propose a framework called EARL, which performs entity linking and relation linking as a joint task. EARL implements two different solution strategies for which we provide a comparative analysis in this paper: The first strategy is a formalisation of the joint entity and relation linking tasks as an instance of the Generalised Travelling Salesman Problem (GTSP). In order to be computationally feasible, we employ approximate GTSP solvers. The second strategy uses machine learning in order to exploit the connection density between nodes in the knowledge graph. It relies on three base features and re-ranking steps in order to predict entities and relations. We compare the strategies and evaluate them on a dataset with 5000 questions. Both strategies significantly outperform the current state-of-the-art approaches for entity and relation linking.