Pune has deployed over 1000 IoT devices (including 1500 CCTV cameras, which are quasi IoT devices), connected with the integrated command and control centre (ICCC). The data feeds are regularly relayed from the sensors. Going ahead the many use cases will need to be explored. Pune is working with IISc and IIT Kanpur for use case development, for example, the availability of parking spaces in the city can be easily identified from sensor data; traffic movements in the city can be tracked and appropriate actions relating to reducing congestion can also be taken based on data relayed from the sensors. Pune is the only city in the country to have participated in a global hackathon, wherein the API based technology architecture allows to expose the data in a secure manner globally to create applications over it.

Fox News Flash top headlines for Sept. 4 are here. Check out what's clicking on Foxnews.com A New Jersey man posed as a member of the United States military on dating websites to bilk more than 30 women out of $2 million, the U.S. Attorney's Office announced. Rubbin Sarpong, 35, of Millville, was arrested Wednesday and charged with conspiracy to commit wire fraud. Sarpong and his alleged conspirators "met and wooed the victims" on online dating sites, New Jersey U.S. Attorney Craig Carpenito said.

Multicolored lights illuminate a rack of computer server units. "Legal confidentiality is a shield for citizens." These are the words of Shami Chakrabarti, the one-time director of the U.K.-based human rights group Liberty, speaking in 2018. Well, it seems that this shield has just been broken, because researchers at the University of Zurich in Switzerland have published a study in which they were able to identify the participants in confidential legal cases, even though such participants had been anonymized. By harnessing these technologies in tandem, the study's authors could mine over 120,000 public legal records and then use an algorithm to identify connections between them.

We address the problem of tuning word embeddings for specific use cases and domains. We propose a new method that automatically combines multiple domain-specific embeddings, selected from a wide range of pre-trained domain-specific embeddings, to improve their combined expressive power. Our approach relies on two key components: 1) a ranking function, based on a new embedding similarity measure, that selects the most relevant embeddings to use given a domain and 2) a dimensionality reduction method that combines the selected embeddings to produce a more compact and efficient encoding that preserves the expressiveness. We empirically show that our method produces effective domain-specific embeddings that consistently improve the performance of state-of-the-art machine learning algorithms on multiple tasks, compared to generic embeddings trained on large text corpora.

The rapid advancement of artificial intelligence (AI) is changing our lives in many ways. One application domain is data science. New techniques in automating the creation of AI, known as AutoAI or AutoML, aim to automate the work practices of data scientists. AutoAI systems are capable of autonomously ingesting and pre-processing data, engineering new features, and creating and scoring models based on a target objectives (e.g. accuracy or run-time efficiency). Though not yet widely adopted, we are interested in understanding how AutoAI will impact the practice of data science. We conducted interviews with 20 data scientists who work at a large, multinational technology company and practice data science in various business settings. Our goal is to understand their current work practices and how these practices might change with AutoAI. Reactions were mixed: while informants expressed concerns about the trend of automating their jobs, they also strongly felt it was inevitable. Despite these concerns, they remained optimistic about their future job security due to a view that the future of data science work will be a collaboration between humans and AI systems, in which both automation and human expertise are indispensable.

"We are already becoming tiny chips inside a giant system that nobody really understands." So wrote Israeli historian Yuval Noah Harari about our current experience of urban living, which, increasingly, is mediated by AI. AI is now an important component of sectors such as healthcare, agriculture, public administration and transportation, and is helping to address major challenges such as ageing and climate change. However, there is currently a lack of transparency in algorithmic governance systems, and this is worsened when these algorithms are integrated into already opaque governance structures in our cities. Moreover, over the past decade, the propagation of sensors and data collection machines in so-called'smart cities' by both the public and the private sectors has created democratic challenges around AI, surveillance capitalism, and protecting citizens' digital rights to privacy and ownership.

Modern Cloud solutions offer attorneys the opportunity to benefit from learnings drawn from a broader pool of contracts for training the AI model. Increase Attorney's Efficiency Invest Solver 2019 5

With this number of users, activity, and content, it makes sense for YouTube to take advantage of the power of artificial intelligence (AI) to help operations. Here are a few ways YouTube, owned by Google, uses artificial intelligence today. In the first quarter of this year, 8.3 million videos were removed from YouTube, and76% were automatically identified and flagged by artificial intelligence classifiers. More than 70% of these were identified before there were any views by users. While the algorithms are not foolproof, they are combing through content much more quickly than if humans were trying to monitor the platform singlehandedly.

A man from Cardiff, UK, says the police breached his human rights when they used facial recognition technology, but today a court ruled that the police's actions were lawful. That is, however, hardly the end of the matter. South Wales Police has been trialling automated facial recognition (AFR) technology since April 2017. Other forces around the country are trialling similar systems, including London's Metropolitan Police. Bridges may have been snapped during a pilot called AFR Locate.

Ensuring fair automated decisions is another related area of upcoming regulations. While there is no consensus in the research community on the right set of fairness metrics, some approaches like equality of opportunity are already required by law in use cases like hiring. Integrating AI explainability in the ML lifecycle can also help provide insights for fair and unbiased automated decisions. Assessing and monitoring these biases, along with data quality and model interpretability approaches, provides a good playbook towards developing fair and ethical AI.