DUBAI: Dubai Government Workshop (DGW) recently organized an interactive seminar entitled "How to Measure Productivity" as part of its ongoing partnership with Dubai Customs. The key features of the Productivity Engine, which aims to utilize Artificial Intelligence (AI) technologies, were reviewed in order to help improve productivity and increase efficiency. The move aligns with DGW s efforts to speed up the digital transformation process and integrate AI to be part and parcel of any tools intended to enhance the efficiency and effectiveness of the government sector. Furthermore, it complements the vision of the wise leadership in steering Dubai and the UAE towards developing an integrated system that employs AI in all vital areas. The workshop was held in the presence of Khalid Ahmed Al Doubi, Director-Corporate Support, DGW and Ahmed Abdul Salam Kazim, Director of Strategy and Corporate Excellence, Dubai Customs, during which time DGW employees were acquainted with the importance of the Productivity Engine.

Today, we're at an inflection point in how we engage with customers to better meet their needs. According to a PwC report, 73% of all people point to customer experience as an important factor in their purchasing decisions. Yet only half of the U.S. consumers say companies are meeting their expectations. In this experience-driven economy, companies who understand the meaning behind customers' needs, listen and learn in every interaction, and consistently create engaging and personal experiences, will win. Companies should adopt AI to get the desired success.

DesignCon, the nation's largest event for chip, board, and systems design engineers, today announced new areas of focus for the 2020 edition highlighting advances in the fields of 5G, artificial intelligence (AI), automotive, and IoT, producing the most in-demand electronics emerging today. Topics will be examined through a 14-track conference schedule spanning technical sessions, boot camps, tutorials, and more to fit the needs of the hardware design engineering community. The DesignCon conference and expo reflects the design engineering industry's growing need for breakthroughs in the development of 5G connectivity, AI, automotive electronics, and IoT. With worldwide spending on AI systems forecasted to reach $79.2 billion in 2022, sales of automotive electronic systems at its highest growth rate on-record, expected worldwide spending on IoT projected to surpass $1 trillion in 2022, and the 5G infrastructure market estimated to reach $4.2 billion in value by 2020, DesignCon is the ideal forum for attendees across these interlaced industries to exchange ideas and develop solutions to meet consumer demand. "The market and value for connected, especially fast connected products, is on the rise," said Suzanne Deffree, brand director, Intelligent Systems & Design, Informa Markets.

Fox News Flash top headlines for Nov. 23 are here. Check out what's clicking on Foxnews.com A California woman has been arrested after going on a first date with a man who arranged to meet her through an online dating app and was later allegedly robbed at gunpoint by two of her male associates, according to the Tulare County Sheriff's Office. Shalena Lopez, 31, and one of the men, Mario Gaona, 39, were arrested Thursday, the sheriff's office said in a news release Friday. The second man, Cesar Domingo Jr., 27, was being sought, according to the sheriff's office.

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead Rudin et al., arXiv 2019 It's pretty clear from the title alone what Cynthia Rudin would like us to do! The paper is a mix of technical and philosophical arguments and comes with two main takeaways for me: firstly, a sharpening of my understanding of the difference between explainability and interpretability, and why the former may be problematic; and secondly some great pointers to techniques for creating truly interpretable models. A model can be a black box for one of two reasons: (a) the function that the model computes is far too complicated for any human to comprehend, or (b) the model may in actual fact be simple, but its details are proprietary and not available for inspection. In explainable ML we make predictions using a complicated black box model (e.g., a DNN), and use a second (posthoc) model created to explain what the first model is doing. A classic example here is LIME, which explores a local area of a complex model to uncover decision boundaries.

A few years ago this month, Portland, Oregon artist Darius Kazemi watched a flood of tweets from would-be novelists. November is National Novel Writing Month, a time when people hunker down to churn out 50,000 words in a span of weeks. To Kazemi, a computational artist whose preferred medium is the Twitter bot, the idea sounded mildly tortuous. "I was thinking I would never do that," he says. "But if a computer could do it for me, I'd give it a shot."

Barcelona-based startup Prometeo has developed an AI-based cognitive health monitoring platform in hope that one day it will be used to monitor the health of firefighters while they are out battling brutal wildfires. Co-founder Salome Valero explained the development of the platform came about following concerns that were raised by one of their own team members who is a veteran firefighter. "When the firefighters are fighting against fire, they are breathing in a lot of toxins which can be dangerous for their health … and some of the immediate effects they feel from breathing in smoke is getting headaches. But there is evidence they can suffer respiratory diseases, cancer, and stress disorders," she told ZDNet, during IBM Cloud Innovation Exchange in Sydney last week. "The problem is there currently isn't a lot of data about firefighters' vitals, but because we have this real-time capability, we are will be able to monitor them." Working primarily with firefighters from Catalonia, Prometeo, which was recently announced as the winner of IBM's Call for Code 2019 competition, has developed a smartphone-sized device that straps on a firefighter's arm.

It's time for employers to start preparing for legislation recently signed into law in Illinois, the Artificial Intelligence Video Interview Act. The new law, which takes effect on January 1, 2020, regulates Illinois employers' use of artificial intelligence (AI) in the interview and hiring process. Under the AI Video Interview Act, employers that record video interviews and use AI technology to analyze applicants' suitability for employment must: Employers that conduct such interviews may not distribute videos to other parties, except as necessary to obtain expert assistance in evaluating a candidate's fitness for a particular position. In addition, an employer has only 30 days to destroy all video copies of the interview if an applicant seeks such destruction. This law highlights a myriad of privacy concerns for employers evaluating the costs and benefits of incorporating AI technology into their hiring practices.

Why did you think to combine Qiskit, a quantum-computing framework, with PyTorch, a machine-learning framework? Karel Dumon: Classical machine learning is currently benefiting hugely from the open-source community, and this is something we want to leverage in quantum too. Our project focuses on the potential application of quantum computing for machine learning, but also on the use of machine learning to help progress quantum computing itself. Through our project, we hope to make it easier for machine learning developers to explore the quantum world. Patrick Huembeli: To that effect, it makes Qiskit very accessible for people with a classical machine learning background -- they can treat the quantum nodes just as another layer of their machine learning algorithm.

And, once again, they're doing it by teaching a computer to find previously unseen changes in patterns in CT scans taken when the lung cancer is first diagnosed compared to scans taken after the first 2-3 cycles of immunotherapy treatment. And, as with previous work, those changes have been discovered both inside -- and outside -- the tumor, a signature of the lab's recent research. "This is no flash in the pan -- this research really seems to be reflecting something about the very biology of the disease, about which is the more aggressive phenotype, and that's information oncologists do not currently have," said Anant Madabhushi, whose Center for Computational Imaging and Personalized Diagnostics (CCIPD) has become a global leader in the detection, diagnosis and characterization of various cancers and other diseases by meshing medical imaging, machine learning and AI. Currently, only about 20% of all cancer patients will actually benefit from immunotherapy, a treatment that differs from chemotherapy in that it uses drugs to help your immune system fight cancer, while chemotherapy uses drugs to directly kill cancer cells, according to the National Cancer Institute. Madabhushi said the recent work by his lab would help oncologists know which patients would actually benefit from the therapy, and who would not.