Dyson is a global technology company. We examine what currently exists and question it. We take things apart and look at them from new angles. We reinvent things that should simply work better. The Machine Learning team works across all product categories at Dyson, alongside leading technical specialists and highly motivated creative people to develop products which challenge convention.

Abdullah bin Sharaf Al-Ghamdi is the newly appointed head of the Saudi Data and Artificial Intelligence Authority. The appointment was made in one of several royal decrees issued by King Salman. Amongst the other decrees, Saleh Al-Othaim was made Al-Ghamdi's deputy. Al-Ghamdi had previously served as head of the National Information Center since August. He graduated with a bachelor's degree in information systems from King Saud University (KSU) in Riyadh in 1991.

This guidance is part of a wider collection about using artificial intelligence (AI) in the public sector. The SQ team is responsible for reviewing the sale of financial products for regulatory compliance. Currently the team is required to check a sample of 10% to 15% of completed sales. A team of 120 reviewers had to look at more than 10 different data sources and 180 data points to find and extract the information they needed to complete the audit. Each review took around 4 hours.

Using a supercomputing system, MIT researchers have developed a model that captures what web traffic looks like around the world on a given day, which can be used as a measurement tool for internet research and many other applications. Understanding web traffic patterns at such a large scale, the researchers say, is useful for informing internet policy, identifying and preventing outages, defending against cyberattacks, and designing more efficient computing infrastructure. A paper describing the approach was presented at the recent IEEE High Performance Extreme Computing Conference. For their work, the researchers gathered the largest publicly available internet traffic dataset, comprising 50 billion data packets exchanged in different locations across the globe over a period of several years. They ran the data through a novel "neural network" pipeline operating across 10,000 processors of the MIT SuperCloud, a system that combines computing resources from the MIT Lincoln Laboratory and across the Institute.

To improve the safety of autonomous systems, MIT engineers have developed a system that can sense tiny changes in shadows on the ground to determine if there's a moving object coming around the corner. Autonomous cars could one day use the system to quickly avoid a potential collision with another car or pedestrian emerging from around a building's corner or from in between parked cars. In the future, robots that may navigate hospital hallways to make medication or supply deliveries could use the system to avoid hitting people. In a paper being presented at next week's International Conference on Intelligent Robots and Systems (IROS), the researchers describe successful experiments with an autonomous car driving around a parking garage and an autonomous wheelchair navigating hallways. When sensing and stopping for an approaching vehicle, the car-based system beats traditional LiDAR -- which can only detect visible objects -- by more than half a second.

AI has been seen as an emerging trend in the government sector. How is it going to leverage this sector with its benefits? FREMONT, CA: Artificial Intelligence has already worked like a magic wand in other sectors. Now, the state government is also thinking of deploying AI. AI empowers the state CIOs in minimizing paperwork, automating tedious tasks, turning over simple jobs, evaluating large volumes of data to understand the society in a better way. However, with AI's promise comes its fear as well.

We're looking for an Android Engineer to join our Perception team at Snap Inc! Working from our New York, NY office, you'll work on bringing our machine learning and computer vision products to life through powerful tools and engaging product experiences.

With the decade winding down it's time for us to set our sights on the next one. The 2020s promises to be anything but dull. From the automation revolution and increasingly dangerous AI to geohacking the planet and radical advances in biotechnology, here are the most futuristic developments to expect in the next 10 years. Making predictions is easy; it's getting them right that's tough. That said, some tangible trends are emerging that should allow us to make some informed guesses about what the future will hold over the next 10 years. Of great concern, of course, is the pending automation revolution and the associated onset of technological unemployment.

Biomedical researchers are embracing artificial intelligence to accelerate the implementation of cancer treatments that target patients' specific genomic profiles, a type of precision medicine that in some cases is more effective than traditional chemotherapy and has fewer side effects. The potential for this new era of cancer treatment stems from advances in genome sequencing technology that enables researchers to more efficiently discover the specific genomic mutations that drive cancer, and an explosion of research on the development of new drugs that target those mutations. To harness this potential, researchers at The Jackson Laboratory, an independent, nonprofit biomedical research institution also known as JAX and headquartered in Bar Harbor, Maine, developed a tool to help the global medical and scientific communities stay on top of the continuously growing volume of data generated by advances in genomic research. The tool, called the Clinical Knowledgebase, or CKB, is a searchable database where subject matter experts store, sort and interpret complex genomic data to improve patient outcomes and share information about clinical trials and treatment options. The challenge is to find the most relevant cancer-related information from the 4,000 or so biomedical research papers published each day, according to Susan Mockus, the associate director of clinical genomic market development with JAX's genomic medicine institute in Farmington, Connecticut.

Christopher Samiullah, Soledad Galli: Testing and Validating Machine Learning Models when Deploying to Production PyData London 2019 Slides - https://www.slideshare.net/solegalli2... Through model deployment, we bridge the gap between the research environment and live systems. Reproducibility between environments is key to maximise the researched value the ML models will bring to an organisation. Therefore, before the models are fully integrated and live, we run thorough testing and reconciliation processes. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other.