MIT researchers developed a picking robot that combines vision with radio frequency (RF) sensing to find and grasps objects, even if they're hidden from view. The technology could aid fulfilment in e-commerce warehouses. System uses penetrative radio frequency to pinpoint items, even when they're hidden from view. In recent years, robots have gained artificial vision, touch, and even smell. "Researchers have been giving robots human-like perception," says MIT Associate Professor Fadel Adib.

Six years ago, Atsushi Nakanishi launched Triple W with nothing but the seed of an idea and an overwhelming passion to realize it. Today, the startup is the creator and seller of DFree -- the world's first wearable device for urinary incontinence. The tiny, noninvasive device uses ultrasound to monitor the volume of urine in the user's bladder in real time. When the bladder reaches its threshold, DFree sends an alert to the user's smartphone to tell them it is time to go to the bathroom. Nakanishi credits the ground-breaking product to a eureka moment in 2013.

One can be forgiven for thinking that everyone in the world is adopting sophisticated, next-gen technologies such as artificial intelligence and autonomous systems, and their company is falling woefully behind. While it's more the case of everyone trying to find their way with still yet-to-be-fully-understood technologies, this fear of falling behind is real, and is driving investment. That's the word from a survey of 200 enterprises from Seeqc, which finds rising investment in deep-tech solutions is largely driven by the threat of industry competition, with substantial R&D budgets and jobs on the line. More than two-thirds, 67%, fear their competitors are further along than their company. That's certainly a way to get the full attention of business leaders controlling the purse strings.

Current experience suggests that artificial intelligence (AI) and machine learning (ML) may be useful in the management of hospitalized patients, …

If you have built Deep Neural Networks before, you might know that it can involve a lot of experimentation. In this article, I will share with you some useful tips and guidelines that you can use to better build better deep learning models. These tricks should make it a lot easier for you to develop a good network. You can pick and choose which tips you use, as some will be more helpful for the projects you are working on. Not everything mentioned in this article will straight up improve your models' performance.

As IBM explain, "at its simplest form, artificial intelligence is a field, which combines computer science and robust datasets to enable problem-solving." It includes the sub-fields of machine learning and deep learning. These two fields use algorithms that are designed to make predictions or classifications based on input data. Of course, as technology becomes more sophisticated, literally millions of decisions need to be made every day and AI speeds things up and takes the burden off humans. The World Economic Forum describes AI as a key driver of the Fourth Industrial Revolution.

"Trust is a must," she said. "The EU is spearheading the development of new global norms to make sure AI can be trusted. By setting the standards, we can pave the way to ethical technology worldwide." Any fast-moving technology is likely to create mistrust, but Vestager and her colleagues decreed that those in power should do more to tame AI, partly by using such systems more responsibly and being clearer about how these work. The landmark legislation – designed to "guarantee the safety and fundamental rights of people and businesses, while strengthening AI uptake, investment and innovation" – encourages firms to embrace so-called explainable AI.

Python continues to lead the way when it comes to Machine Learning, AI, Deep Learning and Data Science tasks. Because of this, we've decided to start a series investigating the top Python libraries across several categories: Of course, these lists are entirely subjective as many libraries could easily place in multiple categories. Now, let's get onto the list (GitHub figures correct as of November 16th, 2018): "pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python." "Matplotlib is a Python 2D plotting library which produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell (à la MATLAB or Mathematica), web application servers, and various graphical user interface toolkits."

Jobs in data science grew nearly 46% in 2020, with salaries in the range of $100,000 to $130,000 annually, according to a recent account in TechRepublic based on information from LinkedIn and LHH, formerly Lee Hecht Harrison, a global provider of talent and leadership development. Related job titles include data science specialist and data management analyst. Novacoast, which helps organizations build a cybersecurity posture through engineering, development, and managed services. Founded in 1996 in Santa Barbara, the company has many remote employees and a presence in the UK, Canada, Mexico, and Guatemala. The company offers a security operations center (SOC) cloud offering called novaSOC, that analyzes emerging challenges.

Gartner predicts that "by 2022, 70 percent of white-collar workers will interact with conversational platforms on a daily basis." As a result, the research group found that more organizations are investing in chatbot development and deployment. IBM Business Partners like Sopra Steria are making chatbot and virtual assistant technology available to businesses. Sopra Steria, a European leader in digital transformation, has developed an intelligent virtual assistant for organizations across several industries who want to use an AI conversational interface to answer recurrent customer service questions. In developing our solution, we at Sopra Steria were looking for AI technology that was easy to configure and could support multiple languages and complex dialogs.