Volvo has created a new business unit for its growing range of autonomous transport solutions. The new business area, Volvo Autonomous Solutions, will accelerate the development, commercialization and sales of autonomous transport solutions. Volvo says this will enable the company to meet "a growing demand" and to offer "the best possible solutions" to customers in such segments as mining, ports and transport between logistics centers, as a complement to today's products and services. With global developments that are characterized by higher demand for transportation, increasingly congested roads and major environmental challenges, the industry needs to provide transport solutions that are safer, have a lower environmental impact and are more efficient. Autonomous transport solutions, based on self-driving and connectivity technologies are well-suited for applications where there is a need to move large volumes of goods and material on pre-defined routes, in repetitive flows.

Since the 1980's, human/machine interactions, and human-in-the-loop (HTL) scenarios in particular, have been systematically studied. It was often predicted that with an increase in automation, less human-machine interaction would be needed over time. Human input is still relied upon for most common forms of AI/ML training, and often even more human insight is required than ever before. As AI/ML evolves and baseline accuracy of models improves, the type of human interaction required will change from creation of generalized ground truth from scratch, to human review of the worst-performing ML predictions in order to improve and fine-tune models iteratively and cost-effectively. Deep learning algorithms thrive on labeled data and can be improved progressively if more training data is added over time.

Hiring managers in search of qualified job candidates who can scale with and contribute to their growing businesses are facing a crisis today. They're not finding the right or in many cases, any candidates at all using resumes alone, Applicant Tracking Systems (ATS) or online job recruitment sites designed for employers' convenience first and candidates last. These outmoded approaches to recruiting aren't designed to find those candidates with the strongest capabilities. Add to this dynamic the fact that machine learning is making resumes obsolete by enabling employers to find candidates with precisely the right balance of capabilities needed and its unbiased data-driven approach selecting candidates works. Resumes, job recruitment sites and ATS platforms force hiring managers to bet on the probability they make a great hire instead of being completely certain they are by basing their decisions on solid data.

There are many metrics to measure the performance of your machine learning model depending on the type of machine learning you are looking to conduct. In this article, we take a look at performance measures for classification and regression models and discuss which is better-optimized. Sometimes the metric to look at will vary according to the problem that is initially being solved. The True Positive Rate, also called Recall, is the go-to performance measure in binary/non-binary classification problems. Most of the time -- if not all of the time -- we are only interested in correctly predicting one class.

Data science has traditionally been an analysis-only endeavor: using historical statistics, user interaction trends, or AI machine learning to predict the impact of deterministically coded software changes. For instance, "how do we think this change to the onboarding workflow will shift user behavior?" This is data science (DS) as an offline toolkit to make smarter decisions. Increasing, though, companies are building statistical or AI/Machine Learning features directly into their products. This can make our applications less deterministic – we may not know exactly how applications behave over time, or in specific situations – and harder to explain.

Make the world work for 100% of humanity, in the shortest possible time, through spontaneous cooperation without ecological offense or disadvantage of anyone. As a design leader, keeping one foot in the present and the other an optimistic future is all in a typical day. However, a little over a year ago, the universe decided to kick one of those feet out from under me and present me with an opportunity to experience life with a physical impairment. For the next three months, that injury left me wheelchair bound and gave me an opportunity to pivot my design from adoption, conversion and retention of users to accessibility and the rapid shift to Inclusive Design. According to the CDC, 1 in 4 US adults live with a disability.

Vehicle manufacturers know that they need to invent in autonomous technologies if they want to continue to remain relevant. As such, it should be no surprise that many car companies are investing in AI technologies to keep themselves competitive and relevant. Interviewed on an AI Today podcast episode, Jim Adler, Founding Managing Director of Toyota AI Ventures shared insights into the sort of investments Toyota AI Ventures is making in the industry, how the automotive industry is benefiting from these investments, and what non-automotive related AI and ML investments they are making. Founded in 2017, Toyota AI Ventures raised a $100 million fund to invest in artificial intelligence, cloud-based data, and robotics that may also leverage AI and cloud-based data. Toyota AI Ventures is a subsidiary of the Toyota Research Institute and helps AI ventures around the world to bring new artificial technology to the market.

Do we have more to fear from artificial intelligence or natural stupidity? This year's best science books offer plenty of both. Artificial intelligence has been much in the news this year, even though it doesn't really exist yet – as was made clear by the story of how people's conversations with Apple's "smart assistant", Siri, were being listened to by real human beings, low-paid workers in the global digital sweatshop. Nevertheless, the arrival of really intelligent machines has the potential to transform our world utterly. Consider ordering a superintelligent computer to make paper clips.

Welcome to the Fun and Easy Machine learning Course in Python and Keras. Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing of field Machine Learning. Each section consists of fun and intriguing white board explanations with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science.