The online games were easy–until I got to challenge number six. I was applying for a job at Unilever, the consumer-goods behemoth behind Axe Body Spray and Hellmann's Real Mayonnaise. I was halfway through a series of puzzles designed to test 90 cognitive and emotional traits, everything from my memory and planning speed to my focus and appetite for risk. A machine had already scrutinized my application to determine whether I was fit to reach even this test-taking stage. Now, as I sat at my laptop, scratching my head over a probability game that involved wagering varying amounts of virtual money on whether I could hit my space bar five times within three seconds or 60 times within 12 seconds, an algorithm custom-built for Unilever analyzed my every click. I furiously stabbed at my keyboard, my chances of joining one of the world's largest employers literally at my fingertips.

Finally, a comprehensive hands-on machine learning course with specific focus on classification based models for the investment community and passionate investors. In the past few years, there has been a massive adoption and growth in the use of data science, artificial intelligence and machine learning to find alpha. However, information on and application of machine learning to investment are scarce. This course has been designed to address that. It is meant to spark your creative juices and get you started in this space.

Bangassou, Central African Republic - As an IT specialist, Djamaladine Mahamat Salet is used to providing solutions to complex problems. When he discovered there were no high-school classes for the displaced children in his community, he decided to start teaching them basic computer skills. The big challenge was that he would have to do so without electricity, an internet connection, and no laptops. "These children are stuck here due to this conflict," said 38-year-old Salet. "I am trying to prepare these students so that when they can finally go on to university, they will thrive."

Today, chatbots used in e-learning are completely changing the workplace, with an aim to increase business productivity, employee efficiency and morale. Bots are communication interfaces that provide ample applications through spacing effect, virtual assistance, new hire onboarding process, learning scoring and more. Anubha Goel is a Senior Executive in the Marketing team at G-Cube Solutions. Her passion is to write innovative content that caters the customized learning needs of diverse audiences from different industry domains.

Google and its partners have slowly but surely trying to revitalize Android-based smartwatches, and it looks like they may use fitness as the way to reel you in. Android Police (which has a history of accurate leaks) claims to have word of Google Coach, a Wear OS-oriented assistant that would use AI to improve your health and fitness. Rather than just track your activities and popping up an endless stream of reminders, it would "proactively" recommend changes to your activity and provide prompts that are genuinely useful. The coaching tool would look at your exercise and suggest new routines, or even provide alternatives if you couldn't make it to the gym on time. And while nutrition tracking by itself isn't new, Google Coach could suggest healthy meals on nights out, generate meal plans (possibly using your calendar) and create shopping lists.

Check out the "Deep Learning models" sessions at the Artificial Intelligence Conference in San Francisco, September 4-7, 2018. One of the key findings of a survey we released earlier this year (How Companies are Putting AI to Work through Deep Learning) was that the leading reason holding companies back from incorporating deep learning was their lack of access to skilled people. One-fifth of respondents pointed to a skills gap as one of the reasons they haven't integrated deep learning, and at the time of the survey, 75% of respondents indicated their company had some combination of internal and external training programs to address this issue. We've continued to monitor interest in topics relevant to building AI products and systems, specifically areas that also warrant investment in skills development. In this post, I'll share results of related studies we've conducted.

Disruption is inevitable, but also deeply feared. We've seen this with every significant technological leap -- from the printing press to automobiles to computers. But, as we enter the next iteration of technology with AI, we know it will have a profound, transformative effect on global business and society. However, we must reflect on how we want this transformation to occur. Early adoption has already begun: AI is transforming everyday activities and processes such as virtual assistants, fraud detection and driverless cars. Various forms of AI solutions are already in the market, including automation, speech recognition, machine learning, decision-making and natural language processing.

Could robots really help teachers engage students more fully? According to Sean Kelly, a sociologist at the University of Pittsburgh School of Education, the answer is yes. Kelly, along with computer scientist Sidney D'Mello, has researched the role of tech in helping teachers ask better, more thought-provoking questions for a number of years. Rather than relying on one-time test scores to judge learning, Kelly's work is a new advancement in the field of building systems for day-to-day classroom observation. His focus is on the quality of instruction, specifically, the type of questions teachers pose that prompt cognitive processing and verbal response from students.

Despite the fact that nonlinear subspace learning techniques (e.g. manifold learning) have successfully applied to data representation, there is still room for improvement in explainability (explicit mapping), generalization (out-of-samples), and cost-effectiveness (linearization). To this end, a novel linearized subspace learning technique is developed in a joint and progressive way, called \textbf{j}oint and \textbf{p}rogressive \textbf{l}earning str\textbf{a}teg\textbf{y} (J-Play), with its application to multi-label classification. The J-Play learns high-level and semantically meaningful feature representation from high-dimensional data by 1) jointly performing multiple subspace learning and classification to find a latent subspace where samples are expected to be better classified; 2) progressively learning multi-coupled projections to linearly approach the optimal mapping bridging the original space with the most discriminative subspace; 3) locally embedding manifold structure in each learnable latent subspace. Extensive experiments are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods.

What parent wouldn't use anything at their disposal to help their kids succeed? We know that sitting down with our kids and helping them grasp the concepts they're learning at school is the best way to help them academically. And they probably don't need more time interacting with electronics, but Alexa offers some skills that can help students when parents aren't available. Luckily, if you have an Amazon Echo smart speaker, (in this case we recommend the Echo Dot for Kids) you have access to a wealth of brain-sharpening, knowledge-enhancing activities. To enable a skill on your Echo device, just say, "Alexa, enable [exact name of skill]."