While building artificial systems does not necessarily require copying nature -- after all, airplanes fly without flapping their wings like birds -- the history of AI and machine learning convincingly demonstrates that drawing inspirations from neuroscience and psychology can lead to significant breakthroughs, with deep neural networks and reinforcement learning being perhaps the two most prominent examples. Taking inspiration from the brain, our IBM Research team recently used machine learning techniques to develop computational models of attention and memory. Our ultimate goal is to build lifelong learning AI systems, able to adapt to new environments while retaining what they have learned so far. This challenge can be broken down into short term adaptation, where there is little time to change a system and train it on what to pay attention to, and long term adaptation that is inspired by how the human brain forms memory and how neuroplasticity (e.g., adult neurogenesis) affects this process. Our team developed two important innovations that enable short-term and long-term adaptation which are a result of reward-driven attention techniques and enabling network "plasticity."

Deep Learning A-Z: Hands-On Artificial Neural Networks by Kirill Eremenko and Hadelin de Ponteves will teach you Deep Learning with Artificial Neural Networks. You will work with Tensorflow and Pytorch to build several different types of Neural Networks. Data Science: Deep Learning in Python by Lazy Programmer Inc. will teach you build Neural Networks from scratch in Python, numpy & TensorFlow. You will learn about the various types and terms associated to neural networks. Natural Language Processing with Deep Learning in Python by Lazy Programmer Inc. will teach you everything about deriving and implementing word2vec, GLoVe, word embeddings, and sentiment analysis with recursive nets.

Over the years, Andrew Ng has worn many hats - Coursera co-founder, former Baidu chief scientist, founding lead of Google Brain team, and Stanford University adjunct professor. But lately, he has emerged as the leading influencer championing artificial intelligence (AI). Well over 1.5 million people have enrolled in his AI courses in Coursera. In a chat with Vinod Mahanta, Ng talks about recent AI controversies: Elon Musk versus Mark Zuckerberg spat on dangers of AI, Facebook AI chatbots creating their own language and job displacements. Edited excerpts: In an experiment recently, Facebook chatbots created their own language and had to be shut down.

After reading a recent pair of articles covering the topic of artificial intelligence (AI), I am confused. On one hand, there's recent PwC findings suggesting AI could drive $15.7 trillion in productivity gains by 2030. On the other, a recent piece from the New York Times makes a compelling case that, despite all the hype, AI's dirty little secret is that "it still has a long, long way to go." There's no question AI is a developing technology. As the New York Times piece points out, we can find plenty of examples of robots falling over while opening doors, driverless cars needing human intervention, and machines that still cannot read reliably at the level of a sixth grader.

Science, technology, engineering, and math (STEM) fields play growing roles in national and international economies by driving innovation and generating high salary jobs. Yet, the US is lagging behind other highly industrialized nations in terms of STEM education and training. Furthermore, many economic forecasts predict a rising shortage of domestic STEM-trained professions in the US for years to come. One potential solution to this deficit is to decrease the rates at which students leave STEM-related fields in higher education, as currently over half of all students intending to graduate with a STEM degree eventually attrite. However, little quantitative research at scale has looked at causes of STEM attrition, let alone the use of machine learning to examine how well this phenomenon can be predicted. In this paper, we detail our efforts to model and predict dropout from STEM fields using one of the largest known datasets used for research on students at a traditional campus setting. Our results suggest that attrition from STEM fields can be accurately predicted with data that is routinely collected at universities using only information on students' first academic year. We also propose a method to model student STEM intentions for each academic term to better understand the timing of STEM attrition events. We believe these results show great promise in using machine learning to improve STEM retention in traditional and non-traditional campus settings.

Earn your certification as a Customer Experience Specialist (CXS) or get college credits to stand out and advance your career. Features powerful keynote addresses, engaging workshops, and valuable networking aimed at driving business success through customer insights and intelligence. Attendees will also receive two special reports focused on research and best practices for CX leadership. Register with the discount code "CustomerThink" to get the lowest available price. A first-of-its kind marketing program for the CX industry, the new CX Playbook Partner Sponsorship Program incorporates five key marketing elements.

Going to school for a formal degree program for isn't always possible or desirable. For those considering an autodidactic alternative, this is for you. There's too much to learn, and the field is advancing rapidly. Master foundational concepts and then focus on projects in a specific domain of interest -- whether it's natural language understanding, computer vision, deep reinforcement learning, robotics, or whatever else. Motivation is far more important than micro-optimizing a learning strategy for some long-term academic or career goal.

In one Pennsylvania high school, more than 15 languages are spoken in a student body of nearly 4,000. WASHINGTON -- In middle school, Junior Alvarado often struggled with multiplication and earned poor grades in math, so when he started his freshman year at Washington Leadership Academy, a charter high school in the nation's capital, he fretted that he would lag behind. But his teachers used technology to identify his weak spots, customize a learning plan just for him and coach him through it. This past week, as Alvarado started sophomore geometry, he was more confident in his skills. "For me personalized learning is having classes set at your level," Alvarado, 15, said in between lessons.

HUANGCHUAN VILLAGE, CHINA – Every day after lunch, Qu Yexiu used to potter around her house in northwest China doing housework and looking after her 2-year-old grandson. Now, every day after lunch, Qu and her grandson visit the newly opened early-childhood development center in their village of Huangchuan in the mountains of Shaanxi province, where he can play with other toddlers. "Things are better now that we have this village center," said Qu, 56. She looks after her two grandchildren while their parents work and live in nearby Anhui province. The other grandchild attends a preschool.

A vice-chancellor's call for universities to train undergraduates "to tell the machines what to do" has rekindled debate about how higher education institutions can best prepare their students for the jobs of the future. Michael Spence outlined plans for the University of Sydney to move towards offering four-year degrees with a greater focus on problem-solving and cultural competency as sector leaders around the world debate whether the rise of artificial intelligence and automation will require providers to prioritise specialist skills in areas such as coding, or broad knowledge that will allow graduates to adapt to a changing workplace. The shift towards longer degrees also runs counter to the push in the UK for more two-year degrees, designed to allow students to start their career more quickly and more cheaply. In an interview with Times Higher Education, Dr Spence outlined how Sydney had streamlined its 122 degree programmes – a portfolio based on the supposition that "if you enter a narrow tube that has a job name at one end, at the other end you'll plop out into the job" – to just 25. The rise of AI means that such jobs "may not exist by the time you end up there, or at least won't necessarily have any longevity", Dr Spence said.