Studies have long debated whether it is best for young children and their school grades to have their mothers at home supporting their development. But new research has found that a stable home life is more important than whether a mother is in or out of employment, allowing parents to develop long-term routines. And by bringing in money and raising the overall family income, working mothers may be able to provide a more stimulating and safer environment for their children. In an article for The Conversation Markus Klein, a lecturer in human development and education policy at Strathclyde University and Michael Kühhirt, a sociology lecturer from the University of Cologne, explore the various factors at work. Women go to great lengths to ensure they combine their careers with their children's developmental needs.

NEW DELHI: Indian Institute of Technology (IIT) Delhi is hosting a week long hackathon on artificial intelligence starting Friday (July 28 – August 4). The main objective of the hackathon is to bring together young talent in the AI space. This hackathon is being led by Anshul Bhagi, an MIT/Harvard alumnus who is also the co-director of OpenEd.ai, a non-profit organisation committed to developing and promoting open-source AI for education. Among other partners for the event are Niti Aayog, Omidyar Network (venture capital firm), IBM, Amazon Web Services, Google Developer Groups, Bhagi shared. "The grand prize is of $6000 sponsored by Omidyar. These organizations are offering a total of $17,000 as prize money for participants," Bhagi said.

Other major examples include Google's DeepMind Health, which last year announced multiple UK-based partnerships, including with Moorfields Eye Hospital in London, in which they're developing technology to address macular degeneration in aging eyes. Image credit: Google DeepMind Health – An OCT scan of one of the DeepMind Health team's eyes In the area of brain-based diseases like depression, Oxford's P1vital Predicting Response to Depression Treatment (PReDicT) project is using predictive analytics to help diagnose and provide treatment, with the overall goal of producing a commercially-available emotional test battery for use in clinical settings. Key players in this domain include the MIT Clinical Machine Learning Group, whose precision medicine research is focused on the development of algorithms to better understand disease processes and design for effective treatment of diseases like Type 2 diabetes. Until that day comes, Google's DeepMind Health is working with University College London Hospital (UCLH) to develop machine learning algorithms capable of detecting differences in healthy and cancerous tissues to help improve radiation treatments.

Artificial intelligence is progressing so rapidly that there are new developments, ideas, and breakthroughs almost every week. As AI becomes more present than futuristic and intertwines with our everyday lives, it's hard to stay on top of the most current research. But you don't need to miss out - here's an update to fill you in on what's been going on this week: Tesla CEO Elon Musk and Facebook CEO Mark Zuckerberg's clash continues as they argue over the future of artificial intelligence. It would appear that the overly publicised disagreement comes down to one thing - both business men protecting their brands. Musk has been very vocal about his worries about the future of AI, saying "I keep sounding the alarm bell, but until people see robots going down the street killing people, they don't know how to react."

Days after Tesla CEO Elon Musk said that artificial intelligence (AI) was the biggest risk, Facebook has shut down one of its AI systems after chatbots started speaking in their own language defying the codes provided. According to a report in Tech Times on Sunday, the social media giant had to pull the plug on the AI system that its researchers were working on "because things got out of hand". "The AI did not start shutting down computers worldwide or something of the sort, but it stopped using English and started using a language that it created," the report noted. Initially the AI agents used English to converse with each other but they later created a new language that only AI systems could understand, thus, defying their purpose. This led Facebook researchers to shut down the AI systems and then force them to speak to each other only in English.

Discover how to use Python--and some essential machine learning concepts--to build programs that can make recommendations. She helps you learn the concepts behind how recommendation systems work by taking you through a series of examples and exercises. Once you're familiar with the underlying concepts, Lillian explains how to apply statistical and machine learning methods to construct your own recommenders. She demonstrates how to build a popularity-based recommender using the Pandas library, how to recommend similar items based on correlation, and how to deploy various machine learning algorithms to make recommendations. At the end of the course, she shows how to evaluate which recommender performed the best.

The other day I was reading an article on how AI has progressed so far and where it is going. I was awestruck and had a hard time digesting the picture the author drew on possibilities in the future. Here is how I reacted. "A surgeon could control a machine scalpel with her motor cortex instead of holding one in her hand, and she could receive sensory input from that scalpel so that it would feel like an 11th finger to her. So it would be as if one of her fingers was a scalpel and she could do the surgery without holding any tools, giving her much finer control over her incisions. An inexperienced surgeon performing a tough operation could bring a couple of her mentors into the scene as she operates to watch her work through her eyes and think instructions or advice to her. And if something goes really wrong, one of them could "take the wheel" and connect their motor cortex to her outputs to take control of her hands."

Most superintelligent systems will by default develop instrumental subgoals that conflict with human interests. This could have catastrophic consequences. If we don't actively work on control mechanisms and safety of AI systems, this will most likely pose an existential risk to humanity. Artificial intelligence is all around us in many parts of every day life; our phones find the fastest way to move from A to B, spam is automatically filtered out of our email inboxes, and Netflix recommends movies and tv-shows tailored to our likings. For the near future, AI promises many changes of how we live: from self-driving cars to intelligent fridges. Obviously, there is a great interest in developing stronger, faster and more intelligent AI systems. Giant companies such as Alphabet (Google), Facebook, Apple, Microsoft, etc. have long realized how profitable and promising this field of research and development is. At the same time, governments have begun to invest in the military use of AI. Overall, there is a big hype around AI topics, especially when mentioning buzzwords such as deep learning.

In this work, we design and evaluate a computational learning model that enables a human-robot team to co-develop joint strategies for performing novel tasks that require coordination. The joint strategies are learned through "perturbation training," a human team-training strategy that requires team members to practice variations of a given task to help their team generalize to new variants of that task. We formally define the problem of human-robot perturbation training and develop and evaluate the first end-to-end framework for such training, which incorporates a multi-agent transfer learning algorithm, human-robot co-learning framework and communication protocol. Our transfer learning algorithm, Adaptive Perturbation Training (AdaPT), is a hybrid of transfer and reinforcement learning techniques that learns quickly and robustly for new task variants. We empirically validate the benefits of AdaPT through comparison to other hybrid reinforcement and transfer learning techniques aimed at transferring knowledge from multiple source tasks to a single target task. We also demonstrate that AdaPT's rapid learning supports live interaction between a person and a robot, during which the human-robot team trains to achieve a high level of performance for new task variants. We augment AdaPT with a co-learning framework and a computational bi-directional communication protocol so that the robot can co-train with a person during live interaction. Results from large-scale human subject experiments (n=48) indicate that AdaPT enables an agent to learn in a manner compatible with a human's own learning process, and that a robot undergoing perturbation training with a human results in a high level of team performance. Finally, we demonstrate that human-robot training using AdaPT in a simulation environment produces effective performance for a team incorporating an embodied robot partner.