Hassaan Ahmed is co-founder of Intellisense Solutions. Machine learning is not new. We have witnessed it since the 1990s, when Amazon introduced a new "recommended for you" section for its users to display more personalized results. When we search for something on Google, machine learning is behind those search results. The "Friends" recommendations or the suggested pages on Facebook or a product recommendation on any e-commerce site all depend on machine learning.

The advocates of machine learning are known to be a fiercely contentious lot, each asserting that its own approach is superior to all others, and that any evidence adduced to the contrary is propaganda, fake news of the worst sort, stemming from jealous advocates of inferior approaches. The closest approximation to the internecine warfare of the machine learning field is the human learning field, in which advocates of public, government-run and union-staffed schools exchange harsh words with advocates of charter schools, with a level of invective and passion that indicates that someone is strongly in favor of hopelessly uneducated machines and/or humans.

The computation model was built on CogSketch, a sketch-understanding system developed in Forbus' laboratory at Northwestern University. Sketching is a natural activity that people do while thinking or trying to communicate an idea, especially when spatial content is involved. Sketching is also heavily used in engineering and geoscience. CogSketch is used to model spatial understanding and reasoning, making it suitable for research based on sketches, but also for testing against a standardized visual intelligence test such as the Raven's Progressive Matrices test.

A Northwestern University team developed a new computational model that performs at human levels on a standard intelligence test. This work is an important step toward making artificial intelligence systems that see and understand the world as humans do. "The model performs in the 75th percentile for American adults, making it better than average," said Northwestern Engineering's Ken Forbus. "The problems that are hard for people are also hard for the model, providing additional evidence that its operation is capturing some important properties of human cognition." The new computational model is built on CogSketch, an artificial intelligence platform previously developed in Forbus' laboratory.

The general idea here is that the work done by a Machine Learning engineer can be automated, thus freeing potential users from the tyranny of needing to have specific expertise. Presumably, the ultimate goal of such automations is to make Machine Learning accessible to more people. After all, if a thing can be done automatically, that means anyone who can press a button can do it, right? I'm going to make a three-part argument here that "Machine Learning Automation" is really just a poor proxy for the true goal of making Machine Learning useable by anyone with data. Furthermore, I think the more direct path to that goal is via the combination of automation and interactivity that we often refer to in the software world as "abstraction". By understanding what constitutes a powerful Machine Learning abstraction, we'll be in a better position to think about the innovations that will really make Machine Learning more accessible.

In 1960s and 70s Britain, immigrant ethnic minority children were dispersed across schools in the hope that it would help them integrate. The process saw children - largely of south Asian and African or Caribbean descent - being "bussed" out of their local areas to go to school. Eleven Local Area Authorities (LEAs) decided there should be no more than 30% of immigrants at any one school. It meant once that quota was reached, children were taken elsewhere. The process, which became known as "bussing", is now at the heart of a project in Bradford where Shabina Aslam is trying to trace children who, like herself, were sent to school away from where they lived.

Recent empirical results on long-term dependency tasks have shown that neural networks augmented with an external memory can learn the long-term dependency tasks more easily and achieve better generalization than vanilla recurrent neural networks (RNN). We suggest that memory augmented neural networks can reduce the effects of vanishing gradients by creating shortcut (or wormhole) connections. Based on this observation, we propose a novel memory augmented neural network model called TARDIS (Temporal Automatic Relation Discovery in Sequences). The controller of TARDIS can store a selective set of embeddings of its own previous hidden states into an external memory and revisit them as and when needed. For TARDIS, memory acts as a storage for wormhole connections to the past to propagate the gradients more effectively and it helps to learn the temporal dependencies. The memory structure of TARDIS has similarities to both Neural Turing Machines (NTM) and Dynamic Neural Turing Machines (D-NTM), but both read and write operations of TARDIS are simpler and more efficient. We use discrete addressing for read/write operations which helps to substantially to reduce the vanishing gradient problem with very long sequences. Read and write operations in TARDIS are tied with a heuristic once the memory becomes full, and this makes the learning problem simpler when compared to NTM or D-NTM type of architectures. We provide a detailed analysis on the gradient propagation in general for MANNs. We evaluate our models on different long-term dependency tasks and report competitive results in all of them.

Ten years from today, the center of our digital lives will no longer be the smart phone, but device that looks like ordinary eyeglasses: except those glasses will have settings for Virtual and Augmented Reality. What you really see and what is computer generated will be mixed so tightly together, that we won't really be able to tell what is real and what is illusion. Instead of touching and sliding on a mobile phone, we will make things happen by moving our eyes or by brainwaves. When we talk with someone or play an online game, we will see that person in the same room with us. We will be able to touch and feel her or him through haptic technology.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

Artificial intelligence and automation are now considered to be amongst the biggest threats to job creation and industry voices across the board agree that so-called traditional jobs may soon cease to exist. According to Infosys' chief executive officer, Vishal Sikka, advances in technology removing a lot of mechanical and scripted jobs, besides many related to business process outsourcing, IT and IT infrastructure operations. But automation will also lead to creation of a new set of jobs, Sikka added. Around 20 crore middle class young people would have no jobs by 2025 if our education system doesn't keep pace with the changes in automation and technology, Mohandas Pai, the chairman of Manipal Global Education Services told BloombergQuint in an interview. And the level of automation is only going to get higher. Both ICICI and HDFC Bank say this is only the start, and there's a lot more to come.