Michael Harrison, who holds a Bachelor of Science degree with a major in Theoretical Physics minor at the Massachusetts Institute of Technology and a Master's degree program in Aerospace Systems Architecture at the University of Southern California, discusses artificial intelligence. To view more trends in robotics and artificial intelligence, visit the Robotics & AI Channel.

Evolving Floor Plans is an experimental research project exploring speculative, optimized floor plan layouts. The rooms and expected flow of people are given to a genetic algorithm which attempts to optimize the layout to minimize walking time, the use of hallways, etc. The creative goal is to approach floor plan design solely from the perspective of optimization and without regard for convention, constructability, etc. The research goal is to see how a combination of explicit, implicit and emergent methods allow floor plans of high complexity to evolve. The floorplan is'grown' from its genetic encoding using indirect methods such as graph contraction and emergent ones such as growing hallways using an ant-colony inspired algorithm.

Having used IBM's Watson technology to create a virtual learning assistant, Pearson has played a significant role in driving forward the development of artificial intelligence in higher education. Now the international education company is poised to take the next step by developing an AI tool that can grade university essays. Pearson's tool, which is currently being developed for piloting in US higher education, is not the first product of its type; similar platforms have been developed at the University of Manchester and at the University of California, Berkeley. But Pearson's global reach and its experience of using Watson – which can analyse huge amounts of text and data and use this to answer complex questions in natural language – could mean that its new tool represents a significant step forward. Milena Marinova, who has been hired by Pearson from chipmaker Intel to lead its work on AI, told Times Higher Education that the new tool would be able to mark essays in a more sophisticated fashion than previous grading assistants. "For any automation or assisted decision-making, abstraction is very difficult, but the new product is going to allow the professor to train the system," explained Ms Marinova, Pearson's senior vice-president for AI products and solutions.

The driving forces behind one of the UK's leading drone insurance providers has given a peek behind the scenes of the business. Flock's CEO, Ed Leon Klinger, and data scientist, Courtenay Mansel, took to the stage at the Data Science London Festival, to talk about Flock's technology and application. The talk covers the basics of Flock's risk analysis and insurance platform, as well as the data science behind it, and its commercial applicability in the drone industry. The talk can be viewed below.

It's never been easier to get started with machine learning. In addition to structured MOOCs, there is also a huge number of incredible, free resources available around the web. Familiarity and moderate expertise in at least one high-level programming language is useful for beginners in machine learning. Unless you are a Ph.D. researcher working on a purely theoretical proof of some complex algorithm, you are expected to mostly use the existing machine learning algorithms and apply them in solving novel problems. This requires you to put on a programming hat. While the debate rage, grab a coffee and read this insightful article to get an idea and see your choices.

The intense focus on the exponential potential of AI and related tools for businesses has thrown up questions around their relevance for the SMEs and whether they would stand to gain from investments in this area. In the early 70s, personal computers were new to business domains with only large-scale companies being able to justify the investment. To a great extent, the scenario with respect to AI and businesses is very similar. However, due to the hype created, small businesses are made to consider AI as probably essential to their survival. Just as in the 80s, when there were exceptions and some businesses considered investment in computers for the future of business, SMEs who see value in such investment to maintain their edge and are able to afford it, should go ahead and evaluate their investment decision based on their business objective and not just because of AI's novelty value.

As convolutional neural networks (CNNs) enable state-of-the-art computer vision applications, their high energy consumption has emerged as a key impediment to their deployment on embedded and mobile devices. Towards efficient image classification under hardware constraints, prior work has proposed adaptive CNNs, i.e., systems of networks with different accuracy and computation characteristics, where a selection scheme adaptively selects the network to be evaluated for each input image. While previous efforts have investigated different network selection schemes, we find that they do not necessarily result in energy savings when deployed on mobile systems. The key limitation of existing methods is that they learn only how data should be processed among the CNNs and not the network architectures, with each network being treated as a blackbox. To address this limitation, we pursue a more powerful design paradigm where the architecture settings of the CNNs are treated as hyper-parameters to be globally optimized. We cast the design of adaptive CNNs as a hyper-parameter optimization problem with respect to energy, accuracy, and communication constraints imposed by the mobile device. To efficiently solve this problem, we adapt Bayesian optimization to the properties of the design space, reaching near-optimal configurations in few tens of function evaluations. Our method reduces the energy consumed for image classification on a mobile device by up to 6x, compared to the best previously published work that uses CNNs as blackboxes. Finally, we evaluate two image classification practices, i.e., classifying all images locally versus over the cloud under energy and communication constraints.

Robots can rapidly acquire new skills from demonstrations. However, during generalisation of skills or transitioning across fundamentally different skills, it is unclear whether the robot has the necessary knowledge to perform the task. Failing to detect missing information often leads to abrupt movements or to collisions with the environment. Active learning can quantify the uncertainty of performing the task and, in general, locate regions of missing information. We introduce a novel algorithm for active learning and demonstrate its utility for generating smooth trajectories. Our approach is based on deep generative models and metric learning in latent spaces. It relies on the Jacobian of the likelihood to detect non-smooth transitions in the latent space, i.e., transitions that lead to abrupt changes in the movement of the robot. When non-smooth transitions are detected, our algorithm asks for an additional demonstration from that specific region. The newly acquired knowledge modifies the data manifold and allows for learning a latent representation for generating smooth movements. We demonstrate the efficacy of our approach on generalising elementary skills, transitioning across different skills, and implicitly avoiding collisions with the environment. For our experiments, we use a simulated pendulum where we observe its motion from images and a 7-DoF anthropomorphic arm.

I'm currently on vacation after completing my 1st year of computer science and i wanted to get into Artificial Intelligence (mainly with neural nets) but i was overwhelmed with the information there is online. Which is the best way for me to fully comprehend AI and learn how to apply it in real situations, for example coding an AI to pratice playing chess or some other simple game? Should i do online courses? Any help is much appreciated, thanks!

In Machine Learning one of the biggest problem faced by the practitioners in the process is choosing the correct set of hyper-parameters. And it takes a lot of time in tuning them accordingly, to stretch the accuracy numbers. For instance lets take, SVC from well known library Scikit-Learn, sklearn.svm.SVC class implements the Support Vector Machine algorithm for classification which contains more than 10 hyperparameters, now adjusting all ten to minimize the loss is very difficult just by using hit and trial. Though Scikit-Learn provides Grid Search and Random Search, but the algorithms are brute force and exhaustive, however hyperopt implements distributed asynchronous algorithm for hyperparameter optimization. Introducing SMBO- Sequential Model Based Global Optimization.