The Eighth International Conference on Computational Creativity (ICCC’17)1 was hosted at the Georgia Institute of Technology in Atlanta, Georgia, USA from June 19th - June 23rd, 2017. The ICCC’17 organising committee consisted of Ashok Goel (General Chair), Kazjon Grace (Workshop Co-chair), Matthew Guzdial (Media Chair), Mikhail Jacob (Local Chair), Anna Jordanous (Program Co-chair), Ruli Manurung (Workshop Co-chair) and Alison Pease (Program Co-chair). This report summarises the main topics addressed.

The idea of temporal abstraction, i.e. learning, planning and representing the world at multiple time scales, has been a constant thread in AI research, spanning sub-fields from classical planning and search to control and reinforcement learning. For example, programming a robot typically involves making decisions over a set of controllers, rather than working at the level of motor torques. While temporal abstraction is a very natural concept, learning such abstractions with no human input has proved quite daunting. In this paper, we present a general architecture, called option-critic, which allows learning temporal abstractions automatically, end-to-end, simply from the agent’s experience. This approach allows continual learning and provides interesting qualitative and quantitative results in several tasks.

Machine learning workflow development is anecdotally regarded to be an iterative process of trial-and-error with humans-in-the-loop. However, we are not aware of quantitative evidence corroborating this popular belief. A quantitative characterization of iteration can serve as a benchmark for machine learning workflow development in practice, and can aid the development of human-in-the-loop machine learning systems. To this end, we conduct a small-scale survey of the applied machine learning literature from five distinct application domains. We collect and distill statistics on the role of iteration within machine learning workflow development, and report preliminary trends and insights from our investigation, as a starting point towards this benchmark. Based on our findings, we finally describe desiderata for effective and versatile human-in-the-loop machine learning systems that can cater to users in diverse domains.

Many machine learning problems can be formulated as consensus optimization problems which can be solved efficiently via a cooperative multi-agent system. However, the agents in the system can be unreliable due to a variety of reasons: noise, faults and attacks. Thus, providing falsified data leads the optimization process in a wrong direction, and degrades the performance of distributed machine learning algorithms. This paper considers the problem of decentralized learning using ADMM in the presence of unreliable agents. First, we rigorously analyze the effect of falsified updates (in ADMM learning iterations) on the convergence behavior of multi-agent system. We show that the algorithm linearly converges to a neighborhood of the optimal solution under certain conditions and characterize the neighborhood size analytically. Next, we provide guidelines for network structure design to achieve a faster convergence. We also provide necessary conditions on the falsified updates for exact convergence to the optimal solution. Finally, to mitigate the influence of unreliable agents, we propose a robust variant of ADMM and show its resilience to unreliable agents.

It might not look that special, but the robot above is, according to a new measure, the most dexterous one ever created. Among other tricks, it could sort through your junk drawer with unrivaled speed and skill. The key to its dexterity is not in its mechanical grippers but in its brain. The robot uses software called Dex-Net to determine how to pick up even odd-looking objects with incredible efficiency. The new robot was built by Ken Goldberg, a professor at UC Berkeley, and one of his graduate students, Jeff Mahler. Goldberg will demonstrate the latest version of it at EmTech Digital, an event in San Francisco organized by MIT Technology Review and dedicated to artificial intelligence.

Artificial intelligence will transform many companies and create completely new types of businesses. The cofounder of Coursera, AI Fund, and Landing.AI shares how businesses can benefit. Artificial intelligence (AI) is at the cutting edge of innovation. But how do companies find the expertise necessary to utilize it, and then take it to market? In this video, recorded at the Aspen Ideas Festival in June, Andrew Ng, cofounder of Coursera, AI Fund, and Landing.AI, discusses the difference between an AI-enabled business versus a true AI company, and how businesses can organize, hire, and make use of AI to add value.

Two high school robotics teams from Pittsburgh, Pa. recently used products from igus, a German-based manufacturer of engineered plastics with a presence in East Providence, R.I., to set four world records in this year's FIRST Tech Challenge global robotics competition. Team #8393 -- The Giant Diencephalic BrainSTEM Robotics Team, captained by senior James Walton -- joined forces with the rookie team they have been mentoring this season (Team #6931 -- the Substantial Monocephalic BrainSTEM Robotics team) to set their third world record at the South Central Regional Qualifier in York, Pennsylvania. The teams set the record for the most points scored in the 2018 FTC Challenge: Relic Recovery. This challenge requires robots to collect and score glyphs (foam cubes) in various patterns, retrieve jewels, transfer relics, park on balancing stones, and navigate to specific parts of the playing field -- sometimes autonomously. The BrainSTEM robots include spinning collectors, elevating depositing-platforms and robotic arms all built onto fast moving omnidirectional drivetrains.

Artificial intelligence (AI) is now a part of our lives whether we know it or not, and whether we accept it or not. It has risen to such a status over the years in a slow but steady manner. Everything that we do like buying clothes and shoes on the internet as well as watching shows on TV is influenced to various extents by AI. However, the question that needs to be asked in this context is what effect would this have on education? The fact of the matter is that AI does not take away, in way, shape, or form, from the classroom. Rather it makes the classroom experience a lot better than before.

Every day we hear about more and more jobs disappearing, yet the data science community cannot keep up with unprecedented demand. When you consider the growth of this industry, it's not surprising to hear there will be a shortage of 1.5 million analysts capable of analyzing big data in the U.S. alone, by 2018, according to McKinsey. Globally, demand for data scientists is projected to exceed supply by more than 50 percent by 2018. It appears women are deterred from jobs in data science for the same reason they are deterred from other STEM fields. But interestingly, some stereotypically female traits are exactly the qualities that make for a successful data scientist.

These complex and abstract representations can then be identified anywhere in the image. One drawback to CNN's is that increasing model power requires increased model depth. This increases the number of parameters in the model, lengthening training time and predisposing to the vanishing gradient problem, where gradients disappear and the model stalls in stochastic gradient descent, failing to converge. The introduction of Residual Networks in 2015 (ResNets) solved some of the problems with increasing network depth, as residual connections (seen above in a DenseNet) allow backpropagation to take a gradient from the last layer and follow it through all the way to the first layer. Recognition that CNN's are agnostic to position, but not orientation is important to note.