Harry Chiang is a Financial Analyst at I Know First. "The big paradox here is that people think technology will lead to banking becoming more and more automated and less and less personalized, but what we've seen coming through here is the view that technology will actually help banking become a lot more personalized." Over the past few years, news articles have casually floated the term'Artificial Intelligence' around at an increasing rate. It's one of those buzzwords that somehow finds its way in to every tech-related conversation. Even the least tech-savvy person has a vague notion of what it is. The problem is, some of the more tech-savvy person don't have a much clearer notion of what it is either. The definition of AI ranges and has vague boundaries.

It was a hectic but interesting week at the 2017 IEEE International Conference on Robotics and Automation in Singapore. Computer/robotic vision and navigation-related (SLAM, localization) are still some of the most popular, or at least well-attended fields at these conferences. My colleagues said that some of the manipulation / grasping sessions were well attended too. The influence and growth in machine learning / deep learning seems to have slowed somewhat. This is possibly related to the fact we're increasingly seeing some of the best robotics learning work going to machine learning or computer vision conferences, or potentially to new conferences like the Conference on Robot Learning.

When Swedbank customers face a problem, they reach out to Nina, the bank's virtual assistant. Visitors to Mizuho Bank are greeted by Pepper, a humanoid robot standing four feet tall. Santander allows payments to be activated by voice, and JP Morgan Chase now uses machine learning to review commercial loan agreements in seconds, a task that used to take 3,60,000 manhours every year. Wherever you look in the world of financial services, you will find some form of artificial intelligence (AI) at work. AI technologies such as machine learning and speech recognition are quietly working behind the scenes to improve lending decisions and prevent fraud.

Scientists have harnessed the power of'the Beast' to answer some of the most baffling questions about religion. The custom-built computer has developed a simulated virtual human mind capable of creating the impacts of terror on behavior - with the hopes of uncovering the truth behind why people become radicalized. The model had revealed that religious ritual observance would increase after terror-inspiring events drove people beyond a certain threshold of fear. Researches at Boston University first designed a computer simulation that predicts how many people would stay in a religion based on its strictness. It was then compared with defection rates from 18 Christian denominations.

Deep learning AI could one day work as an early warning system to allow earlier medical intervention to patients. This AI will tell people when they're likely to die -- and that's a good thing. That's because scientists from the University of Adelaide in Australia have used deep learning technology to analyze the computerized tomography (CT) scans of patient organs, in what could one day serve as an early warning system to catch heart disease, cancer, and other diseases early so that intervention can take place. Using a dataset of historical CT scans, and excluding other predictive factors like age, the system developed by the team was able to predict whether patients would die within five years around 70 percent of the time. The work was described in an article published in the journal Scientific Reports.

As part of yesterday's WorldWide Developer Conference (WWDC) Google launched their foray into home connectivity with the HomePad called by Apple CEO Tim Cook, "breakthrough home speaker with amazing sound and incredible intelligence that will reinvent home audio." Don't overlook the last word, in his statement, despite the desire some will have to group HomePad with voice activatedAmazon Echo and Google Home, the product really belongs in a separate category, and here's why. The HomePod is a 7-inch tall smart speaker covered in a "seamless 3D mesh" fabric It contains a four-inch subwoofer According to Apple it " uses an advanced algorithm that continuously analyzes the music and dynamically tunes the low frequencies for smooth, distortion‑free sound." This includes "seven beamforming tweeters" that possess spatial awareness and direct the sound beams throughout the room. It automatically analyzes the acoustics, adjusting the sound based on the speaker's location, and steers the music in the optimal direction. According to Tim Cook: "Just like with portable music, we want to reinvent home music."

In the hit movie Avengers: Age of Ultron, the Iron Man shows the'brains' of a computer system to his colleague, the Incredible Hulk. "I mean, look at this! They're like neurons firing," the Hulk exclaims, pointing to a pulsating, blue orb which represented super baddie Ultron's consciousness. We'd like to think that's what neural networks look like too. They are a rising field of artificial intelligence, and a new trend that is coming to a government near you. Neural networks describe a computing technique that closely imitates human brain functions. "By using neural networks, we try to mimic nature's ability to learn how certain things work," Associate Professor Andy Chun from City University of Hong Kong's Department of Computer Science tells GovInsider.

Multi-agent forward search (MAFS) is a state-of-the-art privacy-preserving planning algorithm. We describe a new variant of MAFS, called multi-agent forward-backward search (MAFBS) that uses both forward and backward messages to reduce the number of messages sent and obtain new privacy properties. While MAFS requires agents to send a state s produced by an action a to all agents that can apply any action in s, MAFBS sends such messages forward only to agents that have an action that requires one of the effects of a. To achieve completeness, it sends messages backward to agents that can supply a missing precondition. This more focused message passing scheme reduces states exchanged, and requires that agents be aware only of other agents that they directly interact with, leading to agent privacy.

This paper describes an architecture for an agent to learn and reason about affordances. In this architecture, Answer Set Prolog, a declarative language, is used to represent and reason with incomplete domain knowledge that includes a representation of affordances as relations defined jointly over objects and actions. Reinforcement learning and decision-tree induction based on this relational representation and observations of action outcomes, are used to interactively and cumulatively (a) acquire knowledge of affordances of specific objects being operated upon by specific agents; and (b) generalize from these specific learned instances. The capabilities of this architecture are illustrated and evaluated in two simulated domains, a variant of the classic Blocks World domain, and a robot assisting humans in an office environment.

In this paper we introduce a novel approach for unsupervised classification of planning instances based on the recent formalism of planning programs. Our approach is inspired by structured prediction in machine learning, which aims at predicting structured information about a given input rather than a scalar value. In our case, each input is an unlabelled classical planning instance, and the associated structured information is the planning program that solves the instance. We describe a method that takes as input a set of planning instances and outputs a set of planning programs, classifying each instance according to the program that solves it. Our results show that automated planning can be successfully used to solve structured unsupervised classification tasks, and invites further exploration of the connection between automated planning and structured prediction.