Colonial First State data scientists are working with a team of computer engineering PhDs from the University of Technology, Sydney, to develop deep learning algorithms to predict investor responses to market shocks and tailor the communication of financial advice. A five-year partnership between Commonwealth Bank of Australia-owned CFS and UTS has resulted in the asset manager providing 20 years of investment and behavioural data for 1 million customers to machine learning researchers at the university, who are using its cutting-edge super-computers to forecast investor reaction. Peter Chun, the general manager of product and investment at Colonial First State, said artificial intelligence and big data analytics will also help the asset manager predict which customers might be more receptive to investment opportunities. He points to the example of the government's non-concessional contribution rules for superannuation; customers have a window of opportunity before June 30 to invest more than the caps.

In order to understand trends in the field, I find it helpful to think of developments in deep learning as being driven by three major frontiers that limit the success of artificial intelligence in general and deep learning in particular. Firstly, there is the available computing power and infrastructure, such as fast GPUs, cloud services providers (have you checked out Amazon's new EC2 P2 instance?) and tools (Tensorflow, Torch, Keras etc), secondly, there is the amount and quality of the training data and thirdly, the algorithms (CNN, LSTM, SGD) using the training data and running on the hardware. Invariably behind every new development or advancement, lies an expansion of one of these frontiers. Much of the progress we have seen this year is driven by an expansion of the former two frontiers; we now have systems that are able to recognize images and speech with an accuracy that rivals that of humans and there is an abundance of data and tools to develop them. However, almost all of these systems rely on supervised learning and thereby on the ready availability of labelled data sets.

LG Electronics will use machine learning technology to offer customized after sales (AS) services for its smartphone users, the company announced. The South Korean tech giant said it will adopt machine learning, big data analytics, and other, latest artificial intelligence technologies sequentially, starting in the first quarter of this year, to offer remote AS services via its apps. Thanks to machine learning, the app's diagnosis will improve over time, data will be processed faster, and LG will be able to offer services customized for the individual customer. The South Korean tech giant said over 80 percent of visits to service centres from its smartphone users were for simple requests or software problems. The remote service will drastically reduce its customers' needs to visit physical service centres, it said Artificial intelligence technology will be put into its "Smart Doctor" app.

Professor Einstein rolls his eyes, sticks out his tongue, and can give a simple explanation of the theory of relativity. With his lifelike rubbery "skin" and bushy mustache, he can almost make you forget he's a robot. The Einstein robot is among dozens roaming the Consumer Electronics Show in Las Vegas that can be your companion, educator or babysit your children. While robots have been around for years, advances in technology and artificial intelligence have allowed developers to give them traits that enable the devices to be seen as members of the family. "We make robots that have personality and come to life," said Andy Rifkin, chief technology officer of Hanson Robotics, the Hong Kong-based firm which is bringing the $299 Einstein robot to consumers this year.

No billion-dollar machine could replace a doctor. But a $25 machine can tell you when you need one. In 1996, the ER at Cook County Hospital of Chicago used an algorithm to determine when a patient with chest pain was in danger of having a heart attack and was thus worth one of its scarce hospital beds. Using a systematic, flowchart-based approach of basic tests, the algorithm proved not only to be quick and efficient, but accurate: It sorted 70 percent more patients into the low-risk category, but caught a higher percentage of heart attacks (95 percent) than human doctors (75-89 percent). And this was before any deep computing was involved. Now consider that there are around 6.4 billion IoT devices in use this year -- nearly one for every living human.

Yiling Chen (yiling@seas.harvard.edu) is Gordon McKay Professor of Computer Science at Harvard University, Cambridge, MA. Arpita Ghosh (arpitaghosh@cornell.edu) is an associate professor of information science at Cornell University, Ithaca, NY. Michael Kearns (mkearns@cis.upenn.edu) is a professor and National Center Chair of Computer and Information Science at the University of Pennsylvania, Philadelphia, PA. Tim Roughgarden (tim@cs.stanford.edu) is an associate professor of CS at Stanford University, Stanford, CA. Jennifer Wortman Vaughan (jenn@microsoft.com) is a senior researcher at Microsoft Research, New York, NY.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

This year's CES displayed a pretty broad mix in terms of production areas, with plenty of representation for categories ranging from wearables, to health, to VR and beyond – but the clear winners were two companies, and both succeeded primarily through partnerships than by direct demonstration of their own consumer products. Of the two, Nvidia had a much more direct presence; the GPU-maker's CEO Jen-Tsun Huang had the opening keynote, and packed a lot of news into the three-part presentation. Nvidia also had its own showfloor presences, including a self-driving car demonstration featuring BB8, its own test car, and an Audi Q7 equipped with the same software and hardware that could also drive itself, with no human behind the wheel. Nvidia's core business has become the core business of virtually every other tech company of size and significance; AI is one of, if not the primary area of interest and investment at Google, Facebook, Apple and others, and Nvidia's GPUs make it possible to create the neural nets and server systems that back machine learning, image recognition and other technologies under the broad AI umbrella. Meanwhile, Nvidia's consumer business is also making some big leaps.

Chinese internet giant Baidu and state-owned passenger vehicle manufacturer BAIC Motor Corp have formed a strategic partnership that will see the companies produce and promote autonomous vehicle technology in China. Announced on Friday at the Consumer Electronics Show (CES) 2017, Baidu's autonomous driving research and development arm, Baidu Intelligent Vehicle, will work with the automaker on two key projects, the first of which is the launch of a BAIC-built vehicle equipped with Baidu's telematics solutions at the Shanghai auto show in April. Level 3 autonomy still requires human drivers to be present and capable of taking the steering wheel if needed to prevent accidents, however continuous monitoring isn't required. As part of the partnership, the companies will also collaborate on HD maps for use in autonomous driving, and BAIC will provide OEM solutions including CarLife, CoDriver, and MapAuto for Baidu's intelligent vehicle program. CarLife, a smartphone integration application for connected cars, has been deployed in nearly 150 car models from 60 automakers including Audi, Mercedes-Benz, Hyundai, and Shanghai GM, and counts more than 700,000 car owners as active users.

Constructing activity budgets for marine animals when they are at sea and cannot be directly observed is challenging, but recent advances in bio-logging technology offer solutions to this problem. Accelerometers can potentially identify a wide range of behaviours for animals based on unique patterns of acceleration. However, when analysing data derived from accelerometers, there are many statistical techniques available which when applied to different data sets produce different classification accuracies. We investigated a selection of supervised machine learning methods for interpreting behavioural data from captive otariids (fur seals and sea lions). We conducted controlled experiments with 12 seals, where their behaviours were filmed while they were wearing 3-axis accelerometers.