Flow cytometry is a high-throughput technology used to quantify multiple surface and intracellular markers at the level of a single cell. This enables to identify cell sub-types, and to determine their relative proportions. Improvements of this technology allow to describe millions of individual cells from a blood sample using multiple markers. This results in high-dimensional datasets, whose manual analysis is highly time-consuming and poorly reproducible. While several methods have been developed to perform automatic recognition of cell populations, most of them treat and analyze each sample independently. However, in practice, individual samples are rarely independent (e.g. longitudinal studies). Here, we propose to use a Bayesian nonparametric approach with Dirichlet process mixture (DPM) of multivariate skew $t$-distributions to perform model based clustering of flow-cytometry data. DPM models directly estimate the number of cell populations from the data, avoiding model selection issues, and skew $t$-distributions provides robustness to outliers and non-elliptical shape of cell populations. To accommodate repeated measurements, we propose a sequential strategy relying on a parametric approximation of the posterior. We illustrate the good performance of our method on simulated data, on an experimental benchmark dataset, and on new longitudinal data from the DALIA-1 trial which evaluates a therapeutic vaccine against HIV. On the benchmark dataset, the sequential strategy outperforms all other methods evaluated, and similarly, leads to improved performance on the DALIA-1 data. We have made the method available for the community in the R package NPflow.

In this paper we formally analyse the use of sparse filtering algorithms to perform covariate shift adaptation. We provide a theoretical analysis of sparse filtering by evaluating the conditions required to perform covariate shift adaptation. We prove that sparse filtering can perform adaptation only if the conditional distribution of the labels has a structure explained by a cosine metric. To overcome this limitation, we propose a new algorithm, named periodic sparse filtering, and carry out the same theoretical analysis regarding covariate shift adaptation. We show that periodic sparse filtering can perform adaptation under the looser and more realistic requirement that the conditional distribution of the labels has a periodic structure, which may be satisfied, for instance, by user-dependent data sets. We experimentally validate our theoretical results on synthetic data. Moreover, we apply periodic sparse filtering to real-world data sets to demonstrate that this simple and computationally efficient algorithm is able to achieve competitive performances.

Automated decision making and the difficulty of ensuring accountability for algorithmic decisions have been in the news. This is a big deal if we are to start addressing some of the serious ethical issues in developing Artificial Intelligence systems that can't easily be made transparent. I'm breaking out of a concentrated book-writing space to offer my voice – and to outline some of the directions I think we should be taking to address the wicked problems of ethics, algorithms and accountability – and hoping also to be standing up to be counted as one of the people opening out discussions in this space, so that it can be more diverse. A few months ago I submitted a response to the UK's Science and Technology Committee consultation on automated decision making. This consultation asked specifically how transparency could be empoyed to allow more scrutiny of algorithmic systems.

The Neurobiofeedback machine gained popularity for its non-invasive and quantitative approach to behavior regulation, but its legitimacy remains in question by pediatricians, therapists, and other professionals. In academic-sounding terms, this machine (which I'll be abbreviating as NBF from now on) is built on the concept of feedback therapy, which exploits our ability to exert and/or regain control over physiological aspects in our body. NBF is a type of Brain-Computer Interface (BCI) machine that senses your brain wave activity in different ways (usually involving hardware-software interaction) and rewards you with an auditory or visual stimulus when your brain wave's frequency matches the desired frequency. This comes from the scientific notion that brain rhythms correspond to certain cognitive states. By "mind games", the'auditory or visual stimulus' I mentioned last paragraph usually comes in the form of a game.

Duckietown is a hands-on, project-based course at MIT that focuses on self-driving vehicles and high-level autonomy. In Spring 2016, Liam Paull served as Duckietown's CEO and Teddy Ort worked as a vehicle autonomy engineer in training. Since the course began at MIT, it has spread to other universities around the globe, and is now taught in universities from Beijing to Zurich.

A facial recognition system can identify someone even if their face is covered up. The Disguised Face Identification (DFI) system uses an AI network to map facial points and reveal the identity of people. It could eventually help to pick out criminals, protesters, or anyone who hides their identity by covering themselves with masks, scarves or sunglasses. The software could also see the end of public anonymity, sparking privacy concerns from one academic, who has labelled it'authoritarian'. A facial recognition system can identify someone even if their face is covered up.

THE human face is a remarkable piece of work. The astonishing variety of facial features helps people recognise each other and is crucial to the formation of complex societies. So is the face's ability to send emotional signals, whether through an involuntary blush or the artifice of a false smile. People spend much of their waking lives, in the office and the courtroom as well as the bar and the bedroom, reading faces, for signs of attraction, hostility, trust and deceit. They also spend plenty of time trying to dissimulate.

Jürgen, it's a privilege to have you here as one of the pioneers of artificial intelligence and, more specifically, deep learning--its hottest field right now. Before you go into all of those fields, we would like to understand the person Jürgen Schmidhuber better. Perhaps you can tell us a few things that you're particularly proud of in your career. One of the things I'm proud of: I think I understand what it means to be curious and how to implement curiosity, which I think is essential to build agents that learn from experience through their own self-generated experiments. Agents who are motivated to invent, in a directed way, action sequences or experiments that lead to data that tell them something about how the world works that they didn't know yet.

On August 24, doc.ai announced that their advanced natural language processing technology platform, based on the blockchain, would timestamp datasets and decentralize artificial intelligence. The startup stated that the platform was "envisioned and built" by researchers from Stanford and Cambridge Universities. The objective of the company is to help healthcare companies improve patient care and experience through an advanced natural dialogue system which will be able to generate insights from combined medical data. According to the World Health Organization, there is a shortage of seven million healthcare professionals globally, and that number is on the rise. There is increasing pressure on doctors who are faced with meeting the challenging needs of the population and keeping up with the latest developments in healthcare and medicine.

Dr Stephen Weng, from the university's NIHR School for Primary Care Research, said: "Cardiovascular disease is the leading cause of illness and death worldwide. Our study shows that artificial intelligence could significantly help in the fight against it by improving the number of patients accurately identified as being at high risk and allowing for early intervention by doctors to prevent serious events like cardiac arrest and stroke. "Current standard prediction models like the ACC are based on eight risk factors including age, cholesterol level and blood pressure but are too simplistic to account for other factors like medications, multiple disease conditions, and other non-traditional biomarkers. These AI algorithms have the potential to help save more lives". Professor Jon Garibaldi and Dr Jenna Reps, of the Advanced Data Analysis Centre in the School of Computer Science, said: "We were curious to find out how four modern machine learning algorithms would perform given the large data set of 378,256 patients from nearly 700 UK GP practices.