Imagine that, in 20 or 30 years, a company creates the first artificially intelligent humanoid robot. She looks like a person, talks like a person, interacts like a person. If you were to meet Ava, you could relate to her even though you know she's a robot. Ava is a fully conscious, fully self-aware being: she communicates; she wants things; she improves herself. She is also, importantly, far more intelligent than her human creators.

For part one of the series, in which we explore the benefits and dangers of the EU's GDPR legislation, check this article.

This paper proposes an agent with particle swarm optimization (PSO) based on a Fuzzy Markup Language (FML) for students learning performance evaluation and educational applications, and the proposed agent is according to the response data from a conventional test and an item response theory. First, we apply a GS-based parameter estimation mechanism to estimate the items parameters according to the response data, and then to compare its results with those of an IRT-based Bayesian parameter estimation mechanism. In addition, we propose a static-IRT test assembly mechanism to assemble a form for the conventional test. The presented FML-based dynamic assessment mechanism infers the probability of making a correct response to the item for a student with various abilities. Moreover, this paper also proposes a novel PFML learning mechanism for optimizing the parameters between items and students. Finally, we adopt a K-fold cross validation mechanism to evaluate the performance of the proposed agent. Experimental results show that the novel PFML learning mechanism for the parameter estimation and learning optimization performs favorably. We believe the proposed PFML will be a reference for education research and pedagogy and an important co-learning mechanism for future human-machine educational applications.

The rapid adoption of machine learning has increased concerns about the privacy implications of machine learning models trained on sensitive data, such as medical records or other personal information. To address those concerns, one promising approach is Private Aggregation of Teacher Ensembles, or PATE, which transfers to a "student" model the knowledge of an ensemble of "teacher" models, with intuitive privacy provided by training teachers on disjoint data and strong privacy guaranteed by noisy aggregation of teachers' answers. However, PATE has so far been evaluated only on simple classification tasks like MNIST, leaving unclear its utility when applied to larger-scale learning tasks and real-world datasets. In this work, we show how PATE can scale to learning tasks with large numbers of output classes and uncurated, imbalanced training data with errors. For this, we introduce new noisy aggregation mechanisms for teacher ensembles that are more selective and add less noise, and prove their tighter differential-privacy guarantees. Our new mechanisms build on two insights: the chance of teacher consensus is increased by using more concentrated noise and, lacking consensus, no answer need be given to a student. The consensus answers used are more likely to be correct, offer better intuitive privacy, and incur lower-differential privacy cost. Our evaluation shows our mechanisms improve on the original PATE on all measures, and scale to larger tasks with both high utility and very strong privacy ($\varepsilon$ < 1.0).

Black-box risk scoring models permeate our lives, yet are typically proprietary or opaque. We propose a transparent model distillation approach to audit such models. Model distillation was first introduced to transfer knowledge from a large, complex teacher model to a faster, simpler student model without significant loss in prediction accuracy. To this we add a third criterion - transparency. To gain insight into black-box models, we treat them as teachers, training transparent student models to mimic the risk scores assigned by the teacher. Moreover, we use side information in the form of the actual outcomes the teacher scoring model was intended to predict in the first place. By training a second transparent model on the outcomes, we can compare the two models to each other. When comparing models trained on risk scores to models trained on outcomes, we show that it is necessary to calibrate the risk-scoring model's predictions to remove distortion that may have been added to the black-box risk-scoring model during or after its training process. We also show how to compute confidence intervals for the particular class of transparent student models we use - tree-based additive models with pairwise interactions (GA2Ms) - to support comparison of the two transparent models. We demonstrate the methods on four public datasets: COMPAS, Lending Club, Stop-and-Frisk, and Chicago Police.

In a future teeming with robots and artificial intelligence, humans seem to be on the verge of being crowded out. But in reality the opposite is true. To be successful, organizations need to become more human than ever. Organizations that focus only on automation will automate away their competitive edge. The most successful will focus instead on skills that set them apart and that can't be duplicated by AI or machine learning.

About this course: What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of being about logic, rather than just data. We talk about why such a framing is useful when thinking about building a pipeline of machine learning models. Then, we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important the phases not be skipped. We end with a recognition of the biases that machine learning can amplify and how to recognize this.

Automation and personalization, although very different in meaning, are very closely connected and go hand in hand with each other. A good question to ask ourselves in this context is – what happens if one interferes with the other? When we talk about the development of businesses, personalization, automation, tools and devices that will take the way we do business to a whole new level, we realize that there is a need to automate personalization more, while customizing automation. This is where automation vs. personalization issue arises. In the context of Artificial Intelligence and Human Resources, automation and personalization are intertwined as well as these two areas.

With Donald Trump, everything old is new again, it seems. His latest effort to grapple with the school shooting in Parkland, Florida, sees him joining his fellow Republicans, such as the Kentucky governor, Matt Bevin, in resuscitating a long-dormant culture war, blaming video games for mass shootings. "I'm hearing more and more people say the level of violence on video games is really shaping young people's thoughts," Bevin said this week at a White House meeting on school security, where he also launched into a tirade about violent films. This echoes the thoughts of Wayne LaPierre, the president of the National Rifle Association (NRA), in 2012 when he tried to pin the Sandy Hook shooting on "vicious violent video games, with names like Bulletstorm, Grand Theft Auto, Mortal Kombat and Splatterhouse". It's a remarkable series of logic leaps that allows a person to scorn a simulator while holding the actual gun whose use is seen as blameless, but here we are again.

IT solutions provider NIIT Technologies Ltd and AI firm Arago have decided to deepen their partnership to optimise the IT operations of Global In-House Centres (GICs) using artificial intelligence. "As global MNCs in India drive innovation through their GICs, there is a need for them to build deeper partnerships with IT service providers to innovate with intelligent automation," said Arvind Mehrotra, president, Infrastructure Management Services. Umamaheshwar Mudigonda, vice-president, Service Provider Business, of Arago, said that the company had high hopes. "We aim to enable the GICs use cutting-edge AI technologies and implement practical solutions, said a press release.