Mumbai: As pro vice-chancellor (product and partner development) of the University of Sunderland in the UK, Prof. John MacIntyre's brief includes covering research, innovation, knowledge exchange, employer engagement and regional economy. Since 1996, MacIntyre has also been the editor-in-chief of Neural Computing and Applications--an international scientific peer- reviewed journal published by Springer Verlag. In an interview, he talks about why artificial intelligence (AI) needs to be looked at more positively and how AI can contribute to society. MacIntyre will also address EmTech India 2017--an emerging tech conference organized by Mint and MIT Technology Review--on 9 March in New Delhi. You completed your PhD in applied AI, focussing on the use of neural networks in predictive maintenance.

Uber will stop using its "Greyball" tool to evade law enforcement efforts, the ride-hail company announced Wednesday, just days after it defended the controversial program as necessary to protect its drivers from harm. "We have started a review of the different ways this technology has been used to date," Uber's chief security officer Joe Sullivan wrote in a blog post. "In addition, we are expressly prohibiting its use to target action by local regulators going forward." Uber's backtracking comes less than a week after the New York Times revealed that the company had been serving up an alternative (and non-functioning) version of its app to public officials in cities where its service violated regulations. It also comes shortly after the company reversed course on applying for a permit to test self-driving cars in California.

A technology initially used to fight traffic fines is now helping refugees with legal claims. When Joshua Browder developed DoNotPay he called it "the world's first robot lawyer". It's a chatbot - a computer program that carries out conversations through texts or vocal commands - and it uses Facebook Messenger to gather information about a case before spitting out advice and legal documents. It was originally designed to help people wiggle out of parking or speeding tickets. But now Browder - a 20-year-old British man currently studying at Stanford University - has adapted his bot to help asylum seekers.

Written by Tom Mayor, national strategy leader for consulting firm KPMG's Industrial Manufacturing practice, and Todd Dubner, a principal in KPMG's Strategy practice. This is one in a series of periodic guest columns by industry thought leaders. Stop and think for a moment: How do you program a Class 8 truck to drive down the interstate? How do you go from there to instructing it to maneuver a 53-foot trailer in an urban area? How many millions or perhaps billions of lines of computer code would be required to figure out all the decisions necessary to make these day-to-day driving functions successful in an autonomous vehicle?

SAN FRANCISCO - Google paraded executives from Disney, Verizon,The Home Depot and others out today to boast about the infrastructure and enterprise readiness of its cloud services. The kickoff to Google Cloud Next showcased companies that are using Google's capabilities for artificial intelligence, machine learning, analytics and offsite data storage. The main message was that Google is enterprise savvy and can deliver superior results to competitors thanks to its heavy investment in smarter computing. Google describes the conference as "an immersive event that brings together executives, customers, partners, developers, IT decision makers and Google engineers to build the future of the cloud." Running through Friday at Moscone Center West here, opening day also featured news about a new partnership with SAP and the acquisition of Kaggle.

Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image-agnostic) and very small perturbation vector that causes natural images to be misclassified with high probability. We propose a systematic algorithm for computing universal perturbations, and show that state-of-the-art deep neural networks are highly vulnerable to such perturbations, albeit being quasi-imperceptible to the human eye. We further empirically analyze these universal perturbations and show, in particular, that they generalize very well across neural networks. The surprising existence of universal perturbations reveals important geometric correlations among the high-dimensional decision boundary of classifiers. It further outlines potential security breaches with the existence of single directions in the input space that adversaries can possibly exploit to break a classifier on most natural images.

We introduce Joint Causal Inference (JCI), a powerful formulation of causal discovery from multiple datasets that allows to jointly learn both the causal structure and targets of interventions from statistical independences in pooled data. Compared with existing constraint-based approaches for causal discovery from multiple data sets, JCI offers several advantages: it allows for several different types of interventions in a unified fashion, it can learn intervention targets, it systematically pools data across different datasets which improves the statistical power of independence tests, and most importantly, it improves on the accuracy and identifiability of the predicted causal relations. A technical complication that arises in JCI is the occurrence of faithfulness violations due to deterministic relations. We propose a simple but effective strategy for dealing with this type of faithfulness violations. We implement it in ACID, a determinism-tolerant extension of Ancestral Causal Inference (ACI) (Magliacane et al., 2016), a recently proposed logic-based causal discovery method that improves reliability of the output by exploiting redundant information in the data. We illustrate the benefits of JCI with ACID with an evaluation on a simulated dataset.

We describe a strategy for constructing a neural network jet substructure tagger which powerfully discriminates boosted decay signals while remaining largely uncorrelated with the jet mass. This reduces the impact of systematic uncertainties in background modeling while enhancing signal purity, resulting in improved discovery significance relative to existing taggers. The network is trained using an adversarial strategy, resulting in a tagger that learns to balance classification accuracy with decorrelation. As a benchmark scenario, we consider the case where large-radius jets originating from a boosted resonance decay are discriminated from a background of nonresonant quark and gluon jets. We show that in the presence of systematic uncertainties on the background rate, our adversarially-trained, decorrelated tagger considerably outperforms a conventionally trained neural network, despite having a slightly worse signal-background separation power. We generalize the adversarial training technique to include a parametric dependence on the signal hypothesis, training a single network that provides optimized, interpolatable decorrelated jet tagging across a continuous range of hypothetical resonance masses, after training on discrete choices of the signal mass.

The voice-activated Echo lets users control other smart home devices, play games and order products on Amazon's site. Amazon's mounting First Amendment battle has reached an anticlimactic end. The company agreed to hand over user data of an Amazon Echo speaker for a murder trial in Arkansas, after it spent months pushing back against a warrant for the information. Amazon changed its position after the user, defendant James Andrew Bates, consented to the disclosure, according to a court filing that was made public Monday. Before Bates consented, Amazon just last month offered a strong defense against releasing the user information, with the company saying Bates' audio recordings with the Echo were protected under the First Amendment.

Technology must be controlled in order to safeguard the future of humanity, Stephen Hawking has warned. The physicist, who has spoken out about the dangers of artificial intelligence in the past, says a'world government' could be our only hope. He says our'logic and reason' could be the only way to defeat the growing threat of nuclear or biological war. We are living through the most dangerous time in the history of the human race, according to Professor Stephen Hawking. 'Since civilisation began, aggression has been useful inasmuch as it has definite survival advantages,' he told The Times. 'It is hard-wired into our genes by Darwinian evolution. 'Now, however, technology has advanced at such a pace that this aggression may destroy us all by nuclear or biological war.