To send a link to this page you must be logged in. Peter Scott from the Next Wave Institute - an ex-NASA scientist and leading expert in Artificial Intelligence (AI), cut the virtual ribbon at the Innovation Lab at Wharfside House in Prentice Road at a launch event. The new hub aims to take Stowmarket a step closer to its ambition of developing a high-tech Centre of Excellence by offering a co-working space with advice and guidance to start-up businesses. The aim is to support innovation, entrepreneurship, business growth and the development of an AI Centre of Excellence. Backed by Mid Suffolk District Council, Tech East and the New Anglia Local Enterprise Partnership (LEP), it is hoped the hub will eventually form the focal point for developing a cluster of technology and manufacturing companies.

The waves of the third artificial intelligence (AI) boom are now sweeping across Japan in the same way as earlier fads did in the 1950s and the 1980s. Referring to the ongoing craze in the country, leading Japanese economic magazine Shukan toyo keizai wrote in its 5 December 2015 issue, "not a single day passes by without hearing about AI." Many companies in Japan are making AI-related announcements one after another. Seminars on AI are held in Tokyo almost every day. But the question we must ask is this: Is the development of AI good news for mankind? From early on, many people in the world outside Japan forecast a dystopian future if AI were to surpass human intelligence. To cite an early example, Bill Joy, a U.S. computer scientist dubbed the Thomas Edison of the Internet, cautioned that robots with higher intelligence may compete with humans and threaten the latter's survival when they become able to self-replicate in "Why the Future Doesn't Need Us," an article he published in 2000. More recently, British theoretical physicist and cosmologist Stephen Hawking expressed the fear that "the development of full artificial intelligence could spell the end of the human race." Speaking in concert, Microsoft founder Bill Gates also said, "I am in the camp that is concerned about the threat of super intelligence [to human beings］." Behind their concern, there is the feeling of unease that humans will stop being the owners of the highest intelligence on earth. High intelligence is the very thing that has allowed humans to consider themselves as special beings distinguished from other animals. What will happen if and when AI surpasses human intelligence? Will humans really be able to continue their dominance as rulers of the earth in this situation? Won't machines deprive humans of many intellectual jobs and dominate them, in effect? These arguments about the possible threats posed by AI have been small in number in Japan until recently, however.

London (CNN Business)Some of the leading artificial intelligence experts from Africa and South America have been denied visas to attend a major industry conference in Canada, dealing a setback to efforts to prevent bias from taking root in the new technology. Conference organizers say Canadian immigration authorities have denied visas to two dozen academics from countries such as Nigeria and Brazil, preventing them from attending the event next month in Vancouver. Katherine Heller, a professor who serves as co-chair of diversity and inclusion at the Neural Information Processing Systems conference, said organizers "are trying extremely hard" to have the visa denials overturned. "It is very significant for the field of AI that all voices be heard," she said. The problem of algorithmic bias in data science has become more pronounced, and there's mounting evidence that AI-powered algorithms display bias against women and some racial groups.

Thanks to approvals from the Food and Drug Administration (FDA) for applications such as primary disease diagnosis, digital pathology is rapidly becoming the new standard of care. However, this advancement creates challenges that Artificial Intelligence could help solve. Digital pathology enables capturing pathology information, such as whole slide images (WSI), and working with it digitally using a specialized scanner. Acquiring, studying and managing data in this way allows sharing between parties on a computer or mobile device. According to experts, the global digital pathology market was worth $689.2 million in 2018.

While cybersecurity has been an essential area of concern for most IT companies and businesses depending on technology, it is the expertise with the latest technologies like AI and Machine Learning that can give them a competitive lead in terms of information security and data safety. These days AI and Machine Learning technologies are in the limelight for many industries and use cases. Cybersecurity remains to be one of the most important beneficiaries of these new technologies. Here through the length of this post, we are going to explain the role of AI and Machine Learning for cybersecurity. In spite of having the capability of mimicking human intelligence, AI is still far short of capabilities to replace human intelligence and the ways of understanding a problem and finding solutions.

SA Bobde, the Chief Justice of India said that the Supreme Court has proposed to introduce a system of AI (artificial intelligence) that would help in better administration of justice delivery. However, he made clear that people should form the impression that the AI would ever replace the judges. The CJI was addressing the Constitution Day function organized by the Supreme Court Bar Association (SCBA). He said – "We propose to introduce, if possible, a system of artificial intelligence. There are many things which we need to look at before we introduce ourselves. We do not want to give the impression that this is ever going to substitute the judges."

This paper proposes a formal approach to learning and planning for agents operating in a priori unknown, time-varying environments. The proposed method computes the maximally likely model of the environment, given the observations about the environment made by an agent earlier in the system run and assuming knowledge of a bound on the maximal rate of change of system dynamics. Such an approach generalizes the estimation method commonly used in learning algorithms for unknown Markov decision processes with time-invariant transition probabilities, but is also able to quickly and correctly identify the system dynamics following a change. Based on the proposed method, we generalize the exploration bonuses used in learning for time-invariant Markov decision processes by introducing a notion of uncertainty in a learned time-varying model, and develop a control policy for time-varying Markov decision processes based on the exploitation and exploration trade-off. We demonstrate the proposed methods on four numerical examples: a patrolling task with a change in system dynamics, a two-state MDP with periodically changing outcomes of actions, a wind flow estimation task, and a multi-arm bandit problem with periodically changing probabilities of different rewards.

Networks are a powerful tool used to analyze complex systems: agents are represented as nodes, and pairwise interactions between agents are recorded as edges between these nodes. Examples of fields of applications include biology, where networks may be used to describe protein-protein interactions; ecology, where they may represent food webs [13] or spatial distributions in crop diversity networks [46]; ethnology, where networks summarize relationships or trades between individuals or communities [40, 36]; sociology, where the recent development of online social networks offers unprecedented possibilities while fostering new challenges [47]. Real-life networks are often modeled as realizations of random graphs or, equivalently, as noisy versions of more structured networks. In this setting, recovering the "noiseless" version of the graph, i.e. estimating the underlying probabilities of interactions between agents, is a key problem that has recently gained considerable attention (see, e.g., [30, 15, 14, 17, 50]). Most methods for recovering structural properties of a network rely on assumptions on the distribution of the underlying random graph. However, in numerous examples, these assumptions are put in default by the behaviour of a small number of individuals, which strongly departs from the behaviour of the majority of agents, introducing outlier profiles. For example, in graphs obtained from survey data, some individuals may be reluctant to participate and for this reason provide false answers; other individuals may even be paid to provide erroneous answers in order to distort the public opinion on a subject [3].

Saliency maps are a popular approach to creating post-hoc explanations of image classifier outputs. These methods produce estimates of the relevance of each pixel to the classification output score, which can be displayed as a saliency map that highlights important pixels. Despite a proliferation of such methods, little effort has been made to quantify how good these saliency maps are at capturing the true relevance of the pixels to the classifier output (i.e. their "fidelity"). We therefore investigate existing metrics for evaluating the fidelity of saliency methods (i.e. saliency metrics). We find that there is little consistency in the literature in how such metrics are calculated, and show that such inconsistencies can have a significant effect on the measured fidelity. Further, we apply measures of reliability developed in the psychometric testing literature to assess the consistency of saliency metrics when applied to individual saliency maps. Our results show that saliency metrics can be statistically unreliable and inconsistent, indicating that comparative rankings between saliency methods generated using such metrics can be untrustworthy.

We propose the Square Attack, a new score-based black-box $l_2$ and $l_\infty$ adversarial attack that does not rely on local gradient information and thus is not affected by gradient masking. The Square Attack is based on a randomized search scheme where we select localized square-shaped updates at random positions so that the $l_\infty$- or $l_2$-norm of the perturbation is approximately equal to the maximal budget at each step. Our method is algorithmically transparent, robust to the choice of hyperparameters, and is significantly more query efficient compared to the more complex state-of-the-art methods. In particular, on ImageNet we improve the average query efficiency for various deep networks by a factor of at least $2$ and up to $7$ compared to the recent state-of-the-art $l_\infty$-attack of Meunier et al. while having a higher success rate. The Square Attack can even be competitive to gradient-based white-box attacks in terms of success rate. Moreover, we show its utility by breaking a recently proposed defense based on randomization. The code of our attack is available at https://github.com/max-andr/square-attack