Artificial Intelligence (AI) may well be the most powerful technology of the 21st century, helping to solve humanity's most complex unsolved problems: environmental, social, and more. Yet sceptics believe that AI's risks are as large as its potential benefits. How can they be avoided? And why isn't the most powerful technology being used more widely today to solve the world's greatest "wicked" problems? Great technological advances are often a double-edged sword.

CHENNAI: Drafting legal contracts -- be it employment documents or the highly complex merger agreements -- has been a constant pain for larger corporates. For smaller enterprises that may not be able to hire legal organisations to vet such documents in detail, it is even harder to manage and map laws to particular contracts. With the development of Artificial Intelligence, legal contract mapping and management has been automated to a large extent and is turning out to be an area of growth for technology start-ups such as SpotDraft, which makes about $5 million in revenue every month. Amid Indian players like VakilSearch, Legal Desk and Near Law, SpotDraft is one of the first few to provide contract management services using Artificial Intelligence. The legal tech start-up, founded by Harvard Law School graduate Shashank Bijapur, along with Madhav Bhagat, a former software developer at Google, looks to expand its operations from its home base in India to European countries, Singapore and Hong Kong, among others, this year to cash in on the rapid growth in the $80 billion contract automation market.

AI-powered chatbots come with many benefits for the businesses that adopt them, but in some instances, they can have greater impact for the everyday user. In this blog, we list four recent articles giving examples of where AI-powered applications and chatbots have been put into practice to help customers and the common man. The developer of the'world's first robot lawyer' application, which helped overturn more than one-hundred parking fines, is now adapting the functionality of the integrated chatbot to provide legal aid to refugees seeking asylum in the US and Canada, as well as asylum support in the UK. The original DoNotPay AI-powered application gives legal aid through a simple chat interface, where a chatbot asks a series of questions to help determine which application a refugee needs to fill out and whether they are eligible for asylum protection under international law. After this, the chatbot takes note of the relevant details required for asylum applications in the US or Canada, auto-fills the application form and sends.

One thing Artificial Intelligence can't be is prejudiced. It should be impossible; machines don't suddenly decide to hate, they're all about the facts. But what if the people programming them are prejudiced themselves? A disturbing new report in Science reveals that some are inadvertently doing just that. Who remembers Microsoft's Tay, a 2016 chatbot designed to ape the verbal machinations of a 19-year-old American girl? The high-minded idea behind it was to, according to Microsoft, "conduct research on conversational understanding."

In the last three decades, we have seen a significant increase in trading goods and services through online auctions. However, this business created an attractive environment for malicious moneymakers who can commit different types of fraud activities, such as Shill Bidding (SB). The latter is predominant across many auctions but this type of fraud is difficult to detect due to its similarity to normal bidding behaviour. The unavailability of SB datasets makes the development of SB detection and classification models burdensome. Furthermore, to implement efficient SB detection models, we should produce SB data from actual auctions of commercial sites. In this study, we first scraped a large number of eBay auctions of a popular product. After preprocessing the raw auction data, we build a high-quality SB dataset based on the most reliable SB strategies. The aim of our research is to share the preprocessed auction dataset as well as the SB training (unlabelled) dataset, thereby researchers can apply various machine learning techniques by using authentic data of auctions and fraud.

In this paper, we propose a deep learning approach to tackle the automatic summarization tasks by incorporating topic information into the convolutional sequence-to-sequence (ConvS2S) model and using self-critical sequence training (SCST) for optimization. Through jointly attending to topics and word-level alignment, our approach can improve coherence, diversity, and informativeness of generated summaries via a biased probability generation mechanism. On the other hand, reinforcement training, like SCST, directly optimizes the proposed model with respect to the non-differentiable metric ROUGE, which also avoids the exposure bias during inference. We carry out the experimental evaluation with state-of-the-art methods over the Gigaword, DUC-2004, and LCSTS datasets. The empirical results demonstrate the superiority of our proposed method in the abstractive summarization.

It reminds me when the US government tried to make exportation of strong encryption algorithms illegal. I think it is still illegal to export encryption software (but not algorithms written in plain text) to some countries. This was driven by the same argument: a fear of data mining, analytics, and mathematics, by government lawmakers who do not understand anything about it. Eventually, just like with encryption laws, the GDPR regulation will die -- not because it is bad, but because it is very poorly designed by incompetent people.

It was a pleasant 21 degrees in New York when computers defeated humanity -- or so many people thought. That Sunday in May 1997, Garry Kasparov, a prodigal chess grandmaster and world champion, was beaten by Deep Blue, a rather unassuming black rectangular computer developed by IBM. In the popular imagination, it seemed like humanity had crossed a threshold -- a machine had defeated one of the most intelligent people on the planet at one of the most intellectually challenging games we know. The age of AI was upon us. While Deep Blue was certainly an impressive piece of technology, it was no more than a supercharged calculating machine.

Organisations including the Food Standards Agency and the Department for Work and Pensions will be able to see UK citizens' entire internet browsing history within weeks. The Investigatory Powers Bill, which was all but passed into law this week, forces internet providers to keep a full list of internet connection records (ICRs) for a year and to make them available to the Government if asked. Those ICRs in effect serve as a full list of every website that people have visited, rather than collecting which specific pages are visited or what's done on them. ICRs will be made available to a wide range of government bodies. Those include expected law enforcement organisations such as the police, the military and the secret service, but also includes bodies such as the Food Standards Agency, the Gambling Commission, councils and the Welsh Ambulance Services National Health Service Trust.

Life-Science companies are obsessed with hording documents. These documents are filled with meaningless data. Axendia research shows that 85% of companies surveyed rely on document driven processes. Much of these meaningless data are unstructured, untagged and untapped. Data are spread across countless repositories.