Since basic OCRs typically spit out jumbled information when taking text from tables and forms, companies have to resort to manual data entry that could be both costly and time consuming. Textract can process millions of pages in just a few hours, which can lower document processing costs. Plus, customers can use it even though they don't have previous machine learning experience. Amazon says Textract can recognize information like names and social security numbers, allowing it to transfer table data from PDFs, for instance, into easily searchable spreadsheets. For much larger stacks of documents, the information it extracts could be used to build smart searches or could be loaded into databases.

If you applied for a mortgage, would you be comfortable with a computer using a collection of data about you to assess how likely you are to default on the loan? If you applied for a job, would you be comfortable with the company's human-resources department running your information through software that will determine how likely it is that you will, say, steal from the company, or leave the job within two years? If you were arrested for a crime, would you be comfortable with the court plugging your personal data into an algorithm-based tool, which will then advise your judge on whether you should await trial in jail or at home? If you were convicted, would you be comfortable with the same tool weighing in on your sentencing? Much of the hand-wringing about advances in artificial intelligence has been concerned with AI's effects on the labor market.

In his latest novel, Machines Like Me, Ian McEwan reimagines what 1980s Britain might have looked like if certain iconic events had swung a different way: Argentina winning the Falklands war; Margaret Thatcher battling Tony Benn for power. And then there's the catalyst for the book – what might have happened if Alan Turing had achieved a major breakthrough in the field of artificial intelligence. As with most visions of AI's future – or hypothetical past – the author speculates a dystopia in which sentient, anthropomorphous robots are embedded into our everyday lives. The pros and cons of AI are debated at an ethical and moral level; with less credence given to the practicality or the technological constraints. Whilst undoubtedly a necessity for a science fiction novel, in the real world, this lack of practical consideration for AI and the related constraints has been responsible for a huge amount of hyperbole in the enterprise technology market; especially in relation to CRM.

Europe is'the' place when it comes to areas like Data Science, Artificial Intelligence or even Deeptech Scaleups. About the AI, it has grown rapidly over the past few years and we've seen the impact on our everyday lives, from facial recognition to the recommendations we're served up. Looks like Artificial Intelligence (AI) is the most influential technology trends for 2019. However, if you look in-depth, you'll find all the intricacies that make Artificial Intelligence possible including machine learning and other deep learning techniques. Being the buzzword at present, Machine learning is the application of AI based around the concept – provide data to machines and let them learn by themselves.

They might actually detect and avoid objects better, says Andres Arrieta, an assistant professor of mechanical engineering at Purdue University, because they would process sensory information faster. Better sensing capabilities would make it possible for drones to navigate in dangerous environments and for cars to prevent accidents caused by human error. Current state-of-the-art sensor technology doesn't process data fast enough -- but nature does. And researchers wouldn't have to create a radioactive spider to give autonomous machines superhero sensing abilities. Instead, Purdue researchers have built sensors inspired by spiders, bats, birds and other animals, whose actual spidey senses are nerve endings linked to special neurons called mechanoreceptors.

A new artificial intelligence created by researchers at the Massachusetts Institute of Technology pulls off a staggering feat: by analyzing only a short audio clip of a person's voice, it reconstructs what they might look like in real life. The AI's results aren't perfect, but they're pretty good -- a remarkable and somewhat terrifying example of how a sophisticated AI can make incredible inferences from tiny snippets of data. In a paper published this week to the preprint server arXiv, the team describes how it used trained a generative adversarial network to analyze short voice clips and "match several biometric characteristics of the speaker," resulting in "matching accuracies that are much better than chance." In practice, the Speech2Face algorithm seems to have an uncanny knack for spitting out rough likenesses of people based on nothing but their speaking voices. The MIT researchers urge caution on the project's GitHub page, acknowledging that the tech raises worrisome questions about privacy and discrimination.

More and more entities are deploying machine learning and artificial intelligence to automate tasks previously performed by humans. Such efforts carry with them real benefits, such as the enhancement of operational efficiency and the reduction of costs, but they also raise a number of concerns regarding their potential impacts on human society, particularly as computer algorithms are increasingly used to determine important outcomes like individuals' treatment within the criminal justice system. This mixture of benefits and concerns is starting to attract the interest of regulators. Efforts in the European Union, Canada, and the United States have initiated an ongoing discussion around how to regulate "automated decision-making" and what principles should guide it. And while not all of these regulatory efforts will directly implicate private companies, they may nonetheless provide insight for companies seeking to build consumer trust in their artificial intelligence systems or better prepare themselves for the overall direction that regulation is taking.

The U.K.'s University of Surrey has announced that it's securing digital government records of national video archives around the world against tampering using blockchain tech and artificial intelligence (AI). In a press release provided to CoinDesk, the university said its Centre for Vision, Speech and Signal Processing (CVSSP) has teamed up with the Open Data Institute and the National Archives in the U.K. to develop what it calls a "highly secure, decentralised computer vision and blockchain based system" called ARCHANGEL, which is designed to preserve the integrity of digital archives for the long term. Computer vision is a field in which computers are programmed to analyze and understand digital images or videos. The system "essentially provides a digital fingerprint for archives, making it possible to verify their authenticity," according to project lead at the University of Surrey, Professor John Collomosse. ARCHANGEL uses blockchain tech as a database maintained by a number of archives.

The First International Workshop on AI and Intelligent Assistance for Legal Professionals in the Digital Workplace (LegalAIIA) will be held at the Cyberjustice Laboratory at the University of Montreal on June 17th. This workshop is part of the 17th International Conference on AI and Law (ICAIL), a biennial conference which has served as an important forum at the intersection the AI and the law since its founding in 1987. The LegalAIIA workshop itself is an offshoot of the successful decade-long DESI (Discovery for Electronically Stored Informed) workshop series, which was pivotal in helping forge an interdisciplinary community of legal and technical practitioners working on advancing the state-of-the-art in electronic discovery practice. The first edition of Legal AIIA, driven by an impressive set of electronic discovery veterans including Jack G. Conrad (Thomson Reuters), Jeremy Pickens (Catalyst Repository Systems), Amanda Jones (H5), Hans Henseler (Magnet Forensics), and Jason R. Baron (Drinker, Biddle & Reath), aims to tackle head on the issue of human-AI collaboration. Accepted papers will focus on evaluating when and how to best leverage a "human-in-the-loop" approach to AI.