UK startup Juro, which is applying a "design centric approach" and machine learning tech to help businesses speed up the authoring and management of sales contracts, has closed $2m in seed funding led by Point Nine Capital. Prior investor Seedcamp also contributed to the round. Juro is announcing Taavet Hinrikus (TransferWise's co-founder) as an investor now too, as well as Michael Pennington (Gumtree co-founder) and the family office of Paul Forster (co-founder of Indeed.com). Back in January 2017 the London-based startup closed a $750,000 (£615k) seed round, though CEO and co-founder Richard Mabey tells us that was really better classed as an angel round -- with Point Nine Capital only joining "late" in the day. "We actually could have strung it out to Series A," he says of the funding that's being announced now. "But we had multiple offers come in and there is so much of an explosion in demand for the [machine learning] that it made sense to do a round now rather than wait for the A. The whole legal industry is undergoing radical change and we want to be leading it."

Facebook founder Mark Zuckerberg has been receiving special coaching on how to present himself when he appears before US politicians demanding to know what he is doing to protect users' data, and how Russia was able to use his platform to allegedly meddle in the 2016 presidential election. Amid continuing controversy over the inappropriate harvesting of the data of up to 87 million Facebook users by British political consulting firm Cambridge Analytica, Mr Zuckerberg will try and reassure Congress he is taking the concerns of them and the general public seriously. He will also try to deflect the efforts of those who favour more stringent government regulation. "It's clear now that we didn't do enough to prevent these tools from being used for harm," he is expected to tell the House Committee on Energy and Commerce, according to written testimony released ahead of his appearance. "We didn't take a broad enough view of our responsibility, and that was a big mistake. It was my mistake, and I'm sorry. I started Facebook, I run it, and I'm responsible for what happens here."

Facebook is contacting users whose data it says may have been shared with Cambridge Analytica, with around 87 million people expected to receive a message in their newsfeed from Monday. The platform says the majority of users who could be affected – around 70 million – are in the US, but some will be in the UK. We would like to hear from people who see the message, and to find out more about how you think Facebook has handled the situation. Are you one of the 87 million users Facebook says it is contacting? Are you aware of any connections or settings that may have made your data vulnerable?

MATLAB is the language of choice for many researchers and mathematics experts for Machine Learning. This video course will help you build a foundation in Machine Learning using MATLAB. You'll start by performing data fitting, pattern recognition, and clustering analysis. Then, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. Finally, you will learn to put it all together through real-world cases covering major Machine Learning algorithms and will now be an expert in performing Machine Learning with MATLAB.

Fast object detector YOLO gets its third major release: …Along with one of the most clearly written and reassuringly honest research papers of recent times. YOLO (You Only Look Once) is a fast, free object detection system developed by researchers at the University of Washington. Its latest v3 update makes it marginally faster by incorporating "good ideas from other people". These include a residual network system for feature extraction which attains reasonably high scores on ImageNet classification while being more efficient than current state-of-the-art systems, and a method inspired by feature pyramid networks that improves prediction of bounding boxes. Reassuringly honest: The YOLOv3 paper is probably the most approachable AI research paper I've read in recent years, and that's mostly because it doesn't take itself too seriously.

Cloud, data analytics and security will dominate CIO spending in 2018 and 2019, although an increasing number of CIOs compared to 2017 will be investing in artificial intelligence, machine learning, Internet of Things, wearables, and augmented reality and virtual reality technologies. Similarly, data centre and server infrastructure are expected to see the biggest decrease, along with CRM and non-mobile devices. While cloud, security and data analytics are set to see significant investment from CIOs, fewer technology leaders expect to be dedicating budget towards these services as in 2017. Some 84% of CIOs expect to be investing in cloud capabilities in the next two years, down five percentage points compared to 2017. Data analytics (79% of CIOs) and security (73%) were also cited by the most number of UK CIOs as areas of investment in 2018 and 2019, although each was down five and nine percentage points respectively compared to 2017 investment.

In the previous article, we went over the basics of the impact that AI can have when introduced within the customer service industry along with some use cases. In this article, we take a look at the implementation and execution aspect of it as well as the benefits it entails for customer support agents along with support center management. Stinting on cost is the first priority for businesses as on today. When it comes to call center practices, it takes a good deal of money and time in hiring and training staff for customer service, as well as in erecting the whole brick-and-mortar infrastructure, even more if it isn't cloud-based. Just 10 support individuals can cost you as much as $35000, or even more if recruits frequently quit (attrition being quite high in the call center industry) -- which is a nightmare.

Pony.ai self-driving cars during a trial run in February in Guangzhou, China. Driverless cars could become a regular feature of the roads as early as April – at least in California, which has decided to allow fully autonomous vehicles to be tested on the roads (none of those pesky humans who have been present in test drives so far). Arizona has already become a fair-weather center for testing driverless vehicles, thanks in large part to the governor's support, and Uber announced last week that it has finished testing its self-driving trucks in Arizona and is now beginning to use them to move goods across the state. The British government launched a review last week of laws governing self-driving vehicles, with the aim of getting autonomous cars on the road by 2021, and other countries around the world are also experimenting with autonomous vehicles. Clearly a big step for the technology and automotive industries – no surprise that companies working on driverless vehicles include Google and Uber, as well as traditional automakers like Audi, BMW, Ford, GM, Volkswagen and Volvo – the advent of human-less driving could also redirect the traffic of our days, how we live our lives and get around.

A hallucinating artificial intelligence might see something like this product of Google's Deep Dream algorithm. As artificial intelligence (AI) allows machines to become more like humans, will they experience similar psychological quirks such as hallucinations or depression? And might this be a good thing? Last month, New York University in New York City hosted a symposium called Canonical Computations in Brains and Machines, where neuroscientists and AI experts discussed overlaps in the way humans and machines think. Zachary Mainen, a neuroscientist at the Champalimaud Centre for the Unknown, a neuroscience and cancer research institute in Lisbon, speculated that we might expect an intelligent machine to suffer some of the same mental problems people do.

The Convergence Thesis We strongly that blockchain technology will prove as the key underlying technology stack for the technologies that emerge with numerous applications on top of it. This view has been influenced by Outlier Ventures' Jamie Burke, who coined the term'convergence'. Blockchain provides an infrastructure for emergent trends like; IoT, 3D Printing, Autonomous Robotics (drones) to scale securely. More specifically, there are a handful of emerging technologies that hold the most promise for this blockchain-enabled convergence: - Artificial Intelligence (AI) - The Internet of Things (IoT) - Autonomous Robotics (Drones & Autonomous Vehicles) - Virtual Reality (VR) & Augmented Reality (AR) - 3D Printing It is the ability of blockchains to enable automated and intelligent M2M (machine-to-machine) networks that could transform design & manufacturing, distribution & logistics as well as retail & commerce. This will impact almost every supply chain from health to construction and manufacturing.