I actually had to double-check my calendar to make sure today wasn't April Fool's. Because watching the intro video of an indoor surveillance drone operated by Amazon seemed like just the sort of geeky joke you'd expect on April 1. But it isn't April Fools, and besides, Google has always been the one with the twisted sense of humor. Amazon has always been the one with the twisted sense of world domination. This was a serious press briefing.

During this period of progressive development and deployment of artificial intelligence, discussions around the ethical, legal, socio-economic and cultural implications of its use are increasing. What are the challenges and the strategy, and what are the values that Europe can bring to this domain? During the European Conference on AI (ECAI 2020), two special events in the format of panels discussed the challenges of AI made in the European Union, the shape of future research and industry, and the strategy to retain talent and compete with other world powers. This article collects some of the main messages from these two sessions, which included the participation of AI experts from leading European organisations and networks. Since the publication of European directives and guidance, such as the EC White Paper on AI and the Trustworthy AI Guidelines, Europe has been laying the foundation for the future vision of AI. The European strategy for AI builds on the well-known and accepted principles found in the Charter of Fundamental Rights of the European Commission and the Universal Declaration of Human Rights to define a human-centric approach, whose primary purpose is to enhance human capabilities and societal well-being.

Massachusetts State Police (MSP) has been quietly testing ways to use the four-legged Boston Dynamics robot known as Spot, according to new documents obtained by the American Civil Liberties Union of Massachusetts. And while Spot isn't equipped with a weapon just yet, the documents provide a terrifying peek at our RoboCop future. This browser does not support the video element. The Spot robot, which was officially made available for lease to businesses last month, has been in use by MSP since at least April 2019 and has engaged in at least two police "incidents," though it's not clear what those incidents may have been. It's also not clear whether the robots were being operated by a human controller or how much autonomous action the robots are allowed.

It enables financial institutions to simplify identifying illicit client relationships, beneficiaries, and links to criminal or terrorist activity during the onboarding phase. FREMONT, CA: Financial regulations globally are cracking down on banks. As Anti Money Laundering and know your customer (KYC) procedures are getting stricter, hefty fines are being imposed on those found to be in breach of the same. Recent studies have discovered that banks across the globe have been charged with a total of USD 26 billion in monetary penalties in Anti Money Laundering (AML) and sanctions violations over the last ten years. As banks and financial institutions continue to search for digital transformation initiatives to streamline and simplify the customer onboarding process and reduce the risk associated with fraud, many are looking to exploit emerging technologies' potential.

Recently computer scientists at USC Institute of Technologies (ICT) set out to assess under what conditions humans would employ deceptive negotiating tactics. Through a series of studies, they found that whether humans would embrace a range of deceptive and sneaky techniques was dependent both on the humans' prior negotiating experience in negotiating as well as whether virtual agents where employed to negotiate on their behalf. The findings stand in contrast to prior studies and show that when humans use intermediaries in the form of virtual agents, they feel more comfortable employing more deceptive techniques than they would normally use when negotiating for themselves. Lead author of the paper on these studies, Johnathan Mell, says, "We want to understand the conditions under which people act deceptively, in some cases purely by giving them an artificial intelligence agent that can do their dirty work for them." Nowadays, virtual agents are employed nearly everywhere, from automated bidders on sites like eBay to virtual assistants on smart phones.

"Yeah, but your scientists were so preoccupied with whether or not they could, they didn't stop to think if they should." It turns out that a $1 billion investment from Microsoft and unfettered access to a supercomputer wasn't enough to keep OpenAI's GPT-3 from being just as bigoted as Tay, the algorithm-based chat bot that became an overnight racist after being exposed to humans on social media. It's only logical to assume any AI trained on the internet – meaning trained on databases compiled by scraping publicly-available text online – would end up with insurmountable inherent biases, but it's still a sight to behold in the the full context (ie: it took approximately $4.6 million to train the latest iteration of GPT-3). What's interesting here is OpenAI's GPT-3 text generator is finally starting to trickle out to the public in the form of apps you can try out yourself. These are always fun, and we covered one about a month ago called Philosopher AI.

Financial crime as a wider category of cybercrime continues to be one of the most potent of online threats, covering nefarious actives as diverse as fraud, money laundering and funding terrorism. Today, one of the startups that has been building data intelligence solutions to help combat that is announcing a fundraise to continue fueling its growth. Ripjar, a UK company founded by five data scientists who previously worked together in British intelligence at the Government Communications Headquarters (GCHQ, the UK's equivalent of the NSA), has raised $36.8 million (£28 million) in a Series B, money that it plans to use to continue expanding the scope of its AI platform -- which it calls Labyrinth -- and scaling the business. Labyrinth, as Ripjar describes it, works with both structured and unstructured data, using natural language processing and an API-based platform that lets organizations incorporate any data source they would like to analyse and monitor for activity. It automatically and in real time checks these against other data sources like sanctions lists, politically exposed persons (PEPs) lists and transaction alerts.

Mexico's increasingly militarized crackdown of powerful drug cartels has left nearly 39,000 unidentified bodies languishing in the country's morgues – a grotesque symbol of the ever-burgeoning war on drugs and rampant violence. Investigative NGO Quinto Elemento Labs, in a recent report, found that an alarming number of people have been simply buried in common graves without proper postmortems, while others were left in funeral homes. The so-called war of drugs has claimed the lives of nearly 300,000 people over the last 14 years, while another 73,000 have gone missing. All the while, these cartels have yet to be designated formal terrorist organizations despite boasting well-documented arsenals of sophisticated weaponry to rival most fear-inducing militias on battlefields abroad. Just last month, reports surfaced that Mexico's Jalisco New Generation Cartel (CJNG) now possess bomb-toting drones – which The Drive's Warzone depicts as "small quadcopter-type drones carrying small explosive devices to attack its enemies."

Financial crime as a wider category of cybercrime continues to be one of the most potent of online threats, covering nefarious actives as diverse as fraud, money laundering and funding terrorism. Today, one of the startups that has been building data intelligence solutions to help combat that is announcing a fundraise to continue fueling its growth. Ripjar, a UK company founded by five data scientists who previously worked together in British intelligence at the Government Communications Headquarters (GCHQ, the UK's equivalent of the NSA), has raised $36.8 million (£28 million) in a Series B, money that it plans to use to continue expanding the scope of its AI platform -- which it calls Labyrinth -- and scaling the business. Labyrinth, as Ripjar describes it, works with both structured and unstructured data, using natural language processing and an API-based platform that lets organizations incorporate any data source they would like to analyse and monitor for activity. It automatically and in real time checks these against other data sources like sanctions lists, politically exposed persons (PEPs) lists and transaction alerts.

We all are moving towards an era of Artificial Intelligence. Earlier when face recognition was something to be amazed at it is now easily implemented using existing libraries and frameworks. Machine learning is now embedded into our lives and it is thickening its grasp with time. Earlier it was a buzzword but now it is a reality that is making our lives easier and better. So let's talk about some of the problems with Machine Learning.