In its 2018 report, Will robots really steal our jobs?, PwC suggested that artificial intelligence (AI) threatened 30% of jobs in the UK. Much of the discussion on the introduction of AI and the potential effects on the legal sector is equally negative. Legal professionals have raised concerns that developments in AI-powered technology could threaten the security of junior roles, such as paralegals and research positions, within the next decade. Impressive advances in AI technology tailored for legal work have led some lawyers to worry that their profession is in jeopardy. However, in reality the likelihood of a lawyer's replacement is relatively low compared to that of a law student or a paralegal.

You can find a video review of this topic at the bottom of the article. Recently the EC released a proposal for a legal framework to regulate artificial intelligence. Unfortunately, it seems to still be quite far from what we need. If you're new here my name's Bobby, I'm am a tech entrepreneur and business advisor based in the Netherlands. On this channel, I explore the strategies and tools that help us understand technology and build better business ventures. Today I look at quite a controversial topic -- the recent development in the regulatory space towards building a legal framework for artificial intelligence.

Even as the search continues over a week later for signs of life in the mangled debris of the fallen Champlain Towers South, the process of seeking answers about why it happened and who is to blame is already underway in Florida's legal system. Authorities have opened criminal and civil investigations into the collapse of the oceanfront condominium building, which left at least 28 confirmed dead and more than 117 unaccounted for. Miami-Dade State Attorney Katherine Fernandez Rundle pledged to bring the matter soon before grand jurors, who could recommend criminal charges or simply investigate the cause to suggest reforms. And at least five lawsuits have been filed on behalf of residents who survived or are feared dead. One lawyer involved in the litigation said the collapse raises widespread concerns about infrastructure issues and the trust we put in those responsible for them.

Studies on online hate speech have mostly focused on the automated detection of harmful messages. Little attention has been devoted so far to the development of effective strategies to fight hate speech, in particular through the creation of counter-messages. While existing manual scrutiny and intervention strategies are time-consuming and not scalable, advances in natural language processing have the potential to provide a systematic approach to hatred management. In this paper, we introduce a novel ICT platform that NGO operators can use to monitor and analyze social media data, along with a counter-narrative suggestion tool. Our platform aims at increasing the efficiency and effectiveness of operators' activities against islamophobia. We test the platform with more than one hundred NGO operators in three countries through qualitative and quantitative evaluation. Results show that NGOs favor the platform solution with the suggestion tool, and that the time required to produce counter-narratives significantly decreases.

Voice assistants changed the way we interact with technology. Why set an alarm manually when Alexa can do it for you? Want to get more out of your Amazon Echo? Tap or click here for new tricks such as using your Echo as a speakerphone to finding the right wine to pair with a particular dish. Tap or click here for my favorite Siri shortcuts.

Conventional wisdom holds that the key to winning in big data is scaling one's data collection, storage, and analysis capabilities faster than one's competitor. The more data you have, the more trends you can uncover, and the better your ML models will be, the thinking goes. But in some situations, holding onto data is a liability that can cost you millions. That's why the smarter companies know when to let the data go. While some data clearly has value and should be kept, other pieces of data can actually be deleterious to a company's financial and legal health and should be disposed as soon as legally possible, says Bill Tolson, vice president of global compliance for Archive360.

Pricing decisions are increasingly made by AI. Thanks to their ability to train with live market data while making decisions on the fly, deep reinforcement learning algorithms are especially effective in taking such pricing decisions. In e-commerce scenarios, multiple reinforcement learning agents can set prices based on their competitor's prices. Therefore, research states that agents might end up in a state of collusion in the long run. To further analyze this issue, we build a scenario that is based on a modified version of a prisoner's dilemma where three agents play the game of rock paper scissors. Our results indicate that the action selection can be dissected into specific stages, establishing the possibility to develop collusion prevention systems that are able to recognize situations which might lead to a collusion between competitors. We furthermore provide evidence for a situation where agents are capable of performing a tacit cooperation strategy without being explicitly trained to do so.

Several high-profile events, such as the use of biased recidivism systems and mass testing of emotion recognition systems on vulnerable sub-populations, have highlighted how technology will often lead to more adverse outcomes for those that are already marginalized. In this paper, I will make a case for thinking about ethical considerations not just at the level of individual models and datasets, but also at the level of AI tasks. I will present a new form of such an effort, Ethics Sheets for AI Tasks, dedicated to fleshing out the assumptions and ethical considerations hidden in how a task is commonly framed and in the choices we make regarding the data, method, and evaluation. Finally, I will provide an example ethics sheet for automatic emotion recognition. Together with Data Sheets for datasets and Model Cards for AI systems, Ethics Sheets aid in the development and deployment of responsible AI systems.

Great problem solvers are made, not born. That's what we've found after decades of problem solving with leaders across business, nonprofit, and policy sectors. These leaders learn to adopt a particularly open and curious mindset, and adhere to a systematic process for cracking even the most inscrutable problems. And when conditions of uncertainty are at their peak, they're at their brilliant best. Six mutually reinforcing approaches underly their success: (1) being ever-curious about every element of a problem; (2) being imperfectionists, with a high tolerance for ambiguity; (3) having a "dragonfly eye" view of the world, to see through multiple lenses; (4) pursuing occurrent behavior and experimenting relentlessly; (5) tapping into the collective intelligence, acknowledging that the smartest people are not in the room; and (6) practicing "show and tell" because storytelling begets action (exhibit). Here's how they do it. As any parent knows, four-year-olds are unceasing askers.

In 2017, Amazon officially announced three new features to its "Amazon Rekognition" software package. The software was launched the previous year with the promise to dramatically increase developers' use of machine learning in the analysis of digital images. The new features included "detection and recognition of text in images, real-time face recognition across tens of millions of faces, and detection of up to 100 faces in challenging crowded photos," per the press release. The breadth of the new features demonstrated how far machine learning had come in just a few years, and the release merely touched the surface of the widespread applicability of Amazon's ever-improving software. Describing the technology's immediate impact in the battle to end human trafficking, as well as its use by social media sites such as Pinterest as a way of extracting "rich text" and thereby improving the cataloging of users' images, Amazon seemed keen to telegraph how widely Rekognition was already used.