It is important to understand the basic nature of machines like computers in order to understand what machine learning is. Computers are devices that follow instructions, and machine learning brings in an interesting outlook, where a computer can learn from the experience without the need for programming. Machine learning transports computers to another level where they can learn intuitively in a similar manner as humans. It has several applications, including virtual assistants, predictive traffic systems, surveillance systems, face recognition, spam, malware filtering, fraud detection, and so on. The police can utilize machine learning effectively to resolve the challenges that they face.
"The big tech is banking heavily on AI, Cloud and 5G technologies to retain customers and drive growth" A global emergency can smother your business, government lawsuits can break your company, competitors with trillion-dollar market value can wipe your organisation off the map. But what would happen when all three come together in the same year? The pandemic brought the world to a standstill. The internet giants, however, came out of it unscathed. Apple, Amazon, Google and Facebook, popularly known as the big four, have not only survived a combination of calamities but registered profits and left the Wall Street analysts dumbfounded.
RUSI's Centre for Financial Crime and Security Studies is launching a one-year study of policy and operational considerations related to the impact of artificial intelligence (AI) on financial crime. The project will explore the opportunities that AI offers for better financial crime detection, as well as the threats posed by the abuse of AI. It will form part of Financial Crime 2.0, a RUSI research programme focused on the intersection of new technology and financial crime. This latest workstream of the Financial Crime 2.0 programme is sponsored by its strategic partner, LexisNexis Risk Solutions. Tom Keatinge, Director of RUSI's Centre for Financial Crime and Security Studies, said: 'We are delighted to continue our Financial Crime 2.0 work, which delves into some of the most exciting, promising and topical issues facing the financial crime expert community'.
This is the sixth, and final episode in a series dedicated to all things A.I. In this episode, Tae Royle, Head of Digital Products APAC from Ashurst Advance Digital is joined by Tara Waters, Partner and Head of Ashurst Advance Digital. This is the sixth and final episode in a series dedicated to all things Artificial Intelligence. My name is Tae Royle head of digital products from Ashurst did that digital and today I'm joined by Tara Waters partner and head of Ashurst Advanced Digital based out of our London office. Naturally we come to the question of what's next? In Lewis Carroll's second novel, Alice enters Wonderland by climbing through a mirror.
Shalini Kantayya describes herself as a filmmaker who's fascinated with disruptive technologies and the good or harm they create. In a data-driven and increasingly automated world, there's a question of how to protect our civil liberties as artificial intelligence grows by the day. MIT researcher Joy Buolamwini discovered that most facial recognition technology does not see dark-skinned faces and women's faces accurately. This led to an investigation of how the technology we typically see as objective can actually encode racism and sexism. Buolamwini, and others working to change technology for the better around the globe, are featured in Kantayya's documentary Coded Bias.
"Trust is a must," she said. "The EU is spearheading the development of new global norms to make sure AI can be trusted. By setting the standards, we can pave the way to ethical technology worldwide." Any fast-moving technology is likely to create mistrust, but Vestager and her colleagues decreed that those in power should do more to tame AI, partly by using such systems more responsibly and being clearer about how these work. The landmark legislation – designed to "guarantee the safety and fundamental rights of people and businesses, while strengthening AI uptake, investment and innovation" – encourages firms to embrace so-called explainable AI.
Even though we're far from achieving critical mass in the legal profession when it comes to the use of predictive coding technologies and approaches in electronic discovery, the use of predictive coding for document review – especially relevancy review – to support discovery is certainly the most common use of artificial intelligence (AI) and machine learning technologies. Some of you reading this blog post may be "old pros" at this point when it comes to the use of predictive coding while others of you still have yet to "dip your toes" into the predictive coding pool. But applying machine learning technology to support document review (which is predictive coding) is far from the only discovery-related workflow and use case where AI and machine learning technology can be applied. There are several others that forward-thinking organizations are looking to also implement to streamline workflows in the discovery life cycle. How could we forget one of the "forgotten ends" that I discussed last week?
The magnitude of how technology has changed the landscape of the legal profession so far is quite astounding, considering how legal professionals used to be insistent on sticking to the status quo. The digital revolution made practicing law significantly easier by supplying lawyers with tools that streamlined parts of their previously outdated workflow, from researching case files to client relations. As law firms are working remotely and switching to a client-centric approach, now is the right time to consider how the latest IT developments will further impact the legal industry. With clients demanding law firms to be faster, cost-effective, and more flexible, law professionals have embraced automation and law firm software as solutions for growing customer expectations. Automation, in particular, helped law firms save time and deliver more productive results.
AI is defined as software that is developed with one or more specified techniques and approaches (including machine learning and deep learning) that can, for a given set of human-defined objectives, generate outputs such as content, predictions, recommendations or decisions influencing the environments they interact with.
There's a decent chance that you never heard the name "Itch.io" until Apple painted a less-than-complete picture of the indie games store on Friday, during its ongoing legal tussle with Epic Games. The dispute sees two multi-billion dollar companies duking it out over in-app purchasing. Epic contends that Apple's restrictive policy which carves out 30 percent of most in-app sales for the iPhone maker, are anti-competitive and therefore illegal. Apple, meanwhile, maintains that its policy barring competing storefronts from the App Store is necessary and justified by the service it provides in offering a curated and secure experience that is ostensibly free from fraud. I'm not here to weigh in on who's right and wrong in a legal war fueled by the immense wealth of two massive companies; better to deal with the fallout once it's all over. But I do want to spare some thought for Itch.io, which got roped into the court proceedings on Friday.