This blog post will take us through how a business which need to ensure customer due diligence (CDD) can automate the KYC (Know Your Customer) processes using deep learning and computer vision based solutions. But before we get started, let's familiarise ourselves with some basic terminology. Customer Due Diligence - CDD involves verifying that your customers are who they say they are and assessing the risks associated with each customer like the possibilities of fraud, money laundering, terrorism financing, etc. This includes verifying your customer's name, address, photograph by analysing bank documents, utility bills, etc. Anti Money Laundering - AML refers to a set of laws, regulations and procedures meant to prevent criminals from disguising illegally obtained assets and funds as legitimate income by safeguarding against trading illegal goods, tax evasion, market manipulation, corruption of public funds, etc. Know Your Business - KYB involves vetting a business trying to establish a relationship with a bank by determining their Ultimate Beneficial Owners (UBO) and enforcing compliance by assessing risks associated with the business. You can learn more about beneficial ownership structures and a risk based approach to counter laundering here.

If you look at the past four years we've been enjoying substantial economic growth, in order for the economy to be resilient to external shocks we have to continue to diversify and explore new niches to sustain our economic growth. So for this reason we have delved into niche economic areas. We started 2 years ago with Blockchain and we've been attracting significant investment to our island, not only in terms of crypto but also in other areas of technological developments. Now we're seeing new development in companies that are investing in technology and coming here to work and operate from Malta. We're also seeing a spill-over effect, such as companies from the iGaming industry who are producing new products supported by blockchain technology.

The conference focuses on four main themes and will be augmented by additional discussions. Our key themes are structural and institutional change in gender equality in R&I; a broader concept of equality in R&I; digitalization and gender equality; and gender equality in innovation. Follow conference live stream from this link. This session will take stock of the advancement over the past 20 years of gender equality policy in European research and innovation, its achievements and continued challenges. Structural change is today the dominant approach to advancing gender equality in Research Funding and Performing Organisations in the EU.

We propose a new model for making generalizable and diverse retrosynthetic reaction predictions. Given a target compound, the task is to predict the likely chemical reactants to produce the target. This generative task can be framed as a sequence-to-sequence problem by using the SMILES representations of the molecules. Building on top of the popular Transformer architecture, we propose two novel pre-training methods that construct relevant auxiliary tasks (plausible reactions) for our problem. Furthermore, we incorporate a discrete latent variable model into the architecture to encourage the model to produce a diverse set of alternative predictions. On the 50k subset of reaction examples from the United States patent literature (USPTO-50k) benchmark dataset, our model greatly improves performance over the baseline, while also generating predictions that are more diverse.

Recent surveys, studies, forecasts and other quantitative assessments of the progress of AI highlighted the rapidly increasing expectations regarding the business benefits of AI and the low incidence of business gains so far; the increasing adoption of AI by businesses worldwide and the challenges in its implementation and integration with exiting processes; and how companies respond to AI by both reducing and training their workforce. The report estimated the combined AI spending from large-capitalization financial institutions at more than $150 billion annually. In the past two years, BB&T Corp. has embraced a digital-first approach to plugging in artificial intelligence and robotics into its back-office, customer-service and compliance operations. That should eclipse the 1,281 companies that raised $16.8 billion in all of 2018, according to the 3Q 2019 PitchBook-NVCA Venture Monitor [VentureBeat] "The values of AI designers or the purchasing administrators are not necessarily the values of the bedside clinician or patient. Those value collisions and tensions are going to be sites of significant ethical conflict"--Danton Char, assistant professor of anesthesiology, perioperative and pain medicine at Stanford University Medical Center "I don't yet fully subscribe to the view that the machine is completely autonomous and operates without human intervention. At least as of today, and probably the foreseeable future, the AI machine is just another tool"--Andrei Iancu, director of the U.S. Patent and Trademark Office, speaking about recognizing AI systems that develop new products as inventors "If leaders think about AI like a balance sheet, then they're missing the point. You need to get emotional attachment to the disruptive nature that it can bring"--Werner Boeing, CIO, Roche Diagnostics "The major upside for us is driving more engagement....Right behind that is the ability to monetize this and generate incremental revenue for us and for our clubs....This data's going to be hugely valuable"--Dave Lehanski, NHL senior vice president of business development and global partnerships

It's likely that most people locked in our jails believe that with a better lawyer, a more lenient judge or a more understanding jury things might have been very different for them. Human error, they will say, is to blame for them being banged up. But can the human element be removed? Law firms are already using computer algorithms to perform background research other tasks traditionally performed by human staff. As computer researchers get closer to creating true Artificial Intelligence, it's predicted to eliminate most paralegal and legal research positions within the next decade.

IT'S ALMOST A BANALITY nowadays to remark that artificial intelligence (AI) is so deeply embedded in our infrastructure that it's affecting decisions everywhere. But what's not trite is considering exactly how it will change markets, medicine, transportation, military operations, politics, social relations, criminal justice, and the likes of you and me -- which will largely depend on big tech companies like Google, Amazon, Facebook, and the rest. If these behemoths continue to grow by supporting products and services that cause harm, then the most important stories we tell about AI won't be about technology, but about capitalism incapacitating democratic governance. In other words: They will be about the private sector dictating the terms of innovation, including the direction of regulation. While the 25 contributors to Possible Minds: 25 Ways of Looking at AI have lots of smart, multidisciplinary things to say about software and society, they mostly underplay or quickly move past the supersized consequences of supersized corporate ambitions.

In response to the serious threat that AI-enabled bots and deepfakes pose for election integrity, the California government has pushed forward progressive pieces of legislation that have influenced federal and international efforts. Passed in 2018, the "Bots Disclosure Act" makes it unlawful to use a bot to influence a commercial transaction or a vote in an election without disclosure in California. This includes bots deployed by companies in other states and countries, which requires those companies to either develop bespoke standards for Californian residents or harmonize their strategies across jurisdictions to maintain efficiency. At the federal level, the "Bots Disclosure and Accountability Act" includes many of the same strategies proposed in California. The California "Anti-Deepfakes Bill" seeks to mitigate the spread and impact of malicious political deepfakes before an election and the federal "Deepfakes Accountability Act" seeks to do the same.

Automation is coming, pant the breathless pundits warning of A.I.-induced job loss. Ratcheting up the fear meter, presidential candidate Andrew Yang recently sounded the alarm for unprecedented employment gutting -- not just among blue-collar professions, but white-collar jobs, too. Meanwhile, renowned studies paint a gloomy picture, one in which rapid A. advances kneecap our middle-class dreams, sapping the hopes of young people who are left to wonder: Will there be a job for me when I graduate? And yet, the on-the-ground reality doesn't fit these sour prognostications. If anything, it offers good news for workers.

Artificial intelligence programs are being used in more applications and more industries all the time.