If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The UK Court of Appeal has unanimously reached a decision against a face-recognition system used by South Wales Police. The judgment, which called the use of automated face recognition (AFR) "unlawful", could have ramifications for the widespread use of such technology across the UK. But there is disagreement about exactly what the consequences will be. Ed Bridges, who initially launched a case after police cameras digitally analysed his face in the street, had appealed, with the support of personal rights campaign group Liberty, against the use of face recognition by police. The police force claimed in court that the technology was similar to the use of closed-circuit television (CCTV) cameras in cities.
A screen shows a demonstration of SenseTime Group's SenseVideo pedestrian and vehicle recognition system at the company's showroom in Beijing. Facial recognition supporters in the US often argue that the surveillance technology is reserved for the greatest risks -- to help deal with violent crimes, terrorist threats and human trafficking. And while it's still often used for petty crimes like shoplifting, stealing $12 worth of goods or selling $50 worth of drugs, its use in the US still looks tame compared with how widely deployed facial recognition has been in China. A database leak in 2019 gave a glimpse of how pervasive China's surveillance tools are -- with more than 6.8 million records from a single day, taken from cameras positioned around hotels, parks, tourism spots and mosques, logging details on people as young as 9 days old. The Chinese government is accused of using facial recognition to commit atrocities against Uyghur Muslims, relying on the technology to carry out "the largest mass incarceration of a minority population in the world today."
The UK government has been funneling millions of dollars into a prediction tool for violent crime that uses artificial intelligence. Now, officials are finally ready to admit that it has one big flaw: It's completely unusable. Police have already stopped developing the system called "Most Serious Violence" (MSV), part of the UK's National Data Analytics Solution (NDAS) project, and luckily was never actually put to use -- yet plenty of questions about the system remain. The tool worked by assigning people scores based on how likely they were to commit a gun or knife crime within the next two years. Two databases from two different UK police departments were used to train the system, including crime and custody records.
It has been more than two months since the killing of George Floyd at the hands of police in the United States. And as protests continue - the message is no longer just about specific incidents of violence, but about what demonstrators say is systemic racism in policing. One of the most obvious examples is the widespread use of facial recognition systems that have been proven to misidentify people of colour.
I work for Icon Solutions. We work in instant payments. What I want to talk about is applying machine learning to fraud detection. When we first started researching it, we found two themes that were going on. We found these hype type things. I'm sure you've all seen this, when will we bow to our machine overlord? By 2025, robots will be playing symphonies and all that stuff. Then we found the other extreme as well, which was the fairly wacky math. What we were looking at is how can we actually apply this technology to our requirements and to those of our clients? I'm going to talk about payments. Then I'm going to do a demonstration. In terms of payments, the way it worked, if you wanted to interact with the bank through most of 20th centuries, you had to go into a branch. That was the only way you could interact with the bank. If somebody wanted to steal money from a bank, they had to rob it. That was basically the only option they had, which is why you can see the big security barriers that they had in the branches at that point in time. Then, moving on to about 1960s, the bank started employing new technologies. They took things like the IBM 360 series, and they actually started using it. Even then it was pretty secure. The people who were using it were people who worked for the bank. It was a closed network. If you wanted to actually get into the systems, you had to go into the bank's offices, and you had to be an employee. The potential for fraud was fairly small.
A new report published by University College London aimed to identify the many different ways that AI could potentially assist criminals over the next 15 years. The report had 31 different AI experts take 20 different methods of using AI to carry out crimes and rank these methods based on various factors. The AI experts ranked the crimes according to variables like how easy the crime would be to commit, the potential societal harm the crime could do, the amount of money a criminal could make, and how difficult the crime would be to stop. According to the results of the report, Deepfakes posed the greatest threat to law-abiding citizens and society generally, as their potential for exploitation by criminals and terrorists is high. The AI experts ranked deepfakes at the top of the list of potential AI threats because deepfakes are difficult to identify and counteract.
Clearview AI is just one of many facial recognition firms scraping billions of online images to create a massive database for purchase – but a new program could block their efforts. Researchers designed an image clocking tool that makes subtle pixel-level changes that distort pictures enough so they cannot be used by online scrapers – and claims it is 100 percent effective. Named in honor of the'V for Vendetta' mask, Fawkes is an algorithm and software combination that'cloaks' an image to trick systems, which is like adding an invisible mask to your face. These altered pictures teach technologies a distorted version of the subject and when presented with an'uncloaked' form, the scraping app fails to recognize the individual. 'It might surprise some to learn that we started the Fawkes project a while before the New York Times article that profiled Clearview.ai in February 2020,' researchers from the SANLab at University of Chicago shared in a statement.
At a time like this, the banking sector is trying its hand, leg and even head to give a head-start to the AI developments. The financial services industry is appealing to enter AI market to avail the luxury of accurate data and investment. The development assists banks with better customer service, fraud detection, reduction of managing cost and easy decision-making through AI analysis. Customers have expectations that can't be turned down. Expectations to get work done faster and with zero error. The only by-standing solution is the utilisation of AI in the everyday banking sector.
Using data science in the banking industry is more than a trend, it has become a necessity to keep up with the competition. Banks have to realize that big data technologies can help them focus their resources efficiently, make smarter decisions, and improve performance. Here is a list of data science use cases in banking area which we have combined to give you an idea how can you work with your significant amounts of data and how to use it effectively. Machine learning is crucial for effective detection and prevention of fraud involving credit cards, accounting, insurance, and more. Proactive fraud detection in banking is essential for providing security to customers and employees.