With plenty of post-recession anti-banking sentiment still lingering, it's common to see fintech and traditional banks framed in oppositional terms. There's some truth to that, especially with disruption-minded digital-only banks, but technological innovations have transformed banking of all stripes -- and nowhere is that clearer than with artificial intelligence. AI has impacted every banking "office" -- front, middle and back. That means even if you know nothing about the way your financial institution uses, say, complex machine learning to fend off money launderers or sift through mountains of data for fraud-related anomalies, you've probably at least interacted with its customer service chatbot, which runs on AI. Read on to learn how else AI is transforming the way banks operate, from investment assistance and consumer lending to credit scoring, smart contracts and more.

BANGKOK - The launch of facial recognition technology at two Indian airports and plans to place it in police stations have stoked fears over privacy and increased surveillance among human rights groups in the country. The "paperless biometric technology" launched in Bengaluru airport this week identifies passengers by their face, doing away with the need to present boarding passes, passports and other identity documents, according to a statement from the airport in India's tech capital. Another airport in the southern Indian city of Hyderabad is also testing facial recognition technology this month. While airlines, airports and the companies developing the software promise greater security and increased efficiency, some technology analysts and privacy experts say the benefits are not clear, and come at the cost of privacy and greater surveillance. This is particularly true of India, which does not have a data protection law or an electronic surveillance framework, said Vidushi Marda, a lawyer and advisor at human rights group Article 19.

Harry Potter fans will remember Professor Albus Dumbledore's nifty memory reviewer - the Pensieve. Throughout the series, several characters used it to store their memories and rewatch them to derive insights. Taking this concept from fiction to reality, Gaurav Shrivastava, Co-founder of Zimmber, built a transparent data machine - called Pensieve - AI with Co-founder Prahlad Routh. The two were ecstatic about implementing text analytics in the legal domain and the ample growth of Machine Learning (ML) and Artificial Intelligence (AI). The duo identified that legal tech could be a thing if Natural Language Processing (NLP) is used rightly, and started Pensieve in Mumbai in 2017.

Facial recognition technology has spread prodigiously. Google, Microsoft, Apple and others have built it into apps to compile albums of people who hang out together. It verifies who you are at airports and is the latest biometric to unlock your mobile phone where facial recognition apps abound. Need to confirm your identity for a £1,000 bank transfer? Just look into the camera.

On 18 July, the House of Commons select committee on science and technology published an assessment of the work of the biometrics commissioner and the forensic science regulator. My guess is that most citizens have never heard of these two public servants, which is a pity because what they do is important for the maintenance of justice and the protection of liberty and human rights. The current biometrics commissioner is Prof Paul Wiles. His role is to keep under review the retention and use by the police of biometric material. This used to be just about DNA samples and custody images, but digital technology promises to increase his workload significantly.

But these cities are in the minority. A new map produced by the digital rights nonprofit Fight for the Future illustrates just how pervasive facial recognition technology is across the country: an overwhelming number of red stars on the map indicate where local and state police are using facial recognition, including in Detroit, Michigan; Lincoln, Nebraska; and Albuquerque, New Mexico. You can toggle on and off to see the map pins for each of the groups using the technology, which include police departments (both city and state), as well as airports that use facial recognition and cities that are starting to mobilize against the technology. Facial recognition has become one of the most hotly debated technologies today because of how inaccurate it is, particularly among brown and black people--the ones who are often the target of police surveillance. However, tech companies like Amazon continue selling it to the government, prompting a backlash and a movement in local politics to create oversight and transparency for how the government is using facial recognition.

There are those who praise the technology as the solution to some of humankind's gravest problems, and those who demonize AI as the world's greatest existential threat. Of course, these are two ends of the spectrum, and AI, surely, presents exciting opportunities for the future, as well as challenging problems to be overcome. One of the issues that's attracted much media attention in recent years has been the prospect of bias in AI. It's a topic I wrote about in TechCrunch (Tyrant in the Code) more than two years ago. The debate is raging on. At the time, Google had come under fire when research showed that when a user searched online for "hands," the image results were almost all white; but when searching for "black hands," the images were far more derogatory depictions, including a white hand reaching out to offer help to a black one, or black hands working in the earth.

How to best explore in domains with sparse, delayed, and deceptive rewards is an important open problem for reinforcement learning (RL). This paper considers one such domain, the recently-proposed multi-agent benchmark of Pommerman. This domain is very challenging for RL --- past work has shown that model-free RL algorithms fail to achieve significant learning without artificially reducing the environment's complexity. In this paper, we illuminate reasons behind this failure by providing a thorough analysis on the hardness of random exploration in Pommerman. While model-free random exploration is typically futile, we develop a model-based automatic reasoning module that can be used for safer exploration by pruning actions that will surely lead the agent to death. We empirically demonstrate that this module can significantly improve learning.

Spam! That's what Lorrie Faith Cranor and Brian LaMacchia exclaimed in the title of a popular call-to-action article that appeared 20 years ago in Communications.10 And yet, despite the tremendous efforts of the research community over the last two decades to mitigate this problem, the sense of urgency remains unchanged, as emerging technologies have brought new dangerous forms of digital spam under the spotlight. Furthermore, when spam is carried out with the intent to deceive or influence at scale, it can alter the very fabric of society and our behavior. In this article, I will briefly review the history of digital spam: starting from its quintessential incarnation, spam emails, to modern-days forms of spam affecting the Web and social media, the survey will close by depicting future risks associated with spam and abuse of new technologies, including artificial intelligence (AI), for example, digital humans. After providing a taxonomy of spam, and its most popular applications emerged throughout the last two decades, I will review technological and regulatory approaches proposed in the literature, and suggest some possible solutions to tackle this ubiquitous digital epidemic moving forward. An omni-comprehensive, universally acknowledged definition of digital spam is hard to formalize. Laws and regulation attempted to define particular forms of spam, for example, email (see 2003's Controlling the Assault of Non-Solicited Pornography and Marketing Act.) However, nowadays, spam occurs in a variety of forms, and across different techno-social systems. Each domain may warrant a slight different definition that suits what spam is in that precise context: some features of spam in a domain, for example, volume in mass spam campaigns, may not apply to others, for example, carefully targeted phishing operations.

Facebook and the digital virus called fake news.