In the post-pandemic, post-Brexit world, businesses of all sorts face a range of new challenges – and many will be wondering if AI-based automation could help them win through. From adding more self-service capabilities for hotel guests through modernising e-commerce fulfilment to replacing missing workers in farming, the opportunities are many, but so are the pitfalls. Given all this, some research that we carried out last year on attitudes to AI – and in particular its subset, machine learning (ML) – is looking even more relevant now than it was then. It gives a picture not just of where AI could add value, but of key routes to get there and of hurdles that must be overcome along the way. As well as asking how our respondents perceived AI and ML, and hearing a lot of weariness with the noise and hype, we asked how well their organisations understood "the AI imperative".
Consumer privacy has made big headlines in the recent years with the Facebook Cambridge Analytica Scandal, Europe's GDPR and high-profile breaches by companies like Equifax. It's clear that the data of millions of consumers is at risk every day, and that companies that wish to handle their data must do so with the highest degree of protection around both security and privacy of that data, especially for companies that build and sell AI-enabled facial recognition solutions. As CEO of an AI-enabled software company specializing in facial recognition solutions, I've made data security and privacy among my top priorities. Our pro-privacy stance goes beyond mere privacy by design engineering methodology. We regularly provide our customers with education and best practices, and we have even reached out to US lawmakers, lobbying for sensible pro-privacy regulations governing the technology we sell.
In 1963, Martin Luther King gave his "I have a dream" speech, words that reflected the thoughts and attitudes of civil rights activists at the time, and lit a torch that lives on in the hearts and minds of those who fight for civil liberties and equality in the western hemisphere. While the world has advanced since Dr. King ushered those words, it's hard to deny that discrimination still rears its ugly head in modern society. We know for a fact that racial discrimination in the workplace is illegal in most of America and Europe. And yet, just in the USA statistics show that things don't seem to have improved regarding hiring practices for black people and Hispanics in the last 25 years. In theory, AI-assisted hiring is built on an underlying model that makes unbiased decisions as long as the data itself isn't biased.
Bottom Line: Barclays' and Kount's co-developed new product, Barclays Transact reflects the future of how companies will innovate together to apply AI-based fraud prevention to the many payment challenges merchants face today. Merchant payment providers have seen the severity, scope, and speed of fraud attacks increase exponentially this year. Account takeovers, card-not-present fraud, SMS spoofing, and phishing are just a few of the many techniques cybercriminals are using to defraud merchants out of millions of dollars. But it doesn't have to be a choice between security and a frictionless transaction. Frustrated by the limitations of existing fraud prevention systems, many payment providers are working as fast as they can to pilot AI- and machine-learning-based applications and platforms.
Hyperdimensional computing (HDC) is an emerging computing approach inspired by patterns of neural activity in the human brain. This unique type of computing can allow artificial intelligence systems to retain memories and process new information based on data or scenarios it previously encountered. Most HDC systems developed in the past only perform well on specific tasks, such as natural language processing (NLP) or time series problems. In a paper published in Nature Electronics, researchers at IBM Research- Zurich and ETH Zurich presented a new HDC system that performs all core computations in-memory and that could be applied to a variety of tasks. "Our work was initiated by the natural fit between the two concepts of in-memory computing and hyperdimensional computing," Abu Sebastian and Abbas Rahimi, the two lead researchers behind the study, told TechXplore.
Three companies – Samsung, IBM and Tencent – dominate the global AI patent race over the past 10 years, while fierce competition between the U.S, and China overshadows other countries and regions, including the EU. These are the key findings of OxFirst, a specialist in IP law and economics (and spin out of Oxford University), which also reported that multiple neural nets, machine learning and speech recognition are driving the market. "Patents are mainly filed in the area of interconnectivity and system architecture, suggesting that top players focus primarily on protecting technologies covering multiple neural nets," OxFirst said in its announcement today. "Other areas of crucial importance are ML and bootstrap methods, alongside procedures used during speech recognition processes; e.g. the further establishment of human-machine dialogue." OxFirst said its sector-specific analysis suggests that major companies have focused on AI in the medical space, particularly medical diagnosis, medical simulation and data mining.
Would you let a machine learning model that has a failure rate of 98% and a false positive rate of 81% into production? Well, these claimed performance figures are from a facial recognition system that is in use by the policing force in South Wales and other parts of the United Kingdom. Dave Gershgorn article starts with a description akin to the setting of a dystopian future where an overseeing governing system monitors everyone; which is hysterically a foreshadowing of a foreseeable future. South Wales Police have been using facial recognition systems since 2017 and have done this in no secrecy from the public. They've made arrests as a result of the facial recognition system.
Innovative Artificial Intelligence (AI) is being used by Hertfordshire County Council's highways team during the coronavirus pandemic. Instead of a driver and inspector team logging faults, the technology allows one inspector to drive while the device records road defects, allowing this work to continue while meeting Covid-19 social distancing guidelines. The county council, which filled over 17,000 potholes last year, was the first to adopt the Vaisala Road AI system for safety inspections. Kevin Carrol, divisional manager of Ringway, the county council's highways contractor, said: "The safety of our […]
The development and deployment of artificial intelligence (AI) tools should take place in a socio-technical framework where individual interests and the social good are preserved but also opportunities for social knowledge and better governance are enhanced without leading to the extremes of'surveillance capitalism' and'surveillance state'. This was one of the main conclusions of the study'The impact of the General Data Protection Regulation on Artificial Intelligence', which was carried out by Professor Giovanni Sartor and Dr Francesca Lagioia of the European University Institute of Florence at the request of the STOA Panel, following a proposal from Eva Kaili (S&D, Greece), STOA Chair. Data protection is at the forefront of the relationship between AI and the law, as many AI applications involve the massive processing of personal data, including the targeting and personalised treatment of individuals on the basis of such data. This explains why data protection has been the area of the law that has most engaged with AI and, despite the fact that AI is not explicitly mentioned in the General Data Protection Regulation (GPDR), many provisions of the GDPR are not only relevant to AI, but are also challenged by the new ways of processing personal data that are enabled by AI. This new STOA study addresses the relation between the GDPR and AI and analyses how EU data protection rules will apply in this technological domain and thus impact both its development and deployment.
The Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) is a collaboration between McGill University and Forschungszentrum Jülich to develop next-generation high-resolution human brain models using cutting-edge Machine- and Deep Learning methods and high-performance computing. HIBALL is based on the high-resolution BigBrain model first published by the Jülich and McGill teams in 2013. Over the next five years, the lab will be funded with a total of up to 6 million Euro by the German Helmholtz Association, Forschungszentrum Jülich, and Healthy Brains, Healthy Lives at McGill University. In 2003, when Jülich neuroscientist Katrin Amunts and her Canadian colleague Alan Evans began scanning 7,404 histological sections of a human brain, it was completely unclear whether it would ever be possible to reconstruct this brain on the computer in three dimensions. At that time, there were no technical possibilities to cope with the huge amount of data.