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) …
Artificial Intelligence (AI) and retail are a good fit. The COVID-19 pandemic has accelerated digital transformation worldwide and is whipping up different business verticals to adopt various AI technologies. As per the UNCTAD survey, more than half of consumers of the emerging and developed economies are shopping online. The part of AI in the retail market in 2020 was valued at USD 1,80 billion and is expected to reach USD 10,90 billion at a CAGR of 35% by 2026. It seems like it is high time for going big or going home for retailers.
COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis.
Griffith University researchers have developed an AI video surveillance system to detect social distancing breaches in an airport without compromising privacy. By keeping image processing gated to a local network of cameras, the team bypassed the traditional need to store sensitive data on a central system. Professor Dian Tjondronegoro from Griffith Business School says data privacy is one of the biggest concerns with this technology because the system has to constantly observe people's activities to be effective. "These adjustments are added to the central decision-making model to improve accuracy." Published in Information, Technology & People, the case study was completed at Gold Coast Airport which, pre-COVID-19 had 6.5 million passengers annually with 17,000 passengers on-site daily.
The 2021 ACM SIGAI Industry Award for Excellence in Artificial Intelligence is granted to DrAidTM - the AI-powered Assistant product for Radiologists developed by VinBrain (a subsidiary of Vingroup in Vietnam). This is one of the world's top awards, and only one AI product is selected as the winner each year. The award will be presented at the International Joint Conference on Artificial Intelligence (IJCAI) 2021 from August 19-26, 2021 in Canada. In 2019, the award was granted to Microsoft Corporation. The ACM SIGAI Industry Award for Excellence in Artificial Intelligence (AI) is one of the world's top awards in the field of AI which is given annually to individuals or teams who have transferred original advanced academic research into AI applications.
Zindi is much more than a machine learning platform. It's the place where people come to learn, collaborate, and connect while solving problems that really matter. In a recent survey, we discovered that about half of the users on Zindi were thinking about starting their own AI business. Zindi co-founder Ekow Duker dives into taking charge of your own destiny, and gives some tips on how to make sure you succeed. Let's start with asking why on earth you would want to do this right now.
Machine-learning algorithms are used to find patterns in data that humans wouldn't otherwise notice, and are being deployed to help inform decisions big and small – from COVID-19 vaccination development to Netflix recommendations. New award-winning research from the Cornell Ann S. Bowers College of Computing and Information Science explores how to help nonexperts effectively, efficiently and ethically use machine-learning algorithms to better enable industries beyond the computing field to harness the power of AI. "We don't know much about how nonexperts in machine learning come to learn algorithmic tools," said Swati Mishra, a Ph.D. student in the field of information science. "The reason is that there's a hype that's developed that suggests machine learning is for the ordained." Mishra is lead author of "Designing Interactive Transfer Learning Tools for ML Non-Experts," which received a Best Paper Award at the annual ACM CHI Virtual Conference on Human Factors in Computing Systems, held in May. As machine learning has entered fields and industries traditionally outside of computing, the need for research and effective, accessible tools to enable new users in leveraging artificial intelligence is unprecedented, Mishra said.
Recently the pandemic has pushed digital transformation to the front of the line. While collaborative tools allowed us to work from home and maintain close contact with our co-workers, the next step is just around the corner, thanks to artificial intelligence and machine learning. In every element of the company, the pandemic is driving a move towards a hybrid work paradigm, changing people's management and the way we work. Enterprises are on the verge of digital transformation and the use of artificial intelligence in HR departments will accelerate this process. Digital transformation improves the customer experience while also unlocking new value.
This year's Olympic Games may be closed to most spectators because of COVID-19, but the eyes of the world are still on the athletes thanks to dozens of cameras recording every leap, dive and flip. Among all that broadcasting equipment, track-and-field competitors might notice five extra cameras--the first step in a detailed 3-D tracking system that supplies spectators with near-instantaneous insights into each step of a race or handoff of a baton. And tracking is just the beginning. The technology on display in Tokyo suggests that the future of elite athletic training lies not merely in gathering data about the human body, but in using that data to create digital replicas of it. These avatars could one day run through hypothetical scenarios to help athletes decide which choices will produce the best outcomes.
Rapid deployment of artificial intelligence and machine learning to tackle coronavirus must still go through ethical checks and balances, or we risk harming already disadvantaged communities in the rush to defeat the disease. This is according to researchers at the University of Cambridge's Leverhulme Centre for the Future of Intelligence (CFI) in two articles published in the British Medical Journal, cautioning against blinkered use of AI for data-gathering and medical decision-making as we fight to regain normalcy in 2021. "Relaxing ethical requirements in a crisis could have unintended harmful consequences that last well beyond the life of the pandemic," said Dr Stephen Cave, Director of CFI and lead author of one of the articles. "The sudden introduction of complex and opaque AI, automating judgments once made by humans and sucking in personal information, could undermine the health of disadvantaged groups as well as long-term public trust in technology." In a further paper, co-authored by CFI's Dr Alexa Hagerty, researchers highlight potential consequences arising from the AI now making clinical choices at scale – predicting deterioration rates of patients who might need ventilation, for example – if it does so based on biased data.
The pandemic has both reinforced and highlighted the underlying trends that preceded it. Not only did 45% of consumers claim that they want to purchase more sustainable products during this period, Accenture reports, they also intend to continue in this dynamic in the future (Keeble, 2020). Faced with the rise of these new expectations, companies have higher demands from their suppliers and are improving the sustainability of their value chains. However, the required transformation does not stop there, it is now necessary to ensure the quality of the information that circulates in the supply chains and empower each actor with suitable technological tools to manage it. To take full advantage of artificial intelligence (AI) and its capabilities, large amounts of data are necessary. As value chain data is too often scattered, a system integrating supply chain data collection and consolidation is needed.