In 2017, China laid out a three-step roadmap to become the world leader in AI by 2030. It hopes to make the industry worth 1 trillion yuan, or $147.7 billion, within the next decade. Already, it has announced billions in funding for innovative startups and launched programmes to entice researchers. It might not be long before it gains an edge over the US. That said, the US continues to pave the way, as is the case with many new fields of technology.
Anyone who runs a business knows that one of the hardest things to do is accuse a customer of malfeasance. That's why, before members of Scandinavian Airlines' (SAS) fraud detection unit accuse a customer of attempting to scam the carrier's loyalty points program, the detectives need confidence that their case is solid. "It would hurt us even more if we accidentally managed to say that something is fraud, but it isn't," said Daniel Engberg, head of data analytics and artificial intelligence for SAS, which is headquartered in Stockholm, Sweden. The airline is currently flying a reduced schedule with limited in-flight services to help slow the spread of COVID-19, the disease caused by the novel coronavirus. Before the restrictions, SAS handled more than 800 departures per day and 30 million passengers per year.
Google warned on Thursday that the EU's definition of artificial intelligence was too broad and that Brussels must refrain from over-regulating a crucial technology. The search and advertising giant made its argument in feedback to the European Commission, the EU's powerful regulator that has reached out to big tech as it draws up ways to set new rules for AI. The EU has not decided yet on how to regulate AI, but is putting most of its focus on what it calls "high risk" sectors, such as healthcare and transport. It's plans, to be spearheaded by EU commissioners Margrethe Vestager and Thierry Breton, are not expected until the end of the year. "A clear and widely understood definition of AI will be a critical foundational element for an effective AI regulatory framework," the company said in its 45-page submission.
General AI (Artificial Intelligence) is coming closer thanks to combining neural networks, narrow AI and symbolic AI. Yves Mulkers, Data strategist and founder of 7wData talked to Wouter Denayer, Chief Technology Officer at IBM Belgium, to share his enlightening insights on where we are and where we are going with Artificial Intelligence. Join us in our chat with Wouter. Yves Mulkers Hi and welcome, today we're together with Wouter Denayer, Chief Technology Officer at IBM. Wouter, you're kind of authority in Belgium and I think outside the borders of Belgium as well on artificial intelligence. Can you tell me a bit more about what you're doing at IBM and What keeps you busy?
Explainable artificial intelligence (AI) is attracting much interest in medicine. Technically, the problem of explainability is as old as AI itself and classic AI represented comprehensible retraceable approaches. However, their weakness was in dealing with uncertainties of the real world. Through the introduction of probabilistic learning, applications became increasingly successful, but increasingly opaque. We argue that there is a need to go beyond explainable AI.
There is a growing consensus that artificial intelligence ethics instruction is critical, and must extend beyond computer sciences courses. Ethics and technology have always been tightly interwoven, but as artificial intelligence (AI) marches forward and impacts society in new and novel ways, the stakes--and repercussions--are growing. "There is potential for (AI) to be used in ways that society disapproves of," observes David S. Touretzky, a research professor in the computer science department at Carnegie Mellon University. One idea that's gaining momentum is AI ethics instruction in schools. Groups such as AI4K12 and the MIT Media Lab have begun to study the issue and develop AI learning frameworks for K-12 students.
As we've seen unfold in recent years, artificial intelligence (AI), machine learning (ML) and data analytics are rapidly changing the speed at which the retail industry operates. As these technologies become increasingly popular among leading retail companies, it's clear that early adopters of AI have seen a sizable financial advantage compared to retailers that haven't yet adopted the technology. Non-adopters will need to erode their margin to stay competitive on price, while adopters with sizable financial gain will be able to weather volatility on price inputs. AI is being used as a differentiating factor between smaller retailers as a way to get ahead and capture market share. The gap between adopters and non-adopters will continue to grow, meaning AI is no longer just a way to get ahead of competitors -- it's become a pivotal part of staying relevant in the industry and maintaining innovation.
Digital marketing relies on leveraging insights from the copious amounts of data that gets created every time a customer interacts with a digital asset. In 2020, we anticipate a significant uptick in the mainstreaming of AI and machine learning use cases in digital marketing across several areas. In the past year, online search has had several AI and machine learning developments. Google is leading the pack with exciting applications in information retrieval. For example, Google's BERT technology can process a word in the context of all the other terms in a sentence, rather than one-by-one in order.