Anna is experienced in driving digital transformation for multinationals. She oversees digitalization strategy and digital business "Schindler Ahead" in 16 countries, harnessing the power of IoT, AI and machine learning and defining the future of business with focus on customer experience. Previously leading digitalization and communications functions at BASF Monomers and Siemens, she built digital roadmaps, scouted new technologies, founded partnerships, commercialized digital products and shaped the companies as technology powerhouses through 360-degree marketing and communications strategy. Being a strong advocate to inspire more people to join tech sector, Anna was invited to speak at TED talk and tech conferences worldwide. Her active support of the innovation ecosystem earned her recognitions of being finalist of "IoT Leader of the Year 2019" by IoT World Asia, "Transformation Leader of the Year" by Women in IT Awards, "Rising Star" and "Innovator 25 APAC" awards by the Holmes Report.

By JULIE OWINO, NAIROBI, Kenya, Apr 18 – Agriculture is slated to take the lead in the adoption of Artificial Intelligence with farmers increasingly now using technology to monitor plant, soil and weather conditions. Speaking during the launch of'AI for Good' at Strathmore University, Microsoft East Africa Manager Sebu Haileleul said there is a large pool of workforce and farmers in the agriculture sector but AI can also be adapted in other industry. "The agriculture sector contributes a lot towards the country economy but certainly AI is not only applicable to the agriculture sector but can be adopted in other industries," said Haieleleul. Currently, farmers have adapted some applications where they can use to access loans from banks. According to Microsoft East Africa government Affairs Director Christopher Akiwumi, the AI adoption process has made it easier for farmers to stimulate their growth through easy and quick Access of loans from various government institutions. However, there is a need to support and raise the skills gap in the wake of the adoption of AI and different technologies in the coming years.

Landslides have caused more than 11,500 fatalities in 70 countries between 2007-2010. Over 1000 people were victims of a landslide that hit Sierra Leone in August 2017. The situation is getting worse as the volume and intensity of rainfall in West Africa is increasing. In April, Colombia's landslide left at least 254 dead and hundreds missing. Landslides are challenging across various levels, for example: social, economic, infrastructural, and environmental.

As Manchester City's players returned to the home dressing room after January's exhilarating, exhausting 2-1 win over Liverpool, music shuddered from speakers. A house remix of Gregory Porter's Liquid Spirit mixed with gleeful shouts as the celebrations began. But in one corner, three men huddled quietly together. Ederson and John Stones stared at a big screen as Harry Dunn, a member of manager Pep Guardiola's backroom staff, zipped through a timeline of the match action to show a replay of Stones clearing the ball off his own goalline, with just 11mm to spare. By the time they were showered, changed and back in the tinted privacy of their cars, Ederson, Stones or any of their team-mates could open the Hudl app on their phone and watch that moment, along with every other involvement they had in the game.

New York Fashion Week, now underway, is a celebration of creativity, crafts, imagination, and human ingenuity. But of course in the year 2019 one aspect of creation is integrating technology, so perhaps it's little wonder that some designers are relying on data collected by the Chinese e-commerce giant Alibaba to inform their looks. "We used big data to help these designers hone in on a few trends and have their collections built around these trends," James Lin, head of fashion at Alibaba North America, told the Nikkei Asian Review. "Based on what our consumers are wanting to buy, are looking for, are searching, we can help these designers create collections that appeal to a very big group." The company is investing a lot of resources and effort in lifestyle products--beauty and fashion items--which appear to maintain steady popularity and are exceptionally able to withstand the pressures of the US-China trade war. The global management consultancy McKinsey predicts that in 2019 China will overtake the US as the world's largest fashion market.

Kai-Fu Lee is a Chinese venture capitalist, technology executive, writer, and computer scientist. He is currently based in Beijing, China. Lee developed the world's first speaker-independent, continuous speech recognition system as his Ph.D. thesis at Carnegie Mellon. He later worked as an executive, first at Apple, then SGI, Microsoft, and then Google.He became the focus of a 2005 legal dispute between Google and Microsoft, his former employer, due to a one-year non-compete agreement that he signed with Microsoft in 2000 when he became its corporate vice president of interactive services.

When Norio Ichihashi is involved in a project, anyone with any sense had better pay attention. After all, Ichihashi is one of the co-founders of Mobileye (MBLY), which was just recently purchased by Intel for $15.3 billion. Ichihashi now has his sights set on a new venture: a new artificial intelligence educational gaming app called Zangu that enables children to learn a new language in as little as 30 days. "There are serious problems with the way children learn a second language," says Kenichi Kainuma, the CEO of AI Teach U Shanghai, which created Zangu, along with Ichihashi. "There's always been problems with children – and even adults – learning a new or a second language."

It may be true data scientists occupy "the sexiest job of the century," but it's also true they're under tremendous pressure to deliver on their rarefied skills, knowledge and pay. We recently spoke (under condition of anonymity) with a data scientist at a North American financial institution, a resource-rich company implementing AI at enterprise scale, and his comments show how Wall Street firms view machine learning as a critical strategic weapon to drive profits and efficiencies. "There's a massive drive at all financial institutions, especially here, to drive efficiencies, for us to gain every little cent across the board," he told us. "…It's part of our internal KPIs (key performance indicators), to find implementable opportunities for efficiency gains in terms of how we perform. This is part of the master goal of the organization."

It may be true data scientists occupy "the sexiest job of the century," but it's also true they're under tremendous pressure to deliver on their rarefied skills, knowledge and pay. We recently spoke (under condition of anonymity) with a data scientist at a North American financial institution, a resource-rich company implementing AI at enterprise scale, and his comments show how Wall Street firms view machine learning as a critical strategic weapon to drive profits and efficiencies. "There's a massive drive at all financial institutions, especially here, to drive efficiencies, for us to gain every little cent across the board," he told us. "…It's part of our internal KPIs (key performance indicators), to find implementable opportunities for efficiency gains in terms of how we perform. This is part of the master goal of the organization."

--Humans are very sophisticated in learning new information on a completely unknown domain because humans can contradistinguish, i.e., distinguish by contrasting qualities. We learn on a new unknown domain by jointly using unsupervised information directly from unknown domain and supervised information previously acquired knowledge from some other domain. Motivated by this supervised-unsupervised joint learning, we propose a simple model referred as Contradis-tinguisher (CTDR) for unsupervised domain adaptation whose objective is to jointly learn to contradistinguish on unlabeled target domain in a fully unsupervised manner along with prior knowledge acquired by supervised learning on an entirely different domain. Most recent works in domain adaptation rely on an indirect way of first aligning the source and target domain distributions and then learn a classifier on labeled source domain to classify target domain. This approach of indirect way of addressing the real task of unlabeled target domain classification has three main drawbacks. In this work, we propose a simple and direct approach that does not require domain alignment. We jointly learn CTDR on both source and target distribution for unsupervised domain adaptation task using contradistinguish loss for the unlabeled target domain in conjunction with supervised loss for labeled source domain. Our experiments show that avoiding domain alignment by directly addressing the task of unlabeled target domain classification using CTDR achieves state-of-the-art results on eight visual and four language benchmark domain adaptation datasets.