Nintendo Co. fell the most in nine months after it missed estimates for quarterly profit and forecast full-year earnings that were short of expectations, raising concern about demand for its Switch game console. The shares fell as much as 4.7 percent in early trading in Tokyo on Friday, the biggest intraday drop since April 26. A day earlier, Nintendo reported operating income of ¥168.7 billion ($1.5 billion) in the three months ended December, but that underwhelmed versus the ¥175.4 billion average projection. The lackluster results may fuel worries about the Switch console's longevity, especially in a year when Microsoft Corp. and Sony Corp. are preparing to launch new machines for the holidays. Nintendo released a lower-cost Switch Lite in the fall, reaching out to more mainstream users, and that console has sold 5.19 million units to date, the company reported.

What exactly is biotechnology, and how could it change our approach to human health? As the age of big data transforms the potential of this emerging field, members of the World Economic Forum's Global Future Council on Biotechnology tell you everything you need to know. What if your doctor could predict your heart attack before you had it – and prevent it? Or what if we could cure a child's cancer by exploiting the bacteria in their gut? These types of biotechnology solutions aimed at improving human health are already being explored. As more and more data (so called "big data") is available across disparate domains such as electronic health records, genomics, metabolomics, and even life-style information, further insights and opportunities for biotechnology will become apparent. However, to achieve the maximal potential both technical and ethical issues will need to be addressed. As we look to the future, let's first revisit previous examples of where combining data with scientific understanding has led to new health solutions. Biotechnology is a rapidly changing field that continues to transform both in scope and impact. Karl Ereky first coined the term biotechnology in 1919.

The below excerpt showcases the distinctiveness and acumen of a holistic AI company – TransOrg Analytics that is consistently striving to roll out intelligent and scalable solutions for the betterment of its customers. TransOrg Analytics is an award-winning player in'Analytics and Advisory' space. Founded in 2009, TransOrg is headquartered in Gurugram, India with a global presence in the US, UK, Singapore, India and the Middle East. Its global clientele includes Fortune 500 companies and industry leaders in sectors like Banking, Financial Services, Insurance, Telecom, Hospitality, CPG, Retail, E-commerce, Travel & Aviation. TransOrg has a strong team of over 80 high-performing Data Scientists, Data Engineers, Visualization experts from top schools and leadership with strong academic credentials and collective work experience of over 100 years with reputed organizations.

The automation of the healthcare sphere is one of the most urgent and, perhaps, the most difficult tasks in the world. A huge amount of money is spent every year to solve it, but many problems and questions remain. The main issue includes proper interaction between various systems, especially the exchange of data at the municipal, regional, and federal levels. Most countries develop their own concepts when it comes to the question «what is a smart hospital»: they adopt normative legal acts, introduce standards, and implement targeted programs. The Smart Hospital concept is aimed at creating a single IT environment composed of automated «bricks» of specialized and auxiliary processes.

The automation of the healthcare sphere is one of the most urgent and, perhaps, the most difficult tasks in the world. A huge amount of money is spent every year to solve it, but many problems and questions remain. The main issue includes proper interaction between various systems, especially the exchange of data at the municipal, regional, and federal levels. Most countries develop their own concepts when it comes to the question «what is a smart hospital»: they adopt normative legal acts, introduce standards, and implement targeted programs. The Smart Hospital concept is aimed at creating a single IT environment composed of automated «bricks» of specialized and auxiliary processes.

Dr. Jianfeng Gao is a veteran computer scientist, an IEEE Fellow and the current head of the Deep Learning Group at Microsoft Research. He and his team are exploring novel approaches to advancing the state-of-the-art on deep learning in areas like NLP, computer vision, multi-modal intelligence and conversational AI. Today, Dr. Gao gives us an overview of the deep learning landscape and talks about his latest work on Multi-task Deep Neural Networks, Unified Language Modeling and vision-language pre-training. He also unpacks the science behind task-oriented dialog systems as well as social chatbots like Microsoft Xiaoice, and gives us some great book recommendations along the way! Jianfeng Gao: Historically, there are two approaches to achieve the goal. One is to use large data. The idea is that if I can collect all the data in the world, then I believe the representation learned from this data is universal. Because I see all of them. The other approach is that, since the goal of this representation is to serve different applications, how about I train the model using application-specific objective functions across many, many different applications? Host: You're listening to the Microsoft Research Podcast, a show that brings you closer to the cutting-edge of technology research and the scientists behind it. Host: Dr. Jianfeng Gao is a veteran computer scientist, an IEEE Fellow and the current head of the Deep Learning Group at Microsoft Research. He and his team are exploring novel approaches to advancing the state-of-the-art on deep learning in areas like NLP, computer vision, multi-modal intelligence and conversational AI. Today, Dr. Gao gives us an overview of the deep learning landscape and talks about his latest work on Multi-task Deep Neural Networks, Unified Language Modeling and vision-language pre-training. He also unpacks the science behind task-oriented dialog systems as well as social chatbots like Microsoft Xiaoice, and gives us some great book recommendations along the way! What gets you up in the morning? Jianfeng Gao: It's like all the world-class research teams, our goal, ultimate goal, is to advance the state-of-the-art and we want to push the AI frontiers by using deep learning technology or developing new deep learning technologies.

Dr. Jianfeng Gao is a veteran computer scientist, an IEEE Fellow and the current head of the Deep Learning Group at Microsoft Research. He and his team are exploring novel approaches to advancing the state-of-the-art on deep learning in areas like NLP, computer vision, multi-modal intelligence and conversational AI. Today, Dr. Gao gives us an overview of the deep learning landscape and talks about his latest work on Multi-task Deep Neural Networks, Unified Language Modeling and vision-language pre-training. He also unpacks the science behind task-oriented dialog systems as well as social chatbots like Microsoft Xiaoice, and gives us some great book recommendations along the way! Jianfeng Gao: Historically, there are two approaches to achieve the goal. One is to use large data. The idea is that if I can collect all the data in the world, then I believe the representation learned from this data is universal. Because I see all of them. The other approach is that, since the goal of this representation is to serve different applications, how about I train the model using application-specific objective functions across many, many different applications? Host: You're listening to the Microsoft Research Podcast, a show that brings you closer to the cutting-edge of technology research and the scientists behind it. Host: Dr. Jianfeng Gao is a veteran computer scientist, an IEEE Fellow and the current head of the Deep Learning Group at Microsoft Research. He and his team are exploring novel approaches to advancing the state-of-the-art on deep learning in areas like NLP, computer vision, multi-modal intelligence and conversational AI. Today, Dr. Gao gives us an overview of the deep learning landscape and talks about his latest work on Multi-task Deep Neural Networks, Unified Language Modeling and vision-language pre-training. He also unpacks the science behind task-oriented dialog systems as well as social chatbots like Microsoft Xiaoice, and gives us some great book recommendations along the way! What gets you up in the morning? Jianfeng Gao: It's like all the world-class research teams, our goal, ultimate goal, is to advance the state-of-the-art and we want to push the AI frontiers by using deep learning technology or developing new deep learning technologies.

If you have a Google Nest Thermostat, you'll know that the smart heating device will send you alerts when there are immediate problems with your HVAC system (Heating Ventilation and Air Conditioning). Now, Google is trialling a new feature that will tell you about any potential problems before they arise, potentially saving your heating from breaking down entirely. Using artificial intelligence, Nest Thermostats, including the Nest Thermostat E and the Nest Learning Thermostat, will learn "how to identify unusual patterns related to your HVAC system," factoring in historical data and the current weather conditions – patterns that you probably wouldn't be aware of until your furnace or AC stops working. In a blog post, Google explains: "Sometimes, your HVAC system shows warning signs that it's having issues. For example, if it takes longer than normal to cool your home, there might be a problem with your cooling system (AC). If your Nest thermostat detects these unusual or unexpected patterns, we may send you an email alert."

'Collective intelligence' is defined as the capacity of human communities to cooperate intellectually in creation, innovation and invention. As our society becomes more and more knowledge-dependent, this collective ability becomes of fundamental importance. It is therefore vital to understand, among other things, how collective intelligence processes can be expanded by digital networks. It is one of the keys to success for modern societies. Pierre Lévy is one of the world's leading thinkers, not only in the vast area of cyberculture, but also in the fundamental field of knowledge and its processes. He was essentially the first to focus research on collective intelligence when it became a determining factor in the competitiveness, creativity and human development of knowledge-based societies. Michael Peters (MP): May I call you'Pierre'? Can you tell us something about your education, especially over the three institutions of your experience as a graduate?