But samples also were sent to a lab where computers using artificial intelligence are changing the way pharmaceutical companies develop drugs. Biological insights driven by machine learning also could help pharmaceutical companies better identify and recruit patients for clinical trials of therapies most likely to work for them, perhaps boosting the chances of those medications' getting approved by regulatory agencies such as the Food and Drug Administration. AI systems trained on various data sources, including preclinical data sets, have helped make "significant performance improvements" by enabling "better selections of which compounds to…make and test" in the lab and by "flagging" whether compounds might have "toxic" effects or "unexpected favorable" ones, he says. German pharmaceutical company Merck KGaA has developed two drugs using computer-vision software, which analyzes images of cells and tissues, and other AI systems capable of drawing insights from public databases of genetic and chemical information, says Joern-Peter Halle, Merck KGaA's head of external innovation.

Artificial intelligence is currently revolutionizing the business world -- and it's just as quickly transforming virtually every element of digital marketing. For marketing leaders, the growth of artificial intelligence (AI) means less guesswork and more efficiency. Marketers should expect to use AI in guiding and implementing every element of their marketing strategy -- from the initial research stages to carrying out automated media buys and providing targeted customer service. In the past, marketers have used research, combined with trial and error to determine the best way to communicate with target markets. With the introduction of artificial intelligence, that guesswork is starting to drastically decrease.

The introduction of new voice-driven digital assistants has captured the imagination of consumers and businesses alike. As more developers build capabilities for these devices, consumers will increase usage, providing the springboard for integration with digital banking solutions such as voice-driven payments. It is becoming clear that the next battle in the tech world will be around voice-driven digital assistants, such as the Apple Siri, Amazon Alexa, Microsoft Cortana, Google Assistant/Now and Samsung Bixby/Viv. While the attention has moved from smartphone assistants to home hubs, the real excitement will begin as the underlying AI (artificial intelligence) and machine learning begins to deliver detailed, contextual, and highly personalized responses that will make a consumer's life easier. Digital assistants will be at the heart of a user's daily activities, whether in an increasingly smart home (using home hub devices like the Amazon Echo and Dot, Google Home, Apple HomePod), a connected car, at work or walking down the street.

DALIAN, China--(BUSINESS WIRE)--New research from Accenture (NYSE:ACN) reveals that artificial intelligence (AI) could accelerate China's economic growth rate from 6.3 percent to 7.9 percent by 2035, by transforming the nature of work and opening new sources of value and growth. The report, titled "How Artificial Intelligence Can Drive China's Growth," explores new insights into AI and its impact on China's economy. Based on analysis and modeling by Accenture Research, in collaboration with Frontier Economics, there is dramatic impact on China's growth when AI is added as a completely new factor of production to the economic growth model. "China has already made great leaps in the development of AI and our research shows that it has the potential to be a powerful remedy for slowing growth," said Chuan Neo Chong, Accenture Greater China Chairwoman. "However, as with any catalyst, it is important to remember the challenges and the risk of unintended consequences. Stakeholders must prepare themselves intellectually, technologically, politically, ethically and socially for the promise of AI."

New research from Accenture (NYSE:ACN) reveals that artificial intelligence (AI) could accelerate China's economic growth rate from 6.3 percent to 7.9 percent by 2035, by transforming the nature of work and opening new sources of value and growth. This Smart News Release features multimedia. AI is poised to boost China's GVA by USD $7,111 billion by 2035 (Graphic: Business Wire) The report, titled "How Artificial Intelligence Can Drive China's Growth," explores new insights into AI and its impact on China's economy. Based on analysis and modeling by Accenture Research, in collaboration with Frontier Economics, there is dramatic impact on China's growth when AI is added as a completely new factor of production to the economic growth model. "China has already made great leaps in the development of AI and our research shows that it has the potential to be a powerful remedy for slowing growth," said Chuan Neo Chong, Accenture Greater China Chairwoman.

AI promises to transform the world. Companies like this one will pave the way. He's been called one of the "foremost thinkers on the topic of artificial intelligence," so it's no surprise that Andrew Ng -- the cofounder of Coursera, the lead developer of Stanford University's main Massive Open Online Course (MOOC) platform, and the founder of the Google Brain project -- is starting another AI company of his own now that he's left Baidu. The resume of this impressive entrepreneur reads like a laundry list of some of the most impressive achievements in AI technology, and it seems safe to assume that Ng's newest venture, known only as deeplearning.ai, Hope will help many of you: deeplearning.ai

The search to improve and eventually perfect artificial intelligence is driving the research labs of some of the most advanced and best-known American corporations. They are investing billions of dollars and many of their best scientific minds in pursuit of that goal. All that money and manpower has begun to pay off.In the past few years, artificial intelligence -- or A.I. -- has taken a big leap -- making important strides in areas like medicine and military technology. What was once in the realm of science fiction has become day-to-day reality. You'll find A.I. routinely in your smart phone, in your car, in your household appliances and it is on the verge of changing everything. On 60 Minutes Overtime, Charlie Rose explores the labs at Carnegie Mellon on the cutting edge of A.I. See robots learning to go where humans can'... It was, for decades, primitive technology. But it now has abilities we never expected. It can learn through experience -- much the way humans do -- and it won't be long before machines, like their human creators, begin thinking for themselves, creatively. Independently with judgment -- sometimes better judgment than humans have. As we first reported last fall, the technology is so promising that IBM has staked its 106-year-old reputation on its version of artificial intelligence called Watson -- one of the most sophisticated computing systems ever built.

The search to improve and eventually perfect artificial intelligence is driving the research labs of some of the most advanced and best-known American corporations. They are investing billions of dollars and many of their best scientific minds in pursuit of that goal. All that money and manpower has begun to pay off.In the past few years, artificial intelligence -- or A.I. -- has taken a big leap -- making important strides in areas like medicine and military technology. What was once in the realm of science fiction has become day-to-day reality. You'll find A.I. routinely in your smart phone, in your car, in your household appliances and it is on the verge of changing everything. It was, for decades, primitive technology. But it now has abilities we never expected. It can learn through experience -- much the way humans do -- and it won't be long before machines, like their human creators, begin thinking for themselves, creatively. Independently with judgment -- sometimes better judgment than humans have. As we first reported last fall, the technology is so promising that IBM has staked its 106-year-old reputation on its version of artificial intelligence called Watson -- one of the most sophisticated computing systems ever built. John Kelly, is the head of research at IBM and the godfather of Watson.

This paper considers the use of Machine Learning (ML) in medicine by focusing on the main problem that this computational approach has been aimed at solving or at least minimizing: uncertainty. To this aim, we point out how uncertainty is so ingrained in medicine that it biases also the representation of clinical phenomena, that is the very input of ML models, thus undermining the clinical significance of their output. Recognizing this can motivate both medical doctors, in taking more responsibility in the development and use of these decision aids, and the researchers, in pursuing different ways to assess the value of these systems. In so doing, both designers and users could take this intrinsic characteristic of medicine more seriously and consider alternative approaches that do not "sweep uncertainty under the rug" within an objectivist fiction, which everyone can come up by believing as true.

In this paper, we present a novel massively parallel algorithm for accelerating the decision tree building procedure on GPUs (Graphics Processing Units), which is a crucial step in Gradient Boosted Decision Tree (GBDT) and random forests training. Previous GPU based tree building algorithms are based on parallel multi-scan or radix sort to find the exact tree split, and thus suffer from scalability and performance issues. We show that using a histogram based algorithm to approximately find the best split is more efficient and scalable on GPU. By identifying the difference between classical GPU-based image histogram construction and the feature histogram construction in decision tree training, we develop a fast feature histogram building kernel on GPU with carefully designed computational and memory access sequence to reduce atomic update conflict and maximize GPU utilization. Our algorithm can be used as a drop-in replacement for histogram construction in popular tree boosting systems to improve their scalability. As an example, to train GBDT on epsilon dataset, our method using a main-stream GPU is 7-8 times faster than histogram based algorithm on CPU in LightGBM and 25 times faster than the exact-split finding algorithm in XGBoost on a dual-socket 28-core Xeon server, while achieving similar prediction accuracy.