"We will see the huge impact this university will have in enabling highly-skilled Emiratis and expats – and anyone from around the world who has the capability and the skillset – to do their masters and PhD [at MBZUAI]… This will create momentum and industry in the UAE based on the latest technologies, to bring us where we want to be, as part of the knowledge economy," Al Hammadi said.

The Stanford University Institute for Human-Centered Artificial Intelligence is calling for the U.S. government to make a $120 billion investment in the nation's AI ecosystem over the course of the next 10 years. The report calls efforts by the Trump administration, like the call for near $1 billion in U.S. non-defense research and development spending in 2020, "encouraging, but not nearly enough." The national AI vision report specifically calls for $2 billion in annual spending to support entrepreneurs and expand innovation, $3 billion on education, and $7 billion on interdisciplinary research to discover breakthrough advances in the field. The report was written by center directors John Etchemendy and Dr. Fei-Fei Li, and it calls underfunding of AI a threat to U.S. global leadership and a "national emergency in the making." Li is a leader at the Stanford Computer Vision Lab, creator of ImageNet, and until last year, served as chief AI scientist for Google Cloud.

EIT Digital is putting its weight behind the concept of autonomous robot colleagues for hard-pressed professionals in elderly care provision by supporting the development of SARA (Social & Autonomous Robotic health Assistant) as part of its focus on Digital Wellbeing. SARA is a consortium-led initiative that aims to improve the quality of care in nursing homes and hospitals by introducing robots as social entities – taking on time-consuming tasks and interacting with patients without requiring a human operator. The consortium includes analytics and data science specialist Bright Cape, Forum Virium Helsinki, GIM Robotics, Curamatik and TU Berlin. The idea is to address the twin challenges of caring for a rapidly ageing population and an acute shortage of healthcare professionals, helping to balance a workload that is under ever-increasing pressure: it is estimated that 13.8% of nurses deal every week with the consequences of heavy work pressure – medication errors, for example – while patients feel the impact on quality of care. While there is nothing new about the idea of robot colleagues in healthcare, most of the current generation of robots perform activities that need to be set up and led by a human operator.

Fralick chairs McAfee's Analytic Center of Excellence and is responsible for the cybersecurity company's technical analytic strategy that integrates into McAfee consumer and enterprise products. Prior to Intel's divestiture of McAfee, she was Chief Data Scientist in Intel's Internet of Things Group where she developed machine learning and deep learning analytics for over eight different markets. And while broadly applying the term AI makes it easier to market and talk about, for Fralick who has 40 years expertise in the field, each type of model under the AI umbrella has different levels of complexity and intelligence behind them. "As a data scientist and an engineer, I look at AI to be very specific mathematically. I look at deep learning to be very different mathematically from machine learning."

Shanghai-based artificial intelligence (AI) startup Emotibot has closed its $45 million Series B, aiming to improve the emotion-sensing capabilities of robots in human-machine interaction. Why it matters: The company lists social media and gaming giant Tencent, video streaming firm iQiyi, and robot maker UBTech as its partners. Details: Emotibot's latest round was led by V Fund Management, Linfeng Capital, and an undisclosed strategic investor. Context: Emotibot focuses on building chatbots that are able to identify emotional responses in humans, which the company believes is the next step in the evolution of artificial intelligence.

Check out the "Text, Language, and Speech" sessions at the O'Reilly Artificial Intelligence conference in London, 14-17 October 2019. As companies begin to explore AI technologies, three areas in particular are garnering a lot of attention: computer vision, natural language applications, and speech technologies. A recent report from the World Intellectual Patent Office (WIPO) found that together these three areas accounted for a majority of patents related to AI: computer vision (49% of all patents), natural language processing (NLP) (14%), and speech (13%). Companies are awash with unstructured and semi-structured text, and many organizations already have some experience with NLP and text analytics. While fewer companies have infrastructure for collecting and storing images or video, computer vision is an area that many companies are beginning to explore.

For years, the business world has been enraptured by the concept of big data. But the era of big data will not last forever. In fact, the replacement knocking on the door is one that might sound counter-intuitive: small data. Conventional wisdom suggests that data aggregation will only increase in size and scale. With an ever-expanding consumer base with evolving tastes, and an explosion of connected devices and digital channels to create and extract data, how could it not? But as we reach the point where most forward-looking businesses have "digitally transformed" and successfully used the vast amount of data to their advantage, the foundation is shaking.

There is no shortage of statistics on the increasing uptake of artificial intelligence (AI) in business. With adoption poised to grow over the next few years, this will undoubtedly lead to the discovery of new use cases and applications of the technology that were never before on the radar of business leaders. While this anticipated growth is exciting from a technological standpoint, it is also fraught with ethical landmines. As the associate director of the Charlotte Visualization Center at the University of North Carolina at Charlotte, I oversee internal and external research on the use of big data in industries such as science, business and medicine, so I'm familiar with the frequent intersection of ethics and AI. In today's digital age, organizations have access to unprecedented volumes of consumer data -- including personally identifiable information (PII), which can range from your name, credit card information and medical records to IP addresses and biometric data such as fingerprints.

A few years ago, Paul Allen, the co-founder of Microsoft, published the results of something called the Great Elephant Census, which counted all the savanna elephants in Africa. What it found rocked the conservation world: In the seven years between 2007 and 2014, Africa's savanna elephant population decreased by about a third and was on track to disappear completely from some African countries in as few as 10 years. To reverse that trend, researchers landed on a technology that is rewriting the rules for everything from our household appliances to our cars: artificial intelligence. AI's ability to find patterns in enormous volumes of information is demystifying not just elephant behavior but human behavior -- specifically poacher behavior -- too. "AI can process huge amounts of information to tell us where the elephants are, how many there are," said Cornell University researcher Peter Wrege. "And ideally tell us what they are doing."

As noted in the paper Derisking ML and AI by McKinsey [4], ML algorithms are typically far more complex than their statistical counterparts and often require design decisions to be made before the training process begins. The benefits of ML include superior performance and accuracy but their complexity leads to an added layer of challenge of interpretation, bias and compliance for ML. This is not just a technical problem though. The paper rightly points out that the degree of interpretability required is a policy choice. Feature Engineering -- ML is more complex because of the inherent difficulty of feature engineering -- that is, which features to use? How sound is each feature? Is it consistent with policy?