How can AI and machine learning be harnessed as creative tools to help artists, writers, poets, film and theatre makers create compelling narratives and experiences for the audiences of the future? A one-day event for UK based artists, writers, poets, film and theatre makers, creative technologists, arts organisations, and cultural stakeholders. AI and machine learning are no longer futuristic technologies but are being increasingly integrated into our everyday lives; used to help us access creative content, from image and video content, to music, radio and podcasts. Artists, writers and organisations are already exploring whether AI can help them develop new creative worlds for people to discover but as a cultural sector there is nothing like the widespread interest and uptake there is in immersive technologies. The Space is interested in exploring what some of the potential barriers to adoption are and how we might facilitate creative access to these powerful new technologies.

The Motor City's historic strength in manufacturing is enabling it to become the center for the future of the automotive industry. Just a few years ago, conventional thinking assumed Silicon Valley's tech heavyweights held the upper hand in producing the next generation of vehicles. That was before the extensive problems experienced by electric-vehicle start-up Tesla Inc. in building EVs at its California plant, among other challenges to the tech-will-prevail thesis. "There was this thinking that Silicon Valley was going to crush Detroit, that they knew how to do it better," said Michelle Krebs, an analyst with Cox Automotive. "Well, reality has set in" that Detroit knows is how to make cars.

I had an interesting conversation the other day with Michael Stephan, a principal in Deloitte Consulting's Human Capital practice. He and I spoke during an interview for an upcoming feature story I'm writing. Stephan consults regularly with some of the largest and most well-known companies in the U.S. A number of them are, not surprisingly, focused on how to prepare their workforces for the changes that automation and machine learning (aka artificial intelligence) will be bringing to many of today's jobs. Managers in particular are seeing their roles greatly altered and this is giving rise to what Deloitte is calling the "superjob," says Stephan. As an example, he cites a company that operates warehouse-distribution centers.

A UCLA research team has devised a technique that extends the capabilities of fluorescence microscopy, which allows scientists to precisely label parts of living cells and tissue with dyes that glow under special lighting. The researchers use artificial intelligence to turn two-dimensional images into stacks of virtual three-dimensional slices showing activity inside organisms. In a study published in Nature Methods, the scientists also reported that their framework, called "Deep-Z," was able to fix errors or aberrations in images, such as when a sample is tilted or curved. Further, they demonstrated that the system could take 2-D images from one type of microscope and virtually create 3-D images of the sample as if they were obtained by another, more advanced microscope. "This is a very powerful new method that is enabled by deep learning to perform 3-D imaging of live specimens, with the least exposure to light, which can be toxic to samples," said senior author Aydogan Ozcan, UCLA chancellor's professor of electrical and computer engineering and associate director of the California NanoSystems Institute at UCLA.

AiCure, a nearly decade-old startup using artificial intelligence to measure medication adherence and other behavior during clinical trials or normal care, has brought in $24.5 million in Series C funding. Palisades Growth Capital led the raise, which also featured new backers Singtel Innov8, Asahi Kasei Corporation, Accelmed Growth Partners, and SpringRock Ventures. The round also included all of the company's existing institutional investors: Baird Capital, New Leaf Venture Partners, the Pritzker Group, Biomatics Capital, Tribeca Venture Partners, and Silicon Valley Bank. AiCure's interactive medical assistant, or IMA, collects visual and audio data from patients to quantify their engagement with a treatment program, allowing the tool to identify patients who are at higher risk of dropout or non-adherence. While care providers can use these capabilities to better target their therapies and cut off preventable medical costs, drug developers can also use these trends to hone in on underperforming trial sites identify enrollees who are deliberately not participating.

"Shrewd corporate executives are realigning their organizations to harness the burgeoning power of cyberintelligence. However, Josh Sullivan and Angela Zutavern take us a step beyond by introducing The Mathematical Corporation. Leaders of mathematical corporations combine data analytics with the mathematical intelligence of machines and their own creativity to enhance the quality of current and future decisions. A must read for leaders striving to stay contemporary in a rapidly evolving world."―Larry Bossidy, retired chairman and CEO of Honeywell, co-author of Execution: The Discipline of Getting Things Done and Confronting Reality "In this interesting and accessible book, Sullivan and Zutavern challenge us to reconsider assumptions about machines'taking over,' relegating the human factor to a bygone era. Their hopeful alternative scenario for the future instead clearly shows the importance of leaders and employees who work creatively in symbiosis with machines to achieve greater productivity, better innovation and higher profits."―Amy

The Government Digital Service (GDS) and the Office for Artificial Intelligence (OAI) have published joint guidance on how to build and use artificial intelligence (AI) in the public sector. OAI, GDS, and The Alan Turing Institute (ATI) have partnered to produce guidance on how to use AI ethically and safely. Every day, artificial intelligence (AI) is changing how we experience the world. We already use AI to find the fastest route home, alert us of suspicious activity in our bank accounts and filter out spam emails. Indeed, Artificial Intelligence and Data was named as one of the four'Grand Challenges' in the Industrial Strategy White Paper, which are global trends that will transform our future and contribute to the government's long-term plan to boost productivity in the UK.

We're looking forward to ESOMAR Fusion 2019 where we'll share our journey with Audeering – a German start-up that develops machine learning to detect emotions in voice – in analyzing'how' people communicate their needs, attitudes and interest. We all know the importance of identifying both rational and emotional consumer needs and drivers of decision-making and this is particularly the case in new product development. However, whilst we have techniques to uncover emotions qualitatively, what about when we need to size the unmet need or opportunity for a new product innovation? Together with Audeering, we had a goal to access their underlying emotions and explore an opportunity or evaluate a new product with greater validity by understanding their emotions in voice.

It's not enough for a healthcare artificial intelligence (AI) algorithm to be highly accurate. To be widely adopted in clinical use, it must demonstrate improvement in quality of care and patient outcomes, according to an opinion article published online October 29 in BMC Medicine. A team from Google Health in London, U.K., led by Dr. Christopher Kelly, PhD, said that further work is needed to develop tools to address bias and unfairness in algorithms, reduce the brittleness of AI and improve the generalizability of models, and develop methods for improving the interpretability of machine-learning predictions. "If these goals can be achieved, the benefits for patients are likely to be transformational," the group wrote. AI faces a number of challenges standing in the way of translation into clinical practice, including those intrinsic to the science of machine learning, logistical difficulties in implementation, and barriers to adoption, as well as sociocultural or pathway changes associated with using the technology, according to the team.

Health care leaders are embracing AI. But by conducting an extensive review of case studies and research literature, we've found that their AI initiatives are predominantly focused on developing algorithms that can predict a problem such as cancer in order to make diagnoses better, faster, and less expensively. Rarely, are their organizations devoting resources to AI efforts aimed at understanding why diseases occur. To intervene as effectively as possible, both kinds of algorithms are crucial. To be clear, we are not downplaying the importance of predictive analytics to help diagnose patients.