Recently at the 6th annual Deep Learning Summit in London, attendees congregated to hear from industry leaders, academics, researchers, and innovative startups, presenting both the latest cross-industry technological advancements and industry application methods. Running in parallel was the AI Assistant Summit and AI in Retail and Advertising Summit, resulting in some of the world's leading experts from Universities, brands, and emerging startups coming together. We began the day with Huma Lodhi, Data Scientist at BP, discussing some of the tricks and tips she has picked up during her work in Deep Learning, with intelligent methodologies using structured and unstructured data as the focal point. "We need to find better methods to use this data for our real-world applications. Examples of this can be Noisy Data, Missing data, or unstructured data. This gives us the principle problem for data, Quantity vs Quality."

The Carl Zeiss Foundation is funding the establishment of a new artificial intelligence (AI) research centre in Mainz, Germany, that will focus on determining why modern machine learning methods are so effective. The new Emergent AI Center is being established at the Institute of Computer Science at Johannes Gutenberg University Mainz (JGU), and will receive €3m over the next three years from the optoelectronics company. A core focus on the AI research centre will be unlocking greater understanding about machine learning, one of the most widely used types of AI that in many fields now matches human-level performance. "Modern machine learning methods allow us to solve many problems using computers which just a few years ago, without doubt, only humans were able to handle successfully," said Professor Michael Wand of the Institute of Computer Science at JGU. "Of course we know exactly how the systems work, but why they work so well remains a mystery." Machine learning advances have occurred due to the development of what are known as deep artificial neural networks.

When data scientists in Chicago, Illinois, set out to test whether a machine-learning algorithm could predict how long people would stay in hospital, they thought that they were doing everyone a favour. Keeping people in hospital is expensive, and if managers knew which patients were most likely to be eligible for discharge, they could move them to the top of doctors' priority lists to avoid unnecessary delays. It would be a win–win situation: the hospital would save money and people could leave as soon as possible. Starting their work at the end of 2017, the scientists trained their algorithm on patient data from the University of Chicago academic hospital system. Taking data from the previous three years, they crunched the numbers to see what combination of factors best predicted length of stay.

It is possible to monitor Sun's extreme ultraviolet (EUV) irradiance using deep learning. Scientists at the NASA Frontier Development Lab (FDL) have shown that deep learning can help get more value out of our current ability to monitor the Sun by providing virtual instruments to supplement physical devices. The Sun is vital for survival, yet solar flares, which typically occurs a few times a year, can cause severe interruptions in space and on Earth. These interruptions can affect rocket, satellites, and even frameworks here on Earth, including GPS route, radio interchanges, and the power grid. FDL team member and co-author Alexander Szenicer (Oxford University) said, "Our research shows how a deep neural network can be trained to mimic an instrument on the Solar Dynamics Observatory (SDO). By inferring what ultraviolet radiation levels that sensor would have detected based on what the other instruments on SDO are observing at any given time, we demonstrate it is possible to increase the scientific productivity of NASA missions and to increase our capability to monitor solar sources of space weather."

Less than two weeks ahead of the Pixel 4 flagship smartphone's debut, Google is temporarily pulling the plug on a controversial "field research" program that offered subjects in US cities a $5 gift certificate in exchange for a scan of their face -- after a New York Daily News report that one Google contracting agency was actively targeting homeless people in Atlanta and tricking unwitting college students into participating by pretending they would merely be testing a new app. Originally, the company told us, the idea was to make sure the Pixel 4's new Face Unlock feature would recognize a diverse array of faces, which could keep it from being biased against people of color -- a legitimate concern for facial recognition tech. Google now tells The New York Times and The Verge that it has immediately suspended the program, and opened an investigation, after reading the Daily News' story. It wouldn't confirm individual allegations, but did say it's true it hired contractors from Randstad for the research, the same contractor named in the Daily News' expose, and Google has reportedly called the alleged details "very disturbing." Google tells The Verge that it made sure to provide directions to its researchers to be transparent with people they approached for a facial scan, so it sounds like Google will be able to dodge some of the blame if the allegations about its contractor are true.

AI has been a popular topic for movies and fascinated audiences for over 60 years. Hollywood hasn't always gotten it right -- in fact, most of the time, they've been totally off. What is it about artificial intelligence that entices directors? What are they missing when they create movies about AI? Let's take a look at a few popular movies about AI, why the topic is popular, why some got it wrong, and a few movies that actually got it right. To most people, artificial intelligence creates a question of what is and what isn't human.

Now researchers at the University of Sydney have used advanced artificial intelligence and machine learning to develop a generalised method to predict when seizures will strike that will not require surgical implants. Dr Omid Kavehei from the Faculty of Engineering and IT and the University of Sydney Nano Institute said: "We are on track to develop an affordable, portable and non-surgical device that will give reliable prediction of seizures for people living with treatment-resistant epilepsy." In a paper published this month in Neural Networks, Dr Kavehei and his team have proposed a generalised, patient-specific, seizure-prediction method that can alert epilepsy sufferers within 30 minutes of the likelihood of a seizure. Dr Kavehei said there had been remarkable advances in artificial intelligence as well as micro- and nano-electronics that have allowed the development of such systems. Now it is completely accessible.

The National Microbiome Data Collaborative (NMDC), a new initiative aimed at empowering microbiome research, is gearing up its pilot phase after receiving $10 million from the U.S. Department of Energy (DOE) Office of Science. Spearheaded by Lawrence Berkeley National Laboratory (Berkeley Lab), in partnership with Los Alamos (LANL), Oak Ridge (ORNL), and Pacific Northwest (PNNL) national laboratories, the NMDC will leverage DOE's existing data-science resources and high-performance computing systems to develop a framework that facilitates more efficient use of microbiome data for applications in energy, environment, health, and agriculture. Nearly every ecosystem and organism on Earth hosts a diverse community of microorganisms – its microbiome. Yet we know little about the functions of individual microbes, let alone how they interact with each other, their hosts, or their environments, and how their activity varies over time or in response to perturbations. The past decade has seen tremendous advances in genome and metagenome DNA-sequencing technologies, which has led to an unprecedented volume of microbiome data being generated.

R&C: To what extent are compliance departments turning to new technologies to help manage financial crime risks? Compared to legacy compliance systems, what kind of opportunities does the latest financial technology offer financial institutions (FIs) in identifying and responding to suspicious activities? LaScala: We have seen an increase in compliance departments leveraging new technologies to manage financial crime risks. Tools, such as machine learning (ML), artificial intelligence (AI) and robotic process automation can increase effectiveness and efficiency of anti-money laundering (AML) programmes. Specifically, they can help automate repetitive tasks and produce more valuable alerts so that compliance departments can better identify risk and spend more time investigating potentially suspicious activity. R&C: How are regulatory authorities responding to FIs' use of technology tools?

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