PMMI is a Business Reporter client. While it has not yet arrived, the time is approaching in packaging and processing manufacturing when it will be nearly impossible to discuss advancements in automation without addressing congruent developments in artificial intelligence (AI). AI and automation are particularly intertwined in robotics, supplying workforce-enhancing solutions that not only complete repetitive tasks but also support and improve decision-making processes. Already, industrial robots with a high risk of breakdown can self-minimise downtime by pre-scheduling maintenance and ordering spare parts or by analysing vision system and sensor data to reduce the time taken to complete a task. Network-connected robots use AI to either learn simultaneously or from one another, reducing the time it takes to understand new jobs.

AI and deep learning have been shining in dealing with unstructured data, from natural language understanding and automatic knowledge base construction to classifying and generating images and videos. Structured data, however, which is trapped in business applications such as product repositories, transaction logs, ERP and CRM systems are being left behind! Tabular data is still being processed by an older generation of data science techniques, like rule-based systems or decision trees. These methods use handcrafted features, are tedious to maintain, and require lots of manually labelled data. While the recent advancement of AI advances allowed mining huge value out of unstructured data, it would be remiss to not pay the same attention to the value of structured data in driving business, revenues, health, security and even governance.

Described as "the science and engineering of making intelligent machines," AI (artificial intelligence) has expanded rapidly and has made a lasting impact in the bioprocessing sector. From diagnosis to personalised treatments, AI is making its mark across modern medicine, with both SMEs and Big Pharma making use of it to increase efficiency in the manufacturing of medicines and to create the best outcomes for patients. Speaking at the BioIndustry Association's 16th Annual bioProcessUK conference last year, Dr Andrew Phillips of Microsoft Research highlighted that genetically programmed organisms are attracting significant investment (more than US$3.8 billion in private investment last year) and are becoming the fastest growing area in pharma. At the start of 2020, we heard that AI is more accurate than doctors in diagnosing breast cancer … so it's clear that AI will continue to be at the forefront of the sector throughout 2020 and beyond. Microsoft has become a big player in our sector, partnering with researchers at Princeton University in the US and two UK companies -- Oxford BioMedica and Synthace -- to develop Station B. The Station B platform, which is being developed in Cambridge, UK, is made up of integrated computer programs that can analyse biomedical data.

Program: Monday Session I Maximilian Kasy, "Adaptive treatment assignment in experiments for policy choice" Bezirgen Veliyev, "Functional Sequential Treatment Allocation" Keynote Uri Shalit about "Machine learning and causal inference: a two-way road": "This talk will have two parts. In the first we will discuss a framework we developed for learning individualized treatment recommendations from observational health data, merging ideas from machine learning and causal inference. We will see examples of our framework applied to two crucial health problems using data from tens of thousands of patients, and discuss some important causal-inference challenges that come to focus in this setting. In the second part we will show how we use ideas from the causal inference literature to address long standing problems in machine learning: off-policy evaluation in a partially observable Markov decision process (POMDP), and learning predictive models that are stable against distributional shifts." Heterogeneous effects of training programmes for unemployed in Belgium" Daniel Jacob, "Does Tenure make you love your Job?" Nicolaj Mühlbach, "Heterogeneous Treatment Effects of an Early Retirement Reform" Tuesday Session III Dmitry Arkhangelsky, "Double-Robust Identification for Causal Panel Data Models" Martin Spindler, "Uniform Inference in High-Dimensional Gaussian Graphical Models" Keynote Stefan Wager about "Designing Loss Functions for Causal Machine Learning": "Given advances in machine learning over the past decades, it is now possible to accurately solve difficult non-parametric prediction problems in a way that is routine and reproducible.

The UK government is launching an investigation to determine the levels of bias in algorithms that could affect people's lives. A browse through our'ethics' category here on AI News will highlight the serious problem of bias in today's algorithms. With AIs being increasingly used for decision-making, parts of society could be left behind. Conducted by the Centre for Data Ethics and Innovation (CDEI), the investigation will focus on areas where AI has tremendous potential – such as policing, recruitment, and financial services – but would have a serious negative impact on lives if not implemented correctly. "Technology is a force for good which has improved people's lives but we must make sure it is developed in a safe and secure way. Our Centre for Data Ethics and Innovation has been set up to help us achieve this aim and keep Britain at the forefront of technological development. I'm pleased its team of experts is undertaking an investigation into the potential for bias in algorithmic decision-making in areas including crime, justice and financial services. I look forward to seeing the Centre's recommendations to Government on any action we need to take to help make sure we maximise the benefits of these powerful technologies for society."

Artificial Intelligence Marketing (AIM) provides superior solutions to bridge the gap between analytics and execution. It is the process of going through massive piles of data to originate positive results. As per the courtesy of Forbes, retailers invested around 5.9 billion US dollars on AIM. North America, Europe, and Asia-Pacific are mainly using this type of digital marketing and advertising. Likewise, remote health monitoring, wearable AR, IoT kitchen appliances, and brain-sensing gadgets lie under the game-changing innovations.

Robotic technology has played an important part in the medical field in the last two decades. The best example in this regard is the Da Vinci system, which is considered the best-selling surgery robot on the market today. This robot can perform high-precision surgical procedures -- down to one millimeter. However, the system comes with a hefty price tag of $2 million, plus the expensive maintenance costs. For those of you who don't know supermicrosurgery refers to a precise reconstructive procedure that connects ultra-thin blood and lymph vessels ranging from 0.3 to 0.8 millimeters.

Luis Perez-Breva is a Massachusetts Institute of Technology (MIT) professor and the faculty director of innovation teams at the MIT School or Engineering. He is also an entrepreneur and part of The Martin Trust Center for MIT Entrepreneurship. Luis works to see how we can use technology to make our lives better and also on how we can work to get new technology out into the world. On an episode of the AI Today podcast, Professor Perez-Breva managed to get us to think deeply into our understanding of both artificial intelligence and machine learning. Are we too focused on data?

John Brownstein is co-founder of HealthMap, a system using artificial intelligence to monitor global disease outbreaks. John Brownstein is co-founder of HealthMap, a system using artificial intelligence to monitor global disease outbreaks. John Brownstein is co-founder of HealthMap, a system using artificial intelligence to monitor global disease outbreaks. John Brownstein is co-founder of HealthMap, a system using artificial intelligence to monitor global disease outbreaks. BOSTON -- Did an artificial intelligence system beat human doctors in warning the world of a severe coronavirus outbreak in China?

Read the paper to learn more about Kaokore dataset, our motivations in making them, as well as creative usage of it! KaoKore is a novel dataset of face images from Japanese illustrations along with multiple labels for each face, derived from the Collection of Facial Expressions. KaoKore dataset is build based on the Collection of Facial Expressions, which results from an effort by the ROIS-DS Center for Open Data in the Humanities (CODH) that has been publicly available since 2018. It provides a dataset of cropped face images extracted from Japanese artworks publicly available from National Institute of Japanese Literature, Kyoto University Rare Materials Digital Archive and Keio University Media Center from the Late Muromachi Period (16th century) to the Early Edo Period (17th century) to facilitate research into art history, especially the study of artistic style. It also provides corresponding metadata annotated by researchers with domain expertise.