Donald Trump has won the United States presidency and Brexit has promised to take the UK out of the European Union. Both campaigns employ Cambridge Analytica, which harvested the data of millions of Facebook users to personalise electoral messaging to them and sway their voting intentions. Millions of people begin to ask themselves whether, in the digital era, they have lost something deeply valuable: their privacy. Two years later, countless European email inboxes would be filling up with messages from companies, asking people for permission to continue processing their data – the aim was compliance with the new General Data Protection Regulation (GDPR). Despite its imperfections, this law has served as a point of reference for laws in Brazil and Japan, and inaugurated the modern era of data protection.

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.

Donald Trump has won the United States presidency and Brexit has promised to take the United Kingdom out of the European Union. Both campaigns employ Cambridge Analytica, who harvest the data of millions of Facebook users to personalise electoral messaging and sway their voting intentions. Millions of people begin to ask themselves whether, in the digital era, they have lost something that they valued dearly: their privacy. Two years later, millions of email inboxes in Europe would be filling up with messages from companies, asking them for permission to continue processing their data, complying with the new General Data Protection Regulation (GDPR). Despite its imperfections, this law has served as a point of reference for laws in Brazil and Japan and began the era of data protection in earnest. Nevertheless, what was once seen as a triumph for privacy is now seen as a roadblock in Europe's quest to develop digital technologies, especially artificial intelligence.

South Eastern Regional College (@S_ERC) is hosting an online international conference to highlight the need for businesses to identify employee digital skills gaps and provide relevant digital skills training for employees in preparation for the move to Artificial Intelligence (AI) technology within the work environment. The Reskilling Revolution in the Artificial Intelligence Era: The Case for Digital Upskilling is online 10am – 1.00pm GMT, Thursday 17 June. However, more and more businesses are also reaping the benefits of AI whether that's a small business replacing paper-based processes with digital, using drones, virtual reality or augmented reality or a total transformational change. The impact for employees could be devastating, with concerns over job role and security, but by providing relevant digital upskilling training, employers can retain valuable employees minimising uncertainty and job losses." Speakers will include leading experts from the field of Artificial Intelligence who will share their knowledge and expertise.

Nadya Bliss is the Executive Director of the Global Security Initiative at Arizona State University. In that capacity, she leads a pan-university organization advancing research, education, and other programming in support of national and global security. She has proven expertise in growing mission-focused research organizations; deep knowledge of the technology transition pipeline; and significant experience identifying advanced research capabilities to address mission needs with almost two decades specifically in defense and security sectors. Dr. Bliss is actively involved in national service, including currently as an Executive Committee member of the Computing Community Consortium and the Vice Chair of the DARPA ISAT (Information Science and Technology) study group.

Artificial intelligence (AI) tools are increasingly being applied in drug discovery. While some protagonists point to vast opportunities potentially offered by such tools, others remain sceptical, waiting for a clear impact to be shown in drug discovery projects. The reality is probably somewhere in-between these extremes, yet it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines. This article presents the views of a diverse group of international experts on the'grand challenges' in small-molecule drug discovery with AI and the approaches to address them.

In an age when artificial intelligence can churn out poetry, the U.S. Patent and Trademark Office is expanding its query into how intellectual property protections need to adapt to cover creations by non-humans.

Montreal-based My Intelligent Machines (MIMS), which is developing a platform to help life sciences companies maximize their R&D and production activities, has closed a $2.6 million seed round. "MIMS will contribute to life sciences companies that are ready to welcome the artificial intelligence era." The round saw participation from Anges Québec, Anges Québec Capital, Consortium MedTeq, Desjardins Capital, Real Ventures, and StandUp Ventures. With this funding, MIMS intends to develop solutions that allow life scientists to stratify human, animal, and plant populations through artificial intelligence and big data, without needing expertise in data science or bioinformatics. "MIMS sets itself apart from other AI startups, because of our ability to leverage what we call'fat data' using AI, which is very difficult to do," said Sarah Jenna, co-founder and CEO of MIMS.

How humans collaborate with one another has been closely linked to technological progress. Before the invention of the telephone, we had to be in the same room to communicate in real-time – and prior to the internet, we had to be in the same building to share our designs, new ideas, or other documents. Collaboration has always been a moving target, and in modern times we shouldn't be surprised that new technological innovations are again shifting how humans work and coordinate together. Today's infographic comes to us from Schneider Electric, and it shows that humans are no longer making things using a linear approach. Instead, modern collaboration is a cyclical process with no defined start or end points – and it often involves more users and stakeholders, continuous access, and an increasingly decentralized workforce.

Welcome to the artificial intelligence era. Unless you have been hiding under a rock for the last decade, you will have come across some form of artificial intelligence tools or solutions in your life. If you are anything like me, you have been excited to welcome the innovations brought on by artificial intelligence. But when it comes to understanding the landscape of the artificial intelligence ecosystem, it can truly be confusing. This ecosystem includes terms such as general artificial intelligence, artificial narrow intelligence, machine learning, deep learning, and so many others.