Organic semiconductors are promising materials for cheap, scalable and sustainable electronics, light-emitting diodes and photovoltaics. For organic photovoltaic cells, it is a challenge to find compounds with suitable properties in the vast chemical compound space. For example, the ionization energy should fit to the optical spectrum of sun light, and the energy levels must allow efficient charge transport. Here, a machine-learning model is developed for rapidly and accurately estimating the HOMO and LUMO energies of a given molecular structure. It is build upon the SchNet model (Sch\"utt et al. (2018)) and augmented with a `Set2Set' readout module (Vinyals et al. (2016)). The Set2Set module has more expressive power than sum and average aggregation and is more suitable for the complex quantities under consideration. Most previous models have been trained and evaluated on rather small molecules. Therefore, the second contribution is extending the scope of machine-learning methods by adding also larger molecules from other sources and establishing a consistent train/validation/test split. As a third contribution, we make a multitask ansatz to resolve the problem of different sources coming at different levels of theory. All three contributions in conjunction bring the accuracy of the model close to chemical accuracy.

Alchemy is a new meta-learning environment rich enough to contain interesting abstractions, yet simple enough to make fine-grained analysis tractable. Further, Alchemy provides an optional symbolic interface that enables meta-RL research without a large compute budget. In this work, we take the first steps toward using Symbolic Alchemy to identify design choices that enable deep-RL agents to learn various types of abstraction. Then, using a variety of behavioral and introspective analyses we investigate how our trained agents use and represent abstract task variables, and find intriguing connections to the neuroscience of abstraction. We conclude by discussing the next steps for using meta-RL and Alchemy to better understand the representation of abstract variables in the brain.

Is artificial intelligence the new alchemy? That is, are the powerful algorithms that control so much of our lives -- from internet searches to social media feeds -- the modern equivalent of turning lead into gold? Moreover: Would that be such a bad thing? According to the prominent AI researcher Ali Rahimi and others, today's fashionable neural networks and deep learning techniques are based on a collection of tricks, topped with a good dash of optimism, rather than systematic analysis. Modern engineers, the thinking goes, assemble their codes with the same wishful thinking and misunderstanding that the ancient alchemists had when mixing their magic potions.

See also article by Sveinsson et al in this issue. Paul H. Yi, MD, was a musculoskeletal radiology fellow at Johns Hopkins Hospital and is affiliate faculty at the Malone Center for Engineering in Healthcare. His research focuses on application and limitations of deep learning in radiology, including the potential for algorithmic bias. He serves on the RSNA Machine Learning Steering Subcommittee and the trainee editorial board of Radiology: Artificial Intelligence and is the journal's podcast co-host. In July 2021, Dr Yi joined the radiology faculty at the University of Maryland and serves as director of the University of Maryland Intelligent Medical Imaging Center.

In all, a few lines of R or Python code will suffice for a piece of machine intelligence and there's a plethora of resources and tutorials online to train your quasi-neural networks, like all sorts of deepfake networks, manipulating image-video-audio-text, with zero knowledge of the world, as Generative Adversarial Networks, BigGAN, CycleGAN, StyleGAN, GauGAN, Artbreeder, DeOldify, etc. They create and modify faces, landscapes, universal images, etc., with zero understanding what it is all about.

January is the customary time to make predictions on what the year holds in store. Working in partnership with companies across multiple industries that are looking to develop data science and AI skills in their workforce, I have a good vantage point on the trends that are developing across the realm of technology. In addition, I have published recent research with colleagues at Cambridge University about the challenges that face organizations with deploying machine learning. From this perspective, there is a clear picture forming that 2021 will be a turning point within leading businesses for making a priority of operationalizing AI. In fact, the second half of 2020 has seen a new crop of tools, platforms and startups receiving investment to provide solutions to this difficult problem.

There has been rapidly growing interest in meta-learning as a method for increasing the flexibility and sample efficiency of reinforcement learning. One problem in this area of research, however, has been a scarcity of adequate benchmark tasks. In general, the structure underlying past benchmarks has either been too simple to be inherently interesting, or too ill-defined to support principled analysis. In the present work, we introduce a new benchmark for meta-RL research, which combines structural richness with structural transparency. Alchemy is a 3D video game, implemented in Unity, which involves a latent causal structure that is resampled procedurally from episode to episode, affording structure learning, online inference, hypothesis testing and action sequencing based on abstract domain knowledge. We evaluate a pair of powerful RL agents on Alchemy and present an in-depth analysis of one of these agents. Results clearly indicate a frank and specific failure of meta-learning, providing validation for Alchemy as a challenging benchmark for meta-RL. Concurrent with this report, we are releasing Alchemy as public resource, together with a suite of analysis tools and sample agent trajectories.

It was the hight of summer 2018 when I had the great fortune to be acquainted with Dr. Julia Mossbridge, who was at that time just preparing for the launch of her newest book'The Premonition Code'. Now on valentines day 2020, we thought it a good time to reshare the amazing conversation that we had about being unconditionally loved by artificial general intelligence. "You see, what I'm suggesting is that love will be the key… by which they acquire a kind of subconscious never before achieved. In our modern, hyper-connected world where many prominent personalities warn about the dangers of artificial intelligence and declare that the AI researchers are summoning the demon, it is rather peculiar to come across the notion of a loving AI. News articles are declaring that AI is getting more emotional, that AI algorithms are better than us at recognizing emotions, that the rise of emotionally intelligent AI is near and questioning whether we can fall in love with an AI? Even Hollywood is exploring love between humans and AI, through movies like Her and Ex Machina. It seems that the Alchemists of our time, rather than striving to transform lead into gold, seek to be loved by silicon. For the second episode of the SingularityNET Podcast we invited one such modern-day Alchemist: Dr. Julia Mossbridge, to be our guest. As the principal founder of the loving AI project, Dr. Mossbridge is at the forefront of researching the ways of creating algorithmic love. But why build loving robots? Would such a love be different? Can machines love us more? Can we be loved unconditionally? And can we love in return? In our podcast, we asked Dr. Julia Mossbridge those questions. And like everything that concerns love, things were a bit complicated. "Powerful infatuations can be induced by the skillful potioneer, but never yet has anyone managed to create the truly unbreakable, eternal, unconditional attachment that alone can be called love." -- J.K.Rowling It is not unreasonable to wonder that if magicians could not create unconditional love, what hope do AI researchers have? And why do we need a love potion for an AGI in the first place? Dr. Julia Mossbridge saw the need to dedicate her time and energy toward the herculean task of creating unconditionally loving AI when she was approached by a group of people who were concerned about the future. More specifically, these people did not want a future where humanity was left wondering "if only we had taught AI to love." As she embarked on her journey, Julia realized that humanity had no other choice but to create unconditionally loving AI. "They [AI] are going to have super intelligence in many ways.

This presentation was recorded at GOTO Copenhagen 2018. Erik Meijer - Think Like A Fundamentalist, Code Like A Hacker ABSTRACT In ancient times, the dream of alchemists was to mutate ordinary metals such as lead into noble metals such as gold. However, by using classic mathematics, modern physicists and chemists are much more successful in understanding and transforming matter than alchemists ever dreamt of. The situation in software seems to be the opposite. Modern computer scientists have been unsuccessful in their quest to reliably turn formal specifications into code and to accurately understand the mechanics of side-effecting computation.

To my readers it will appear as though I am writing some article on old Greek mythology, but you will soon realize that the world remains the same the more it changes. Recently Ali Rahimi, a researcher in artificial intelligence at Google, compared machine learning with alchemy. Later a few technology journalists, more than ever before, started writing about the relationship between technology and alchemy. Alchemy is about using the "trial and error" method and coming out with a formula (mostly secret or something that cannot be deconstructed). Similarly, in machine learning a model is designed out of data, this model constantly learns and produces an output but nobody know how decisions are made.