"The skull acts as a bastion of privacy; the brain is the last private part of ourselves," Australian neurosurgeon Tom Oxley says from New York. Oxley is the CEO of Synchron, a neurotechnology company born in Melbourne that has successfully trialled hi-tech brain implants that allow people to send emails and texts purely by thought. In July this year, it became the first company in the world, ahead of competitors like Elon Musk's Neuralink, to gain approval from the US Food and Drug Administration (FDA) to conduct clinical trials of brain computer interfaces (BCIs) in humans in the US. Synchron has already successfully fed electrodes into paralysed patients' brains via their blood vessels. The electrodes record brain activity and feed the data wirelessly to a computer, where it is interpreted and used as a set of commands, allowing the patients to send emails and texts.

Invaio Sciences, a Flagship Pioneering company, is a multi-platform technology company that unlocks the potential of the planet's interdependent systems to address pressing agricultural, nutritional, and environmental challenges. Founded by Flagship Pioneering in 2018, Invaio leverages discoveries from diverse fields including human therapeutics, agriculture, environmental science, and advanced manufacturing. The company's deep understanding of the physiology of insects, plants and trees, together with its novel integrated solutions approach, promises to refine agricultural practices and reduce the need for pesticides globally. We don't think in small steps. We think in giant leaps.

The 18th International Conference for Artificial Intelligence and Law (ICAIL 2021) was organized at the University of São Paulo School of Law, Brazil. ICAIL is a biannual conference organized under the auspices of the International Association for Artificial Intelligence and Law (iaail.org) For the first time, the ICAIL conference was organized entirely online, due to the overall Covid-19 pandemic situation. Despite these unusual circumstances, the conference came out as a considerable success, attracting almost 1400 registered participants, the highest number ever. The conference talks were streamed publicly on the YouTube channel and the discussions and networking were enabled on the platforms accessible for the registered participants.

Your organization knows it, and so do your customers; they engage with your business only when you clearly communicate how you can help them. Getting your signal through the noise has never been more challenging. Businesses are sending out an estimated seven trillion messages a month, most of them ignored--or worse, actively avoided; the customers who receive them may cut off contact entirely. Why are so few messages hitting the mark? Because it's difficult to tailor marketing campaigns and messages to thousands or millions of customers at once while incorporating the complex data, channels, and other inputs that give these messages the authenticity that drives outcomes. With cookies and other third-party customer data on the way out, companies need to make better use of their proprietary data and get a competitive advantage from their content.

In July of last year, a DJI Mavic 2 drone approached a Pennsylvania power substation. Two 4-foot nylon ropes dangled from its rotors, a thick copper wire connected to the ends with electrical tape. The device had been stripped of any identifiable markings, as well as its onboard camera and memory card, in an apparent effort by its owner to avoid detection. Its likely goal, according to a joint security bulletin released by DHS, the FBI, and the National Counterterrorism Center, was to "disrupt operations by creating a short circuit." The drone crashed on the roof of an adjacent building before it reached its ostensible target, damaging a rotor in the process.

There's a massive announcement set to take place later this year, and it could change the $12 trillion healthcare industry forever. Over the last 2 years, we've seen a huge transformation as businesses across nearly every industry have gone digital. And with the health crisis sweeping the globe last year, the healthcare sector was no different. Mentioned in today's commentary includes: Brookfield Renewable Partners L.P. (NYSE: BEP), LifeStance Health Group, Inc. (NASDAQ: LFST), Teladoc Health, Inc. (NYSE: TDOC), Mind Medicine (MindMed) Inc. (NASDAQ: MNMD), American Well Corporation (NYSE: AMWL). That's why we're now on the verge of a healthcare revolution, set to leverage the latest technology to disrupt a bloated and complicated system.

The legal framework for the development and use of Artificial Intelligence (AI) was launched in Brazil. There was already a lot about regulation in full progress in the European Union and the United States, but here the matter was delayed. The landscape is starting to change. In late September, the Chamber of Deputies approved a bill that establishes the foundations and principles for AI in the country. The framework defines the rules of the game for AI development and outlines how it will be applied to the daily lives of companies and citizens. In line with the General Data Protection Law (LGPD), this regulation serves as a guide for the work of Brazilian companies in the startup and research and development (R&D) sectors in this important area of innovation.

Source separation models either work on the spectrogram or waveform domain. In this work, we show how to perform end-to-end hybrid source separation, letting the model decide which domain is best suited for each source, and even combining both. The proposed hybrid version of the Demucs architecture won the Music Demixing Challenge 2021 organized by Sony. This architecture also comes with additional improvements, such as compressed residual branches, local attention or singular value regularization. Overall, a 1.4 dB improvement of the Signal-To-Distortion (SDR) was observed across all sources as measured on the MusDB HQ dataset, an improvement confirmed by human subjective evaluation, with an overall quality rated at 2.83 out of 5 (2.36 for the non hybrid Demucs), and absence of contamination at 3.04 (against 2.37 for the non hybrid Demucs and 2.44 for the second ranking model submitted at the competition).

The Wasserstein distance, rooted in optimal transport (OT) theory, is a popular discrepancy measure between probability distributions with various applications to statistics and machine learning. Despite their rich structure and demonstrated utility, Wasserstein distances are sensitive to outliers in the considered distributions, which hinders applicability in practice. Inspired by the Huber contamination model, we propose a new outlier-robust Wasserstein distance $\mathsf{W}_p^\varepsilon$ which allows for $\varepsilon$ outlier mass to be removed from each contaminated distribution. Our formulation amounts to a highly regular optimization problem that lends itself better for analysis compared to previously considered frameworks. Leveraging this, we conduct a thorough theoretical study of $\mathsf{W}_p^\varepsilon$, encompassing characterization of optimal perturbations, regularity, duality, and statistical estimation and robustness results. In particular, by decoupling the optimization variables, we arrive at a simple dual form for $\mathsf{W}_p^\varepsilon$ that can be implemented via an elementary modification to standard, duality-based OT solvers. We illustrate the benefits of our framework via applications to generative modeling with contaminated datasets.

Machine learning on source code (MLOnCode) is a popular research field that has been driven by the availability of large-scale code repositories and the development of powerful probabilistic and deep learning models for mining source code. Code-to-code recommendation is a task in MLOnCode that aims to recommend relevant, diverse and concise code snippets that usefully extend the code currently being written by a developer in their development environment (IDE). Code-to-code recommendation engines hold the promise of increasing developer productivity by reducing context switching from the IDE and increasing code-reuse. Existing code-to-code recommendation engines do not scale gracefully to large codebases, exhibiting a linear growth in query time as the code repository increases in size. In addition, existing code-to-code recommendation engines fail to account for the global statistics of code repositories in the ranking function, such as the distribution of code snippet lengths, leading to sub-optimal retrieval results. We address both of these weaknesses with \emph{Senatus}, a new code-to-code recommendation engine. At the core of Senatus is \emph{De-Skew} LSH a new locality sensitive hashing (LSH) algorithm that indexes the data for fast (sub-linear time) retrieval while also counteracting the skewness in the snippet length distribution using novel abstract syntax tree-based feature scoring and selection algorithms. We evaluate Senatus via automatic evaluation and with an expert developer user study and find the recommendations to be of higher quality than competing baselines, while achieving faster search. For example, on the CodeSearchNet dataset we show that Senatus improves performance by 6.7\% F1 and query time 16x is faster compared to Facebook Aroma on the task of code-to-code recommendation.