The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Eight months in, 2021 has already become a record year in brain-computer interface (BCI) funding, tripling the $97 million raised in 2019. BCIs translate human brainwaves into machine-understandable commands, allowing people to operate a computer, for example, with their mind. Just during the last couple of weeks, Elon Musk's BCI company, Neuralink, announced a $205 million in Series C funding, with Paradromics, another BCI firm, announcing a $20 million Seed round a few days earlier. Almost at the same time, Neuralink competitor Synchron announced it has received the groundbreaking go-ahead from the FDA to run clinical trials for its flagship product, the Stentrode, with human patients. Even before this approval, Synchron's Stentrode was already undergoing clinical trials in Australia, with four patients having received the implant.

Last week, Apple announced two backdoors in the US into the encryption that protects its devices. One will monitor iMessages: if any photos sent by or to under-13s seem to contain nudity, the user may be challenged and their parents may be informed. The second will see Apple scan all the images on a phone's camera roll and if they're similar to known sex-abuse images flag them as suspect. If enough suspect images are backed up to an iCloud account, they'll be decrypted and inspected. If Apple thinks they're illegal, the user will be reported to the relevant authorities. Action on the circulation of child sexual abuse imagery is long overdue.

Alexa, Siri and Cortana may sound like the top three hipster baby names in 2021, but they are actually Amazon, Apple and Microsoft's virtual assistants. In recent years, we have experienced a boom in speech recognition tools that understand what we are saying. And soon they could also understand how we are feeling. The list of companies working on the development of emotion recognition technology is growing exponentially, and investors appear to be excited when it comes to emotionally intelligent tech. The industry is undoubtedly booming, with estimates predicting that the global emotional intelligence market will grow to $64m by 2027. The most common form of emotion detection software uses cameras to record and analyse facial expressions, body movements and gestures to detect how people are feeling.

Microscopic epidemic models are powerful tools for government policy makers to predict and simulate epidemic outbreaks, which can capture the impact of individual behaviors on the macroscopic phenomenon. However, existing models only consider simple rule-based individual behaviors, limiting their applicability. This paper proposes a deep-reinforcement-learning-powered microscopic model named Microscopic Pandemic Simulator (MPS). By replacing rule-based agents with rational agents whose behaviors are driven to maximize rewards, the MPS provides a better approximation of real world dynamics. To efficiently simulate with massive amounts of agents in MPS, we propose Scalable Million-Agent DQN (SMADQN). The MPS allows us to efficiently evaluate the impact of different government strategies. This paper first calibrates the MPS against real-world data in Allegheny, US, then demonstratively evaluates two government strategies: information disclosure and quarantine. The results validate the effectiveness of the proposed method. As a broad impact, this paper provides novel insights for the application of DRL in large scale agent-based networks such as economic and social networks.

Initialization plays a critical role in the training of deep neural networks (DNN). Existing initialization strategies mainly focus on stabilizing the training process to mitigate gradient vanish/explosion problems. However, these initialization methods are lacking in consideration about how to enhance generalization ability. The Information Bottleneck (IB) theory is a well-known understanding framework to provide an explanation about the generalization of DNN. Guided by the insights provided by IB theory, we design two criteria for better initializing DNN. And we further design a neuron campaign initialization algorithm to efficiently select a good initialization for a neural network on a given dataset. The experiments on MNIST dataset show that our method can lead to a better generalization performance with faster convergence.

The question whether an artificial intelligence ("AI") system can be named as an inventor in a patent application has obvious implications for the life science community, where AI's presence is now well established and growing. For example, AI is currently used to predict biological targets of prospective drug molecules, identify candidates for drug design, decode genetic material of viruses in the context of vaccine development, determine three-dimensional structures of proteins, including their folding form, and many more potential therapeutic applications. In a landmark decision issued on July 30, 2021, an Australian court declared that an AI system called DABUS can be legally recognized as an inventor on a patent application. It came just days after the Intellectual Property Commission of South Africa granted a patent recognizing DABUS as an inventor. These decisions, as well as at least one other pending case in the U.S. concerning similar issues, have generated excitement and debate in the life sciences community about AI-conceived inventions.

Lady Gaga sings'I'm on the edge of glory and I'm hanging on a moment of truth'. Until now, the longstanding crusade to allow inventions generated by the AI machine DABUS to be patentable under existing national patent laws across different jurisdictions had not had much success. Lawyers with the "Artificial Inventor Project" had filed patent applications around the world for DABUS' 'inventions' but received a steady stream of rejections from national IP offices and courts (for instance see our Lens posts on refusals by the UKIPO, UK High Court, EPO and USPTO). Surprisingly, DABUS has had better results in recent weeks in respect of its South African and Australian applications. Is this the edge of glory?

Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting triplets of aspect terms, their associated sentiments, and the opinion terms that provide evidence for the expressed sentiments. Previous approaches to ASTE usually simultaneously extract all three components or first identify the aspect and opinion terms, then pair them up to predict their sentiment polarities. In this work, we present a novel paradigm, ASTE-RL, by regarding the aspect and opinion terms as arguments of the expressed sentiment in a hierarchical reinforcement learning (RL) framework. We first focus on sentiments expressed in a sentence, then identify the target aspect and opinion terms for that sentiment. This takes into account the mutual interactions among the triplet's components while improving exploration and sample efficiency. Furthermore, this hierarchical RLsetup enables us to deal with multiple and overlapping triplets. In our experiments, we evaluate our model on existing datasets from laptop and restaurant domains and show that it achieves state-of-the-art performance. The implementation of this work is publicly available at https://github.com/declare-lab/ASTE-RL.

We present VESPA, an intentionally simple yet novel zero-shot system for layout, locale, and domain agnostic document extraction. In spite of the availability of large corpora of documents, the lack of labeled and validated datasets makes it a challenge to discriminatively train document extraction models for enterprises. We show that this problem can be addressed by simply transferring the information extraction (IE) task to a natural language Question-Answering (QA) task without engineering task-specific architectures. We demonstrate the effectiveness of our system by evaluating on a closed corpus of real-world retail and tax invoices with multiple complex layouts, domains, and geographies. The empirical evaluation shows that our system outperforms 4 prominent commercial invoice solutions that use discriminatively trained models with architectures specifically crafted for invoice extraction. We extracted 6 fields with zero upfront human annotation or training with an Avg. F1 of 87.50.

"In God we trust, all others must bring data." The "data economy" has been underway for a couple of decades now, where data has been recognized as an asset vital to the business. Enterprises over time have developed robust processes, policies, organizations, and platforms to manage, monitor, and measure the quality, relevancy and availability of data for business needs at every step of a data life cycle. In the modern era, the "Economics of Artificial Intelligence (AI)" is what revolutionizes business today. AI is perhaps best applied when it replaces human intelligence.