The emergence of artificial intelligence (AI) in health care offers unprecedented opportunities to improve patient and clinical team outcomes, reduce costs, and impact population health. While there have been a number of promising examples of AI applications in health care, it is imperative to proceed with caution or risk the potential of user disillusionment, another AI winter, or futher exacerbation of existing health- and technology-driven disparities. This Special Publication synthesizes current knowledge to offer a reference document for relevant health care stakeholders. It outlines the current and near-term AI solutions; highlights the challenges, limitations, and best practices for AI development, adoption, and maintenance; offers an overview of the legal and regulatory landscape for AI tools designed for health care application; prioritizes the need for equity, inclusion, and a human rights lens for this work; and outlines key considerations for moving forward. AI is poised to make transformative and disruptive advances in health care, but it is prudent to balance the need for thoughtful, inclusive health care AI that plans for and actively manages and reduces potential unintended consequences, while not yielding to marketing hype and profit motives.

Multimodal analysis that uses numerical time series and textual corpora as input data sources is becoming a promising approach, especially in the financial industry. However, the main focus of such analysis has been on achieving high prediction accuracy while little effort has been spent on the important task of understanding the association between the two data modalities. Performance on the time series hence receives little explanation though human-understandable textual information is available. In this work, we address the problem of given a numerical time series, and a general corpus of textual stories collected in the same period of the time series, the task is to timely discover a succinct set of textual stories associated with that time series. Towards this goal, we propose a novel multi-modal neural model called MSIN that jointly learns both numerical time series and categorical text articles in order to unearth the association between them. Through multiple steps of data interrelation between the two data modalities, MSIN learns to focus on a small subset of text articles that best align with the performance in the time series. This succinct set is timely discovered and presented as recommended documents, acting as automated information filtering, for the given time series. We empirically evaluate the performance of our model on discovering relevant news articles for two stock time series from Apple and Google companies, along with the daily news articles collected from the Thomson Reuters over a period of seven consecutive years. The experimental results demonstrate that MSIN achieves up to 84.9% and 87.2% in recalling the ground truth articles respectively to the two examined time series, far more superior to state-of-the-art algorithms that rely on conventional attention mechanism in deep learning.

While recent progress in deep reinforcement learning has enabled robots to learn complex behaviors, tasks with long horizons and sparse rewards remain an ongoing challenge. In this work, we propose an effective reward shaping method through predictive coding to tackle sparse reward problems. By learning predictive representations offline and using these representations for reward shaping, we gain access to reward signals that understand the structure and dynamics of the environment. In particular, our method achieves better learning by providing reward signals that 1) understand environment dynamics 2) emphasize on features most useful for learning 3) resist noise in learned representations through reward accumulation. We demonstrate the usefulness of this approach in different domains ranging from robotic manipulation to navigation, and we show that reward signals produced through predictive coding are as effective for learning as hand-crafted rewards.

In October last year, for example, AI-generated art hit the headlines when auction house Christie's New York sold an AI-created artwork for $432,000. AI is also being used in music production, with a new industry being built around the use of AI in music. The musician Taryn Southern has used an artificial intelligence platform called Amper to create an entire album, called I AM AI. The album was the first LP to be entirely composed and produced using AI. A patented AI system called "DABUS", created by Dr Stephen Thaler, can devise and develop new ideas.

This is the third Synced year-end compilation of "Artificial Intelligence Failures." Despite AI's rapid growth and remarkable achievements, a review of AI failures remains necessary and meaningful. Our aim is not to downplay or mock research and development results, but rather to take a look at what went wrong with the hope we can do better next time. A leading facial-recognition system identified three-time Super Bowl champion Duron Harmon of the New England Patriots, Boston Bruins forward Brad Marchand, and 25 other New England professional athletes as criminals. Amazon's Rekognition software incorrectly matched the athletes to a database of mugshots in a test organized by the Massachusetts chapter of the American Civil Liberties Union (ACLU).

Is it possible that lakes and forests might have rights before robots? Voters in Toledo have granted "irrevocable rights for the Lake Erie Ecosystem to exist, flourish and naturally evolve" which, according to this story, would give it legal standing to file lawsuits to protect itself from polluters (through the mouthpiece of a human guardian). It's an amazingly bold statement that is rife with thorny questions. Humans have had say over nature ever since Adam and Eve, and most political and cultural uses or abuses have been based on the shifting perspectives of their progeny. Nature is something "out there" that only gains meaning or purpose when defined by us.

Companies regularly spend millions of dollars producing electronically-stored documents in legal matters. Recently, parties on both sides of the 'legal aisle' are accepting the use of machine learning techniques like text classification to cull massive volumes of data and to identify responsive documents for use in these matters. While text classification is regularly used to reduce the discovery costs in legal matters, it also faces a peculiar perception challenge: amongst lawyers, this technology is sometimes looked upon as a "black box", little information provided for attorneys to understand why documents are classified as responsive. In recent years, a group of AI and ML researchers have been actively researching Explainable AI, in which actions or decisions are human understandable. In legal document review scenarios, a document can be identified as responsive, if one or more of its text snippets are deemed responsive. In these scenarios, if text classification can be used to locate these snippets, then attorneys could easily evaluate the model's classification decision. When deployed with defined and explainable results, text classification can drastically enhance overall quality and speed of the review process by reducing the review time. Moreover, explainable predictive coding provides lawyers with greater confidence in the results of that supervised learning task. This paper describes a framework for explainable text classification as a valuable tool in legal services: for enhancing the quality and efficiency of legal document review and for assisting in locating responsive snippets within responsive documents. This framework has been implemented in our legal analytics product, which has been used in hundreds of legal matters. We also report our experimental results using the data from an actual legal matter that used this type of document review.

The 2019 edition of Opus Research's Decision Makers' Guide to Enterprise Intelligent Assistants report determined [24]7 AIVA to be a top solution for enterprises, and the only virtual agent solution capable of delivering across a breadth of simple FAQs to complex, conversational issues to online transactions. The Opus report presents a comprehensive assessment of 16 enterprise-grade Intelligent Assistant solution providers, with a focus on natural language processing, machine learning, AI, analytics and customer management integration to power digital self-service solutions. The report highlights [24]7 AIVA's ability to support both voice and digital channels and deliver unified self-service, calling out the company's differentiators as being a unique blend of AI and human insights, two decades of unparalleled experience in customer journeys across all channels, and proprietary insights including more than 150 patents and patent applications. "We analyzed a short-list of the leading providers in natural language processing, machine learning, AI and analytics to develop the industry's most comprehensive assessment of today's virtual agents and digital self-service solutions," said Dan Miller, lead analyst, Opus Research. An agent can take over a bot conversation at any time, and hand the conversation back to the bot to complete the interactions.

Wild animals are experts at staying out of sight, but a new partnership between Google and the conservation organization Wildlife Insights will try to help scientists capture and analyze pictures of them in their natural habitat. The program will use an artificial intelligence program to sort through photographs taken by small sensor driven camera installations placed in wilderness areas around the world. Google's AI and Cloud services will help researchers analyse and archive the enormous volume of images captured through the program as part of an effort to improve animal conservation strategies all around the world. The camera traps were originally developed in 1990 and in the intervening years have been placed everywhere from Mexico to Madagascar. To date, 4.553 million pictures have been taken from 8,209 camera deployments.

How can we tackle gender imbalance in the personalities of AI learning tools? The expected growth in use of artificial intelligence (AI) in learning applications is raising concerns about both the potential gendering of these tools and the risk that they will display the inherent biases of their developers. Well, to make it easier for us to integrate AI tools and chatbots into our lives, designers often give them human attributes. For example, applications and robots are often given a personality and gender. Unfortunately, in many cases, gender stereotypes are being perpetuated.