Fluence announced an agreement with The AES Corporation, a Fortune 500 global energy company, to implement the AI-powered Fluence IQ Bidding Application to maximize the value of a 1.1GW portfolio of solar and energy storage projects in the Western US. In October of last year, Fluence acquired AMS' software and digital intelligence platform for renewables and energy storage. The platform is designed to improve revenue of energy storage assets in wholesale markets. AMS' technology uses artificial intelligence, advanced price forecasting, portfolio optimization and market bidding to ensure energy storage and flexible generation assets are responding optimally to price signals sent by the market. The way the Fluence IQ Bidding Application works is it recommends bids into daily and hourly auctions for energy and grid services, anticipating opportunities using advanced forecasting to take advantage of favorable pricing, while minimizing exposure to unfavorable pricing.

Smart farming using AI and IoT is no longer a distant dream, smart farms are here to stay thanks to amazing advancements in AI and IoT devices. Over the past decades, the agriculture sector has undergone significant changes. Today, it's possible to grow plants even in the most hostile climatic regions. Crops are more resistant to insects, weeds, and climate change than ever before. Lastly, it's possible to breed high-yielding farm animals. But despite all these advancements, a large population of the world is still undernourished.

Machine learning global investment is expected to grow at a rate of 44.1% CAGR from 2016 to 2022 and increased up to 8.81 billion. Altogether, Machine learning has become a vital tactic for organizations in order to drive customer engagement, increase ROI, and gain competitive advantages. Currently, ML has received adoption worldwide and is thriving as a major technological advancement in data generation & analysis. The major driving factors are improved customer data insight, trend analysis & forecasting, and enhanced business operations. In the endeavor to infuse this technology, machine learning companies are supporting both large and small enterprises. That is boosting its significance in the current marketplace. Netflix, Upwork, Google, YouTube, and many other giant brands are using AI and machine learning algorithms. If you are thinking about infusing machine learning and AI algorithms within your business platforms, then it is essential to go along with an innovative and value-driven approach.

Fleur had the pleasure of speaking with Ian Kuali'i, a multi-disciplinary self-taught artist of Hawaiian/Apache ancestry working in the forms of murals, large-scale hand cut paper and site-specific installations. From a single sheet of paper using only an xacto blade as his tool, Ian's portraits, journal entries and scenes are masterfully rendered in hand cut paper with a blend of loose urban contemporary techniques and collaged found materials. Ian describes his creative process as "The meditative process of destroying to create." Fleur: Can you please introduce yourself? My name is Ian Joseph Kekoa Hardwick-Kuali'i or just simply Ian Kuali'i.

UNSW is also taking a leading role in pushing forward the codification of the use of AI in the law9 and in understanding and highlighting the ethical boundaries where AI techniques can and should really be used for the benefit of society. All up, UNSW has over 300 researchers involved in pushing forward the power of AI related techniques for the good of society. These researchers participate in over a dozen research centres, labs and facilities across all faculties at UNSW, including at ADFA@UNSW Canberra providing Australia's defence forces with world leading teaching and research. This unique capability has developed organically over time but currently lacks a cohesive means to bring them all together. UNSW.ai is designed to harness the power of this capability to accelerate our development of new and more powerful AI techniques while ensuring their integrity, security and appropriate use.

In 1996, philosopher Helen Nissenbaum issued a clarion call concerning the erosion of accountability in society due to the ubiquitous delegation of consequential functions to computerized systems. Using the conceptual framing of moral blame, Nissenbaum described four types of barriers to accountability that computerization presented: 1) "many hands," the problem of attributing moral responsibility for outcomes caused by many moral actors; 2) "bugs," a way software developers might shrug off responsibility by suggesting software errors are unavoidable; 3) "computer as scapegoat," shifting blame to computer systems as if they were moral actors; and 4) "ownership without liability," a free pass to the tech industry to deny responsibility for the software they produce. We revisit these four barriers in relation to the recent ascendance of data-driven algorithmic systems--technology often folded under the heading of machine learning (ML) or artificial intelligence (AI)--to uncover the new challenges for accountability that these systems present. We then look ahead to how one might construct and justify a moral, relational framework for holding responsible parties accountable, and argue that the FAccT community is uniquely well-positioned to develop such a framework to weaken the four barriers.

Taxonomies are fundamental to many real-world applications in various domains, serving as structural representations of knowledge. To deal with the increasing volume of new concepts needed to be organized as taxonomies, researchers turn to automatically completion of an existing taxonomy with new concepts. In this paper, we propose TaxoEnrich, a new taxonomy completion framework, which effectively leverages both semantic features and structural information in the existing taxonomy and offers a better representation of candidate position to boost the performance of taxonomy completion. Specifically, TaxoEnrich consists of four components: (1) taxonomy-contextualized embedding which incorporates both semantic meanings of concept and taxonomic relations based on powerful pretrained language models; (2) a taxonomy-aware sequential encoder which learns candidate position representations by encoding the structural information of taxonomy; (3) a query-aware sibling encoder which adaptively aggregates candidate siblings to augment candidate position representations based on their importance to the query-position matching; (4) a query-position matching model which extends existing work with our new candidate position representations. Extensive experiments on four large real-world datasets from different domains show that \TaxoEnrich achieves the best performance among all evaluation metrics and outperforms previous state-of-the-art methods by a large margin.

Despite achieving incredibly low perplexities on myriad natural language corpora, today's language models still often underperform when used to generate text. This dichotomy has puzzled the language generation community for the last few years. In this work, we posit that the abstraction of natural language as a communication channel (\`a la Shannon, 1948) can provide new insights into the behaviors of probabilistic language generators, e.g., why high-probability texts can be dull or repetitive. Humans use language as a means of communicating information, and do so in an efficient yet error-minimizing manner, choosing each word in a string with this (perhaps subconscious) goal in mind. We propose that generation from probabilistic models should mimic this behavior. Rather than always choosing words from the high-probability region of the distribution--which have a low Shannon information content--we sample from the set of words with an information content close to its expected value, i.e., close to the conditional entropy of our model. This decision criterion can be realized through a simple and efficient implementation, which we call typical sampling. Automatic and human evaluations show that, in comparison to nucleus and top-k sampling, typical sampling offers competitive performance in terms of quality while consistently reducing the number of degenerate repetitions.

Four Democratic senators and House representatives have called on several government departments to stop using Clearview AI's facial recognition system. The Government Accountability Office said in August that the Departments of Justice, Defense, Homeland Security and the Interior were all using the contentious technology for "domestic law enforcement." Pramila Jayapal and Ayanna Pressley urged the agencies to refrain from using Clearview's products and other facial recognition tools. "Clearview AI's technology could eliminate public anonymity in the United States," the lawmakers wrote to the agencies in their letters, which were obtained by The Verge. They said that, combined with the facial recognition system, the database of billions of photos Clearview scraped from social media platforms "is capable of fundamentally dismantling Americans' expectation that they can move, assemble or simply appear in public without being identified."

One of the stars of the working-out-from-home boom is struggling. Peloton won't go quietly though and is making some big changes. The company will replace the CEO and co-founder, John Foley, who will become executive chairman, with former Spotify COO Barry McCarthy reportedly set to step into his shoes. While Foley is sticking around, the company is cutting around 2,800 corporate positions -- these won't include Peloton's instructors who lead its live classes. The company said in a press release about the lay-offs that its "monthly membership will be complimentary for impacted team members for an additional 12 months."