This week, researchers at the Beijing Academy of Artificial Intelligence (BAAI) announced the release of Wu Dao 2.0, a multimodal AI model capable of generating text indiscernible from human-crafted prose -- and more. Containing 1.75 trillion parameters, the parts of the machine learning model learned from historical training data, Wu Dao 2.0 is 10 times larger than OpenAI's 175-billion-parameter GPT- 3. Wu Dao 2.0 is the latest example of what OpenAI policy director Jack Clark calls model diffusion, or multiple state and private actors developing GPT-3-style AI models. For example, Russia and France are training smaller-scale systems via Sberbank and LightOn's PAGnol, while Korea's Naver Labs is investing in the recently created HyperCLOVA. Clark notes that because these models reflect and magnify the data they're trained on, different countries care about how their own cultures are represented in the models. The Wu Dao 2.0 announcement, then, is part of a general trend of nations asserting their own AI capabilities via training frontier models like GPT-3.

The Joint Artificial Intelligence Center will lead implementation of responsible AI across the Defense Department, according to a new directive. In a departmentwide memo signed last week, Deputy Defense Secretary Kathleen Hicks enumerated foundational tenets for responsible AI, reaffirmed the ethical AI principles the department adopted last year, and mandated the JAIC director start work on four activities for developing a responsible AI ecosystem. "As the DoD embraces artificial intelligence (AI), it is imperative that we adopt responsible behavior, processes, and outcomes in a manner that reflects the Department's commitment to its ethical principles, including the protection of privacy and civil liberties," Hicks said in the memo, which was announced June 1. "A trusted ecosystem not only enhances our military capabilities, but also builds confidence with end-users, warfighters, and the American public." Hicks assigned the JAIC director to coordinate responsible AI through a working council, which must in turn hammer out a strategy and implementation pathway, create a talent management framework, and report on how responsible AI can be integrated into acquisitions.

The design of optimal auctions is a problem of interest in economics, game theory and computer science. Despite decades of effort, strategyproof, revenue-maximizing auction designs are still not known outside of restricted settings. However, recent methods using deep learning have shown some success in approximating optimal auctions, recovering several known solutions and outperforming strong baselines when optimal auctions are not known. In addition to maximizing revenue, auction mechanisms may also seek to encourage socially desirable constraints such as allocation fairness or diversity. However, these philosophical notions neither have standardization nor do they have widely accepted formal definitions. In this paper, we propose PreferenceNet, an extension of existing neural-network-based auction mechanisms to encode constraints using (potentially human-provided) exemplars of desirable allocations. In addition, we introduce a new metric to evaluate an auction allocations' adherence to such socially desirable constraints and demonstrate that our proposed method is competitive with current state-of-the-art neural-network based auction designs. We validate our approach through human subject research and show that we are able to effectively capture real human preferences. Our code is available at https://github.com/neeharperi/PreferenceNet

The director of Israel's domestic security service, the Shin Bet, warned on Saturday of growing incitement that could lead to politically motivated violence. Why it matters: Nadav Argaman's rare public statement raised concerns about the threat of a Jan. 6-style attack in Israel to prevent a peaceful transition of power if Prime Minister Benjamin Netanyahu is ousted after 12 years in office.

Surveyor Health Corporation launched the SurveyorAI platform, a patented technology that drives efficiency and affordability for population medication management using drug knowledge from FDB (First Databank). The company also released the results of a Stanford-guided two-year study and peer reviewed research in the Journal of Managed Care & Specialty Pharmacy. The study validates the platform's ability to enhance remote medication management while lowering costs, reducing utilization, and improving health. The results of the study are based on a pilot conducted with IEHP, Inland Empire Health Plan, one of the top 10 largest Medicaid health plans and the largest not-for-profit Medicare-Medicaid plan in the country. "Adverse drug reactions result in 275,689 deaths and cost more than $528 billion per year*," said Erick Von Schweber, co-founder of Surveyor Health Corporation.

Jurisprudence has always had to face new challenges posed by innovations, socio-economic developments, and changes in the political landscape. Most recently, various aspects of our life are increasingly becoming entangled with artificial intelligence (AI). The legal fraternity requires much better acquaintance with the technical space as the new policies that they will debate will directly influence the products developed by engineers. To understand the parallelism which one can draw between Artificial Intelligence and Law, let's walk through a few autonomous systems where AI is already confronting the legal field. Constant advancements across a spectrum of technologies brought autonomous cars to reality straight out of sci-fi movies.

The Department of Information Technology, National Institute of Technology Karnataka (NIT-K), Surathkal, has decided to start a new four-year B.Tech. The Academic Senate, Board of the institute and the Union Ministry of Education have approved the course. Admissions will be through JEE (Main) score. Karanam Uma Maheshwar Rao, Director, NIT-K, said in a release on Thursday that this degree would prepare students for industry or further study by offering specialisations in different areas of AI such as data science, human-centred computing, cyber-physical systems, and robotics. "Its curriculum will focus on the use of inputs such as video, speech, and big data to make decisions or enhance human capabilities."

In this blog, I have tried summarizing the paper Multi-Sentence Compression: Finding Shortest Paths in Word Graphs as per my understanding. Please feel free to comment your thoughts on the same! Sentence compression is the task of compressing a long sentence into a shorter one by deleting redundant words. This paper proposes a novel unsupervised multi-sentence compression technique wherein the goal is to represent a set of related sentences by a single sentence in such a way that it preserves the important parts of the main content and is also grammatically correct at the same time. One of the possible use cases for Sentence Compression (SC) is in Extractive Text Summarization, wherein a typical flow is to rank sentences based on a certain scoring strategy followed by selecting top-k sentences as representative summary.

Last month, we announced the Informatica Intelligent Data Management Cloud. One of the key attributes of our industry-first offering is AI native at scale. CLAIRE is the AI powerhouse behind our Intelligent Data Management Cloud. Built on an enterprise unified metadata foundation, it provides AI-driven automation of data management activities. In this blog post, I'll discuss 10 ways AI improves master data management (MDM).

Confusion Matrix The Confusion Matrix is a table that summarizes the number of true and false predictions made by a classifier. It is used to measure the performance of a classification model. It can be used to assess the performance of a classification model by calculating performance indicators such as accuracy, precision, recall, and F1 score. If you are working with an unbalanced dataset, you had better use the confusion matrix as the endpoint for your machine learning model. Here are the basic terms that will help us identify the metrics we are looking for.