Algorithmic Transparency in Action: How and Why Do Companies Disclose Information on Algorithms? Does the Accent of an Intelligent Conversational Agent Matter? What makes users reliant on the virtual assistant?

Today Insilico Medicine announced the completion of a second closing of its Series D round, led by Prosperity7 Ventures, the diversified growth fund of Saudi Aramco Ventures, bringing the total Series D financing to $95 million. Other global investors with expertise in the biopharmaceutical and life sciences sectors also participated. The financing brought in Prosperity7 as a new investor, alongside current investors in the Series D round, including a large, diversified asset management firm on the US West Coast, B Capital Group, Warburg Pincus, BHR Partners, Qiming Venture Partners, Deerfield, Pavilion Capital, BOLD Capital Partners, and WS Investment Company. Insilico's founder and CEO, Alex Zhavoronkov, PhD, also invested in the Series D round. Insilico Medicine plans to grow its presence in Saudi Arabia, building on the recent investment from Prosperity7.

A year ago researchers from the University of Zurich showcased their autonomous drones that were able to beat the fastest human pilots. However, that race wasn't "fair" in the sense that the AI algorithm commanding the drones had extra information that human pilots didn't have. In particular, the algorithm had access to near-perfect location and velocity estimation of the drones using motion capture systems, high-quality maps of the race course beforehand, and stereo cameras that can give depth information. This year, the team's autonomous drones raced on even playing fields without these handicaps, and its AI was able to beat the best human-controlled time by 0.5s in a three-lap race, a significant lead in the world of drone racing. Our take: This development is representative of AI progress ins many fields, where the researchers first make a working system with additional assumptions and then slowly chip away at these assumptions for a more robust and adaptable AI system.

Artificial Intelligence (AI) is a household name. All around us, businesses are increasingly leveraging AI for a wide range of uses. According to Allied Market Research, the global AI market size was valued at $65.48 billion in 2020, projected to reach $1,581.70 billion by 2030, growing at a CAGR of 38%. However, when it comes to women in AI, a World Economic Forum report states that only 22% of AI professionals globally are female, compared to 78% male, which accounts for a gender gap of 72%. And there is ample room for growth.

Semantic segmentation is a popular research topic in computer vision, and many efforts have been made on it with impressive results. In this paper, we intend to search an optimal network structure that can run in real-time for this problem. Towards this goal, we jointly search the depth, channel, dilation rate and feature spatial resolution, which results in a search space consisting of about 2.78*10^324 possible choices. To handle such a large search space, we leverage differential architecture search methods. However, the architecture parameters searched using existing differential methods need to be discretized, which causes the discretization gap between the architecture parameters found by the differential methods and their discretized version as the final solution for the architecture search. Hence, we relieve the problem of discretization gap from the innovative perspective of solution space regularization. Specifically, a novel Solution Space Regularization (SSR) loss is first proposed to effectively encourage the supernet to converge to its discrete one. Then, a new Hierarchical and Progressive Solution Space Shrinking method is presented to further achieve high efficiency of searching. In addition, we theoretically show that the optimization of SSR loss is equivalent to the L_0-norm regularization, which accounts for the improved search-evaluation gap. Comprehensive experiments show that the proposed search scheme can efficiently find an optimal network structure that yields an extremely fast speed (175 FPS) of segmentation with a small model size (1 M) while maintaining comparable accuracy.

We present BlenderBot 3, a 175B parameter dialogue model capable of open-domain conversation with access to the internet and a long-term memory, and having been trained on a large number of user defined tasks. We release both the model weights and code, and have also deployed the model on a public web page to interact with organic users. This technical report describes how the model was built (architecture, model and training scheme), and details of its deployment, including safety mechanisms. Human evaluations show its superiority to existing open-domain dialogue agents, including its predecessors (Roller et al., 2021; Komeili et al., 2022). Finally, we detail our plan for continual learning using the data collected from deployment, which will also be publicly released. The goal of this research program is thus to enable the community to study ever-improving responsible agents that learn through interaction.

This dissertation explores the linguistic and computational aspects of the meaning relations that can hold between two or more complex linguistic expressions (phrases, clauses, sentences, paragraphs). In particular, it focuses on Paraphrasing, Textual Entailment, Contradiction, and Semantic Similarity. In Part I: "Similarity at the Level of Words and Phrases", I study the Distributional Hypothesis (DH) and explore several different methodologies for quantifying semantic similarity at the levels of words and short phrases. In Part II: "Paraphrase Typology and Paraphrase Identification", I focus on the meaning relation of paraphrasing and the empirical task of automated Paraphrase Identification (PI). In Part III: "Paraphrasing, Textual Entailment, and Semantic Similarity", I present a novel direction in the research on textual meaning relations, resulting from joint research carried out on on paraphrasing, textual entailment, contradiction, and semantic similarity.

Late last month, Australia's leading scientists, researchers and businesspeople came together for the inaugural Australian Defence Science, Technology and Research Summit (ADSTAR), hosted by the Defence Department's Science and Technology Group. In a demonstration of Australia's commitment to partnerships that would make our non-allied adversaries flinch, Chief Defence Scientist Tanya Monro was joined by representatives from each of the Five Eyes partners, as well as Japan, Singapore and South Korea. Two streams focusing on artificial intelligence were dedicated to research and applications in the defence context. A friend who works in cybersecurity asked me this. In the world of information security, social engineering is the game of manipulating people into divulging information that can be used in a cyberattack or scam.

Researchers have warned against possible harms from AI that processes and generates text.Credit: Getty An international team of around 1,000 largely academic volunteers has tried to break big tech's stranglehold on natural-language processing and reduce its harms. Trained with US$7-million-worth of publicly funded computing time, the BLOOM language model will rival in scale those made by firms Google and OpenAI, but will be open-source. BLOOM will also be the first model of its scale to be multilingual. The collaboration, called BigScience, launched an early version of the model on 17 June, and hopes that it will ultimately help to reduce harmful outputs of artificial intelligence (AI) language systems. Models that recognize and generate language are increasingly used by big tech firms in applications from chat bots to translators, and can sound so eerily human that a Google engineer this month claimed that the firm's AI model was sentient (Google strongly denies that the AI possesses sentience).

Google experienced a major international internet outage on Tuesday, technology platforms reported. The realtime online platform Downdetector reported users had registered problems with Google explorer, the world's dominant search engine from 2.12am BST (9.12pm EST, 11.12AM AEST. As of 11.38AM, there had been 4,113 confirmed reports of Google outages. User reports indicate Google is having problems since 9:12 PM EDT. Users said sister platforms Gmail, Google maps and Google images were also experiencing problems.