Timothy Gardner is the Founder and the CEO of Riffyn. He was previously Vice President of Research & Development at Amyris, where he led the engineering of yeast strain and processes technology for large-scale bio-manufacturing of renewable chemicals. Earlier, he was an Assistant Professor of Biomedical Engineering at Boston University, the Founder of Cellicon Biotechnologies, and a Programmer at ALK Associates. Tim has been recognized for his pioneering work in Synthetic Biology by Scientific American, the New Scientist, Nature, Technology Review, and the New York Times. He also served as an advisor to the European Union Scientific Committees and the Boston University Engineering Alumni Advisory Board.

Bracco Diagnostics Inc., the U.S. subsidiary of Bracco Imaging S.p.A., a leading global company in the diagnostic imaging business, announced the results of an experimental artificial intelligence (AI) study of two gadolinium-based contrast agents (GBCAs) which found that ProHance (Gadoteridol) Injection, 279.3 mg/mL and Gadavist provided similar degree and pattern of contrast enhancement in brain magnetic resonance imaging (MRI) of patients with glioblastoma multiforme (GBM) previously enrolled in a large scale, multicenter, randomized, double blinded controlled clinical study (the TRUTH study).1 Full study results will be presented at the Radiological Society of North America (RSNA) Annual Meeting on Wednesday, December 4, in Chicago, IL. GBCAs are widely used imaging agents with a favorable safety profile. While recent research has shown that the gadolinium from these agents may remain in the body for months to years after injection,2 the American College of Radiology and the Food and Drug Administration agree that there are no known adverse clinical consequences associated with gadolinium retention in the brain based on the available data.3,4 Nevertheless, some practitioners have concerns, and questions have been raised over whether using a GBCA that retains less would come with a tradeoff in the effectiveness of the contrast enhancement. The purpose of this study was to use AI to determine the effectiveness of standard concentration ProHance (0.5mmol/ml) compared to double concentration Gadavist (1.0 mmol/ml), since animal studies have shown that Gadavist retains two to seven times more in the brain versus ProHance, at up to 4 weeks after injection5-6.

In 2010, US authors in top-rated AI journals outnumbered Chinese counterparts by two to one. That ratio has now reversed. Last year, 1,073 AI experts based at Chinese universities were credited in AI journals such as the Institute of Electrical and Electronics Engineers's Transactions on Neural Networks, compared to 492 US authors. Australia and Israel also do well on this metric. When experts are ranked according to their'H-index' – a metric of productivity and the citation impact of the publications of a scientist or scholar – Americans occupy 626 of the 1,000 top spots, including all of the top ten spots at the time of our analysis. New Zealand, Saudi Arabia and Finland's AI academics are also highly ranked.

Global Augmented Analytics Market was valued US$ 4.6Bn in 2018 and is expected to reach US$ 20.2Bn by 2026 at a CAGR of 19.98%. This report provides a detailed analysis of the market segment based on insurance type, sales channel and region. This report also focuses on the top players in North America, Europe, Asia Pacific, Middle East & Africa, and South America. The objective of the report is to present a comprehensive assessment of the market and contains thoughtful insights, facts, historical data, industry-validated market data and projections with a suitable set of assumptions and methodology. The report also helps in understanding the global augmented analytics market dynamics, structure by identifying and analysing the market segments and project the global market size.

The market study on the global Healthcare Cognitive Computing market will encompass the entire ecosystem of the industry, covering five major regions namely North America, Europe, Asia Pacific, Latin America and Middle East & Africa, and the major countries falling under those regions. The study will feature estimates in terms of sales revenue and consumption from 2019 to 2025, at the global level and across the major regions mentioned above. The study has been created using a unique research methodology specifically designed for this market. Quantitative information includes Healthcare Cognitive Computing market estimates & forecast for a upcoming years, at the global level, split across the key segments covered under the scope of the study, and the major regions and countries. Sales revenue and consumption estimates, year-on-year growth analysis, price estimation and trend analysis, etc. will be a part of quantitative information for the mentioned segments and regions/countries.

We propose the Heterogeneous Thurstone Model (HTM) for aggregating ranked data, which can take the accuracy levels of different users into account. By allowing different noise distributions, the proposed HTM model maintains the generality of Thurstone's original framework, and as such, also extends the Bradley-Terry-Luce (BTL) model for pairwise comparisons to heterogeneous populations of users. Under this framework, we also propose a rank aggregation algorithm based on alternating gradient descent to estimate the underlying item scores and accuracy levels of different users simultaneously from noisy pairwise comparisons. We theoretically prove that the proposed algorithm converges linearly up to a statistical error which matches that of the state-of-the-art method for the single-user BTL model. We evaluate the proposed HTM model and algorithm on both synthetic and real data, demonstrating that it outperforms existing methods.

To achieve these common goals, agents in a MAS should be capable of interacting with other agents, not simply by exchanging data, but by engaging as in social activities, such as those people participate in their daily lives: cooperation, coordination, negotiation, and the like. In MASs, agents are assumed to be autonomous - capable of making independent decisions about to do in order to satisfy their design objectives, and thus they need mechanisms that allow them to synchronize and to coordinate their activities at run time [31]. Although one of the main issues in MASs is the agents' coordination structure, this is not hard-wired at design time, as MASs are typically in standard concurrent/distributed systems. One well-known strategy for coordination in MAS is the design of multi-agent coordinated plans [7][35][36][33][14] that include, not only usual agents' actions defined by their effectors, but also communication actions to achieve the necessary synchronization and coordination. To represent communication actions, some specific languages were created, e.g.

We present ALFRED (Action Learning From Realistic Environments and Directives), a benchmark for learning a mapping from natural language instructions and egocentric vision to sequences of actions for household tasks. Long composition rollouts with non-reversible state changes are among the phenomena we include to shrink the gap between research benchmarks and real-world applications. ALFRED consists of expert demonstrations in interactive visual environments for 25k natural language directives. These directives contain both high-level goals like "Rinse off a mug and place it in the coffee maker." and low-level language instructions like "Walk to the coffee maker on the right." ALFRED tasks are more complex in terms of sequence length, action space, and language than existing vision-and-language task datasets. We show that a baseline model designed for recent embodied vision-and-language tasks performs poorly on ALFRED, suggesting that there is significant room for developing innovative grounded visual language understanding models with this benchmark.

We present SafeLife, a publicly available reinforcement learning environment that tests the safety of reinforcement learning agents. It contains complex, dynamic, tunable, procedurally generated levels with many opportunities for unsafe behavior. Agents are graded both on their ability to maximize their explicit reward and on their ability to operate safely without unnecessary side effects. We train agents to maximize rewards using proximal policy optimization and score them on a suite of benchmark levels. The resulting agents are performant but not safe---they tend to cause large side effects in their environments---but they form a baseline against which future safety research can be measured.

To build AI-based systems that users and the public can justifiably trust one needs to understand how machine learning technologies impact trust put in these services. To guide technology developments, this paper provides a systematic approach to relate social science concepts of trust with the technologies used in AI-based services and products. We conceive trust as discussed in the ABI (Ability, Benevolence, Integrity) framework and use a recently proposed mapping of ABI on qualities of technologies. We consider four categories of machine learning technologies, namely these for Fairness, Explainability, Auditability and Safety (FEAS) and discuss if and how these possess the required qualities. Trust can be impacted throughout the life cycle of AI-based systems, and we introduce the concept of Chain of Trust to discuss technological needs for trust in different stages of the life cycle. FEAS has obvious relations with known frameworks and therefore we relate FEAS to a variety of international Principled AI policy and technology frameworks that have emerged in recent years.