The European Commission and European Investment Fund (EIF) has launched a new investment scheme for artificial intelligence (AI) and blockchain in Europe, the organization announced in a Nov. 20 blog post. "With the European Commission, we are launching a dedicated investment scheme that will make EUR 100m available to venture capital funds or other investors that support AI and blockchain-based products and services. Because these are cornerstone investments, we expect a total of EUR 300m to be generated for AI and blockchain from other private investors'crowding in.'" The project will focus on development and growth beyond the research and proof of concept stage. Western Europe is expected to spend $674 million on blockchain technology in 2019, making it the second highest-spending region in the world.

Expeditionary Artificial Intelligence is the theme for the May 2020 research and experimentation week during which Private Industry, Academia, and Non-Government Organizations will collaborate on developing and demonstrating Machine and Deep Learning, sensor, networked all-domain Autonomous Systems, and other technologies. The May 2020 experiment will be co-hosted by The Sea Land Air Military Research initiative (SLAMR) and Joint Interagency Field Experimentation (JIFX) Program. The initial TechOp Day at the Naval Postgraduate School (NPS) was part of the laying the groundwork with Private Industry and other partners. This video was produced by NPS and can be watched at NPS' YouTube channel at https://www.youtube.com/user/NPSvideo.

Recently the concept of transformative AI (TAI) has begun to receive attention in the AI policy space. TAI is often framed as an alternative formulation to notions of strong AI (e.g. artificial general intelligence or superintelligence) and reflects increasing consensus that advanced AI which does not fit these definitions may nonetheless have extreme and long-lasting impacts on society. However, the term TAI is poorly defined and often used ambiguously. Some use the notion of TAI to describe levels of societal transformation associated with previous 'general purpose technologies' (GPTs) such as electricity or the internal combustion engine. Others use the term to refer to more drastic levels of transformation comparable to the agricultural or industrial revolutions. The notion has also been used much more loosely, with some implying that current AI systems are already having a transformative impact on society. This paper unpacks and analyses the notion of TAI, proposing a distinction between TAI and radically transformative AI (RTAI), roughly corresponding to societal change on the level of the agricultural or industrial revolutions. We describe some relevant dimensions associated with each and discuss what kinds of advances in capabilities they might require. We further consider the relationship between TAI and RTAI and whether we should necessarily expect a period of TAI to precede the emergence of RTAI. This analysis is important as it can help guide discussions among AI policy researchers about how to allocate resources towards mitigating the most extreme impacts of AI and it can bring attention to negative TAI scenarios that are currently neglected.

The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses the random method to determine the initial cluster centers, which make clustering results prone to local optima and then result in worse clustering performance. Many initialization methods have been proposed, but none of them can dynamically adapt to datasets with various characteristics. In our previous research, an initialization method for K-means based on hybrid distance was proposed, and this algorithm can adapt to datasets with different characteristics. However, it has the following drawbacks: (a) When calculating density, the threshold cannot be uniquely determined, resulting in unstable results. (b) Heavily depending on adjusting the parameter, the parameter must be adjusted five times to obtain better clustering results. (c) The time complexity of the algorithm is quadratic, which is difficult to apply to large datasets. In the current paper, we proposed an adaptive initialization method for the K-means algorithm (AIMK) to improve our previous work. AIMK can not only adapt to datasets with various characteristics but also obtain better clustering results within two interactions. In addition, we then leverage random sampling in AIMK, which is named as AIMK-RS, to reduce the time complexity. AIMK-RS is easily applied to large and high-dimensional datasets. We compared AIMK and AIMK-RS with 10 different algorithms on 16 normal and six extra-large datasets. The experimental results show that AIMK and AIMK-RS outperform the current initialization methods and several well-known clustering algorithms. Furthermore, AIMK-RS can significantly reduce the complexity of applying it to extra-large datasets with high dimensions. The time complexity of AIMK-RS is O(n).

Synthetic medical data which preserves privacy while maintaining utility can be used as an alternative to real medical data, which has privacy costs and resource constraints associated with it. At present, most models focus on generating cross-sectional health data which is not necessarily representative of real data. In reality, medical data is longitudinal in nature, with a single patient having multiple health events, non-uniformly distributed throughout their lifetime. These events are influenced by patient covariates such as comorbidities, age group, gender etc. as well as external temporal effects (e.g. flu season). While there exist seminal methods to model time series data, it becomes increasingly challenging to extend these methods to medical event time series data. Due to the complexity of the real data, in which each patient visit is an event, we transform the data by using summary statistics to characterize the events for a fixed set of time intervals, to facilitate analysis and interpretability. We then train a generative adversarial network to generate synthetic data. We demonstrate this approach by generating human sleep patterns, from a publicly available dataset. We empirically evaluate the generated data and show close univariate resemblance between synthetic and real data. However, we also demonstrate how stratification by covariates is required to gain a deeper understanding of synthetic data quality.

William Karnes, MD, is director of the high-risk program and colonoscopy quality at the UCI Health H.H. Chao Comprehensive Digestive Disease Center in Orange, Calif., and chief medical officer of Docbot, a technology that uses artificial intelligence to detect abnormalities from colonoscopy capsule video. Here, Dr. Karnes shares his thoughts with Becker's ASC Review on the future of AI in the gastroenterology specialty, and how the technology could help patients and physicians. Question: Can you tell me a little more about the Docbot technology and how you got involved? Dr. William Karnes: The story goes back to 2012 when I came to UCI and Dr. Chan brought me on to wipe out colon cancer in Orange County. It was a three-pronged approach but one of the most important ones.

AI systems are already creating works that, if created by a human, would warrant IP protection. Reports of AI-created art, music, literary works, data, technology, formulas, flavors, and other innovations are becoming increasingly common. In 2016, for example, a new "Rembrandt" portrait was unveiled in the Netherlands, generated by an AI system that analyzed more than 300 real paintings of the Dutch master and then used AI, facial recognition, and 3D printing technologies to create an entirely new work in the same style. In 2018, a portrait created using AI sold for $432,500 at auction. A novel publicized as being the first book written by AI was published last year.

NEW DELHI: Chief Justice of India (CJI) S A Bobde said on Tuesday that the Supreme Court has proposed to introduce a system of artificial intelligence (AI) which would aid in the administration of justice delivery. The CJI however made it clear that there should not be any impression that introduction of AI would ever substitute the judges. "We propose to introduce, if possible, a system of artificial intelligence. There are many things which we need to look at before we introduce it. We do not want to give the impression that this is ever going to substitute the judges," Justice Bobde said at the Constitution Day function organised by the Supreme Court Bar Association (SCBA).

If a Russian or Chinese Anti-Satellite (ASAT) weapon streamed into space and exploded U.S. military satellites, friendly forces would instantly become very vulnerable to significant and extremely destructive enemy attacks - space-based infrared missile detection could be destroyed, GPS communications could be knocked out, guided weapons could jam and derail before hitting their targets and war-critical command and control could simply be "taken out." Any, all or part of this could happen in as little as 10 to 15 minutes once a satellite attacking missile is launched from the ground. Lives will hang in the balance as alerts are sent through U.S. command and control and decision-makers scramble to determine the best countermeasure with which to protect its space assets. Space war is no longer a distant prospect to envision years down the road --- it is here. Recognizing the seriousness of this vulnerability, the Pentagon, U.S. Space Command, Missile Defense Agency and industry are moving quickly to integrate Machine Learning and AI into space-based systems and technology.

One of the most common use cases for the role of AI in cybersecurity is support for human analysts. After all, AI is unlikely to ever replace experienced security analysts. It is far more likely to augment humans in areas where machines excel, such as analyzing big data, eliminating fatigue and freeing up humans from tedious tasks so they can utilize more sophisticated skills, like creativity, nuance and expertise. In some cases, analyst augmentation involves incorporating predictive analytics into security operations center (SOC) workflows for triage or querying big data sets. Darktrace's Cyber AI Analyst is a software program that supports human analysts by only surfacing high-priority events. Meanwhile, it queries massive data and pivots across networks to gather context for investigations, conduct them and sort out low-priority cases.