The last decade has seen the rise of Adversarial Machine Learning (AML). This discipline studies how to manipulate data to fool inference engines, and how to protect those systems against such manipulation attacks. Extensive work on attacks against regression and classification systems is available, while little attention has been paid to attacks against time series forecasting systems. In this paper, we propose a decision analysis based attacking strategy that could be utilized against Bayesian forecasting dynamic models.

… an effort that involved the implementation and use of artificial intelligence, machine learning, and high-performance computing.

Jeffrey Austin White has joined Holmes Murphy as its Chief Analytics Officer (CAO), bringing his nearly three decades of expertise focused on strategic investments in talent and technology to the national insurance brokerage. In this new role for Holmes Murphy, White will work to increase emphasis on innovation and data intelligence, helping to drive the company's potential and performance for clients. He will largely be responsible for defining and driving analytics and business intelligence initiatives, including assessing the current state of data and analytics capabilities, developing an analytics strategy, and leveraging innovations in artificial intelligence to propel Holmes Murphy and its clients forward. White will also be working to provide insight on potential opportunities for BrokerTech Ventures (BTV), the first broker-led convening platform and accelerator program focused on delivering innovation to the insurance broker industry. "Holmes Murphy continues to raise the bar when it comes to developing technology and innovations within the insurance industry. I'm excited to play a role in advancing their data analytics and AI capabilities," said White.

A new survey from Gartner found that a majority of CIOs are focusing their investments this year and next year on AI and distributed cloud …

A student-led team of robotics experts will participate in the penultimate event next week for an international challenge that could pave the way for future breakthrough innovations in the world of autonomous vehicles. It should, given Pittsburgh universities' history of performing well in challenges such as the DARPA Grand Challenge, DARPA Urban Challenge or the 2012-2015 Robotics Challenge. But this time it's not a Carnegie Mellon University-led team that is preparing – it's a student group of roboticists from the University of Pittsburgh readying itself for next week's Indy Autonomous Challenge finals on October 23rd. The goal of the challenge is a race around the famous Indianapolis Motor Speedway in self-driving cars – the grand prize of $1 million is up for grabs. Leading the team is Nayana Suvarna, the head of Pitt's Robotics & Automation Society (RAS), a robotics club at the school that pursues robotics education opportunities (the school doesn't have a formal robotics program).

It's been a hot, hot year in the world of data, machine learning and AI. Just when you thought it couldn't grow any more explosively, the data/AI landscape just did: rapid pace of company creation, exciting new product and project launches, a deluge of VC financings, unicorn creation, IPOs, etc. It has also been a year of multiple threads and stories intertwining. One story has been the maturation of the ecosystem, with market leaders reaching large scale and ramping up their ambitions for global market domination, in particular through increasingly broad product offerings. Some of those companies, such as Snowflake, have been thriving in public markets (see our MAD Public Company Index), and a number of others (Databricks, Dataiku, Datarobot, etc.) have raised very large (or in the case of Databricks, gigantic) rounds at multi-billion valuations and are knocking on the IPO door (see our Emerging MAD company Index – both indexes will be updated soon). But at the other end of the spectrum, this year has also seen the rapid emergence of a whole new generation of data and ML startups. Whether they were founded a few years or a few months ago, many experienced a growth spurt in the last year or so. As we will discuss, part of it is due to a rabid VC funding environment and part of it, more fundamentally, is due to inflection points in the market. In the last year, there's been less headline-grabbing discussion of futuristic applications of AI (self-driving vehicle, etc.), and a bit less AI hype as a result. Regardless, data and ML/AI-driven application companies have continued to thrive, particularly those focused on enterprise use cases. Meanwhile, a lot of the action has been happening behind the scenes on the data and ML infrastructure side, with entire new categories (data observability, reverse ETL, metrics stores, etc.) appearing and/or drastically accelerating. To keep track of this evolution, this is our eighth annual landscape and "state of the union" of the data and AI ecosystem – co-authored this year with my FirstMark colleague John Wu. (For anyone interested, here are the prior versions: 2012, 2014, 2016, 2017, 2018, 2019 (Part I and Part II) and 2020.) For those who have remarked over the years how insanely busy the chart is, you'll love our new acronym – Machine learning, Artificial intelligence and Data (MAD) – this is now officially the MAD landscape! We've learned over the years that those posts are read by a broad group of people, so we have tried to provide a little bit for everyone – a macro view that will hopefully be interesting and approachable to most; and then a slightly more granular overview of trends in data infrastructure and ML/AI for people with deeper familiarity with the industry. This (long!) post is organized as follows: Let's start with the high level view of the market. As the number of companies in the space keeps increasing every year, the inevitable questions are: why is this happening?

Jeff Shuren, director of the FDA's Center for Devices and Radiological Health, on Thursday called out the need for better methodologies for identification and improvement of algorithms prone to mirroring "systemic biases" in the healthcare system and the data used to train artificial intelligence and machine learning-based devices, speaking at an FDA public workshop on the topic. The medical device industry should develop a strategy to enroll racially and ethnically diverse populations in clinical trials. "It's essential that the data used to train [these] devices represent the intended patient population with regards to age, gender, sex, race and ethnicity," Shuren said. The virtual workshop comes nine months after the agency released an action plan for establishing a regulatory approach to AI/ML-based Software as a Medical Device (SaMD). Among the five actions laid out in the plan, FDA intends to foster a patient-centered approach that includes device transparency for users.

Artificial intelligence in healthcare is the use of machine-learning algorithms and software to analyze, process and present complex medical and health care data. It has been widely used to support clinical decisions, improve workflows and predict health outcomes. Thus, wide application of AI in the healthcare sector is likely to propel the growth of the market. The growth of the artificial intelligence in healthcare market is attributed to the rising application of artificial intelligence in healthcare, growing investment in AI healthcare start-ups, and increasing cross-industry partnerships and collaborations. However, dearth of skilled AI workforce and imprecise regulatory guidelines for medical software is the major factor hindering the market growth.

The most enduring idea from George Orwell's 1984 is that even as we design and produce language, we are shaped in turn by the language we use--words are crucial in explaining complex ideas and in the process of meaning-making. The dynamic and fast-evolving world of technology, innovation, politics, and security sees the equally frenetic adoption of catchphrases. 'Cyber sovereignty,' 'geotech,' 'ethics washing' and more are fast becoming common lexicons, deployed differentially and differently. Sometimes used with care and thought, and on most occasions with an incomplete understanding of the distinct contexts that shape their use. 'New normal' was one such phrase that captured a collective experience, marking an apparent inflection point in our relationship with technology.

A combination of high-resolution satellite imaging and'deep learning' has identified more than 1.8 billion trees across the West African Sahara, Sahel and sub-humid zone – significantly more trees than were previously thought to exist in the region. The collaboration between NASA and several geoscience departments across the world used 11,128 satellite images from four satellites to count individual trees across 1.3 million square kilometres. The deep-learning approach has, for the first time, allowed researchers to identify individual trees across the dryland expanse. Because of the absence of closed canopies, many parts of the Sahara and the Sahel have previously been mapped with zero per cent tree cover. 'You need high-resolution satellite images to be able to detect individual trees and not just to make estimations based on identified areas of canopy cover,' says Martin Brandt from the University of Copenhagen.