Prophet models are effective, interpretable, and easy to use. But which one is better? In this post we will explore the implementation differences of Prophet and Neural Prophet and run a quick case study. But before we start coding, let's quickly cover some background information, more of which can be found here. Prophet (2017) is the predecessor to NeuralProphet (2020) -- the latter incorporates some autoregressive deep learning.

As the health and safety of our candidates and our employees come first, we're excited to provide virtual experiences for interviews and new hire on-boarding. Currently, reopening of offices is planned for January 2022. Dataminr puts real-time AI and public data to work for our clients, generating relevant and actionable alerts for global corporations, public sector agencies, newsrooms, and NGOs. Our real-time alerts enable tens of thousands of users at hundreds of public and private sector organizations to learn first of breaking events around the world, develop effective risk mitigation strategies, and respond with confidence as crises unfold. Dataminr is making its mark for growth and innovation, recently earning recognition on the Deloitte Technology Fast 500, Forbes AI 50 and Forbes Cloud 100 lists.

French president warns against telling Europeans what words to say and not to say; author Douglas Murray reacts on'Fox & Friends.' Norway's data privacy watchdog on Wednesday fined gay dating app Grindr 65 million kroner ($7.16 million) for sending sensitive personal data to hundreds of potential advertising partners without users' consent -- a breach of strict European Union privacy rules. The Norwegian Data Protection Authority said it imposed its highest fine to date because the California-based company didn't comply with the EU's tough data protection regulations. Norway isn't a member of the 27-nation bloc but closely mirrors EU rules. Grindr said the agency's findings related to consent policies from years ago, not its current practices, and that it is considering its next steps, including an appeal. The data watchdog "relies on a series of flawed findings, introduces many untested legal perspectives, and the proposed fine is therefore still entirely out of proportion with those flawed findings," said Grindr's chief privacy officer, Shane Wiley.

The widespread diffusion of synthetically generated content is a serious threat that needs urgent countermeasures. The generation of synthetic content is not restricted to multimedia data like videos, photographs, or audio sequences, but covers a significantly vast area that can include biological images as well, such as western-blot and microscopic images. In this paper, we focus on the detection of synthetically generated western-blot images. Western-blot images are largely explored in the biomedical literature and it has been already shown how these images can be easily counterfeited with few hope to spot manipulations by visual inspection or by standard forensics detectors. To overcome the absence of a publicly available dataset, we create a new dataset comprising more than 14K original western-blot images and 18K synthetic western-blot images, generated by three different state-of-the-art generation methods. Then, we investigate different strategies to detect synthetic western blots, exploring binary classification methods as well as one-class detectors. In both scenarios, we never exploit synthetic western-blot images at training stage. The achieved results show that synthetically generated western-blot images can be spot with good accuracy, even though the exploited detectors are not optimized over synthetic versions of these scientific images.

Monumental advances in deep learning have led to unprecedented achievements across a multitude of domains. While the performance of deep neural networks is indubitable, the architectural design and interpretability of such models are nontrivial. Research has been introduced to automate the design of neural network architectures through neural architecture search (NAS). Recent progress has made these methods more pragmatic by exploiting distributed computation and novel optimization algorithms. However, there is little work in optimizing architectures for interpretability. To this end, we propose a multi-objective distributed NAS framework that optimizes for both task performance and introspection. We leverage the non-dominated sorting genetic algorithm (NSGA-II) and explainable AI (XAI) techniques to reward architectures that can be better comprehended by humans. The framework is evaluated on several image classification datasets. We demonstrate that jointly optimizing for introspection ability and task error leads to more disentangled architectures that perform within tolerable error.

The DNA sequencing and DNA-RNA binding proteins is critical for identifying and modeling disease spread and building regulatory processes with deep learning algorithms. Implementation of deep convolutional neural networks with algorithms has achieved breakthrough performance on DNA sequencing by analyzing the protein binding microarrays and RNAcompete assays with distributed training on GPUs.

Defense startup Anduril has moved closer to its ambition to build a new industry giant with the acquisition of drone maker Area-I. Combining Anduril's cutting-edge AI with Area-I's proven air vehicles could breed a formidable new range of smart drones. This was the first time the Valkyrie had launched another drone. Area-I was in the headlines this week with the U.S. Air Force reporting the successful launch of one of their Altius-600 drones from an XQ-58 Valkyrie unmanned jet, hinting at plans for a future of unmanned motherships releasing fleets of drones. The Anduril acquisition is likely to take the already successful Altius to another level by opening up a new range of missions. Anduril, founded in 2017, aims to bring a fast-paced Silicon Valley approach to the defense sector, challenging the existing giants like Boeing BA, Northrop Grumman NOC and Raytheon which are geared to the traditional crawling pace of military acquisition: "We deploy in hours, not years," states their website.

A headline that many think will dominate in 2022 is regulation and data governance. A shift is underway and will continue next year in how both government agencies and private organizations are approaching AI ethics, according to analyst firm Cognilytica. Until recently, enterprises in large numbers were acquiring and implementing various AI tools and platforms largely without considering the consequences of biased and unexplainable algorithms. But by the end of 2022, vendors will not be able to sell major AI systems to a large business or government agency without following specific guidelines relating to bias, transparency, explainability and data set collection. There's already a framework for ethical AI in the U.S. Department of Defense, noted Ronald Schmelzer, a Cognilytica analyst.

On November 10, 2021, the New York City Council passed a bill prohibiting employers and employment agencies from using automated employment decision tools to screen candidates or employees, unless a bias audit has been conducted prior to deploying the tool (the "Bill"). The Bill defines an "automated employment decision tool" as any computational process (either derived from machine learning, statistical modeling, data analytics, or artificial intelligence) that issues a simplified output (e.g., a score, classification or recommendation) to substantially assist or replace human decision-making for employment decisions that have an impact natural persons. Under the Bill, use of such a tool is permissible if it has been the subject of a bias audit (i.e., an impartial evaluation by an independent auditor, conducted no more than one year prior to the use of the tool) and a summary of the audit results are made publicly available on the website of the employer or employment agency before deployment of the tool. Moreover, employers or employment agencies are required to provide notice to employees or candidates residing in NYC that an automated employment decision tool will be used to assess or evaluate their candidacy, no less than ten business days before using the tool. Candidates also have the right to request an alternative selection process or accommodation under the Bill.

Next-generation solutions and products are hitting a wall with wi-fi: it's not fast enough, and latency and connectivity issues mean it's not reliable enough. What's an innovator to do? Focus on what's next: 5G and software-defined networking. Nick McKeown, senior vice president and general manager of the network and edge group at Intel Corporation says this technical leap is what will make future innovation possible, "Once you've got a software platform where you can change its behavior, you can start introducing previously absurd-sounding ideas," including, he continues, "fanciful ideas of automatic, real-time, closed-loop control of an entire network." While nascent, these technological advancements are already showing promise in practical applications. For example, in industrial settings where there's more analysis happening at the edge, having greater observability into the network is allowing for fine timescale responses to mechanical errors and broken equipment. "Corrective action could be something as mundane as a broken link, a broken piece of equipment, but it could actually be a functional incorrectness in the software that is controlling it," says McKeown. Grad students and programmers are taking advantage of the advancements in network technology to try out new ideas through academic projects. "One of the key ideas," says McKeown, "is to verify in real time that the network is operating according to a specification, formally checking against that specification in real time, as packets fly around in the network. This has never been done before." And although this idea remains in the realm of research projects, McKeown believes it exemplifies the promise of a software-based 5G networking future. Software-defined 5G networking promises applications that we can't yet even imagine, says McKeown. "New IoT apps combined with both public and private 5G is going to create a'Cambrian explosion' of new ideas that will manifest in ways that if we were to try to predict, we would get it wrong." Laurel Ruma: From MIT Technology Review, I'm Laurel Ruma and this is Business Lab. The show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.