Artificial Intelligence (AI) has become ubiquitous in the past several years. There is not a part of our businesses, cultures, governments and consumer markets. The continuous research and innovation directed by tech giants are driving the adoption of advanced technologies in industry verticals, such as automotive, healthcare, retail, finance, and manufacturing, staffing and education. Technology has always been an essential element for these industries, but artificial intelligence has brought technology to the center of organizations. For instance, from self-driving vehicles to crucial life-saving medical gear, AI is being infused virtually into every apparatus and program.

As generative AI and its eerily human chatbots explode into the public realm -- including Google's Bard, released yesterday -- Silicon Valley looks ripe for another big era of disruption. Think about the era of personal computers, or online businesses, or social platforms, when an accessible, unpredictable new idea shakes up the establishment. But unlike earlier disruptions, the reality of the generative AI race is already looking a little … top-heavy. With AI, the big innovation isn't the kind of cheap, accessible technology that helps garage startups grow into world-changing new companies. The models that underpin the AI era can be extremely, extremely expensive to build.

Riverside Research is an independent National Security Nonprofit dedicated to research and development in the national interest. We provide high-end technical services, research and development, and prototype solutions to some of the country's most challenging technical problems. The Riverside Research Engineering and Support Solutions (ESS) Division conducts Research and Development (R&D) of cyber-physical attacks against embedded systems. The RF Research Engineer will perform theoretical and hands-on work to study electromagnetic emissions from microelectronic devices. They will use RF measurement equipment, analog and digital signal processing techniques, and custom analysis software to test embedded systems in a laboratory environment.

This company is a startup focused on hypersonic aircraft development and revolutionizing air travel.The are collaborating with government organizations such as the US Air Force and NASA to develop a series of unmanned aircraft that mitigate risk and address critical national security issues. These autonomous planes provide vital data and assurance required for the certification, production, operation, and maintenance of safe and comfortable commercial aircraft. They are looking for a Lead Airframe Structures Engineer to handle the development, design, analysis, testing, and life cycle of a structural system or component for the hypersonic aircraft. The ideal candidate will have 5 years of experience in airframe structural design, analysis, and optimization for aircrafts or spacecrafts.

STOCKHOLM/LONDON – Rapid technological advances such as the ChatGPT generative artificial intelligence (AI) app are complicating efforts by European Union lawmakers to agree on landmark AI laws, sources with direct knowledge of the matter have said. The European Commission proposed the draft rules nearly two years ago in a bid to protect citizens from the dangers of the emerging technology, which has experienced a boom in investment and consumer popularity in recent months. The draft needs to be thrashed out between EU countries and EU lawmakers, called a trilogue, before the rules can become law. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

America's Federal Trade Commission has warned it may crack down on companies that not only use generative AI tools to scam folks, but also those making the software in the first place, even if those applications were not created with that fraud in mind. Now the US government agency is wagging its finger at those using generative machine-learning tools to hoodwink victims into parting with their cash and suchlike as well as the people who made the code to begin with. Commercial software and cloud services, as well as open source tools, can be used to churn out fake images, text, videos, and voices on an industrial scale, which is all perfect for cheating marks. Picture adverts for stuff featuring convincing but faked endorsements by celebrities; that kind of thing is on the FTC's radar. "Evidence already exists that fraudsters can use these tools to generate realistic but fake content quickly and cheaply, disseminating it to large groups or targeting certain communities or specific individuals," Michael Atleson, an attorney for the FTC's division of advertising practices, wrote in a memo this week.

"Computers need to be accountable to machines," a top Microsoft executive told a roomful of reporters in Washington, DC, on February 10, three days after the company launched its new AI-powered Bing search engine. Computers need to be accountable to people!" he said, and then made sure to clarify, "That was not a Freudian slip." Slip or not, the laughter in the room betrayed a latent anxiety. Progress in artificial intelligence has been moving so unbelievably fast lately that the question is becoming unavoidable: How long until AI dominates our world to the point where we're answering to it rather than it answering to us? First, last year, we got DALL-E 2 and Stable Diffusion, which can turn a few words of text into a stunning image. Then Microsoft-backed OpenAI gave us ChatGPT, which can write essays so convincing that it freaks out everyone from teachers (what if it helps students cheat?) to journalists (could it replace them?) to disinformation experts (will it amplify conspiracy ...

The complexity and ambiguity of financial and economic systems, along with frequent changes in the economic environment, have made it difficult to make precise predictions that are supported by theory-consistent explanations. Interpreting the prediction models used for forecasting important macroeconomic indicators is highly valuable for understanding relations among different factors, increasing trust towards the prediction models, and making predictions more actionable. In this study, we develop a fundamental-based model for the Canadian-U.S. dollar exchange rate within an interpretative framework. We propose a comprehensive approach using machine learning to predict the exchange rate and employ interpretability methods to accurately analyze the relationships among macroeconomic variables. Moreover, we implement an ablation study based on the output of the interpretations to improve the predictive accuracy of the models. Our empirical results show that crude oil, as Canada's main commodity export, is the leading factor that determines the exchange rate dynamics with time-varying effects. The changes in the sign and magnitude of the contributions of crude oil to the exchange rate are consistent with significant events in the commodity and energy markets and the evolution of the crude oil trend in Canada. Gold and the TSX stock index are found to be the second and third most important variables that influence the exchange rate. Accordingly, this analysis provides trustworthy and practical insights for policymakers and economists and accurate knowledge about the predictive model's decisions, which are supported by theoretical considerations.

Systems with artificial intelligence components, so-called AI-based systems, have gained considerable attention recently. However, many organizations have issues with achieving production readiness with such systems. As a means to improve certain software quality attributes and to address frequently occurring problems, design patterns represent proven solution blueprints. While new patterns for AI-based systems are emerging, existing patterns have also been adapted to this new context. The goal of this study is to provide an overview of design patterns for AI-based systems, both new and adapted ones. We want to collect and categorize patterns, and make them accessible for researchers and practitioners. To this end, we first performed a multivocal literature review (MLR) to collect design patterns used with AI-based systems. We then integrated the created pattern collection into a web-based pattern repository to make the patterns browsable and easy to find. As a result, we selected 51 resources (35 white and 16 gray ones), from which we extracted 70 unique patterns used for AI-based systems. Among these are 34 new patterns and 36 traditional ones that have been adapted to this context. Popular pattern categories include "architecture" (25 patterns), "deployment" (16), "implementation" (9), or "security & safety" (9). While some patterns with four or more mentions already seem established, the majority of patterns have only been mentioned once or twice (51 patterns). Our results in this emerging field can be used by researchers as a foundation for follow-up studies and by practitioners to discover relevant patterns for informing the design of AI-based systems.

Federated Learning (FL) has gained widespread popularity in recent years due to the fast booming of advanced machine learning and artificial intelligence along with emerging security and privacy threats. FL enables efficient model generation from local data storage of the edge devices without revealing the sensitive data to any entities. While this paradigm partly mitigates the privacy issues of users' sensitive data, the performance of the FL process can be threatened and reached a bottleneck due to the growing cyber threats and privacy violation techniques. To expedite the proliferation of FL process, the integration of blockchain for FL environments has drawn prolific attention from the people of academia and industry. Blockchain has the potential to prevent security and privacy threats with its decentralization, immutability, consensus, and transparency characteristic. However, if the blockchain mechanism requires costly computational resources, then the resource-constrained FL clients cannot be involved in the training. Considering that, this survey focuses on reviewing the challenges, solutions, and future directions for the successful deployment of blockchain in resource-constrained FL environments. We comprehensively review variant blockchain mechanisms that are suitable for FL process and discuss their trade-offs for a limited resource budget. Further, we extensively analyze the cyber threats that could be observed in a resource-constrained FL environment, and how blockchain can play a key role to block those cyber attacks. To this end, we highlight some potential solutions towards the coupling of blockchain and federated learning that can offer high levels of reliability, data privacy, and distributed computing performance.