If the technological innovation coming out of Silicon Valley is as important as venture capitalists insist, the past few days suggest they haven't been very responsible stewards of it. The collapse of Silicon Valley Bank late last week may have resulted from a perfect storm of ugly events. But it was also emblematic of a startup ecosystem and venture-capital apparatus that are too unstable, too risky, and too unmoored from reality to be left in charge of something as important as the direction of our technological development. As the startups that make up Silicon Valley Bank's customer base scrambled to figure out whether they would be able to make payroll, a group of extremely online venture capitalists spent four days emoting on Twitter, ginning up confusion and hysteria about the threat of a systemic risk if depositors didn't get all their money back, pronto. All weekend, they screamed that there would be an economic collapse, that they were concerned about the workers, that the Federal Reserve was responsible, that-that-that … until finally, on Sunday evening, they got what they wanted: the government promising full account access to all Silicon Valley Bank depositors. By now, it is relatively clear what happened at Silicon Valley Bank.

As technology continues to evolve, so do cyber threats. In 2021 alone, cybercrime caused losses of around $1 trillion worldwide and is expected to grow to 10.5 trillion annually by 2025. To stay ahead of attackers and strengthen defenses against them, cybersecurity companies are seeking ways to enhance technology. One promising technology that's becoming increasingly popular in this space is ChatGPT – an emerging standard that promises improved efficiency when handling sensitive data. ChatGPT has the potential to revolutionize cybersecurity by providing a faster and more efficient method for analyzing data, detecting threats, and responding to incidents.

Sen. John Thune, R-S.D., on his bill that would give the Biden administration greater power to regulate the Chinese-owned company. Advances in neuroscience, engineering and artificial intelligence have brought us a world of greater brain transparency. With consumer brain wearables coming of age, we can now monitor brain activity as easily as we track heart rate, blood oxygen levels and steps taken in a day; which will revolutionize our relationship with technology and understanding of ourselves and each other. Consumer brain wearables can empower us to take charge of our mental health and wellbeing, but they also threaten our last bastion of freedom. Consumer brain wearables are devices with brain sensors embedded into everyday technology like ear buds, headphones and watches that can detect brain activity to give us insight into our own minds.

Paige, a global leader in end-to-end digital pathology solutions and clinical AI applications, announced the availability of Mindpeak's AI algorithms for IHC biomarker quantification in the Paige Platform. Mindpeak, a leader in image analysis AI software, brings state-of-the-art AI algorithms for analyzing IHC slides of lung and breast tissue into the Paige Platform. These two leading pathology AI providers have combined their technologies to help pathologists deliver faster, more accurate, and more reproducible quantitative cancer diagnoses, even in the most challenging and complex cases. "The Paige Platform is one of the most robust and easily integrated solutions available for both routine and AI-augmented digital workflows. The platform offers open compatibility with Mindpeak's image analysis algorithms and AI solutions to support a complete and optimized workflow experience for pathologists" This new integration allows a complete and seamless workflow on the Paige Platform through FullFocus, Paige's FDA-cleared whole-slide image viewer*.

A team of Chinese researchers funded by China's People's Liberation Army (PLA) recently claimed to have used artificial intelligence (AI) to design an electrical layout of a warship with 100 percent accuracy and at an unprecedented speed. A team of researchers from the China Ship Design and Research Center, headed by Luo Wei, a senior engineer with the ship design center, published a paper in the Chinese-language journal Computer Integrated Manufacturing Systems on February 27. The researchers claimed in the paper that their AI designer took only a day to complete work that humans would need nearly a year to achieve with the most advanced computer tools. Considering the scale and complexity of modern warships, mistakes are sure to happen during the design process, and it can take several hours to discover and rectify them. However, when the researchers put the AI designer to the test, with more than 400 challenging tasks, they found that the AI could accomplish 100 percent accuracy.

ChatGPT has answers for almost everything, but there's one answer we may not know for a while: will this tool turn out to be the genie its creators regret taking out of the bottle over unintended consequences in cybersecurity? BlackBerry surveyed 1,500 IT decision makers across North America, UK, and Australia and half (51 percent) predicted we're less than a year away from a cyberattack credited to ChatGPT. Three-quarters of respondents believe foreign states are already using ChatGPT for malicious purposes against other nations. The survey also exposed a perception that, while respondents see ChatGPT as being used for'good' purposes, 73 percent acknowledge its potential threat to cybersecurity and are either very or fairly concerned, proving Artificial Intelligence (AI) is a double-edged sword. The emergence of chatbots and AI-powered tools presents new challenges in cybersecurity, especially when they end up in the wrong hands.

It's critical to understand what you're worth as you navigate the field Coursera released this article regarding the latest trends. From the article: "In 2022, Glassdoor ranked data scientists as the third-best job in America [1]. If you enjoy analyzing data to identify patterns and solve problems, this career path holds plenty of well-paid opportunities for you. You can use this article to find out how much a data scientist makes and how you can increase your salary in this role. Between 2021 and 2031, the US Bureau of Labor Statistics estimates the employment rate for data scientists to grow by 21 percent by 2030 [2].

This work addresses the task of risk evaluation in traffic scenarios with limited observability due to restricted sensorial coverage. Here, we concentrate on intersection scenarios that are difficult to access visually. To identify the area of sight, we employ ray casting on a local dynamic map providing geometrical information and road infrastructure. Based on the area with reduced visibility, we first model scene entities that pose a potential risk without being visually perceivable yet. Then, we predict a worst-case trajectory in the survival analysis for collision risk estimation. Resulting risk indicators are utilized to evaluate the driver's current behavior, to warn the driver in critical situations, to give suggestions on how to act safely or to plan safe trajectories. We validate our approach by applying the resulting intersection warning system on real world scenarios. The proposed system's behavior reveals to mimic the general behavior of a correctly acting human driver.

State-sponsored trolls are the main actors of influence campaigns on social media and automatic troll detection is important to combat misinformation at scale. Existing troll detection models are developed based on training data for known campaigns (e.g.\ the influence campaign by Russia's Internet Research Agency on the 2016 US Election), and they fall short when dealing with {\em novel} campaigns with new targets. We propose MetaTroll, a text-based troll detection model based on the meta-learning framework that enables high portability and parameter-efficient adaptation to new campaigns using only a handful of labelled samples for few-shot transfer. We introduce \textit{campaign-specific} transformer adapters to MetaTroll to ``memorise'' campaign-specific knowledge so as to tackle catastrophic forgetting, where a model ``forgets'' how to detect trolls from older campaigns due to continual adaptation. Our experiments demonstrate that MetaTroll substantially outperforms baselines and state-of-the-art few-shot text classification models. Lastly, we explore simple approaches to extend MetaTroll to multilingual and multimodal detection. Source code for MetaTroll is available at: https://github.com/ltian678/metatroll-code.git.

Most US school districts draw "attendance boundaries" to define catchment areas that assign students to schools near their homes, often recapitulating neighborhood demographic segregation in schools. Focusing on elementary schools, we ask: how much might we reduce school segregation by redrawing attendance boundaries? Combining parent preference data with methods from combinatorial optimization, we simulate alternative boundaries for 98 US school districts serving over 3 million elementary-aged students, minimizing White/non-White segregation while mitigating changes to travel times and school sizes. Across districts, we observe a median 14% relative decrease in segregation, which we estimate would require approximately 20\% of students to switch schools and, surprisingly, a slight reduction in travel times. We release a public dashboard depicting these alternative boundaries (https://www.schooldiversity.org/) and invite both school boards and their constituents to evaluate their viability. Our results show the possibility of greater integration without significant disruptions for families.