Innovations in artificial intelligence are making it faster and cheaper for political campaigns to identify, turn out and extract money from voters. The big picture: Consultants for both major parties are hoovering up voter data to hone advanced fundraising and persuasion tactics. These data tools are especially useful in down-ballot local races. What's happening: A host of consultancies are now marketing their use of AI and machine learning to boost political clients. What they're saying: "This is a super-weapon that Democrats have," boasted Martin Kurucz, Sterling's managing partner, who says his firm has worked with about 1,000 Democratic campaigns and political committees.

EU lawmakers held their first political debate on the AI Act on Wednesday (5 October) as the discussion moved to more sensitive topics like the highly debated issue of biometric recognition. The AI Act is a landmark EU legislation intended to regulate Artificial Intelligence introducing a series of obligations proportional to the potential harm of the technologies' applications. So far, the co-rapporteurs of the European Parliament, the social democrat Brando Benifei and the liberal Dragoș Tudorache, have limited the discussion to the more technical aspects, hoping to build momentum before addressing the more political hurdles. This approach was not without its successes since the file progressed in several parts. In the meeting, the MEPs formally agreed on the first two batches of compromises on administrative procedures, conformity assessment, standards, and certificates.

Robotic Process Automation (RPA) is one of the most popular technologies for automating business processes. This technology enables fast and, above all, efficient automation of standardized processes. However, the range of uses is limited by the need for structured data and programmable decision-making. However, this shortcoming can be overcome through the use of artificial intelligence. In the following, we will show you RPA and AI, and how artificial intelligence can help RPA bots become smarter.

In case you didn't know, it's Fat Bear Week--the annual tournament that pits the brown bears of Katmai National Park against each other to see who gained the most weight over the year. You can vote for your favorite bear in a March Madness-style bracket where one is crowned the biggest bear of the bunch by the week's end. It's a fun and cheeky project hosted by the U.S. National Parks Service and Explore.org, a multimedia organization best known for the live cams of wildlife like the Brooks Falls Brown Bears of Katmai National Park. While the stream enjoys a healthy and devoted following throughout the year--folks who have created fan wikis, forums, and stan communities for individual bears--its viewer count balloons like its eponymous creatures when Fat Bear Week rolls around. And, as it turns out, it's these very same viewers who can help keep the bears alive and thriving in a world that's rapidly endangering their ecosystems. "We have thousands who watch the bear cams, especially right now with Fat Bear Week," Ed Miller, the co-founder of the BearID Project conservation group, told The Daily Beast.

AlphaTensor was designed to perform matrix multiplications, but the same approach could be used to tackle other mathematical challenges.Credit: DeepMind Researchers at DeepMind in London have shown that artificial intelligence (AI) can find shortcuts in a fundamental type of mathematical calculation, by turning the problem into a game and then leveraging the machine-learning techniques that another of the company's AIs used to beat human players in games such as Go and chess. The AI discovered algorithms that break decades-old records for computational efficiency, and the team's findings, published on 5 October in Nature1, could open up new paths to faster computing in some fields. "It is very impressive," says Martina Seidl, a computer scientist at Johannes Kepler University in Linz, Austria. "This work demonstrates the potential of using machine learning for solving hard mathematical problems." Advances in machine learning have allowed researchers to develop AIs that generate language, predict the shapes of proteins2 or detect hackers.

In order to share the magic of DALL·E 2 with a broad audience, we needed to reduce the risks associated with powerful image generation models. To this end, we put various guardrails in place to prevent generated images from violating our content policy. This post focuses on pre-training mitigations, a subset of these guardrails which directly modify the data that DALL·E 2 learns from. In particular, DALL·E 2 is trained on hundreds of millions of captioned images from the internet, and we remove and reweight some of these images to change what the model learns. Since training data shapes the capabilities of any learned model, data filtering is a powerful tool for limiting undesirable model capabilities.

Today's report on AI of retinal vessel images to help predict the risk of heart attack and stroke, from over 65,000 UK Biobank participants, reinforces a growing body of evidence that deep neural networks can be trained to "interpret" medical images far beyond what was anticipated. Add that finding to last week's multinational study of deep learning of retinal photos to detect Alzheimer's disease with good accuracy. In this post I am going to briefly review what has already been gleaned from 2 classic medical images--the retina and the electrocardiogram (ECG)--as representative for the exciting capability of machine vision to "see" well beyond human limits. Obviously, machines aren't really seeing or interpreting and don't have eyes in the human sense, but they sure can be trained from hundreds of thousand (or millions) of images to come up with outputs that are extraordinary. I hope when you've read this you'll agree this is a particularly striking advance, which has not yet been actualized in medical practice, but has enormous potential.

Investall, the New York FinTech and data science company which provides wealth managers and consumers groundbreaking data science-powered risk analytics and investment technology, is excited to announce Wall Street E-Commerce expert Steve Cortright has joined Investall as Chief Executive Officer to lead our innovative FinTech company into the next inflection point of growth. As a senior Wall Street technology executive, Steve brings deep experience in managing technology utilization and operations for e-commerce financial services firms to Investall. Recommended AI News: Eko Awarded $2.7 Million SBIR Grant from NIH to Develop Pulmonary Hypertension AI "The future of financial intelligence relies on quality data to make critical business decisions and delivery of it through the digitization of infrastructures," stated Mr. Cortright. "Investall is innovating with disruptive AI powered technologies. It is my great privilege to be part of the company's growth."

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Devansh Bansal is the VP of the Emerging Tech Business Unit at Damco Solutions. The widespread adoption of chatbots was imminent with the stellar rise and consolidation of instant messaging. However, the accelerated pace at which chatbots have evolved from accepting scripted responses to holding natural-sounding conversations has been unprecedented. According to Google Trends, the interest in AI Chatbots has increased ten-fold over the last five years! With chatbots getting smarter, value-driven, and user-friendly, it has fueled customer-led demand for chatbot-driven interaction at every touchpoint.