Lifeboat Foundation: Safeguarding Humanity
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Category: robotics/AI – Page 813

Artificial intelligence (AI) has been helping humans in IT security operations since the 2010s, analyzing massive amounts of data quickly to detect the signals of malicious behavior. With enterprise cloud environments producing terabytes of data to be analyzed, threat detection at the cloud scale depends on AI. But can that AI be trusted? Or will hidden bias lead to missed threats and data breaches?

Bias can create risks in AI systems used for cloud security. There are steps humans can take to mitigate this hidden threat, but first, it’s helpful to understand what types of bias exist and where they come from.

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Today marks nine months since ChatGPT was released, and six weeks since we announced our AI Start seed fund. Based on our conversations with scores of inception and early-stage AI founders, and hundreds of leading CXOs (chief experience officers), I can attest that we are definitely in exuberant times.

In the span of less than a year, AI investments have become de rigueur in any portfolio, new private company unicorns are being created every week, and the idea that AI will drive a stock market rebound is taking root. People outside of tech are becoming familiar with new vocabulary.

Large language models. ChatGPT. Deep-learning algorithms. Neural networks. Reasoning engines. Inference. Prompt engineering. CoPilots. Leading strategists and thinkers are sharing their view on how it will transform business, how it will unlock potential, and how it will contribute to human flourishing.

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Since its public launch last year, the artificially intelligent chatbot ChatGPT has simultaneously wowed and frightened the world with its deep knowledge, its surprising empathy, and its undeniable potential to change the world in unforeseen, possibly miraculous or calamitous, ways. Now, it’s making it possible to digitally resurrect the dead in the form of chatbots trained on data of the deceased.

Developed by OpenAI, ChatGPT is an AI program called a large language model. Trained on more than 300 billion words from all sorts of sources on the Internet, ChatGPT responds to prompts from humans by predicting the word it should use next based on both its training and the prompt. The result is a stream of communication that’s both informative and human-like. ChatGPT has passed difficult tests, written scientific papers, and convinced many Microsoft scientists that it actually can understand language and utilize reason.

ChatGPT and other large language models can also receive more specific training to shape their responses. Programmer Jason Rohrer realized that he can create chatbots that emulate specific people by feeding ChatGPT examples of how they communicate and details of their lives. He started off with Star Trek’s Mr. Spock, as any good nerd would. He next launched a website called Project December, which allows paying customers to input all sorts of data and information and make their own personalized chatbots, even ones based upon deceased friends and family.

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At 14, Anton received an old laptop that changed everything. Now he’s using AI to help himself and others achieve their potential.

Neither keyboards nor voice-to-text work well for Anton, a developer with cerebral palsy. He uses AI and LLMs to pursue his passion for programming and shows others how they can harness these technologies to accomplish more.

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This talks about the changing dynamics of jobs and relation to AI. While there are a lot of apprehensions of AI killing jobs but it highlights that there are several new jobs being created by AI. It also stresses the need for professionals and students to reskill themselves in areas as diverse as AI and automation. So the argument that AI is going to kill jobs is not valid. Instead it enforces the argument that reskilling is most important.

LinkedIn: https://www.linkedin.com/in/tarah-ai-8316b7153/
Twitter: https://twitter.com/tarahtech.

#reskilling #AI #Automation #jobs #newskills #oldskills #reinvention #humanresources.
#AI #DeepLearning #ReinforcementLearning #MachineLearning #ML #DL #DataScience #ArtificialIntelligence #Classification #Jobs #Regression #Clustering #Intelligence #Learn #Intelligence #Knowledge #LearnFromHome #BI #BA #Analytics #Insights #Visualization #Graphs #Robots #Speech #BackPropagation #CNN #RNN #LSTM #NeuralNetworks #Network #Prediction #BigData #Hadoop

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The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care, and life sciences companies. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed.

KEYWORDS: Artificial intelligence, clinical decision support, electronic health record systems.

Artificial intelligence (AI) and related technologies are increasingly prevalent in business and society, and are beginning to be applied to healthcare. These technologies have the potential to transform many aspects of patient care, as well as administrative processes within provider, payer and pharmaceutical organisations.

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“It’s a time of huge uncertainty,” says Geoffrey Hinton from the living room of his home in London. “Nobody really knows what’s going to happen … I’m just sounding the alarm.”

In The Godfather in Conversation, the cognitive psychologist and computer scientist ‘known as the Godfather of AI’ explains why, after a lifetime spent developing a type of artificial intelligence known as deep learning, he is suddenly warning about existential threats to humanity.

A University of Toronto University Professor Emeritus, Hinton explains how neural nets work, the role he and others played in developing them and why the kind of digital intelligence that powers ChatGPT and Google’s PaLM may hold an unexpected advantage over our own. And he lays out his concerns about how the world could lose control of a technology that, paradoxically, also promises to unleash huge benefits – from treating diseases to combatting climate change.

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Deep Learning (DL) performs classification tasks using a series of layers. To effectively execute these tasks, local decisions are performed progressively along the layers. But can we perform an all-encompassing decision by choosing the most influential path to the output rather than performing these decisions locally?

In an article published today in Scientific Reports, researchers from Bar-Ilan University in Israel answer this question with a resounding “yes.” Pre-existing deep architectures have been improved by updating the most influential paths to the output.

“One can think of it as two children who wish to climb a mountain with many twists and turns. One of them chooses the fastest local route at every intersection while the other uses binoculars to see the entire ahead and picks the shortest and most significant route, just like Google Maps or Waze. The first child might get a , but the second will end up winning,” said Prof. Ido Kanter, of Bar-Ilan’s Department of Physics and Gonda (Goldschmied) Multidisciplinary Brain Research Center, who led the research.

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Join Dr. Ben Goertzel, the visionary CEO and Founder of SingularityNET, as he delves into the compelling realm of large language models. In this Dublin Tech Summit keynote presentation, Dr. Goertzel will navigate the uncharted territories of AI, discussing the imminent impact of large language models on innovation across industries. Discover the intricacies, challenges, and prospects of developing and deploying these transformative tools. Gain insights into the future of AI, as Dr. Goertzel unveils his visionary perspective on the role of large language models in shaping the AI landscape. Tune in to explore the boundless potentials of AI and machine learning in this thought-provoking session.

Themes: AI & Machine Learning | Innovation | Future of Technology | Language Models | Industry Transformation.
Keynote: Dr. Ben Goertzel, CEO and Founder, SingularityNET
#dubtechsummit

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