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new The Institute is developing a set of missions to sit under the Defence and National Security Grand Challenge, to be launched soon. These will focus on scientific areas such as multi-modal data and signals analysis, human machine teaming, behavioural analysis, cyber security, reinforcement learning, AI explainability, model security, privacy technology, and digital twins.The Alan Turing Institute, 4h ago
new IMT is recognised for its expertise in fields that influence our future: Eco-designed and high-performance materials, advanced manufacturing processes and devices, industry 4.0 & 5.0, industrial ecology, low-carbon energy, 5G and 6G telecommunications, IoT, cybersecurity, resilience and risk sciences, data sciences and artificial intelligence, digital health, etc.JEC World 2023 - English, 5h ago
new One of the key benefits of AI tools in GRC is automation. GRC tasks can be time-consuming and complex, requiring businesses to stay up to date with changing regulations and laws. AI tools can help automate many of these tasks, ensuring that businesses are always compliant with regulations. For example, AI tools can help businesses create and update policies and procedures that ensure compliance with regulations. They can also help with monitoring compliance by analyzing data and identifying potential risks.ISACA, in future
new Accelerating the industry’s effort in this area, ipoque introduced encrypted traffic intelligence (ETI) across its suite of OEM DPI solutions, last year. “At its core, ETI features advanced AI-based analysis using ML, DL and high-dimensional data analysis. This includes ML / DL algorithms such as k-nearest neighbours (k-NN), decision tree learning models, convolutional neural networks (CNN), recurrent neural networks (RNN) and long short-term memory (LSTM) networks that boast over 6,000 features - including statistical, time series and packet-level features,” added Dr. Mieth. “We merge these with statistical and behavioural/heuristic analysis and DNS / service caching to accurately and reliably detect encrypted applications and services”.electronicspecifier.com, 19h ago
new There’s no doubt that AI is revolutionary to many key industries. But, with greater data science comes greater data responsibility; especially when it comes to healthcare, education, finance and more. We’ve seen great progress on this with the advent of Trusted Research Environments (TREs) – computing environments with restrictions in place to keep sensitive data secure. Discover and learn more about TREs with Hari Sood (The Alan Turing Institute) through a comprehensive overview as well as live demos, games and discussion.turing.ac.uk, 4h ago, Event
new One key area of investment is in artificial intelligence (AI) and automation technologies. To keep pace with the rapid changes happening in the world, map creators are deploying AI models to help with many of the fundamentals of mapmaking, such as spotting road changes, updating speed limits, and predicting traffic.Geospatial World, 9h ago

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new Long Short-Term Memory (LSTM) is a neural network, more specifically, a Recurrent Neural Network type designed to address learning long-term dependencies in sequence prediction tasks. Unlike other neural network architectures, LSTM includes feedback connections that allow it to process entire sequences of data rather than individual data points like images.MarkTechPost, 10h ago
new ...was filed in November against Github, Microsoft, and several OpenAI entities, alleging that many of Github's open-source AI-training libraries contain stolen images, stripped of attribution, copyright and license terms, and that these have been used to create considerable commercial value.New Atlas, 14h ago
new Machine Learning Datasets–These datasets serve specifically for machine learning and artificial intelligence projects. Specialized marketplace providers or research organizations often provide them.Cryptopolitan, 19h ago

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My LISP (Learning, Intelligence + Signal Processing) lab is focused on asking fundamental questions such as “Can intelligence be learned?” at the intersection of signal processing, machine learning, game theory, extremal graph theory, and computational neuroscience. My students and I are developing geometric and topological methods to learn and understand information in general—signals (neural, images, videos, hyper-spectral, audio, language, RF), graphs (social networks, communication networks), and human interactions via game theory.dartmouth.edu, 5d ago
Meta believes that the whole artificial intelligence (AI) – civil society, industry, academic researchers, and policymakers – needs to work together to develop clear guidelines around responsible artificial intelligence in general and some responsible massive language models specifically.E-Crypto News - Exclusive Cryptocurrency and Blockchain News Portal, 21d ago
The case for building Scalable Neuromorphic Networks is this: like humans, smarter chips have a larger, tighter neural network. Indeed, neural networks are the current state-of-the-art for machine learning. This isn’t robotics, where a non-sentient arm follows explicit instructions. Instead, machine learning uses algorithms and statistical models to analyze and then draw inferences from patterns in data.The IEEE Photonics Society, 5d ago
new Graph transformers are a type of machine learning algorithm that operates on graph-structured data. Graphs are mathematical structures composed of nodes and edges, where nodes represent entities and edges represent relationships between those entities.MarkTechPost, 2d ago
...(CNN), recurrent neural networks (RNN) and long short-term memory (LSTM) networks that boast over 6000 features - including statistical, time series and packet-level features” added Dr Mieth. “We merge these with statistical and behavioural/heuristic analysis and DNS/service caching to accurately and reliably detect encrypted applications and services”.FutureCIO, 5d ago
My research encompasses information sharing and data fusion, and applying machine learning techniques to tackle challenges across such domains as health, business, finance and social networks. For instance analysing brain-image data from MRI scans or EEG activity to detect abnormal patterns that could indicate particular diseases; scrutinising financial transaction data to detect fraud; or evaluating social media data (from Twitter for example) to detect misinformation.The Lighthouse, 14d ago

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new ...principle in equipping business advisors with AI understanding. Chambers of commerce, business development centres, and start-up hubs are homes for services that are offered to companies locally. This implies constant dialogue with the company networks on behalf of business advisors employed by these organisations. Increasing the business advisors’ understanding of AI helps to make it a part of their company interactions – spreading the knowledge about AI-related business opportunities.The European Business Review, 19h ago
new ...1. Focusing on developing alignment techniques compatible with the current AI capabilities paradigm, such as reinforcement learning from human feedback (RLHF).2. Designing AI systems with general learning processes, potentially studying human value formation and replicating it in AI systems.3. Prioritizing long-term research and collaboration to ensure future AI capabilities advances remain compatible with alignment methodologies.4. Approaching AI alignment with a focus on minimizing the creation of hostile intelligences, and promoting AI systems resistant to adversarial attacks.5. Being cautious about relying on intuitions from other fields, focusing on understanding ML's specific properties to inform alignment strategies, and being open to evidence that disconfirms pessimistic beliefs.lesswrong.com, 19h ago
new Enterprise automation so far has been mostly reactive, implemented as a piecemeal noninvasive method to automate routine, repetitive tasks, and structured processes and data. Business drivers, goals, and means for all the three vectors (Business processes, IT operations and Software development) of enterprise automation have expanded for the next chapter of the digital journey. Digital businesses need proactive, predictive end-to-end automation that leverages optimal blending of intelligent automation toolbox (e.g., process mining, conversational AI, machine learning and IDP) beyond RPA, supports human and machine synergies, and robust governance to accelerate strategic business innovation and unlock the success divide with competition.IDC: The premier global market intelligence company, 19h ago, Event
new Deep-learning models and techniques, such as generative adversarial networks (GANs) and self-supervised learning, have recently been used by academics to tackle this challenge. These investigations, however, call for either fine-tuning toward the particular stimuli utilized in the fMRI experiment or training new generative models with fMRI data from scratch. These attempts have demonstrated great but constrained performance in terms of pixel-wise and semantic fidelity, in part due to the small amount of neuroscience data and in part due to the multiple difficulties associated with building complicated generative models.MarkTechPost, 23h ago
new The past few years have been marked by a literal exponential increase in the number of publications with the words “machine learning,” “artificial intelligence,” and “deep learning” in their titles. These tools now pervade materials science workflows and have been integrated with experimental/computational automation to form autonomous research agents, capable of planning, executing, and analyzing entire scientific campaigns. Lurking beneath the surface truly amazing accomplishments are serious questions around trust, bias, reproducibility, and equity which will ultimately determine the overall adoption of AI and autonomy by the broader community. Here, I will speak to recent work done by our group to systematically (1) remove human bias from experimental data analysis, (2) identify and actively remediate bias in large datasets , and (3) foster and promote a community of equity and reproducibility within the materials AI sub-domain. Specific case studies will center around standard electrochemical impedance spectroscopy analysis, building stability model predictions for complex alloys from large theoretical datasets, and maximizing the amount of information extracted from imaging techniques.Faculty of Applied Science & Engineering, 19h ago, Event
new Linear algebra is a branch of mathematics often utilized in machine learning. This continuous form of mathematics allows for modeling natural phenomena and performing computations efficiently, making it popular across computer science and artificial intelligence fields alike.IoT Worlds, 1d ago

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...along with Yoshua Bengio and Yann LeCun for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing. In a 1986 paper, “Learning Internal Representations by Error Propagation,” co-authored with David Rumelhart and Ronald Williams, Hinton demonstrated that the backpropagation algorithm allowed neural nets to discover their own internal representations of data, making it possible to use neural nets to solve problems that had previously been thought to be beyond their reach. The backpropagation algorithm is standard in most neural networks today.acm.org, 9d ago, Event
Intelligent Data Analytics for Power Apparatus Health Monitoring: AI and Machine Learning Paradigms reviews key implementations of machine learning and data analytics techniques for the optimization of digital power transformers. The work addresses health monitoring fully across the constitutive structure of modern transformers, with coverage of DGA-based intelligent data analytics, transformer winding, bushing and arrestor health monitoring, core, conservator, and tank and cooling systems. Chapters address advanced AI/ML methods including deep convolutional neural network, fuzzy reinforcement learning, modified fuzzy Q learning, gene expression programming, extreme-learning machine, and much more. Primarily intended for researchers and practitioners, the book speeds and simplifies the diagnosis and resolution of health and condition monitoring queries using advanced techniques, particularly with the goal of improved performance, reduced cost, optimized customer behavior and satisfaction, and ultimately increased profitability.elsevier.com, 15d ago
Accelerating the industry’s effort in this area, ipoque introduced encrypted traffic intelligence (ETI) across its suite of OEM DPI solutions, last year. “At its core, ETI features advanced AI-based analysis using ML, DL and high-dimensional data analysis. This includes ML / DL algorithms such as k-nearest neighbors (k-NN), decision tree learning models, convolutional neural networks (CNN), recurrent neural networks (RNN) and long short-term memory (LSTM) networks that boast over 6000 features – including statistical, time series and packet-level features” added Dr. Mieth. “We merge these with statistical and behavioral / heuristic analysis and DNS / service caching to accurately and reliably detect encrypted applications and services”.advanced-television.com, 6d ago

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new The book, Missing Links in AI Governance, includes 18 articles on AI governance written by academics, civil society representatives, innovators and policy makers at a time when technological revolutions provide new scientific, economic and social opportunities while raising ethical questions and posing regulatory challenges.Canadian Manufacturing, 1d ago
new Cutting corners and “banking” on AI and other automation to save the day becomes an excuse for the lack of oversight, accountability, and responsibility.Security Boulevard, 1d ago
new ..., with intrusion attempts involving cloud-conscious threat actors tripling year over year. From cybercriminal gangs to state-funded...VentureBeat, 20h ago
new Deep neural networks (DNNs) are promising models of the cortical computations supporting human object recognition. However, despite their ability to explain a significant portion of variance in neural data, the agreement between models and brain representational dynamics is far from perfect. We address this issue by asking which representational features are currently unaccounted for in neural time series data, estimated for multiple areas of the ventral stream via source-reconstructed magnetoencephalography data acquired in human participants (nine females, six males) during object viewing. We focus on the ability of visuo-semantic models, consisting of human-generated labels of object features and categories, to explain variance beyond the explanatory power of DNNs alone. We report a gradual reversal in the relative importance of DNN versus visuo-semantic features as ventral-stream object representations unfold over space and time. Although lower-level visual areas are better explained by DNN features starting early in time (at 66 ms after stimulus onset), higher-level cortical dynamics are best accounted for by visuo-semantic features starting later in time (at 146 ms after stimulus onset). Among the visuo-semantic features, object parts and basic categories drive the advantage over DNNs. These results show that a significant component of the variance unexplained by DNNs in higher-level cortical dynamics is structured and can be explained by readily nameable aspects of the objects. We conclude that current DNNs fail to fully capture dynamic representations in higher-level human visual cortex and suggest a path toward more accurate models of ventral-stream computations.interestingengineering.com, 22h ago
new From a compute perspective, AI everywhere requires a variety of processors to make it all happen: CPUs, GPUs, adaptable FPGAs and other accelerators across a range of capabilities, form factors and environmental tolerances. It requires chips for training the most complex of neural networks and chips for inferencing data in real time at the ruggedized edge. There’s chips for desktop AI, for AI in the cloud and AI in your handheld device. Putting together coherent AI deployments across this multi-layered landscape is an enormous challenge, stretching the very notion of heterogenous computing.High-Performance Computing News Analysis | insideHPC, 1d ago
new The IBKR Quant Blog delivers news and tutorials from 60 contributors. The topics covered range from Python and R programming, deep learning, API, AI, Blockchain to other transformative technologies influencing modern markets.IBKR Campus, 23h ago

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Accelerating the industry’s effort in this area, ipoque introduced encrypted traffic intelligence (ETI) across its suite of OEM DPI solutions, last year. “At its core, ETI features advanced AI-based analysis using ML, DL and high-dimensional data analysis. This includes ML / DL algorithms such as k-nearest neighbors (k-NN), decision tree learning models, convolutional neural networks (CNN), recurrent neural networks (RNN) and long short-term memory (LSTM) networks that boast over 6000 features – including statistical, time series and packet-level features” added Dr. Mieth. “We merge these with statistical and behavioral/heuristic analysis and DNS / service caching to accurately and reliably detect encrypted applications and services”.Bisinfotech, 5d ago
New guidelines from Cambridge University Press will help researchers use generative artificial intelligence (AI) tools like ChatGPT while upholding academic standards around transparency, plagiarism, accuracy and originality.researchinformation.info, 7d ago
Neural networks have also found applications in finance, where they aid with time series forecasting, algorithmic trading and securities classification. Furthermore, neural networks are utilized to forecast stock prices and detect fraudulence.IoT Worlds, 14d ago
Any AI facial recognition algorithms used in law enforcement need to be held to the highest standards of precision and accuracy across all demographics. However, with more development and innovation, neural networks could be highly effective for law enforcement applications.dzone.com, 13d ago
..., or neuro-evolution, is a type of artificial intelligence that generates artificial neural networks (ANN), parameters, and rules using evolutionary algorithms.indiaai.gov.in, 11d ago
Foundation models can be presented as generative models of behavior and the environment. The paper discusses how skill discovery can be an example of behavior. On the other hand, foundation models can be generative models of the environment for conducting model-based rollouts. These models can even describe different components of decision-making, such as states (S), behaviors (A), dynamics (T), and task specifiers (R), through generative modeling or representation learning with examples of plug-and-play vision-language models, model-based representation learning and so on.MarkTechPost, 10d ago

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new Summary: Critics argue developers of generative AI systems such as ChatGPT and DALL-E have unfairly trained their models on copyrighted works. Those concerns are misguided. Moreover, restricting AI systems from training on legally accessed data would significantly curtail the development and adoption of generative AI across many sectors. Policymakers should focus on strengthening other IP rights to protect creators. (...Center for Data Innovation, 1d ago
new He also warned that market pressures will likely push tech companies toward secrecy rather than openness with their AI models, and that the “media circus” around ChatGPT is a “wake-up call” about the potential of powerful AI systems to both do good for society as well as create significant ethical concerns.VentureBeat, 1d ago
new The study found that occupations with higher wages generally face a higher risk of automation through generative AI tools, with roles requiring “programming and writing skills positively associated with LLM exposure”.TechCentral.ie, 1d ago

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new Training generative AI on well-defined rules and linked terms to generate more accurate SQL queries that align with business rules and data schema...prnewswire.com, 1d ago, Event
new A traffic "Stop" sign on the roadside can be misinterpreted by a driverless vehicle as a speed limit sign when minimal graffiti is added. Wearing a pair of adversarial spectacles can fool facial recognition software into thinking that we are Brad Pitt. The vulnerability of artificial intelligence (AI) systems to such adversarial interventions raises questions around security and ethics, and many governments are now considering proposals for their regulation. I believe that mathematicians can contribute to this landscape. We can certainly get involved in the conflict escalation issue, where new defence strategies are needed to counter an increasingly sophisticated range of attacks. Perhaps more importantly, we also have the tools to address big picture questions, such as: What is the trade-off between robustness and accuracy? Can any AI system be fooled? Do proposed regulations make sense? Focussing on deep learning algorithms, I will describe how mathematical concepts can help us to understand and, where possible, ameliorate current limitations in AI technology.ICMS - International Centre for Mathematical Sciences, 1d ago, Event
new The excellent performance of transformers in computer vision and natural language processing justifies research into the internal representations of these systems. Methods that involve training classifiers to infer latent features (such as part-of-speech and syntactic structure) are prevalent.MarkTechPost, 1d ago
new ...“We focus on AI techniques that deliver value, and our ECO platform is designed to fulfil the real needs of governments and businesses. We help institutions and corporations translate millions of documents using custom engines, mask data using full anonymization or pseudonymization techniques, classify information, and extract knowledge from data,” says Pangeanic’s CEO, Manuel Herranz. “Our users can now benefit from summarizing documents through our customization of OpenAI’s ChatGPT and GPT-4, and do so securely, without revealing any personal details. Pangeanic offers accuracy, governance and return on investment, leading linguistic input to build AI-driven solutions. We will continue to be at the forefront of the market as the major linguistic models evolve.”...Slator, 1d ago
new This allows Croquet to synchronise the evolution of state over time via computations on the network, eliminating requirements to transmit physics, artificial intelligence (AI), and other complex computations.XR Today, 1d ago, Event
new Paired together, Interplay’s low-code environment and generative AI give customers what they need to rapidly implement the most cutting-edge AI capabilities that would otherwise require significant resource investments and complex coding. Organizations can now leverage Interplay and generative AI to enhance and automate their existing product descriptions, create personalized product images on their retail sites, personalize ad campaigns with users’ images and themes, build intelligent FAQs, and offer new conversational experiences, among other possibilities. Interplay and its generative AI integrations are applicable across industries, from retail to banking to insurance to healthcare.MarTech Series, 1d ago

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new The design and growth of semiconductor IPs are continually being streamlined. There are various methods to bypass the constraints of conventional semiconductor applied sciences, together with graphene transistors and self-organizing molecular units, carbon nanotubes, and quantum computing. This enables for an increase within the demand for semiconductor IP.Bitcoin Press UK, 1d ago
new These factors have led to an increased interest in legaltech, not only among clients, entrepreneurs and investors. "Firstly, the legal industry is highly traditional and often faces inefficiencies in terms of time, cost and access to legal services. Legaltech solutions can streamline these processes and make legal services more accessible, affordable and efficient. Additionally, the Indian government's focus on digitalization and technology adoption has opened up opportunities for legaltech startups to innovate and disrupt the industry," said Sumit Agrawal, founder, Regstreet Law Advisors and former SEBI Officer. Regstreet is a boutique law firm advising in the financial regulatory sector. The company has advised many legaltech startups in India who offer services such as online legal advice, compliance and internal audit softwares with disclosures on stock exchanges for insider trading and takeover laws, surveillance alert systems, document automation, contract management, e-discovery, legal research, and dispute resolution.Entrepreneur, 1d ago
new To address these risks, you should establish strict guidelines for using ChatGPT, such as implementing access controls, data sanitization processes, and periodic reviews of AI-generated content. Additionally, you should ensure that AI-generated content is supplemented with human oversight and verification to guarantee accuracy and maintain client trust. By fostering a responsible and cautious approach to ChatGPT integration, MSPs and their vendors can better harness the potential benefits of AI while minimizing potential harm to their clients and the broader digital landscape.The ChannelPro Network, 1d ago
new Most leading banks are investing heavily in digital technologies, including AI, automation, cloud computing, data analytics, blockchains, open APIs, payments, cybersecurity, and more, to take advantage of them and remain competitive in the industry, but how can legacy banks leverage this new transformation?...Fintechnews Middle East, 1d ago, Event
new The phishing emails, which use ChatGPT or similar natural-language machine-learning models to mimic the language and tone of authentic workplace emails, are so hard to distinguish from genuine emails it is in turn taking AI engines to weed them out, creating a spy-versus-spy, AI-versus-AI arms race, says Chad Skipper, global security technologist at VMWare, which makes software for data centres.Australian Financial Review, 1d ago
new ...– What was once just a screwdriver is now an IIoT device continuously streaming information on torque, wear, and 3D position. Predictive maintenance processes analyse data from millions of tools, devices, and machines to determine precisely when calibration and maintenance are needed and what the service should include. Although sensors have collected industrial data for decades, the low latency and intelligence of 5G introduce a more dynamic maintenance loop where equipment breakdowns and excessive wear are all but eliminated. Hyperscale cloud computing power converts the sensory feedback into actionable maintenance schedules, work orders, and procurement plans.Bisinfotech, 1d ago

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new Glenn is highlighting the use of generative AI being prohibited when you "generate and distribute content intended to misinform, misrepresent or mislead," including "sensitive areas (e.g. health, finance, government services, or legal)." Sounds like the YMYL categories? Sure.seroundtable.com, 1d ago
new A graph database stores two kinds of data: “entities” and the “relationships” between them. Data entities are stored as vertices (or sometimes nodes), and data relationships are stored as edges. Vertices represent nouns: people, places, products, locations, payments, and more. Edges represent the verbs or relationships that connect various vertices. This network of interconnected vertices and edges is called a graph.dzone.com, 1d ago
new Scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) had an inkling that it might, so they set off to examine if logic-aware language models could significantly avoid more harmful stereotypes. They trained a language model to predict the relationship between two sentences, based on context and semantic meaning, using a dataset with labels for text snippets detailing if a second phrase “entails,” “contradicts,” or is neutral with respect to the first one. Using this dataset — natural language inference — they found that the newly trained models were significantly less biased than other baselines, without any extra data, data editing, or additional training algorithms.Freethink, 1d ago, Event

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On the other hand, for commercial neural network accelerators, such as Google TPU, Intel Compute Stick and NVDLA, there are relatively fewer successful attacks. Focussing on that direction, in this work, we study the vulnerabilities of commercial open-source accelerator NVDLA and present the first successful model recovery attack. For this purpose, we use power and timing side-channel leakage information from Convolutional Neural Network (CNN) models to train CNN based attack models. Utilizing these attack models, we demonstrate that even with a highly pipelined architecture, multiple parallel execution in the accelerator along with Linux OS running tasks in the background, recovery of number of layers, kernel sizes, output neurons and distinguishing different layers, is possible with very high accuracy. Our solution is fully automated, and portable to other hardware neural networks, thus presenting a greater threat towards IP protection.iacr.org, 5d ago
Today, neural networks allow businesses to grab advantages like predictive maintenance, new revenue flows, asset management, etc. It is possible via deep neural networks (DNN) and the deep Learning (DL) method involving multiple layers for data processing. They detect hidden data trends and valuable information from a significant dataset by employing classification, clustering, and regression. It results in effective business solutions and the facilitation of business applications.IoT Central, 5d ago
Ai is an umbrella term for a collection of technologies that perform complex tasks without human input, such as natural language processing and computer vision. These advancements have enabled engineers to construct robots, self-driving cars, recognize speech and images, writing codes, forecast market trends and more with these systems.IoT Worlds, 14d ago
Another hybrid major, actuarial mathematics looks at how actuarial risk is determined, using mathematical models. As actuarial science is a subset of mathematics, actuarial mathematicians specifically evaluate the mathematical theory behind risk calculation. These people can find work in insurance firms, banks, and financial institutions.Herald Community Newspapers, 4d ago
Bloomberg continues to deploy artificial intelligence and machine learning in managing structured product market data and trading capabilities. For example, machine learning techniques are used to find market consensus, identify reliable sources of data and build models for trading system compliance.Risk.net, 6w ago
Machine learning (ML) models already drive much of contemporary society, and newer ML models, such as ChatGPT and DALL-E, demonstrate impressive competence in tasks such as text and image generation once outside the bounds of artificial intelligence (AI). However, when algorithmic systems are applied to social data, flags have been raised about the occurrence of algorithmic bias against historically marginalized groups. Further, some users of the popular portrait-creating LENSA have reported misogynistic and distorted body images generated from head-only selfies. Those working in AI and the broader algorithmic fairness community point to human biases against marginalised groups and social stereotypes that algorithms inherit from the data set on which they operate as a source of such bias and distortion in AI output.The University of Sussex, 14d ago

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new It bears repeating that the use of augmented reality by most business organizations and, ultimately, consumers remains the wave of the future. Despite the technical challenges encountered when making AR glasses and their uneven and unpredictable adoption curve seen to date, most entities around the world are realigning their future strategies around AR glasses with AI. The expected ubiquitous use of AI across applications in areas such as education, e-commerce, health care, communications, defense, security and more that hand the glove with AR smart glasses, examples like visual search, language translation, voice control, the list goes on and on.Insider Monkey, 2d ago, Event
new Generative AI has been evolving since its introduction at a great pace. The development of Large Language Models (LLMs) can be termed as one of the major reasons for the sudden growth in the amount of recognition and popularity generative AI is receiving. LLMs are AI models that are designed to process natural language and generate human-like responses. OpenAI’s GPT-4 and Google’s BERT are great examples that have made significant advances in recent years, from the development of chatbots and virtual assistants to content creation. Some of the domains in which Generative AI is being used are – content creation, development of virtual assistants, human imitating chatbots, gaming, and so on. Generative AI is also used in the healthcare industry to generate personalized treatment plans for patients, improve the accuracy of medical diagnoses, etc.MarkTechPost, 2d ago
new For a number of years, machine studying and synthetic intelligence have been used inside fintech to ship robo-advisory functionalities to traders. Web3 has taken inspiration from these purposes of AI. Platforms supply information on market costs, macroeconomic information and alternate information like social media, producing user-specific insights.Asia Cryptos, 2d ago
Legal Research/Legal Analytics/Cyber Security/Predictive Technology/ComplianceLegalTech Artificial IntelligenceIn Chapter 4, on the basis of applications, the LegalTech Artificial Intelligence market from 2018 to 2028 covers:...openPR.com, 3d ago
new Because of the fast interconnection between the thousands of computers (nodes) within them, supercomputers will always outperform cloud computing to tackle pressing global challenges – for example, by creating digital twins of factories, fusion reactors, the planet and, in our work, even cells, organs and people. These models can accelerate energy, climate and medical research, respectively.Times Higher Education (THE), 2d ago
new One of the most important things to understand about NLP is that not every chatbot can be built using NLP. However, for the healthcare industry, NLP-based chatbots are a surefire way to increase patient engagement. This is because only NLP-based healthcare chatbots can truly understand the intent in patient communication and formulate relevant responses. This is in stark contrast to systems that simply process inputs and use default responses.web3newshubb.com, 2d ago

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It includes using cognitive computing systems to manage renewable energy, machine learning models and algorithms, biometrics, data-driven speech algorithms, graph analytics, and cybersecurity.Geekflare, 19d ago
..., including General Robert B. Neller’s keynote observing how and why human intelligence and decision-making will always be at the core of defense-related AI advancements. Also from TMI22: UT Austin Professor and Co-Director of the National AI Institute for Foundations of Machine Learning (IFML) Alex Dimakis explains how AI can “dream.” The esteemed professor sets expectations of how future applications of generative models will change the way we approach and solve problems like training MRIs to detect tumors more accurately with AI and analyzing seismic data to predict natural events more reliably and detect underground substrata features.SparkCognition, 10d ago
So the book explores the US/China rivalry in artificial intelligence and the book comes looks at what's happening with artificial intelligence as a general purpose technology, much like electricity or computer networks or the internal combustion engine. And like those other technologies, it has a wide variety of applications across society. The book concludes that the four key battlegrounds of global competition: in artificial intelligence, or data, computing hardware or compute, human talent, and the institutions necessary to successfully adopt AI systems.Business Insider, 8d ago

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new ...(Just last week, the website Publishers Lunch reported that The Authors Guild has added a new clause to its model book contract prohibiting publishers from “using or sublicensing books under contract to train artificial intelligence technologies.” The guild is spooked that digital publisher Findaway Voices is working with Apple on machine learning while Bookwire has teamed up with Google Books for AI-narrated audiobooks. It hopes to stop data miners from training AI models to compete with human work. )...nationalpost, 2d ago, Event
This statistical, but essentially somewhat hollow, approach to knowledge prompted researchers, including former Google AI researchers Emily Bender and Timnit Gebru, to warn of the “dangers of stochastic parrots” that come with large language models. Language model AIs tend to encode bias, stereotypes, and negative sentiment in training data, and researchers and others using these models tend to “confuse performance gains with actual natural language understanding.”...newsbeezer.com, 3d ago
This shift is resulting in new operating models across the sector. In the banking sector, AI applications include chatbots powered by Natural Language Processing (NLP), fraud detection where AI can identify previously undetected transactional patterns and suspicious behaviour, credit risk management and accurate forecasting and prediction.Ventureburn, 3d ago
For a number of years, machine studying and synthetic intelligence have been used inside fintech to ship robo-advisory functionalities to buyers. Web3 has taken inspiration from these functions of AI. Platforms supply information on market costs, macroeconomic information and alternate information like social media, producing user-specific insights.Web3 Rodeo, 3d ago
Jon Haslanger is a technologist, innovator and leader with a passion for using AI to solve critical business problems. With 15+ years safely leading teams in high risk industries, he is well acquainted with the challenges and importance of managing workplace safety. Jon's experience with applied data science and machine learning allows him to leverage expertise, quickly unlock benefits for companies from their AI investments.SparkCognition, 3d ago, Event
...in 1995, coded using AIML (Artificial Intelligence Markup Language) based on heuristic pattern matching. The Open Source community subsequently gains interest and thus actively contributed to all sorts of research repositories which brings us the vast collection of machine learning models today.web3newshubb.com, 3d ago

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The overlapping methods and applications involved in data mining and Machine Learning (ML) often make the terms, wrongly, interchangeably used. These are widely different concepts, despite functional similarities such as working with large datasets. While data mining is concerned only with pattern identification, ML further utilizes pattern recognition to develop a system of future prediction without human intervention. This blog discusses in depth the essentials of data mining vs. machine learning features and why these are distinct data science branches.Emeritus Online Courses, 11d ago
Heuristic search and other forms of combinatorial search optimization are very active areas of research in artificial intelligence, robotics, planning, and other areas of computer science. The International Symposium on Combinatorial Search (SoCS) is meant to bring researchers in such areas together to exchange ideas and crossfertilize the field. SoCS is a forum for researchers and submissions in all fields that use combinatorial search, including artificial intelligence, planning, robotics, constraint programming, metareasoning, operations research, navigation, and bioinformatics. The symposium also welcomes submissions presenting real-world applications of heuristic search.AAAI, 7d ago, Event
Traditionally, organizations implicitly trusted the networks used to connect to company resources. But malicious actors could abuse that trust and move laterally within organizations undetected. This lateral movement is often a prelude to the exfiltration of high-value data and the execution of destructive attacks like ransomware. Therefore, zero trust (ZT) architecture assumes that networks are inherently untrusted and require visibility into and analysis of internal network traffic.SecurityBrief New Zealand, 18d ago
...for the 2023–2024 strategic plan, Ofcom mentioned its ongoing efforts to develop “spectrum sandboxes” to understand the evolving needs of spectrum users and calibrate authorisation approaches accordingly. Similar arrangements for specialised services—like machine-to-machine communications, remote-assisted surgery, and driverless transportation solutions—could help Ofcom better understand, classify, and regulate different categories of specialised services.Competitive Enterprise Institute, 18d ago
TrendForce says generative AI represents an integration of different types of algorithms, pre-trained models, and multimodal machine learning. Notable ones include Generative Adversarial Networks (GAN), Contrast Language-Image Pre-Training (CLIP), Transformer, and Diffusion. Generative AI searches for patterns in the existing data or batches of information and efficiently outputs content that is suitable for scenarios such as data collection and analysis, social interactions, copywriting, etc. There are already many apps powered by generative AI in the market right now, and the most common kinds of output from them include texts, images, music, and software codes.evertiq.com, 18d ago
...and data fabric architectures. There are numerous AI techniques for identifying connections in datasets and making intelligent suggestions about them to accelerate the population of a knowledge graph for a domain. Examples of inference techniques include approaches like semantic inferencing, in which self-describing statements about data are combined to devise new ones.VentureBeat, 14d ago

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With the advancements in deep learning & Tensorflow, the speaker will explain the applicability and effectiveness of simple models and how the hype around deep learning is sometimes not just frivolous but dangerous. We all want to implement complex neural network architectures & get carried away by the paraphernalia around them. We tend to rush towards models and neural networks hence in this session he will demonstrate how sometimes complex problems could be solved with simple models.Zephyrnet, 3d ago
Large language models such as ChatGPT give the impression of intelligence. They are capable of information recall, language translation, writing programming code all whilst generating an explanation of the output along the way. This has prompted claims that these models possess human-level intelligence, and even consciousness, or sentience. In this talk, Dr. Matthew Shardlow will describe the inner working of the transformer model that underlies GPT and other similar models driving recent advances in Artificial Intelligence. The talk will then examine chatGPT from the perspective of integrated information theory, concluding with a discussion of the limitations of AI-based language models.bcs.org, 4d ago
Neural network regression is a powerful tool for solving real-world problems. It is a type of artificial intelligence that uses a network of interconnected nodes to learn from data and make predictions. Neural networks are particularly useful for problems that involve complex relationships between inputs and outputs, such as predicting stock prices or forecasting weather.TodayHeadline, 4d ago

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...• Use MIM to pre-train language models of various sizes using publicly available code and linguistic data, assess them using both programming and human languages, and demonstrate that MIM outperforms several baselines in common evaluation criteria. Ultimately, a few models and pieces of code are made public.MarkTechPost, 3d ago
New Internet of Things analytics technologies could help incorporate new algorithms, architectures and data structures alongside machine learning functionalities. Decentralized analytics architecture can help design secure IoT networks without compromising knowledge-sharing functionalities.EthozEdge, 4d ago
The automotive industry is involved with designing, developing, manufacturing, selling, and marketing motor vehicles. Why is it one of the largest and fastest industries to generate humongous revenue? Also, car manufacturers use industrial robots in constructing a vehicle and autonomous cars navigating traffic with ML. It helps automobile engineers to develop the next generation of electric and low-emission vehicles. Automobile applications make use of microprocessors, sensors, and fast communications. There are advancements in braking systems, traction control, and even accident avoidance.Top ITFirms - Result of In-depth Research & Analysis, 4d ago
...neural networks by using normalisation to identify best parameters settings...emeritus.org, 4d ago
AI has proven useful in many areas like clerical tasks of managing or analyzing medical records and processing insurance claims. To give early warning or predictive diagnosis, it may also be used to analyze data gathered from patient wearables or in-home sensors used in virtual hospital environments (more on that in next trend). All of these applications combined point to artificial intelligence and machine learning becoming major healthcare trends in the future year.CRN - India, 4d ago
Using various examples and theories from the history of philosophy and contemporary ethics research, I will try to illustrate that praise for good outcomes produced by AI technologies is harder to deserve than blame for bad outcomes produced by AI technologies might be. As I discuss this asymmetry between praise and blame for good and bad outcomes caused by AI technologies, I will consider examples such as text produced by large language models (such as ChatGPT), accidents caused by self-driving cars, medical diagnoses or treatment recommendations made by medical AI technologies, AI used in military contexts, and more.Schwartz Reisman Institute, 4d ago

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...1) Detect efficient + effective revenue motions➟ Replicate best practices across the team2) Correct pipeline & activity inefficiencies➟ Briefings to leaders & performers3) Protect against revenue threats➟ Automated alerts"The integration of AI and machine learning technologies in RevOps tooling is enabling teams to analyze vast amounts of data more efficiently, automate routine tasks, and provide more accurate forecasts and insights to contributors and leaders. These technologies will continue to shape the way RevOps teams operate and companies make decisions." Matt added...PRLog, 3d ago
EEG endpoints will increase our understanding of different epilepsy syndromes by capturing changes and different patterns in brain data. For example, EEG data could help clinicians determine if there is underlying epileptiform activity in children displaying abnormal behaviors, and thereby inform decision-making on further genetic testing.Drug Discovery and Development, 3d ago
The machine learning industry size will witness a notable gain in the wake of the growing use of AI and IoT devices, according to the “Machine Learning Industry Data Book, 2023 – 2030,” published by Grand View Research. Forward-looking companies have exhibited traction for machine learning (ML) to streamline tasks with efficiency, agility and reliability across business verticals. For instance, ML has received an impetus in cybersecurity to create antivirus software. An unprecedented spike in data and the use of various sorts of data to train ML models will further gain ground. Organizations are expected to bank on ML-powered services for applications, including voice transcription, sentiment analysis, text-to-speech, translation and anomaly detection.AiThority, 4d ago
His 1950 paper "Computing Machinery and Intelligence" introduced what is now known as the Turing test for measuring machine intelligence, sparking a debate over whether machines can think. A proponent of thinking machines, Turing believed that a "child machine" could be educated and eventually achieve an adult level of intelligence.techxplore.com, 3d ago
Trying to surveil their data and prevent harmful speech and images on these private databases is a major headache for the corporate owners. It will become even more so if Section 230 of the legislation passed by Congress, the Communications Decency Act, is amended to make these platform owners less protected from lawsuits for the harms that communications on their platforms cause.Pearls and Irritations, 3d ago
Natural language processing (NLP) specialists are artificial intelligence (AI) engineers specializing in spoken and written human language. Engineers who work on voice assistants, speech recognition, document processing, and other projects employ NLP technology. For NLP engineering, organizations require a particular degree in computational linguistics. Companies may also be interested in hiring applicants with computer science, mathematics, or statistics backgrounds.NASSCOM Community | The Official Community of Indian IT Industry, 4d ago

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...techUK looks forward to continued Government engagement on AI sandboxing; TDM across a broad spectrum of AI and other relevant AI proposals; industry access to data; the Future of Transport Bill; drones; AIaaS; space and satellite technology; cyber security; and the future of emerging technologies.techuk.org, 4d ago
The UK government has committed to creating a code of practice for generative artificial intelligence (AI) companies to facilitate their access to copyrighted material, and following up with specific legislation if a satisfactory agreement cannot be reached between AI firms and those in creative sectors.ComputerWeekly.com, 4d ago
For a number of years, machine studying and synthetic intelligence have been used inside fintech to ship robo-advisory functionalities to traders. Web3 has taken inspiration from these functions of AI. Platforms supply information on market costs, macroeconomic information and alternate information like social media, producing user-specific insights.Bitcoin Press UK, 4d ago

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Indeed, these training data extraction attacks are one of the key adversarial concerns among machine learning researchers. Also known as "exfiltration via machine learning inference," the attacks could gather sensitive information or steal intellectual property, according to...Dark Reading, 14d ago
Half (51%) 35 central banks said they use ML/AI technologies. The challenges institutions face include data quality (49%), acquiring relevant skills (40%), algorithmic fairness (29%), other constraints (23%) privacy (20%) and ethics (14%).Central Banking, 21d ago
Technologies covered by the bill include not only social media and artificial intelligence, but also quantum computing, e-commerce, financial technology services, cloud services and storage, satellite and mobile networks, video games, internet infrastructure providers, and payment apps. The measure also orders the U.S. government to share evidence on technologies regarded as national safety risks with the intelligence committee.SC Media, 11d ago
...has always used AI and ML to power and improve some of its algorithms. Signal learning algorithms for process learning, clustering algorithms for incident grouping, Bayesian models for symbolic predictions, neural networks of all kinds for classification and regression — all sorts of models are used in code produced by Nozomi Networks engineers, either in final products being sold to customers or “just” used in research projects for future use.Security Boulevard, 6d ago
...“AI is being used to ingest, identify and classify datasets from a variety of sources,” she said. “It continuously mines content to surface unseen patterns and trends, providing organizations with greater visibility and actionable insights to aid in decision-making. Businesses are using AI to automate otherwise manual tasks like data capture, de-duplication, anomaly detection and data validation. They are also training models to apply regulatory policies and ethical standards automatically, ensuring those principles are embedded from the beginning.”...VentureBeat, 11d ago
This sector has embraced AI, and autonomous car computers are being trained to make choices using deep learning models. AI technology now powers elements like accident detection features, GPS systems, radar systems, smart cameras, lane controls, and sensors.ReadITQuik, 13d ago

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In 1936, Turing published what has been called the most influential mathematics paper, establishing the idea of a universal computing machine able to perform any conceivable computation. Such hypothetical computers are called Turing machines. His 1950 paper “Computing Machinery and Intelligence” introduced what is now known as the Turing test for measuring machine intelligence, sparking a debate over whether machines can think. A proponent of thinking machines, Turing believed that a “child machine” could be educated and eventually achieve an adult level of intelligence.newswise.com, 4d ago
On issues of control and, more generally, on the evolving human-computer relationship, writings, such as those by statistician I. J. Good on the prospects of an “intelligence explosion” followed up by mathematician and science fiction author Vernor Vinge’s writings on the inevitable march towards an AI “singularity,” propose that major changes might flow from the unstoppable rise of powerful computational intelligences. Popular movies have portrayed computer-based intelligence to the public with attention-catching plots centering on the loss of control of intelligent machines. Well-known science fiction stories have included reflections (such as the “Laws of Robotics” described in Asimov’s Robot Series) on the need for and value of establishing behavioral rules for autonomous systems. Discussion, media, and anxieties about AI in the public and scientific realms highlight the value of investing more thought as a scientific community on preceptions, expectations, and concerns about long-term futures for AI.AAAI, 4d ago
We also note, "Facial recognition, tracking software, internet shutdowns, surveillance spyware, and drones are among the tools used by government intelligence agencies and police forces to surveil and repress populations, particularly marginalized groups, by discriminating based on demographic categorisation."...reachingcriticalwill.org, 4d ago
The latest report, “GPT for You and Me: Applying AI Language Processing to Cyber Defenses,” details projects developed by Sophos X-Ops using GPT-3's large language models to simplify the search for malicious activity in datasets from security software, more accurately filter spam, and speed up analysis of “living off the land” binary (LOLBin) attacks.Digitalisation World, 4d ago
Data, whether big or small, are key to the current digital transformation, whether directly in business intelligence and analytics, or as the raw material for artificial intelligence and machine learning algorithms. With almost all leading R&D organisations perceiving that data management should be a priority for their business, the ability to understand, interpret and leverage data in a variety of forms has become an essential skill for business professionals. Data literacy helps professionals separate useful information from background noise and enables them to take appropriate and informed actions.Hertie School, 4d ago
Computer Science involves the study of computers using computation, mathematics, algorithms and more. This involves understanding how computers actually work (hardware) and learning the programming languages that run these computers (software), all while using computing technology that follows algorithms and protocols to process information.IDP Education, 5d ago

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However, recent research suggests that 200-year-old maths could help understand how neural networks perform complex tasks. This could increase neural networks’ accuracy and their learning speed, researchers say. To analyse a neural network designed to carry out physics, senior author...Analytics India Magazine, 18d ago
Large Language Models (LLMs) are foundational machine learning models that use deep learning algorithms to process and understand natural language. These models are trained on massive amounts of text data to learn patterns and entity relationships in the language. LLMs can perform many types of language tasks, such as translating languages, analyzing sentiments, chatbot conversations, and more. They can understand complex textual data, identify entities and relationships between them, and generate new text that is coherent and grammatically accurate.Zephyrnet, 8d ago
Future computing technologies will repeatedly challenge Moore’s Law, with Quantum being the ultimate disruption point. By 2025, the industry will have powerful processors in mobile devices capable of running trained deep networks for cognitive functions such as vision, speech, and security. Industries of the future will witness humans and robots working collaboratively through sensored human-machine interfaces...Frost & Sullivan, 6d ago