Machine learning (ML)-based data flow solutions have made it possible to ingest and process data from a large number of applications at an affordable cost. This not only helps expand the overall scope of threat detection, but also helps significantly accelerate the development and production of threat detection applications. Solutions that offer advanced capabilities like in-memory data transformation and distributed in-memory stateful processing also bolster insider threat detection by enabling faster data quality scoring, cleansing, and enrichment. Recent advances in ML have helped create dynamic models that periodically learn normal baseline behavior and detect anomalies based on both dynamic and static factors such as identities, roles, and excess access permissions; correlated with log and event data. Using ML models on the log and complex event data can help reduce false positives from thousands to tens per day and make the end-to-end process of identifying suspicious behavior automated, accurate, and timely.

For the last two years, insurance chatbots have become the hottest trend in technology. Many areas of the insurance industry are now using chatbots to help both customers and agents by enabling intelligent conversations with humans. As more companies develop the latest technologies to make bots more intelligent, they'll be able to provide more applications in the insurance industry in the years to come. For many people, property and casualty insurance is complicated to understand. Chatbots for insurance agents can assist clients with understanding the processes and costs of purchasing different forms of insurance, renewing policies, and making claims.

"Without understanding the cause and effect of interactions within the world, no AI model, algorithm, technique, application, or technology is real and true", be it: Natural language generation converting structured data into the native language; Speech recognition converting human speech into a useful and understandable format by computers; Virtual agents, computer applications that interact with humans to answer their queries, from Google Assistant to the Watson; Biometrics, to identify individuals based on their biological characteristics or behaviors, with fingerprints and faces, hand veins, irises, or voices biometric modalities; Decision management systems for data conversion and interpretation into predictive models; Machine learning empowering machine to make sense from data sets without being actually programmed, to make informed decisions with data analytics and statistical models; Robotic process automation configuring a robot (software application) to interpret, communicate and analyze data; Peer-to-peer network connecting between different systems and computers for data sharing without the data transmitting via server; Deep learning platforms based on ANNs teaching computers and machines to learn by example just the way humans do; Generative AI (GANs, Transformers, Autoencoders) referring to unsupervised and semi-supervised machine learning algorithms that enable computers to use existing content like text, audio and video files, images, or code to create new possible content as completely original artifacts. It leverages AI and ML algorithms to generate artificial content such as text, images, audio and video content based on its training data to trick the user into believing the content is real, facing legal challenges concerning data privacy; Generative AI models with image generation algorithms generating photographs of human faces, objects and scenes, image-to-image conversion, text-to-image translation, film restoration, semantic-image-to-photo translation, face frontal view generation, photos to emojis, face aging, media and entertainment: deep fake technology; AI optimized hardware support artificial intelligence models, as #neuralnetworks, #deeplearning, and #computervision, including CPUs, GPUs, TPUs, OPUs to handle scalable workloads, special purpose built-in silicon for neural networks, neuromorphic chips, etc.; Real AI is NOT about representing computational models of intelligence, described as structures, models, and operational functions that can be programmed for problem-solving, inferences, language processing, etc. Real AI is about the computational models of reality and mentality, described as causal structures, models, and operational functions that can be programmed for problem-solving and inferences for a wide range of goals in a wide range of environments.

We just shouldn’t be too fussy. Everything is a learning curve including developers & AI researchers trying to build AI stuff. It might not be AI right now, but give them the benefit of the doubt of…

After a couple of weeks of experimentation, I realised the AI had the potential to describe imaginary artworks. To my delight, I discovered I could prompt it to write the kind of text you see on a wall label next to a painting in an art gallery. This would prove to be the start of a fascinating collaborative journey with GPT-3 and a suite of other AI art tools, leading to work that has ranged from a physical sculpture of toilet plungers to full-size oil paintings on the wall of a Mayfair art gallery. In recent months, AI-generated art has provoked much debate about whether it will be bad news for artists. There's little doubt that there will be disruptive changes ahead, and there are still important questions about bias, ethics, ownership and representation that need to be answered.

Abstract: Poisson log-linear models are ubiquitous in many applications, and one of the most popular approaches for parametric count regression. In the Bayesian context, however, there are no sufficient specific computational tools for efficient sampling from the posterior distribution of parameters, and standard algorithms, such as random walk Metropolis-Hastings or Hamiltonian Monte Carlo algorithms, are typically used. Herein, we developed an efficient Metropolis-Hastings algorithm and importance sampler to simulate from the posterior distribution of the parameters of Poisson log-linear models under conditional Gaussian priors with superior performance with respect to the state-of-the-art alternatives. The key for both algorithms is the introduction of a proposal density based on a Gaussian approximation of the posterior distribution of parameters. Via simulation, we obtained that the time per independent sample of the proposed samplers is competitive with that obtained using the successful Hamiltonian Monte Carlo sampling, with the Metropolis-Hastings showing superior performance in all scenarios considered.

AI Explorations and Their Practical Use in School Environments is an ISTE initiative funded by General Motors. The program provides professional learning opportunities for educators, with the goal of preparing all students for careers with AI. Recently, we spoke with three more participants of the AI Explorations program to learn about its ongoing impact in K-12 classrooms. Here, they share how the program is helping their districts implement AI curriculum with an eye toward equity in the classroom. Monica Rodriguez is a kindergarten teacher with Ector County Independent School District in Odessa, Texas.

There are two realities when it comes to artificial intelligence. In one, the future's so bright you need to put on welding goggles just to glance at it. AI is a backbone technology that's just as necessary for global human operations as electricity and the internet. But in the other reality, winter is coming. An "AI winter," is a period in which nothing can grow.

Again, this is in a browser! For kicks and giggles, I wanted to see if I could run the full blown model building process. For context, I believe notebooks are great for exploration but can become brittle when moving to repeatable processes. Eventually MLOps requires the movement of the salient code to their own scripts modules/scripts. If you sneak a peek above, you will see a notebooks folder and then a folder that contains the model training Python files.

With a wide breadth of open source, accelerated AI frameworks at their fingertips, medical AI developers and data scientists are introducing new algorithms for clinical applications at an extraordinary rate. Many of these models are nothing short of groundbreaking, yet 87% of data science projects never make it into production. In most data science teams, model developers lack a fast, consistent, easy-to-use, and scalable way to develop and package trained AI models into market-ready medical AI applications. These applications can help clinicians streamline imaging workflows, uncover hidden insights, improve productivity, and connect multi-modal patient information for deeper patient understanding. MONAI, the Medical Open Network for AI, is bridging this gap from development to clinical deployment with MONAI Deploy.