Mediaboss Marketing

Hello,

I hope this email finds you well, coding away and innovating in the dynamic world of Machine Learning.

Today, I am excited to announce a collaboration between Machine Learning Mastery and KDnuggets. Together, we've created something unique to enrich your Machine Learning journey.

I present to you our brand new ebook, "Maximizing Productivity with ChatGPT". While we've been known for our technical, code-heavy books that have guided many through the intricate pathways of Machine Learning, this time we're offering something different but equally impactful.

This ebook shifts the focus from pure coding and technical aspects, to understanding, interacting, and leveraging one of the most advanced AI tools in the market - ChatGPT. This is an evolution from our prior books, aimed at broadening your perspective and deepening your understanding of AI applications.

You'll discover:

In celebration of this launch, we're offering an exclusive 20% early bird discount with the code "20offearlybird" at checkout. But don't delay - this offer ends soon!

Maximizing Productivity with ChatGPT

This ebook is a testament to the fact that not all roads to mastering Machine Learning and AI are paved with code alone. Harnessing the power of AI also involves understanding its applications and learning how to effectively interact with it. "Maximizing Productivity with ChatGPT" offers you exactly that - an avenue to explore and master the usage of AI beyond the traditional coding confines.

If you have any questions, please don't hesitate to hit reply and send me an email directly. Here's to harnessing the power of AI together.

- Jason, Machine Learning Mastery Founder

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Unlock the Power of AI A Special Release by KDnuggets and ... - KDnuggets

Improved weather and climate forecasting using machine learning and artificial intelligence is the focus of a new University of Hawaii at Mnoa project. Results are expected to have a major impact in Hawaii and other tropical climate areas around the world.

Associate Professor Peter Sadowski from the Information and Computer Sciences Department in the College of Natural Sciences earned a five-year, $424,293 CAREER grant from the National Science Foundation (NSF). CAREER grants are designed to support early-career faculty to serve as academic role models in research and education.

One of the risks of climate change for Hawaii is extreme weather events, and current scientific models are poor at estimating these risks, Sadowski said. This project will provide a completely new approach modeling these risks, using the latest advancements in AI (artificial intelligence).

Sadowskis project will develop machine-learning methods to predict the risk of adverse weather and climate events. AI will be used to develop new data-driven computational methods for modeling risk and apply these methods to weather applications.

In particular, these models will be applied to forecasting solar irradiance and precipitation, two areas that are particularly important for tropical islands such as the Hawaiian Islands. Estimating the risk of rapid changes in solar power generation is necessary for managing energy grids that are seeing a rapid increase in variable renewable sources, and floods claim hundreds of lives and billions in property damage each year in the U.S. alone.

Artificial intelligence methods have greatly improved translating text into predictions using images and video. A key development is the ability to learn probabilistic models of images and video. The research will leverage existing data from numerical simulations of atmospheric variables, observations from satellites and ground-based weather station data from the NSF-funded CHANGE-HI project. The machine-learning methods developed by this project will complement existing physics-based weather prediction models by providing location-specific forecasts with increased speed, higher resolution and probabilistic accuracy.

This research will be paired with an educational outreach program that includes a summer data science course for high school students and a workshop to share data science teaching materials with Hawaiis K12 teachers.

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$424K grant to better predict weather, climate through machine ... - University of Hawaii

Some of the significant applications of machine learning in healthcare are Personal Assistants and Chatbots, the finance and banking industry, medical and healthcare, and autonomous vehicles.

FREMONT, CA: Due to the emergence of AI tools like ChatGPT and DALL-E, machine learning, or ML, has garnered significant attention. A widely recognized concept, it allows computers to adapt and learn from experiences. Despite its growing popularity, machine learning is already prevalent in many real-life situations.

The examples of machine learning in action are as follows:

Finance and Banking for Individuals: In the finance and banking sectors, machine learning algorithms provide valuable applications. Advanced fraud detection techniques are one way banks use AI and ML algorithms to protect their customer's assets. By leveraging image and text recognition methods, machine learning models for fraud detection in banking can distinguish between legal and illegal transactions.

Personal finance can also benefit from machine learning, particularly when it comes to portfolio management. As robo-advisors, online investment platforms use machine learning to build, monitor, and automate diversified portfolio management. Without human supervision, these platforms construct portfolios based on preferences for specific assets or risks.

Additionally, machine learning can be used to forecast the market in personal finance. Based on historical data, BL algorithms can predict stock prices and market trends. By acquiring this insight, individuals will be able to develop effective trading strategies and identify favorable trading opportunities.

Medical Diagnosis and Healthcare: Machine learning has also become a crucial tool for medical diagnosis, patient care, and overall outcomes in the healthcare industry. Through collaboration with various healthcare technologies, it improves wellness in a variety of ways.

The following are six critical applications of machine learning in healthcare:

In order to diagnose and prognosis diseases more accurately, machine learning algorithms analyze patient data, including symptoms, medical records, lab results, and imaging scans.

In radiology, machine learning aids radiologists in diagnosing diseases by automatically detecting abnormalities, identifying features, and detecting abnormalities in X-rays, MRI scans, and pathology slides.

By enabling clinical trial optimization, patient recruitment, and identifying suitable candidates for specific treatments, machine learning models optimize drug discovery processes.

Machine learning develops personalized treatment plans by analyzing a patient's characteristics, genetic information, treatment history, and clinical data.

In conjunction with Internet of Things (IoT) wearable devices, machine learning enables predictive analytics.

Autonomous vehicles: Tesla is a prominent example of how machine learning is used in the development of modern cars. Tesla's cars are equipped with AI hardware provided by NVIDIA that incorporates unsupervised ML models for self-learning object recognition. Tesla is not the only company with self-driving features.

They are equipped with cameras, LiDAR, radar, and GPS to gather comprehensive information about their surroundings. The data is then processed to ensure accurate perception and effective decision-making. A self-driving car uses Simultaneous Localization and Mapping (SLAM) techniques to create updated maps to aid navigation.

In self-driving cars, ML models assist in real-time decision-making by determining optimal paths. Furthermore, these models facilitate the development of adaptive systems capable of detecting and predicting potential vehicle malfunctions.

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What are the Critical Applications of Machine Learning in Healthcare - CIO Applications

OpenAI, Microsoft, Google, and Anthropic have come together to establish a forum dedicated to promoting the safe and responsible development of large machine-learning models. This initiative is aimed at coordinating safety research and defining best practices for what are known as frontier AI models, which surpass the capabilities of existing models and have the potential to pose significant risks.

The use of generative AI models, such as the ones powering chatbots like ChatGPT, enables the extrapolation of a vast amount of data rapidly, allowing the models to provide responses in the form of prose, poetry, and images. While these models have numerous applications, regulatory bodies like the European Union and industry leaders, including OpenAIs CEO Sam Altman, have emphasized the need for guardrail measures to address the potential risks associated with AI.

Microsoft President Brad Smith stated that companies developing AI technology have the responsibility to ensure its safety, security, and human control. The newly formed industry body, Frontier Model Forum, aims to collaborate with policymakers and academia, facilitating the sharing of information between companies and governments. Notably, the forum will not engage in lobbying activities but will initially focus on establishing a public library of benchmarks and technical evaluations for frontier AI models.

To streamline its efforts, the forum plans to establish an advisory board, secure funding through a working group, and create an executive board. Anna Makanju, Vice President of Global Affairs at OpenAI, emphasized the urgency of the forums work and its ability to make meaningful contributions to advancing AI safety.

Overall, this collaborative effort among industry leaders is a significant step towards ensuring the responsible development and deployment of large machine-learning models, thereby addressing potential risks and safeguarding public safety.

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Forum Launched to Support Safe and Responsible Development of ... - Fagen wasanni

The University of Vienna is internationally renowned for its excellence in teaching and research, and counts more than 7,500 academics from all disciplines. This breadth of expertise offers unique opportunities to address the complex challenges of modern society, to develop comprehensive new approaches, and educate the problem-solvers of tomorrow from a multidisciplinary perspective.

At the Faculty of Business, Economics and Statistics, the University of Vienna seeks to appoint a

Tenure-Track Professor in the field ofFoundations of Machine Learning in Finance

The position:

We are looking for scientists with demonstrated excellence in machine learning approaches to financial problems combined with their mathematical, probabilistic and statistical foundations. This includes (but is not limited to) the analysis of deep (reinforcement) learning algorithms, signature methods, optimal control and game theory as well as their applications to various problems in banking, finance and economics, such as risk management and hedging, financial time-series prediction and market generation, model calibration or market making. The position will contribute on one hand to the mathematical foundations of data science and on the other hand will focus on financial and economic applications that can be solved by novel machine learning algorithms. In this respect, the Vienna scientific landscape offers many cooperation opportunities, in particular within the Research Network Data Science @ Uni Vienna, a platform for fundamental research in data science and practical applications based on data science techniques.

Your academic profile:

We expect the successful candidate to acquire, within three years, proficiency in German sufficient for teaching in bachelors programmes and for participation in university committees.

We offer:

Application documents:

If you have any questions, please contact:

tenuretrack.personal@univie.ac.at

We look forward to new personalities in our team!

The University of Vienna has an anti-discriminatory employment policy and attaches great importance to equal opportunities, the advancement of women and diversity. We lay special emphasis on increasing the number of women in senior and in academic positions among the academic and general university staff and therefore expressly encourage qualified women to apply. Given equal qualifications, preference will be given to female candidates.

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Reference no.: TT0623Wiwi01

Application deadline: 15 September 2023

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Professor in the Field of Foundations of Machine Learning in ... - Times Higher Education