Mediaboss Marketing

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.

See the original post here:
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.

View post:
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.

Space for personalities. Since 1365.

Privacy Policy

Reference no.: TT0623Wiwi01

Application deadline: 15 September 2023

See original here:
Professor in the Field of Foundations of Machine Learning in ... - Times Higher Education

What the AI and Machine Learning on Microsoft Azure Advanced Specialization Means for Wipro and Its Customers

Partners like Wipro with the AI and Machine Learning on Microsoft Azure Advanced Specialization have the tools and knowledge necessary to develop AI solutions per customers requirements, build AI into their mission-critical applications and put responsible AI into action.

Achieving the AI and Machine Learning in Microsoft Azure Specialization is a proud moment for us, showcasing our deep expertise through third-party audit validation, said Don McCormick, Vice President and Head of the Wipro-Microsoft Partnership. It also highlights our commitment to foster a strong partnership with Microsoft, utilizing our solutions and accelerators built with Microsoft technologies to empower our clients to fully realize the benefits of AI and machine learning. This is our fourteenth Microsoft Advanced Specialization and we are honored to be recognized for our partnership with Microsoft. We look forward to continuing to work together to drive innovation for all our customers.

Learn more about Wipros partnership with Microsoft Azure.

Here is the original post:
Wipro Earns Advanced Specialization in AI and Machine Learning ... - Wipro

How do we make autonomous cars safer?

That question, which is critical as self-driving cars are increasingly found on American roads, is just one that NAU researcher Truong Nghiem hopes to answer with a new project that looks at ways to integrate machine learning and physical principles into large-scale cyber-physical systems.

Nghiem, an assistant professor in the School of Informatics, Computing, and Cyber Systems, received an NSF CAREER grant for this project, which aims to develop a comprehensive and flexible framework for effective and efficient machine learning with physical constraints, which can fundamentally change how we apply machine learning to complex systems like smart energy systems, industrial automation systems and autonomous robots and cars. The CAREER award is the National Science Foundations most prestigious award for early-career faculty.

A critical challenge is how to guarantee the performance and safety of these systems, as they are typically performance- and/or safety-critical, where any failure could have devastating consequences, Nghiem said. Our approach is to tightly integrate machine learning and physical principles. The framework developed in this project will be a foundation for such an integration and will be a stepping stone toward solving the challenge. It will help make future autonomous cyber-physical systems reliable and safe.

A cyber-physical system (CPS) is an engineered system that is built from, and depends on, seamless integration of computational and physical components. They are the foundation of many modern engineering systems that make up our daily life, including cars, robots, medical devices, power grids and more, and they are becoming even more common as our lives become more automated.

Many of these systems employ machine learning and, increasingly, artificial intelligence. However, machine learning, which isnt always informed by physics, doesnt always provide the best way to teach these systems. Nghiems research focuses on physics-informed machine learning (PIML), which is capable of developing methods that seamlessly embed knowledge of a physical system into machine learning, leading to robust, accurate and consistent models.

In autonomous cars, rovers, drones and similar systems, that means fewer system errors and a safer experience for the vehicle and nearby people. However, current PIML methods are functionally too small to meet those needs.

Enter composite physics-informed machine learning, or CPIML. Nghiems project aims to advance the data-driven learning of complex, large-scale systems by synthesizing many PIML and physical component modelsits the physics equivalent of LEGO blocks that can be put together to build much larger, more complex models, with each block being an already-developed model or piece of machine learning.

This groundbreaking solution will require integrating the cyber world (machine learning, AI and computing) and the physical world (dynamic and control systems) in engineered systems, so that each world is aware of and can integrate with the other. The result will be a safer world through which people move.

Smart and autonomous cyber-physical systems will tremendously impact our lives in the near future, Nghiem said. Our productivity will substantially increase with autonomous helper robots, advanced industrial automation (Industry 4.0) and many autonomous systems in our work and personal life. Our energy infrastructures will be more efficient and reliable, and our transportation will be safer and faster. These all depend on modern technologies, including cyber-physical systems and recent advancements in machine learning and AI.

Nghiems research will also offer valuable opportunities for graduate and undergraduate students to engage in software development and real-world applications.

Heidi Toth | NAU Communications (928) 523-8737 | heidi.toth@nau.edu

Go here to see the original:
How NAU is making self-driving cars safer and smarter The NAU ... - NAU News