Chicago - David Higginson has some advice for hospitals and health systems looking to use machine learning.

"Get started," he says.

Higginson, the chief innovation officer of Phoenix Children's Hospital, offered a presentation on machine learning at the HIMSS Global Health Conference & Exhibition. He described how machine learning models helped identify children with malnutrition and people who would be willing to donate to the hospital's foundation.

After the session, he spoke with Chief Healthcare Executive and offered some guidance for health systems looking to do more with machine learning.

"I would say get started by thinking about how you going to use it first," Higginson says. "Don't get tricked into actually building the model."

"Think about the problem, frame it up as a prediction problem," he says, while adding that not all problems can be framed that way.

"But if you find one that is a really nice prediction problem, ask the operators, the people that will use it everyday: 'Tell me how you'd use this,'" Higginson says. "And work with them on their workflow and how it's going to change the way they do their job.

"And when they can see it and say, 'OK, I'm excited about that, I can see how it's going to make a difference,' then go and build it," he says. "You'll have more motivation to do it, you'll understand what the goal is. But when you finally do get it, you'll know it's going to be used."

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David Higginson of Phoenix Children's Hospital on using machine ... - Chief Healthcare Executive

Data augmentation is the process of applying various transformations to the training data. It helps increase the diversity of the dataset and prevent overfitting. Overfitting mostly occurs when you have limited data to train your model.

Here, you will learn how to use TensorFlow's data augmentation module to diversify your dataset. This will prevent overfitting by generating new data points that are slightly different from the original data.

You will use the cats and dogs dataset from Kaggle. This dataset contains approximately 3,000 images of cats and dogs. These images are split into training, testing, and validation sets.

The label 1.0 represents a dog while the label 0.0 represents a cat.

The full source code implementing data augmentation techniques and the one that does not are available in a GitHub repository.

To follow through, you should have a basic understanding of Python. You should also have basic knowledge of machine learning. If you require a refresher, you may want to consider following some tutorials on machine learning.

Open Google Colab. Change the runtime type to GPU. Then, execute the following magic command on the first code cell to install TensorFlow into your environment.

Import TensorFlow and its relevant modules and classes.

The tensorflow.keras.preprocessing.image will enable you to perform data augmentation on your dataset.

Create an instance of the ImageDataGenerator class for the train data. You will use this object for preprocessing the training data. It will generate batches of augmented image data in real time during model training.

In the task of classifying whether an image is a cat or a dog, you can use the flipping, random width, random height, random brightness, and zooming data augmentation techniques. These techniques will generate new data which contains variations of the original data representing real-world scenarios.

Create another instance of the ImageDataGenerator class for the test data. You will need the rescale parameter. It will normalize the pixel values of the test images to match the format used during training.

Create a final instance of the ImageDataGenerator class for the validation data. Rescale the validation data the same way as the test data.

You do not need to apply the other augmentation techniques to the test and validation data. This is because the model uses the test and validation data for evaluation purposes only. They should reflect the original data distribution.

Create a DirectoryIterator object from the training directory. It will generate batches of augmented images. Then specify the directory that stores the training data. Resize the images to a fixed size of 64x64 pixels. Specify the number of images that each batch will use. Lastly, specify the type of label to be binary (i.e., cat or dog).

Create another DirectoryIterator object from the testing directory. Set the parameters to the same values as those of the training data.

Create a final DirectoryIterator object from the validation directory. The parameters remain the same as those of the training and testing data.

The directory iterators do not augment the validation and test datasets.

Define the architecture of your neural network. Use a Convolutional Neural Network (CNN). CNNs are designed to recognize patterns and features in images.

model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3), activation='relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())

model.add(Dense(128, activation='relu'))

model.add(Dropout(0.5))

model.add(Dense(1, activation='sigmoid'))

Compile the model by using the binary cross-entropy loss function. Binary classification problems commonly use It. For the optimizer, use the Adam optimizer. It is an adaptive learning rate optimization algorithm. Finally, evaluate the model in terms of accuracy.

Print a summary of the model's architecture to the console.

The following screenshot shows the visualization of the model architecture.

This gives you an overview of how your model design looks.

Train the model using the fit() method. Set the number of steps per epoch to be the number of training samples divided by the batch_size. Also, set the validation data and the number of validation steps.

The ImageDataGenerator class applies data augmentation to the training data in real time. This makes the training process of the model slower.

Evaluate the performance of your model on the test data using the evaluate() method. Also, print the test loss and accuracy to the console.

The following screenshot shows the model's performance.

The model performs reasonably well on never seen data.

When you run code that does not implement the data augmentation techniques, the model training accuracy is 1. Which means it overfits. It also performs poorly on data it has never seen before. This is because it learns the peculiarities of the dataset.

TensorFlow is a diverse and powerful library. It is capable of training complex deep learning models and can run on a range of devices from smartphones to clusters of servers. It has helped power edge computing devices that utilize machine learning.

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The M.Sc. program in Machine Learning & Data Science at the Efi Arazi School of Computer Science aims to provide a deep theoretical understanding of machine learning and data-driven methods as well as a strong proficiency in using these methods. As part of this unique program, students with solid exact science backgrounds, but not necessarily computer science backgrounds, are trained to become data scientists. Headed by Prof. Zohar Yakhini and PhD Candidate Ben Galili, this program provides students with the opportunity to become skilled and knowledgeable data scientists by preparing them with fundamental theoretical and mathematical understandings, as well as endowing them with scientific and technical skills necessary to be creative and effective in these fields. The program offers courses in statistics and data analysis, different levels of machine -learning courses as well as unique electives such as a course in recommendation systems and on DNA and sequencing technologies.

M.Sc. student Guy Assa, preparing DNA for sequencing on a nanopore device, in Prof. Noam Shomrons DNA sequencing class, part of the elective curriculum (Credit: private photo)

In recent years, data science methodologies have become a foundational language and a main development tool for science and industry. Machine learning and data-driven methods have developed considerably and now penetrate almost all areas of modern life. The vision of a data-driven world presents many exciting challenges to data experts in diverse fields of application, such as medical science, life science, social science, environmental science, finance, economics, business.

Graduates of the program are successful in becoming data scientists in Israeli hi-tech companies. Lior Zeida Cohen, a graduate of the program says After earning a BA degree in Aerospace Engineering from the Technion and working as an engineer and later leading a control systems development team, I sought out a graduate degree program that would allow me to delve deeply into the fields of Data Science and Machine Learning while also allowing me to continue working full-time. I chose to pursue the ML & Data Science Program, at Reichman University. The program provided in-depth study in both the theoretical and practical aspects of ML and Data Science, including exposure to new research and developments in the field. It also emphasized the importance of learning the fundamental concepts necessary for working in these domains. In the course of completing the program, I began work at Elbit Systems as an algorithms developer in a leading R&D group focusing on AI and Computer Vision. The program has greatly contributed to my success in this position".

As a part of the curriculum, the students execute collaborative research projects with both external and internal collaborators, in Israel and around the world; One active collaboration is with the Leibniz Institute for Tropospheric Research (TROPOS) in Leipzig, Germany. In this collaboration, the students, led by Prof. Zohar Yakhini and Dr. Shay Ben-Elazar, a Principal Data Science and Engineering Manager at Microsoft Israel, as well as Dr. Johannes Bhl from TROPOS, are using data science and machine learning tools in order to infer properties of stratospheric layers by using data from sensory devices. The models developed in the project provide inference from simple devices that achieves an accuracy which is close to that which is obtained through much more expensive measurements. This improvement is enabled through the use of neural network models (deep learning).

Results from the TROPOS project: a significant improvement in the inference accuracy. Left panel: actual atmospheric status as obtained from the more expensive measurements (Lidar + Radar) Middle panel: predicted status as inferred from Lidar measurements using physical models. Right panel: status determined by the deep learning model developed in the project.

Additional collaborations include a number of projects with Israeli hospitals such as Sheba Tel Hashomer, Beilinson Hospital, and Kaplan Medical Center, as well as with the Israel Nature and Parks Authority and with several hi-tech companies.

PhD candidate Ben Galili, Academic Director of Machine Learning and Data Science Program (Credit: private photo)

Several research and thesis projects are led by students in the program addressing data analysis questions related to spatial biology the study of molecular biology processes in their bigger location context. One project, led by student Guy Attia and supervised by Dr. Leon Anavy addressed imputation methods for spatial transcriptomics data. A second one, led by student Efi Herbst, aims to expand the inference scope of data from spatial transcriptomics, into molecular properties that are not directly measured by the technology device.

According to Maya Kerem, a recent graduate, the MA program taught me a number of skills that would enable me to easily integrate into a new company based on the knowledge I gained. I believe that this program is particularly unique because it always makes sure that the learnings are applied to industry-related problems at the end of each module. This is a hands-on program at Reichman University, which is what drew me to enroll in this MA program.

For more info

This article was written in cooperation with Reichman University

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An M.Sc. computer science program in RUNI, focusing on machine learning - The Jerusalem Post

The world of artificial intelligence (AI) is growing exponentially, with machine learning playing an instrumental role in bringing intelligent systems to life. As a result, machine learning engineers are in high demand in the tech industry. If youre contemplating a career in this captivating domain, this article will give you a comprehensive understanding of a machine learning engineers role, their primary responsibilities, average salary, and the steps to becoming one.

A machine learning engineer is a specialized type of software engineer who focuses on the design, implementation, and optimization of machine learning models and algorithms. They serve as a link between data science and software engineering, working in close collaboration with data scientists to transform prototypes and ideas into scalable, production-ready systems. Machine learning engineers play a vital role in converting raw data into actionable insights and ensuring that AI systems are efficient, accurate, and dependable.

Machine learning engineers have a wide range of responsibilities, including:

The average salary of a machine learning engineer can vary based on factors such as location, experience, and company size. According to Glassdoor, as of 2023, the average base salary for a machine learning engineer in the United States is approximately $118,000 per year. However, experienced professionals and those working in high-demand areas can earn significantly higher salaries.

To become a machine learning engineer, follow these steps:

the key traits that contribute to the success of a machine learning engineer.

Machine learning engineers often face complex challenges that require innovative solutions. A successful engineer must possess excellent analytical and problem-solving skills to identify patterns in data, understand the underlying structure of problems, and develop effective strategies to address them. This involves breaking down complex problems into smaller, more manageable components, and using a logical and methodical approach to solve them.

A solid foundation in mathematics and statistics is crucial for machine learning engineers, as these disciplines underpin many machine learning algorithms and techniques. Engineers should have a strong grasp of linear algebra, calculus, probability, and optimization methods to understand and apply various machine learning models effectively.

Machine learning engineers must be proficient in programming languages such as Python, R, or Java, as these are often used to develop machine learning models. Additionally, they should be well-versed in software engineering principles, including version control, testing, and code optimization. This knowledge enables them to create efficient, scalable, and maintainable code that can be seamlessly integrated into production environments.

Successful machine learning engineers must be adept at using popular machine learning frameworks and libraries such as TensorFlow, PyTorch, and Scikit-learn. These tools streamline the development and implementation of machine learning models, allowing engineers to focus on refining their algorithms and optimizing their models for better performance.

The field of machine learning is constantly evolving, with new techniques, tools, and best practices emerging regularly. A successful machine learning engineer must possess an innate curiosity and a strong desire for continuous learning. This includes staying up-to-date with the latest research, attending conferences and workshops, and engaging in online communities where they can learn from and collaborate with other professionals.

Machine learning projects often require engineers to adapt to new technologies, tools, and methodologies. A successful engineer must be adaptable and flexible, willing to learn new skills and pivot their approach when necessary. This agility enables them to stay ahead of the curve and remain relevant in the fast-paced world of AI.

Machine learning engineers frequently work in multidisciplinary teams, collaborating with data scientists, software engineers, and business stakeholders. Strong communication and collaboration skills are essential for effectively conveying complex ideas and concepts to team members with varying levels of technical expertise. This ensures that the entire team works cohesively towards a common goal, maximizing the success of machine learning projects.

Developing effective machine learning models requires a high degree of precision and attention to detail. A successful engineer must be thorough in their work, ensuring that their models are accurate, efficient, and reliable. This meticulous approach helps to minimize errors and ensures that the final product meets or exceeds expectations.

Becoming a machine learning engineer requires a strong foundation in mathematics, computer science, and programming, as well as a deep understanding of various machine learning algorithms and techniques. By following the roadmap outlined in this article and staying current with industry trends, you can embark on a rewarding and exciting career as a machine learning engineer. Develop an understanding of data preprocessing, feature engineering, and data visualization techniques.

Learn about different machine learning algorithms, including supervised, unsupervised, and reinforcement learning approaches. Gain practical experience through internships, personal projects, or freelance work. Build a portfolio of machine learning projects to showcase your skills and knowledge to potential employers.

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What is a Machine Learning Engineer? Salary & Responsibilities - Unite.AI

TOKYO, April 24, 2023 (GLOBE NEWSWIRE) -- The Global Machine Learning as a Service Market Size accounted for USD 7.1 Billion in 2022 and is projected to achieve a market size of USD 173.5 Billion by 2032 growing at a CAGR of 37.9% from 2023 to 2032.

Machine Learning as a Service Market Research Report Highlights and Statistics:

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Machine Learning as Service Market Report Coverage:

Machine Learning as a Service Market Overview:

The increasing adoption of cloud-based technologies and the need for managing the enormous amount of data generated has led to the rise in demand for MLaaS solutions. MLaaS provides pre-built algorithms, models, and tools, making it easier and faster to develop and deploy machine learning applications. This service is being used in various industries such as healthcare, retail, BFSI, manufacturing, and others.

The healthcare industry is using MLaaS for patient monitoring and disease prediction. In retail, MLaaS is being used for personalized recommendations and fraud detection. MLaaS is also being utilized for financial fraud detection, sentiment analysis, recommendation systems, predictive maintenance, and much more.

The Natural Language Processing (NLP) segment is expected to grow rapidly during the forecast period. NLP is being used by organizations to analyze customer feedback, improve customer experience, and automate customer service. MLaaS vendors such as Amazon Web Services, IBM Corporation, Google LLC, Microsoft Corporation, and Oracle Corporation offer various pricing models and features, making the Machine Learning as a Service market competitive.

Trends in the Machine Learning as a Service Market:

Machine Learning as a Service Market Dynamics

Growth Hampering Factors in the Market for Machine Learning as a Service:

Check the detailed table of contents of the report @

https://www.acumenresearchandconsulting.com/table-of-content/machine-learning-as-a-service-mlaas-market

Market Segmentation:

By Type of component

By Application

By Size of Organization

End User

Machine Learning as a Service Market Overview by Region:

North America's Machine Learning as a Service market share is the highest globally, due to the high adoption of cloud computing and the presence of several major players in the region. The United States is the largest market for MLaaS in North America, driven by the increasing demand for predictive analytics, the growing use of deep learning, and the rising adoption of artificial intelligence (AI) across various industries. For instance, companies in the healthcare sector are using MLaaS for predicting patient outcomes, and retailers are using it to analyze customer behavior and preferences to deliver personalized experiences.

The Asia-Pacific region's Machine Learning as a Service Market share is also huge and is growing at the fastest rate, due to the increasing adoption of cloud computing, the growth of IoT devices, and the rise of e-commerce in the region. China is the largest market for MLaaS in the Asia Pacific region, with several major companies investing in AI and machine learning technologies. For example, Alibaba, the largest e-commerce company in China, is using MLaaS for predictive analytics and recommendation engines. Japan is another significant market for MLaaS in the region, with companies using it for predictive maintenance and fraud detection.

Europe is another key market for Machine Learning as a Service, with countries such as the United Kingdom, Germany, and France driving growth in the region. The adoption of MLaaS in Europe is being driven by the growth of e-commerce and the increasing demand for personalized experiences. For example, companies in the retail sector are using MLaaS to analyze customer data and make personalized product recommendations. The healthcare sector is also a significant user of MLaaS in Europe, with providers using it for predictive analytics and diagnosis.

The MEA and South American regions have a growing Machine Learning as a Service market share, however it is expected to grow at a steady pace.

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Machine Learning as a Service Market Key Players:

Some of the major players in the Machine Learning as a Service market include Amazon Web Services, Google LLC, IBM Corporation, Microsoft Corporation, SAP SE, Oracle Corporation, Hewlett Packard Enterprise Development LP, Fair Isaac Corporation (FICO), Fractal Analytics Inc., H2O.ai, DataRobot, Alteryx Inc., Big Panda Inc., RapidMiner Inc., SAS Institute Inc., Angoss Software Corporation, Domino Data Lab Inc., TIBCO Software Inc., Cloudera Inc., and Databricks Inc. These companies offer a wide range of MLaaS solutions, including predictive analytics, machine learning algorithms, natural language processing, deep learning, and computer vision.

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A new machine-learning method could help us gauge the time of our internal body clock, helping us all make better health decisions, including when and how long to sleep.

The research, which has been conducted by the University of Surrey and the University of Groningen, used a machine learning programme to analyse metabolites in blood to predict the time of our internal circadian timing system.

To date the standard method to determine the timing of the circadian system is to measure the timing of our natural melatonin rhythm, specifically when we start producing melatonin, known as dim light melatonin onset (DLMO).

Professor Debra Skene, co-author of the study from the University of Surrey, said:

After taking two blood samples from our participants, our method was able to predict the DLMO of individuals with an accuracy comparable or better than previous, more intrusive estimation methods.

The research team collected a time-series of blood samples from 24 individuals 12 men and 12 women. All participants were healthy, did not smoke and had regular sleeping schedules seven days before they visited the University clinical research facility. The research team then measured over 130 metabolite rhythms using a targeted metabolomics approach. These metabolite data were then used in a machine learning programme to predict circadian timing.

Professor Skene continued:

"We are excited but cautious about our new approach to predicting DLMO as it is more convenient and requires less sampling than the tools currently available. While our approach needs to be validated in different populations, it could pave the way to optimise treatments for circadian rhythm sleep disorders and injury recovery.

Smart devices and wearables offer helpful guidance on sleep patterns but our research opens the way to truly personalised sleep and meal plans, aligned to our personal biology, with the potential to optimise health and reduce the risks of serious illness associated with poor sleep and mistimed eating."

Professor Roelof Hut, co-author of the study from University of Groningen, said:

Our results could help to develop an affordable way to estimate our own circadian rhythms that will optimize the timing of behaviors, diagnostic sampling, and treatment.

The study has been published by PNAS

###

Notes to editors

Professor Debra Skene and Professor Roelof Hut are available for interview upon request however requests will be limited due to their work commitments.

For more information, please contact the University of Surrey's press office via mediarelations@surrey.ac.uk

Proceedings of the National Academy of Sciences

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Machine learning estimation of human body time using metabolomic profiling

24-Apr-2023

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Say the words dream vacation and everyone will picture something different. This brings a particular challenge to the modern travel marketer especially in a world of personalization, when all []

Say the words dream vacation and everyone will picture something different. This brings a particular challenge to the modern travel marketer especially in a world of personalization, when all travelers are looking for their own unique experiences. Fortunately, artificial intelligence (AI) provides a solution that allows travel marketers to draw upon a variety of sources when researching the best ways to connect with potential audiences.

By utilizing and combining data from user-generated content, transaction history and other online communications, AI and machine-learning (ML) solutions can help to give marketers a customer-centric approach, while successfully accounting for the vast diversity amongst their consumer base.

AI creates significant value for travel brands, which is why 48% of business executives are likely to invest in AI and automation in customer interactions over the next two years, according to Deloitte. Using AI and a data-driven travel marketing strategy, you can predict behaviors and proactively market to your ideal customers. There are as many AI solutions in the market as there are questions that require data, so choosing the right one is important.

For example, a limited-memory AI solution can skim a review site, such as TripAdvisor, to determine the most popular destinations around a major travel season, like summertime. Or, a chatbot can speak directly with visitors to your site, and aggregate their data to give brands an idea on what prospective consumers are looking for. Other solutions offer predictive segmentation, which can separate consumers based on their probability of taking action, categorize your leads and share personalized outreach on their primary channels. Delivering personalized recommendations are a major end goal for AI solutions in the travel industry. For example, Booking.com utilizes a consumers search history to determine whether they are traveling for business or leisure and provide recommendations accordingly.

A major boon of todays AI and machine-learning solutions are their ability to monitor and inform users of ongoing behavioral trends. For example, who could have predicted the popularity of hotel day passes for remote workers, as little as three years ago? Or the growing consumer desire for sustainable toiletries? Trends change every year or, more accurately, every waking hour so, having a tool that can stay ahead of the next biggest thing is essential.

In an industry where every element of the customers experience travel costs, hotels, activities is meticulously planned, delivering personalized experiences is critical to maintaining a customers interest. Consumers want personalization. As Google reports, 90% of leading marketers indicate that personalization significantly contributes to business profitability.

Particularly in the travel field, where there are as many consumer preferences as there are destinations on a map, personalization is essential in order to gain their attention. AI capabilities can solve common traveler frustrations, further enhancing the consumer experience. Natural language processors can skim through review sites, gathering the generalized sentiment from prior reviews and determining common complaints that may arise. Through these analyses from a range of sources from across a consumers journey, you can catch problems before they start.

For travel marketers already dealing with a diverse audience, and with a need for personalization to effectively stand out amongst the competition, AI and ML solutions can effectively help you plan and execute personalized outreach, foster brand loyalty and optimize the consumer experience. With AI working behind the scenes, your customers can look forward to fun in the sun, on the slopes, or wherever their destination may be.

Janine Pollack is the Executive Director, Growth & Content, and self-appointed Storyteller in Chief at MNI Targeted Media. She leads the brands commitment to generating content that informs and inspires. Her scope of work includes strategy and development for Fortune Knowledge Groups thought leadership programs and launching Fortunes The Most Powerful Woman podcast. She is proud to have partnered with The Hebrew University on the inaugural Nexus: Israel program, featuring worldwide luminaries. Janine has also written lifetime achievements for Sports Business Journal. She earned her masters from the Northwestern University Medill School of Journalism and B.A. from The American University in Washington D.C.

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Activating vacation mode: Utilizing AI and machine learning in your ... - TravelDailyNews International

What is one downside to deep learning?

Deep learning is a subset of machine learning that involves training artificial neural networks to recognize patterns in data. While deep learning has shown remarkable success in recent years, enabling breakthroughs in fields such as computer vision, natural language processing, and robotics, it is not without its flaws. One of the major challenges facing deep learning is its slow adaptability to changing environments and new data.

Deep learning algorithms typically train on large datasets. To recognize patterns in the data. These patterns can become used to make predictions or classify new data. That the model has not seen before. However, the performance of deep learning models usually deteriorate sover time. As the data trained on becomes outdated. Or no longer reflects the real-world conditions. Known as the problem of concept drift. Where the statistical properties of the data change over time. As a result, leading to degraded performance of the model.

There are several techniques that have become proposed to address the problem of concept drift in deep learning. One approach uses a continuous learning framework. Where the model becomes updated over time with new data to prevent the accumulation of errors due to concept drift. Another approach uses transfer learning. Where a pre-trained model fine-tuned on new data to adapt to the changing environment.

Despite these approaches, deep learning models still struggle with slow adaptability to new data and changing environments. Due in part to the fact that deep learning models highly parameterized and require large amounts of data to learn complex representations of the input data. As a result, updating the model with new data can be computationally expensive and time-consuming, making it difficult to adapt quickly to changing conditions.

In conclusion, the slow adaptability of deep learning models to changing environments. And new data becomes a major flaw. Moreover, one that needs to be addressed to enable their wider adoption in real-world applications. While techniques such as continuous learning and transfer learning show promise. More research becomes needed to develop more efficient and effective approaches to address this challenge. By addressing this flaw, deep learning can continue to revolutionize fields ranging from healthcare to finance to transportation, enabling new breakthroughs and transforming our world.

What is an example of a concept drift

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What is one downside to deep learning? - Rebellion Research

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More

Adoption of artificial intelligence (AI) has more than doubled globally since 2017, according to a state of AI report from McKinsey from late 2022, and the level of investment has increased as well.

Five years ago, 40% of organizations using AI said that over 5% of their digital budgets was spent on AI. Now, 52% report that level of investment, and things look set to improve further with 63% of respondents saying they expect investment to increase over the next three years.

Rising adoption is leading to job opportunities both within the sector, and as a result of AI-driven automation. According to the International Federation of Robotics, there has been a 14% increase year-over-year in the number of automated jobs, with junior workers the most affected. Figures from the World Economic Forum predict that the rapid growth of AI will create another 95 million high-paying jobs by 2025.

Since the beginning of 2023, awareness and understanding of AI and machine learning (ML) technologies have crossed the rubicon. No longer the province of the tech-savvy, Open AIs ChatGPT has brought about mass adoption of generative AI in particular.

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ChatGPT is just one of an increasing number of generative AI tools, including Bing Chat and Google Bard. DeepMinds Alpha Code writes computer programs at a competitive level; Jasper is an AI copywriter, and DALL-E, MidJourney and Stable Diffusion can all create realistic images and art from a description you provide

With the progression in AI capabilities, this is a sector that is in growth, but also one where you can build a career with plenty of prospects; the Bureau of Labor Statistics predicts a 31.4% increase in jobs for data scientists and mathematical science professionals which are crucial to AI by 2030.

Plus, AI and ML jobs tend to pay well. While salary will differ from role to role, the top paying states for AI, according to talent.com, are New York ($200,000), Maryland ($171,300), and Virginia ($170,000).

Want to get ahead of the crowd? The VentureBeat Job Board offers a first port of call for thousands of tech jobs discover three interesting AI/ML roles below.

Roblox is building the tools and platform that empowers its community to bring any experience that they can imagine to life. As a Technical Director on the Game Engine team, you will work on state-of-the-art real time AI and machine learning projects and will shape the vision of low compute cost, real-time machine learning at Roblox, taking leads on designing core components of Engine team ML operations. A strong understanding of deep learning frameworks including PyTorch and Tensorflow, as well as machine learning feature development workflows from training to deployment is required. Youll be experienced in shipping ML features in production environments and have experience optimizing and deploying models to mobile devices. Knowledge of state-of-the-art deep learning network architectures and primitives as well as 10 or more years in two or more languages (Python, C++, Rust, Lua, Go or JavaScript) is required. See the full list of requirements for this position.

The Data Automation team at Bloomberg develops machine learning models and infrastructure to extract key information from all kinds of financial documents. As Senior Machine Learning Engineer Data Automation, you will own the research on ground-breaking ML/NLP techniques and design efforts for the most efficient and practical application of those techniques to sophisticated business problems. You will use our automated ML suite, equipped with annotation platforms, for collecting training data and hyper-tuning models, as well as deploy your application on our scalable ETL infrastructure. Youll need to have four or more years of experience with an object-oriented programming language such as Java or Python, and have subject matter expertise in one or more of the following: artificial intelligence (AI), natural language processing (NLP), machine learning (ML), statistical models, and text analytics on large data sets. Interested? See the full job description here.

Meta is seeking a Software Engineer ML Systems (Technical Leadership) to join its research and development teams. You will drive the organizations goal towards relevant machine learning techniques, and build and optimize intelligent systems that improve Metas products and experiences. You will also assist in goal setting related to project impact, AI system design, and ML excellence, develop custom/novel architectures, define use cases, and develop methodology and benchmarks to evaluate different approaches. A Bachelors degree in computer science, computer engineering, a relevant technical field, or equivalent practical experience is required, as is vast experience communicating and working across functions to drive solutions. Experience developing AI algorithms or AI-system infrastructure in C/C++ or Python is also a requirement. See all the details for this job.

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The industry of online casino is truly profitable and lucrative, while it brings huge revenue to different countries all over the world. Since the technological growth and prevalent use of artificial intelligence, the assumptions are that casino industry is going to grow even further. Many online casinos are already using machine learning in database management. In comparison with other industries, users of online casinos are not predictable. That is the reason why companies need to collect and analyze data in order to understand their customers. In the text below, we will take a look at some examples of how machine learning is affecting online casinos as well as how it is personalizing the Online Casino Experience.

Collecting and Analyzing Data

The gambling industry has long analyzed consumer behavior to better understand the preferences of its players. Before the use of AI, casinos took advantage of club cards and loyalty programs however in this day and age collecting data from consumers is easy.

The data shows how players interact on a casino site as well as what their preferences are. By collecting data gaming companies want to understand why their consumers choose specific titles and why they engage in a specific way to then use that data to further improve the casino experience.

To analyze data, complex systems are necessary, but machine learning simplifies the process by collecting, analyzing, and presenting information that gaming companies can interpret. Leading online casinos are utilizing this technology to enhance user experience and entice more players.

By tracking consumer behavior they can create a unique online casino experience for each player. In other words, players can access sites that are tailored to their preferences. Once a player logs in they are presented with their favorite games based on their previous behavior on the site.

Personalized Bonuses and Promotions

Marketing plays a crucial role in the success of any online business, and for gambling sites, welcome bonuses are a major attraction for new customers. However, most operators offer the same promotion to everyone, as customizing offers for each user would be time-consuming and resource-intensive. Thankfully, artificial intelligence can help solve this problem.

Machine learning algorithms can analyze user data and behaviour to determine the best offer for each player, providing a more personalized experience that meets their individual needs and interests. They can offer the best Casino Rewards in Canada to all their players.

This approach allows online casinos to offer the same level of customer satisfaction to all players, not just VIPs, by tailoring promotions and rewards to each user. By enhancing the customer experience in this way, casinos can increase player loyalty, ultimately leading to greater success in the highly competitive online gaming industry.

Detecting and Preventing Fraud

Online casinos are a thriving business, and with that comes the increased risk of fraud. Traditional measures, such as employing security personnel and installing cameras, are effective for brick-and-mortar casinos, but not for online ones. Machine learning is a valuable tool in identifying cheating behaviour, but how can it prevent cheaters from accessing gaming sites?

The development of cyber-security is the answer. Soon, advancements in this field will enable casinos to better protect themselves from unscrupulous players, ensuring a safe and enjoyable experience for all.

Faster Withdrawals

The advent of machine learning is transforming the online casino industry by revolutionizing the withdrawal process and making it faster and more efficient for players. Traditional withdrawal processes required various checks and verifications, causing the process to take several days.

By analyzing the transaction history and the behavior of players machine learning can detect suspicious activity thus allowing online casinos to identify and prevent fraudulent withdrawals in time. Besides the safety benefits of machine learning it also helps make all withdrawals faster.

Additionally, it can help make the verification process easier by automatically verifying if

documents are authentic and detecting anything suspicious. Thus online casinos can offer their players a more reliable, faster, and efficient withdrawal process, which in turn leads to greater player satisfaction and new players.

Final Thoughts

To conclude, by personalizing the player experience, machine learning is revolutionizing the online casino industry. By collecting and examining data on the behaviour, preferences, and transaction history of players, machine learning can grant insight into information that is needed to be able to create personalized games, promotions, incentives, and bonuses.

By offering players a personalized online casino experience, casinos not only increase the satisfaction of players but also encourage the loyalty and retention of planets which is significant in a market as competitive as the online casino market.

Other than personalized player experience, machine learning also helps operators to identify and prevent fraudulent activities, ensuring a safer and more secure online gaming environment for players. Furthermore, the automation of different processes, such as verification of a document or detection of a fraud, speeds up transaction time and reduces processing costs. That results in a more efficient and more effective online casino operations.

Thus, introducing machine learning to this industry is a significant step towards a more personalized as well as rewarding player experience. Since technology continues to expand, the expectancy is that machine learning will become even more common, enabling online casinos to offer increasingly engaging and personalized experiences to their players.

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