GBT Technologies Inc.

SAN DIEGO, Sept. 01, 2022 (GLOBE NEWSWIRE) -- GBT Technologies Inc. (OTC PINK:GTCH) ("GBT or the Company), is implementing a machine learning driven, pattern matching technology within its Epsilon, microchips reliability verification and correction Electronic Design Automation (EDA) tool. Design rules are getting increasingly complex with each new process node and design firms are facing new challenges in the physical verification domain. One of the major areas that are affected by the process physics, is reliability Verification (RV). Microchips are major components nearly in every major electronics application. Civil, military and space exploration industries require reliable operations for many years, and in severe environments. High performance computing systems require advanced processing with high reliability to ensure the consistency and accuracy of the processed data. Complex integrated circuits are in the heart of these systems and need to function with high level of dependability. Particularly in the fields of medicine, aviation, transportation, data storage and industrial instrumentation, microchips reliability factor is crucial. GBT is implementing new machine learning driven, pattern matching techniques within its Epsilon system with the goal of addressing the advanced semiconductors physics, ensuring high level of reliability, optimal power consumption and high performance. As Epsilon analyzes the layout of an integrated circuit (IC), it identifies reliability weak spots, which are specific regions of an ICs layout, and learns their patterns. As the tool continues analyzing the layout it records problematic zones taking into account the patterns orientations and placements. In addition, it is designed to understand small variations in dimensions of the pattern, as specified by the designer or an automatic synthesis tool. As the weak spots are identified, the tool will take appropriate action to modify and correct them. A deep learning mechanism will be performing the data analysis, identification, categorization, and reasoning while executing an automatic correction. The Machine Learning will understand the patterns and record them in an internal library for future use. Epsilons pattern matching technology will be analyzing the chips data according to a set of predefined and learned-from-experience rules. Its cognitive capabilities will make it self-adjust to newest nodes with new constraints and challenges, with the goal of providing quick and reliable verification and correction of an IC layout.

Story continues

The Company released a video which explain the potential functions of the Epsilon tool: https://youtu.be/Mz4IOGRHeqw

"The ability to analyze and address advanced ICs reliability parameters is necessary to mitigate risk of system degradation, overheating, and possible malfunction. It can affect microchips performance, power consumption, data storage and retrieval, heat and an early failure which may be critical in vital electronic systems. Epsilon analyzes a microchip data for reliability, power and electrothermal characteristics, and performs auto-correction in case violations found. We are now implementing an intelligent technology for Epsilon with the goal of utilizing pattern matching algorithms to formulate a smart detection of reliability issues within integrated circuits layout. The new techniques will analyze and learn weak spots within microchips data, predicting failure models that are based on the process physics and electrical constraints knowledge. It will take into consideration each devices function, connectivity attributes, electrical currents information, electrothermal factors and more to determine problematic spots and perform auto-correction. Particularly for FinFet and GAA FET (Gate All Around FET) technologies, a devices functionality is developed with major reliability considerations ensuring power management efficiency, optimal thermal analysis aiming for long, reliable life span. Using smart pattern matching methods, we plan to improve reliability analysis, achieving consistency and accuracy across designs within advanced manufacturing processes. As dimensions of processes shrink, ICs layout features become much more complex to analyze for electrical phenomenon. To provide an intelligent answer for these complexities, we are implementing deep learning-based pattern matching technology with the goal of ensuring efficient, green microchips power consumption, higher performance, optimized thermal distribution, and ultimately superior reliability stated Danny Rittman, the Companys CTO.

There is no guarantee that the Company will be successful in researching, developing or implementing this system. In order to successfully implement this concept, the Company will need to raise adequate capital to support its research and, if successfully researched and fully developed, the Company would need to enter into a strategic relationship with a third party that has experience in manufacturing, selling and distributing this product. There is no guarantee that the Company will be successful in any or all of these critical steps.

About Us

GBT Technologies, Inc. (OTC PINK: GTCH) (GBT) (http://gbtti.com) is a development stage company which considers itself a native of Internet of Things (IoT), Artificial Intelligence (AI) and Enabled Mobile Technology Platforms used to increase IC performance. GBT has assembled a team with extensive technology expertise and is building an intellectual property portfolio consisting of many patents. GBTs mission, to license the technology and IP to synergetic partners in the areas of hardware and software. Once commercialized, it is GBTs goal to have a suite of products including smart microchips, AI, encryption, Blockchain, IC design, mobile security applications, database management protocols, with tracking and supporting cloud software (without the need for GPS). GBT envisions this system as a creation of a global mesh network using advanced nodes and super performing new generation IC technology. The core of the system will be its advanced microchip technology; technology that can be installed in any mobile or fixed device worldwide. GBTs vision is to produce this system as a low cost, secure, private-mesh-network between all enabled devices. Thus, providing shared processing, advanced mobile database management and sharing while using these enhanced mobile features as an alternative to traditional carrier services.

Forward-Looking Statements

Certain statements contained in this press release may constitute "forward-looking statements". Forward-looking statements provide current expectations of future events based on certain assumptions and include any statement that does not directly relate to any historical or current fact. Actual results may differ materially from those indicated by such forward-looking statements because of various important factors as disclosed in our filings with the Securities and Exchange Commission located at their website (http://www.sec.gov). In addition to these factors, actual future performance, outcomes, and results may differ materially because of more general factors including (without limitation) general industry and market conditions and growth rates, economic conditions, governmental and public policy changes, the Companys ability to raise capital on acceptable terms, if at all, the Companys successful development of its products and the integration into its existing products and the commercial acceptance of the Companys products. The forward-looking statements included in this press release represent the Company's views as of the date of this press release and these views could change. However, while the Company may elect to update these forward-looking statements at some point in the future, the Company specifically disclaims any obligation to do so. These forward-looking statements should not be relied upon as representing the Company's views as of any date subsequent to the date of the press release.

Contact:

Dr. Danny Rittman, CTO press@gopherprotocol.com

View post:
GBT is Implementing Machine Learning Driven, Pattern Matching Technology for its Epsilon, Mi-crochip Reliability Verification and Correction EDA Tool...

A support vector machine (SVM) is defined as a machine learning algorithm that uses supervised learning models to solve complex classification, regression, and outlier detection problems by performing optimal data transformations that determine boundaries between data points based on predefined classes, labels, or outputs. This article explains the fundamentals of SVMs, their working, types, and a few real-world examples.

A support vector machine (SVM) is a machine learning algorithm that uses supervised learning models to solve complex classification, regression, and outlier detection problems by performing optimal data transformations that determine boundaries between data points based on predefined classes, labels, or outputs. SVMs are widely adopted across disciplines such as healthcare, natural language processing, signal processing applications, and speech & image recognition fields.

Technically, the primary objective of the SVM algorithm is to identify a hyperplane that distinguishably segregates the data points of different classes. The hyperplane is localized in such a manner that the largest margin separates the classes under consideration.

The support vector representation is shown in the figure below:

As seen in the above figure, the margin refers to the maximum width of the slice that runs parallel to the hyperplane without any internal support vectors. Such hyperplanes are easier to define for linearly separable problems; however, for real-life problems or scenarios, the SVM algorithm tries to maximize the margin between the support vectors, thereby giving rise to incorrect classifications for smaller sections of data points.

SVMs are potentially designed for binary classification problems. However, with the rise in computationally intensive multiclass problems, several binary classifiers are constructed and combined to formulate SVMs that can implement such multiclass classifications through binary means.

In the mathematical context, an SVM refers to a set of ML algorithms that use kernel methods to transform data features by employing kernel functions. Kernel functions rely on the process of mapping complex datasets to higher dimensions in a manner that makes data point separation easier. The function simplifies the data boundaries for non-linear problems by adding higher dimensions to map complex data points.

While introducing additional dimensions, the data is not entirely transformed as it can act as a computationally taxing process. This technique is usually referred to as the kernel trick, wherein data transformation into higher dimensions is achieved efficiently and inexpensively.

The idea behind the SVM algorithm was first captured in 1963 by Vladimir N. Vapnik and Alexey Ya. Chervonenkis. Since then, SVMs have gained enough popularity as they have continued to have wide-scale implications across several areas, including the protein sorting process, text categorization, facial recognition, autonomous cars, robotic systems, and so on.

See More: What Is a Neural Network? Definition, Working, Types, and Applications in 2022

The working of a support vector machine can be better understood through an example. Lets assume we have red and black labels with the features denoted by x and y. We intend to have a classifier for these tags that classifies data into either the red or black category.

Lets plot the labeled data on an x-y plane, as below:

A typical SVM separates these data points into red and black tags using the hyperplane, which is a two-dimensional line in this case. The hyperplane denotes the decision boundary line, wherein data points fall under the red or black category.

A hyperplane is defined as a line that tends to widen the margins between the two closest tags or labels (red and black). The distance of the hyperplane to the most immediate label is the largest, making the data classification easier.

The above scenario is applicable for linearly separable data. However, for non-linear data, a simple straight line cannot separate the distinct data points.

Heres an example of non-linear complex dataset data:

The above dataset reveals that a single hyperplane is not sufficient to separate the involved labels or tags. However, here, the vectors are visibly distinct, making segregating them easier.

For data classification, you need to add another dimension to the feature space. For linear data discussed until this point, two dimensions of x and y were sufficient. In this case, we add a z-dimension to better classify the data points. Moreover, for convenience, lets use the equation for a circle, z = x + y.

With the third dimension, the slice of feature space along the z-direction looks like this:

Now, with three dimensions, the hyperplane, in this case, runs parallel to the x-direction at a particular value of z; lets consider it as z=1.

The remaining data points are further mapped back to two dimensions.

The above figure reveals the boundary for data points along features x, y, and z along a circle of the circumference with radii of 1 unit that segregates two labels of tags via the SVM.

Lets consider another method of visualizing data points in three dimensions for separating two tags (two different colored tennis balls in this case). Consider the balls lying on a 2D plane surface. Now, if we lift the surface upward, all the tennis balls are distributed in the air. The two differently colored balls may separate in the air at one point in this process. While this occurs, you can use or place the surface between two segregated sets of balls.

In this entire process, the act of lifting the 2D surface refers to the event of mapping data into higher dimensions, which is technically referred to as kernelling, as mentioned earlier. In this way, complex data points can be separated with the help of more dimensions. The concept highlighted here is that the data points continue to get mapped into higher dimensions until a hyperplane is identified that shows a clear separation between the data points.

The figure below gives the 3D visualization of the above use case:

See More: Narrow AI vs. General AI vs. Super AI: Key Comparisons

Support vector machines are broadly classified into two types: simple or linear SVM and kernel or non-linear SVM.

A linear SVM refers to the SVM type used for classifying linearly separable data. This implies that when a dataset can be segregated into categories or classes with the help of a single straight line, it is termed a linear SVM, and the data is referred to as linearly distinct or separable. Moreover, the classifier that classifies such data is termed a linear SVM classifier.

A simple SVM is typically used to address classification and regression analysis problems.

Non-linear data that cannot be segregated into distinct categories with the help of a straight line is classified using a kernel or non-linear SVM. Here, the classifier is referred to as a non-linear classifier. The classification can be performed with a non-linear data type by adding features into higher dimensions rather than relying on 2D space. Here, the newly added features fit a hyperplane that helps easily separate classes or categories.

Kernel SVMs are typically used to handle optimization problems that have multiple variables.

See More: What is Sentiment Analysis? Definition, Tools, and Applications

SVMs rely on supervised learning methods to classify unknown data into known categories. These find applications in diverse fields.

Here, well look at some of the top real-world examples of SVMs:

The geo-sounding problem is one of the widespread use cases for SVMs, wherein the process is employed to track the planets layered structure. This entails solving the inversion problems where the observations or results of the issues are used to factor in the variables or parameters that produced them.

In the process, linear function and support vector algorithmic models separate the electromagnetic data. Moreover, linear programming practices are employed while developing the supervised models in this case. As the problem size is considerably small, the dimension size is inevitably tiny, which accounts for mapping the planets structure.

Soil liquefaction is a significant concern when events such as earthquakes occur. Assessing its potential is crucial while designing any civil infrastructure. SVMs play a key role in determining the occurrence and non-occurrence of such liquefaction aspects. Technically, SVMs handle two tests: SPT (Standard Penetration Test) and CPT (Cone Penetration Test), which use field data to adjudicate the seismic status.

Moreover, SVMs are used to develop models that involve multiple variables, such as soil factors and liquefaction parameters, to determine the ground surface strength. It is believed that SVMs achieve an accuracy of close to 96-97% for such applications.

Protein remote homology is a field of computational biology where proteins are categorized into structural and functional parameters depending on the sequence of amino acids when sequence identification is seemingly difficult. SVMs play a key role in remote homology, with kernel functions determining the commonalities between protein sequences.

Thus, SVMs play a defining role in computational biology.

SVMs are known to solve complex mathematical problems. However, smooth SVMs are preferred for data classification purposes, wherein smoothing techniques that reduce the data outliers and make the pattern identifiable are used.

Thus, for optimization problems, smooth SVMs use algorithms such as the Newton-Armijo algorithm to handle larger datasets that conventional SVMs cannot. Smooth SVM types typically explore math properties such as strong convexity for more straightforward data classification, even with non-linear data.

SVMs classify facial structures vs. non-facial ones. The training data uses two classes of face entity (denoted by +1) and non-face entity (denoted as -1) and n*n pixels to distinguish between face and non-face structures. Further, each pixel is analyzed, and the features from each one are extracted that denote face and non-face characters. Finally, the process creates a square decision boundary around facial structures based on pixel intensity and classifies the resultant images.

Moreover, SVMs are also used for facial expression classification, which includes expressions denoted as happy, sad, angry, surprised, and so on.

In the current scenario, SVMs are used for the classification of images of surfaces. Implying that the images clicked of surfaces can be fed into SVMs to determine the texture of surfaces in those images and classify them as smooth or gritty surfaces.

Text categorization refers to classifying data into predefined categories. For example, news articles contain politics, business, the stock market, or sports. Similarly, one can segregate emails into spam, non-spam, junk, and others.

Technically, each article or document is assigned a score, which is then compared to a predefined threshold value. The article is classified into its respective category depending on the evaluated score.

For handwriting recognition examples, the dataset containing passages that different individuals write is supplied to SVMs. Typically, SVM classifiers are trained with sample data initially and are later used to classify handwriting based on score values. Subsequently, SVMs are also used to segregate writings by humans and computers.

In speech recognition examples, words from speeches are individually picked and separated. Further, for each word, certain features and characteristics are extracted. Feature extraction techniques include Mel Frequency Cepstral Coefficients (MFCC), Linear Prediction Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC), and others.

These methods collect audio data, feed it to SVMs and then train the models for speech recognition.

With SVMs, you can determine whether any digital image is tampered with, contaminated, or pure. Such examples are helpful when handling security-related matters for organizations or government agencies, as it is easier to encrypt and embed data as a watermark in high-resolution images.

Such images contain more pixels; hence, it can be challenging to spot hidden or watermarked messages. However, one solution is to separate each pixel and store data in different datasets that SVMs can later analyze.

Medical professionals, researchers, and scientists worldwide have been toiling hard to find a solution that can effectively detect cancer in its early stages. Today, several AI and ML tools are being deployed for the same. For example, in January 2020, Google developed an AI tool that helps in early breast cancer detection and reduces false positives and negatives.

In such examples, SVMs can be employed, wherein cancerous images can be supplied as input. SVM algorithms can analyze them, train the models, and eventually categorize the images that reveal malign or benign cancer features.

See More: What Is a Decision Tree? Algorithms, Template, Examples, and Best Practices

SVMs are crucial while developing applications that involve the implementation of predictive models. SVMs are easy to comprehend and deploy. They offer a sophisticated machine learning algorithm to process linear and non-linear data through kernels.

SVMs find applications in every domain and real-life scenarios where data is handled by adding higher dimensional spaces. This entails considering factors such as the tuning hyper-parameters, selecting the kernel for execution, and investing time and resources in the training phase, which help develop the supervised learning models.

Did this article help you understand the concept of support vector machines? Comment below or let us know on Facebook, Twitter, or LinkedIn. Wed love to hear from you!

Here is the original post:
All You Need to Know About Support Vector Machines - Spiceworks News and Insights

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Watch here.

Over the last two years, one of the most common ways for organizations to scale and run increasingly large and complex artificial intelligence (AI) workloads has been with the open-source Ray framework, used by companies from OpenAI to Shopify and Instacart.

Ray enables machine learning (ML) models to scale across hardware resources and can also be used to support MLops workflows across different ML tools. Ray 1.0 came out in September 2020 and has had a series of iterations over the last two years.

Today, the next major milestone was released, with the general availability of Ray 2.0 at the Ray Summit in San Francisco. Ray 2.0 extends the technology with the new Ray AI Runtime (AIR) that is intended to work as a runtime layer for executing ML services. Ray 2.0 also includes capabilities designed to help simplify building and managing AI workloads.

Alongside the new release, Anyscale, which is the lead commercial backer of Ray, announced a new enterprise platform for running Ray. Anyscale also announced a new $99 million round of funding co-led by existing investors Addition and Intel Capital with participation from Foundation Capital.

MetaBeat 2022

MetaBeat will bring together thought leaders to give guidance on how metaverse technology will transform the way all industries communicate and do business on October 4 in San Francisco, CA.

Ray started as a small project at UC Berkeley and it has grown far beyond what we imagined at the outset, said Robert Nishihara, cofounder and CEO at Anyscale, during his keynote at the Ray Summit.

Its hard to understate the foundational importance and reach of Ray in the AI space today.

Nishihara went through a laundry list of big names in the IT industry that are using Ray during his keynote. Among the companies he mentioned is ecommerce platform vendor Shopify, which uses Ray to help scale its ML platform that makes use of TensorFlow and PyTorch. Grocery delivery service Instacart is another Ray user, benefitting from the technology to help train thousands of ML models. Nishihara noted that Amazon is also a Ray user across multiple types of workloads.

Ray is also a foundational element for OpenAI, which is one of the leading AI innovators, and is the group behind the GPT-3 Large Language Model and DALL-E image generation technology.

Were using Ray to train our largest models, Greg Brockman, CTO and cofounder of OpenAI, said at the Ray Summit. So, it has been very helpful for us in terms of just being able to scale up to a pretty unprecedented scale.

Brockman commented that he sees Ray as a developer-friendly tool and the fact that it is a third-party tool that OpenAI doesnt have to maintain is helpful, too.

When something goes wrong, we can complain on GitHub and get an engineer to go work on it, so it reduces some of the burden of building and maintaining infrastructure, Brockman said.

For Ray 2.0, a primary goal for Nishihara was to make it simpler for more users to be able to benefit from the technology, while providing performance optimizations that benefit users big and small.

Nishihara commented that a common pain point in AI is that organizations can get tied into a particular framework for a certain workload, but realize over time they also want to use other frameworks. For example, an organization might start out just using TensorFlow, but realize they also want to use PyTorch and HuggingFace in the same ML workload. With the Ray AI Runtime (AIR) in Ray 2.0, it will now be easier for users to unify ML workloads across multiple tools.

Model deployment is another common pain point that Ray 2.0 is looking to help solve, with the Ray Serve deployment graph capability.

Its one thing to deploy a handful of machine learning models. Its another thing entirely to deploy several hundred machine learning models, especially when those models may depend on each other and have different dependencies, Nishihara said. As part of Ray 2.0, were announcing Ray Serve deployment graphs, which solve this problem and provide a simple Python interface for scalable model composition.

Looking forward, Nishiharas goal with Ray is to help enable a broader use of AI by making it easier to develop and manage ML workloads.

Wed like to get to the point where any developer or any organization can succeed with AI and get value from AI, Nishihara said.

VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Learn more about membership.

Read the original here:
Ray, the machine learning tech behind OpenAI, levels up to Ray 2.0 - VentureBeat

Headquarters of the Council of Higher Scientific Research (CSIC).

a bone scan Every two years for all women aged 50-69. Since 1990, that is The biggest testing challenge for the national health systemAnd it aims to prevent one of the most common cancers in Spain, that is Mother, The method is X-rays that detect potentially cancerous areas; If something suspicious is found, that test is followed by more tests, often High probability of false positives, harmful and costly,

they are curvature This is the main reason why screening is limited to the highest risk groups. By adding predictive algorithms to mammograms, the risk areas of a patients breasts would be limited and the reliability of diagnosis increased to 90 percent. Therefore, they can be done with Often and the age range of the women they target Expansion,

It is a process that already exists, which uses artificial intelligenceand that . develops a team of Superior Council of Scientific Inquiry (CSIC), specifically the Institute of Corpuscular Physics (IFIC). it is part of the scope of machine learning (machine learning) in precision medicine, and a research network that seeks to increase the efficiency with which each patient is treated and optimize health care resources.

To understand how, you must first understand the concepts that come into play. The first is artificial intelligence. the ability of a computer or robot to perform tasks normally associated with intelligent beings, defined as sara degli-apostic You carlos sierra, author of the CSIC white paper on the subject. That is, they are the processes that are used replace human work with robotsWith the aim of accomplishing this with greater accuracy and greater efficiency.

And where can artificial intelligence work in medicine today? On many fronts, he replies. dolores del castilloResearchers from CSICs Center for Automation and Robotics, From the administrative to the management of clinical documentation. And, in a more specific way, in the analysis of images, or in the monitoring and follow-up of patients. And where are the still bigger limits? Above all, in the field of health care, in legal and ethical aspects when dealing with important matters. And whats more, theres still a long way to go, explains Del Castillo, who works on the projects, among others. neurological movement disorderTraining for a large section of healthcare workers.

We find the second concept as a subfield of artificial intelligence, along with its advantages and disadvantages: machine learning, This can be translated as machine learning. That is, artificial intelligence that works through computers thatand find patterns in population groups, With these patterns, predictions are made about what is most likely to happen. machine learning translate data Algorithm,

Precision medicine to predict disease

and after artificial intelligence and machine learningThere is a third concept: the precision medicine, The one that suits the person, his genes, his background, his lifestyle, his socialization. a model that must first be able predictable disease, Second, Francisco Albiol from IFIC, continues to assess each patient, apply the best treatment based on clinical evidence, identify the most complex cases, and assess their inclusion in management programs.

It makes sense high impact disease, and does not make sense for serious diseases; For example, distinguishing the flu from a cold in primary care, as the benefits will not compensate for the effort required.

The key to the use of artificial intelligence in medicine is also cost optimization, which is very important for public health. Spains population has increased from 42 to 47 million people between 2003 and 2022, that is, more than 10 percent. and from 2005 to 2022, The average age of the population has increased from 40 to 44, We are getting older and older.

Therefore, Dolores del Castillo says, the best valued projects and, therefore, likely to be funded, are those that incorporate artificial intelligence techniques to address the prevention, diagnosis and treatment of cardiovascular diseases, neurodegenerative diseases, cancer and obesity. There is also a special focus on personal and home medicine, elderly care, and new drug offerings. The need for healthcare has been heightened by our demographics, and The aim should be to reduce and simplify the challenges with technology, we tried machine learning, summarizes Albiol.

Albiol is one of the scientists who led a program to improve breast cancer detection through algorithms. He defends, like other researchers, that if we mix machine learning with precision medicine, we should be talking about 4p medicine. Which brings together four features: Predictive, personal, preventive and participatory,

Because most purists confine precision medicine to the field of patient genetics, and would not include it in the bag that takes more characteristics into account. Those who do say that we are talking about something much broader: Applied to precision medicine, machine learning allows for Analyze large amounts of very different types of data (genomic, biochemical, social, medical imaging) and model them to be able to offer together individual diagnosismore precise and thus more effective treatment, summarizes researcher Lara Loret Iglesias of the Institute of Physics of Cantabria.

Lloret is part of a network of scientists who, like Albiol or Del Castillo, are dedicated to projects on machine learning and precision medicine. One of them developed by his team, which he leads together with fellow physicist Miriam Kobo Cano, is called Branyas. It is in honor of Spains oldest woman, Maria Branyas, who managed to overcome Covid-19: she has done so at the age of 113. In this they bring together the many casuistries of more than 3,000 elderly people, much less just genetics: machine learning establish Risk profile of getting sick or dying as a result of coronavirus, We derived data from the analysis of three risk profiles: a sociodemographic, a biological and an extended biological, which will add information on issues such as aspects related to the intestinal microbiota, vaccination and immunity.

Precision Medicine, Cancer and Alzheimers

also explain this Joseph Lewis Arcosfrom the Artificial Intelligence Research Institute. common diseases There are cancer and Alzheimers linked to precision medicine, but they have stood out with the Ictus project. Launched in the middle of a pandemic (which has made things difficult, he admits), he has treated patients at Barcelonas Belwitz Hospital who suffered strokes and, after a severe and acute phase, Have become long term,

In particular, those with movement difficulty in one hand or both. made over 700 sessions In which patients have been asked to play the keyboard of the electronic piano. Then, they transferred the analysis of finger movements to the results to see what the patterns of difficulties and improvements are. And theyve gotten particularly positive feedback among users because its not only doing an exercise, but it affects a very emotional part. The goal is now to expand it to hospitals in the United Kingdom.,

and future? Dolores del Castillo replies, I believe that the challenge of artificial intelligence in medicine is to incorporate research results into daily practice in a generalized way, but always without forgetting that it is the experts who have is the last word. To do that, doctors need to be able to rely on these systems and Interact with them in the most natural and simple wayEven helping with its design.

Lara Loret believes that we have to be able to build generalizable prediction systems, that is, the efficiency of the model does not depend on unnecessary things such as which machine the data is taken in, or how the calibration is. Francisco Albiol focuses on a problem that may be in the long run must have a solutionAt present, larger hospitals are preferred in these technologies than smaller cities or towns. convenience and reduce costs It also has to do with reaching out to everyone.

While it may include statements, data or notes from health institutions or professionals, the information contained in medical writing is edited and prepared by journalists. We advise the reader to consult a health professional on any health-related questions.

Continue reading here:
'Machine Learning' Predicts The Future With More Reliable Diagnostics - Nation World News