Koshiro Sonomoto, PhD

Credit: Lupus KCR 2023

A low-cost predictive machine learning model successfully predicted achievement of remission based on Clinical Disease Activity (CDAI) measures among a cohort of patients with rheumatoid arthritis (RA) after 6 months of treatment with tumor necrosis factor (TNF) inhibitors, according to research published in Rheumatology and Therapy.1

As investigators only used CDAI measures at baseline at month 6, they believe this method demonstrates the achievability of creating regional cohorts to create low-cost models tailored to specific institutions or geographical regions.

Currently, treatment guidelines suggest initiating therapy with methotrexate, followed by either biologic disease-modifying antirheumatic drugs (bDMARDs) or Janus kinase (JAK) inhibitors if methotrexate is ineffective. However, recent studies have favored bDMARDs over JAKs and, therefore, treatment with a TNF is becoming an increasingly popular option for this patient population. Despite these encouraging findings, only 70% of patients initiating a TNF show a favorable response.2

Numerous efforts have been reported to predict the efficacy of TNF in advance, wrote a team of investigators led by Koshiro Sonomoto, PhD, associated with the University of Occupational and Environmental Health in Japan. However, access to these advanced technologies may be limited to certain countries and advanced facilities due to cost, labor requirements, and the need for process standardization. Conversely, predictive models based on routine clinical data are more accessible.

Patients with RA beginning treatment with a TNF as the first targeted synthetic (ts)/bDMARD after inadequate response to methotrexate were included in the analysis. Data were collected from the FIRST registry between August 2003 and October 2022. The analysis of baseline characteristics and 6-month CDAI used a variety of machine learning approaches, such as lasso logistic regression (Lasso), logistic regression with stepwise variable selection, support vector machine, and decision tree, along with 48 factors available in routine clinical practice for the prediction model.

A total of 4706 patients in the FIRST registry initiated treatment with b/tsDMARDs during the study period, of which 2223 were receiving methotrexate. Of these patients, 1630 received a TNF and 79 received a JAK. The average age of patients was 59.2 years, they had an average body mass index of 22.1, a mean disease duration of 75.7 months, and all were Asian. The mean dose of methotrexate was 11.3 mg/week and the mean CDAI score was 26.1.

The models exclusively relied on patient-reported outcomes and quantitative parameters, as opposed to subjective physician input.

Of the approaches, Lasso demonstrated advantages in predicting CDAI remission, with a specificity of 69.9%, sensitivity of 61.7%, and a mean area under the curve of .704. Patients who were predicted to respond to TNF achieved CDAI remission at an average rate of 53.2%, compared with only 26.4% of predicted non-responders.

These results could also help to identify an alternative b/tsDMARDs class for patients who were predicted to be TNF non-responders.

Investigators mentioned limitations, including the decreasing accuracy of the Lasso-generated remission predictive model among a Calendar cohort. In this group, which was split into a 9:1 ratio with a cutoff of October 2019, an increase in censoring was reported in the validation cohort. They suggest this could be due to COVID-19 complications.

This approach holds the potential to improve rheumatoid arthritis management by reducing the need for trial-and-error approaches and facilitating more personalized and effective treatment strategies, investigators concluded. While further validation is necessary, the study also suggests that creating cost-effective models tailored to specific regions or institutions is possible.

References

See the article here:
Machine Learning Approach Effectively Predicts Remission in RA Following TNF Treatment - MD Magazine

This blog post is co-written with Pradeep Prabhakaran from Cohere.

Today, we are excited to announce that Cohere Command R and R+ foundation models are available throughAmazon SageMaker JumpStartto deploy and run inference. Command R/R+ are the state-of-the-art retrieval augmented generation (RAG)-optimized models designed to tackle enterprise-grade workloads.

In this post, we walk through how to discover and deploy Cohere Command R/R+ via SageMaker JumpStart.

Cohere Command R is a family of highly scalable language models that balance high performance with strong accuracy. Command R family include Command R and Command R+ models are optimized for RAG based workflows such as conversational interaction and long context tasks, enabling companies to move beyond proof of concept and into production. These powerful models are designed to handle complex tasks with high performance and strong accuracy, making them suitable for real-world applications.

Command R boasts high precision on RAG and tool use tasks, low latency and high throughput, a long 128,000-token context length, and strong capabilities across 10 key languages: English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Arabic, and Chinese.

Command R+ is the newest model, optimized for extremely performant conversational interaction and long-context tasks. It is recommended for workflows that lean on complex RAG functionality and multi-step tool use (agents), while Cohere R is well-suited for simpler RAG and single-step tool use tasks, as well as applications where price is a major consideration.

With SageMaker JumpStart, you can choose from a broad selection of publicly available foundation models. ML practitioners can deploy foundation models to dedicated SageMaker instances from a network-isolated environment and customize models using SageMaker for model training and deployment. You can now discover and deploy Cohere Command R/R+ models with a few choices inAmazon SageMaker Studioor programmatically through the SageMaker Python SDK. Doing so enables you to derive model performance and machine learning operations (MLOps) controls with SageMaker features such asSageMaker Pipelines,SageMaker Debugger, or container logs.

The model is deployed in an AWS secure environment and under your virtual private cloud (VPC) controls, helping provide data security. Cohere Command R/R+ models are available today for deployment and inferencing in Amazon SageMaker Studio inus-east-1(N. Virginia),us-east-2(Ohio),us-west-1(N. California),us-west-2(Oregon),Canada (Central), eu-central-1 (Frankfurt), eu-west-1(Ireland), eu-west-2 (London), eu-west-3 (Paris), eu-north-1 (Stockholm), ap-southeast-1 (Singapore), ap-southeast-2 (Sydney), ap-northeast-1(Tokyo) , ap-northeast-2(Seoul), ap-south-1(Mumbai), and sa-east-1(Sao Paulo).

You can access the foundation models through SageMaker JumpStart in the SageMaker Studio UI and the SageMaker Python SDK. In this section, we go over how to discover the models in SageMaker Studio.

From the SageMaker JumpStart landing page, you can easily discover various models by browsing through different hubs, which are named after model providers. The Cohere Command R and R+ models are available in the Cohere hub. If you dont see these models, ensure you have the latest SageMaker Studio version by shutting down and restarting Studio Classic Apps.

To find the Command R and R+ models, search for Command R in the search box located at the top left of the SageMaker JumpStart landing page. Each model can be deployed on Amazon Elastic Compute Cloud (EC2) P5 instances powered by NVIDIA H100 Tensor Core GPUs (p5.48xlarge) and Amazon EC2 P4de instances powered by NVIDIA A100 Tensor Core GPUs (ml.p4de.24xlarge).

To illustrate model deployment, well deploy Cohere Command R+ on NVIDIA H100. Choose the model card to open the corresponding model detail page.

When you choose Deploy, a window appears prompting you to subscribe to the model on AWS Marketplace. Choose Subscribe, which redirects you to the AWS Marketplace listing for Cohere Command R+ (H100). Follow the on-screen instructions to complete the subscription process.

Once subscribed, return to the model detail page and choose Deploy in the window. The deployment process initiates.

Alternatively, you can choose Notebooks on the model card and open the example notebook in JupyterLab. This notebook provides end-to-end guidance on deploying the model for inference and cleaning up resources. You can also find this example notebook in the Cohere SageMaker GitHub repository. To ensure the security of the endpoint, you can configure AWS Key Management Service (KMS) key for a SageMaker endpoint configuration.

If an endpoint has already been created, you can simply connect to it:

Once your endpoint has been connected, you can perform real-time inference using the co.chat endpoint.

Command R/R+ is optimized to perform well in 10 key languages, as listed in the introduction. Additionally, pre-training data have been included for the following 13 languages: Russian, Polish, Turkish, Vietnamese, Dutch, Czech, Indonesian, Ukrainian, Romanian, Greek, Hindi, Hebrew, Persian.

The model has been trained to respond in the language of the user. Heres an example in Spanish:

Heres what the response might look like:

Command R/R+ can also perform cross-lingual tasks, such as translation or answering questions about content in other languages.

Command R/R+ can ground its generations. This means that it can generate responses based on a list of supplied document snippets, and it includes citations in its response indicating the source of the information.

For example, the code snippet that follows produces an answer to How deep is the Mariana Trench along with inline citations based on the provided on-line documents.

Command R/R+, comes with a Tool Use API that enables the language model to interact with user-defined tools to automate highly sophisticated tasks. Command R/R+ in Tool Use mode creates API payloads (JSONs with specific parameters) based on user interactions and conversational history. These can be used to instruct any other application or tool.

For example, an application can be instructed to automatically categorize and route support tickets to the appropriate individual, change a status in customer relationship management software (CRM), or retrieve relevant snippets from a vector database. It comes in two variants; single-step and multi-step:

To explore these capabilities further, you can refer to the provided Jupyter notebook and Coheres AWS GitHub repository, which offer additional examples showcasing various use cases and applications.

After youve finished running the notebook and exploring the Cohere Command R and R+ models, its essential to clean up the resources youve created to avoid incurring unnecessary charges. Follow these steps to delete the resources and stop the billing:

In this post, we explored how to leverage the powerful capabilities of Coheres Command R and R+ models on Amazon SageMaker JumpStart. These state-of-the-art large language models are specifically designed to excel at real-world enterprise use cases, offering unparalleled performance and scalability. With their availability on SageMaker JumpStart and AWS Marketplace, you now have seamless access to these cutting-edge models, enabling you to unlock new levels of productivity and innovation in your natural language processing projects.

See the article here:
Cohere Command R and R+ are now available in Amazon SageMaker JumpStart | Amazon Web Services - AWS Blog

Summary

Google's foray into AI, exemplified by its popular AI chatbot, Gemini, is extending to more of its services and apps. Notably, the Chrome browser is poised to showcase its potential in incorporating AI features. In a recent update, we shared the news of a potential Gemini integration in Google Chrome for desktop to enhance the address bar. Now, the Chrome address bar, also known as the Omnibox, is set to elevate its intelligence by integrating cutting-edge machine-learning models.

As noted in the Chromium Blog, starting with Chrome version M124, Google is integrating machine learning models into Chrome's address bar to provide users with more accurate and relevant web page suggestions. These machine-learning models will also help increase the relevance of search suggestions.

Chrome software engineer Justin Donnelly sheds light on the challenges faced by the engineering team in enhancing Omnibox. The hand-crafted formulas it used previously were not well-suited to modern scenarios, leading to the scoring system remaining unchanged for a significant period. Moreover, altering a feature used billions of times daily posed another significant challenge for the team.

Donnelly added that the machine learning models used to train the Chrome Omnibox could identify some interesting patterns. For example, when users select a URL and then immediately return to Omnibox to search for another URL, the ML system decreases the relevance score of that URL. In future attempts, the ML system won't prioritize the URL with a lower score in that context.

According to Chrome software engineers, integrating machine learning models into the Omnibox holds immense potential for enhancing the user experience. These models could potentially adapt to the day's time, offering users more relevant results. Donnelly also revealed that the Chrome engineering team is exploring training specialized versions of the model for mobile, enterprise, or academic environments, further enhancing the user experience on various platforms.

The feature will be available on Google Chrome for Windows, Mac, and ChromeOS. Meanwhile, a similar feature is more likely to be added to the Android version of Chrome soon for a unified experience.

The rest is here:
Machine learning comes to Chrome's address bar on Windows, Mac, and ChromeOS - Android Police

Why it matters: Google is supercharging the address bar of its popular web browser with machine-learning capabilities. Known as the "omnibox" since it pulls double duty as both a URL entry field and search box, this unassuming text field is about to get a major upgrade.

The omnibox has evolved well beyond its humble beginnings as a place to type website addresses. It can now handle all sorts of queries and tasks by leveraging Google's vast search prowess. However, the suggestions and results it surfaces have been driven by a relatively rigid "set of hand-built and hand-tuned formulas." That's all about to change.

In a recent post on the Chromium blog, Justin Donnelly, the engineering lead for Chrome's omnibox, revealed that his team has been hard at work adapting machine learning models to drastically improve the omnibox's "relevance scoring" capabilities. In other words, omnibox will get much better at understanding the context behind your queries and providing more useful suggestions tailored to your needs.

According to Donnelly, when he surveyed colleagues on how to enhance the omnibox experience, improving the scoring system topped the wishlist. While the current rule-based approach works for a vast number of cases, it lacks flexibility and struggles to adapt to new scenarios organically. Enter machine learning.

By analyzing massive datasets of user interactions, browsing patterns, and historical data points like how frequently you visit certain sites, the new AI models can generate far more nuanced relevance scores. For instance, it learned that if you swiftly leave a webpage, chances are it wasn't what you were looking for, so suggestions for that URL get demoted.

As you use the smarter omnibox over time across Windows, Mac, and ChromeOS, it will continue refining and personalizing its suggestions based on your evolving interests and habits. Donnelly's team also plans to explore incorporating time-of-day awareness, specialized models for different user groups like mobile or enterprise, and other contextual signals.

Of course, enabling such deep personalization requires handing over more personal browsing data to Google's machine-learning models. How comfortable you are with that trade-off is a personal decision.

Google has been gradually rolling out these omnibox improvements over recent Chrome updates, with the machine learning models really flexing their muscles starting with version M124 expected in the coming months. And while not mentioned in the blog post, it's safe to assume the update would trickle down to mobile as well eventually.

See the original post:
Google supercharges Chrome's omnibox address bar with machine learning - TechSpot

The terms machine learning and deep learning can seem interchangeable to most people, but they arent. Both considered subdivisions within the world of artificial intelligence (AI), the two have many differences, especially in the architecture and use cases.

Machine learning, for instance, uses structured data and algorithms to train models, with the more data at disposal generally equating with more accurate and better trained models. The idea is to eliminate the need for human intervention. Deep learning, on the other hand, is a subset of machine learning and uses neural networks to imitate the way humans think, meaning the systems designed require even less human intervention.

Differentiating the two, in this way, is crucial to AI research and practical application of both, particularly as businesses attempt to integrate such technologies into their core processes, and recruit for skilled individuals to fill technical roles.

The chances are that youve probably used an application or system built on machine learning. Whether youve interacted with a chatbot, utilized predictive text, or gone to watch a show after Netflix recommended it to you, machine learning was likely at the core of these systems. Machine learning is a subset of AI, and a blanket term used to define machines that learn from datasets.

Using structured data that comes in the form of text, images, numbers, financial transactions, and many other things, machine learning can replicate the process of human learning. Collected data is used as training material to direct the machine learning model. Theoretically, the greater the volume of data that is used, the higher the quality of the model. Machine learning is all about allowing computers to self-program via training datasets and infrequent human interventions.

Supervised learning, semi-supervised learning, unsupervised learning, and reinforcement learning are all differing strands of machine learning processes.

The first of these techniques, supervised learning, involves machine learning scientists feeding labeled training data into algorithms to clearly define variables. This is so that the algorithm can start to understand connections. In contrast, unsupervised learning uses unlabelled data and allows the algorithms to actively search for relationships and connections. Acting as the logical midpoint between these processes, semi-supervised learning aids the models own comprehension of the data. Reinforcement learning, on the other hand, works by letting a machine complete a set of decisions for the purpose of achieving an objective in an unknown environment.

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A subset of machine learning, deep learning deploys systems of artificial neural networks to mimic the cognitive operations of the human mind.

A lack of sufficient compute power has, until now, created barriers to neural network learning capabilities. Huge strides in big data analytics have changed the landscape significantly, with larger and more complex neural networks now able to take form. This means that machines can now understand, learn, and react to complex scenarios quicker than human beings.

These neural networks are constructed in layers and designed to enable the transmission of data from node to node, much like neurons in the brain. Vast datasets are required to build these models but, once theyve been constructed, they can give users instant results with little needed in the way of human intervention. There are many, varied ways in which deep learning can be performed.

Convolutional Neural Networks (CNNs): These comprise multiple layers and are mostly used for image processing and object detection.

Recurrent Neural Networks (RNNs): These are types of artificial neural network that use sequential data or time series data. They are frequently used in problems, such as language translation, natural language processing (NLP), speech recognition, and image captioning.

Long Short-Term Memory Networks (LSTMs): These are types of Recurrent Neural Network (RNN) that can learn and remember long-term dependencies. They can be useful for complex problem domains like machine translation, speech recognition, and more.

Generative Adversarial Networks (GANs): These are generative deep learning algorithms that produce new data instances that look like the training data. It comprises two parts; a generator, which learns to generate false data, and a discriminator, which learns from that fake information. These networks have been used to produce fake images of people who have never existed as well as new and unique music.

Radial Basis Function Networks (RBFNs): These networks have an input layer, a hidden layer, and an output layer and are typically used for classification, regression, and time-series predictions.

Multilayer Perceptrons (MLPs): These are a type of feedforward (this means information moves only forward in the network) neural networks. These have an input layer and an output layer that are fully connected. There may also be hidden layers. These are used in speech-recognition, image-recognition, and machine-translation software.

Deep Belief Networks (DBNs): This looks like another feedforward neural network with hidden layers, but isnt. These are a sequence of restricted boltzmann machines which are sequentially connected. These are used to identify, gather and generate images, video sequences and motion-capture data.

Despite the frequent confusion about their similarities, deep learning is very much a subset of machine learning. Deep learning, however, is differentiated from its counterpart by the data types it interacts with and the ways in which it can learn.

Machine learning uses structured, labelled data to predict outcomes. This means a machine learning models input data defines specific features and is organised into tables. While it gets progressively better at carrying out the task in hand, there still requires there to be a human to intervene at points to ensure the model is working in the required way. In other words, if the predictions are not accurate, an engineer will make any adjustments needed to get back on track.

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That being said, deep learning systems involve algorithms that can autonomously decide on the accuracy of their predictions. This works via the presence of neural networks in deep learning models.

Another difference is that where machine learning can use small amounts of data to make predictions, deep learning needs much, much more data to make more accurate predictions.

While machine learning needs little time to train typically a few seconds to a few hours deep learning takes far longer as the algorithms used here involve many layers.

Outputs also differ between the two. Machine learning tends to output numerical values, such as a score or classification, while deep learning can output in multiple formats, such as text, scores, or even sounds and images.

Machine learning is already in use in a variety of areas that are considered part of day-to-day life, including on social media, on email platforms and, as mentioned, on streaming services like Netflix. These types of applications lend themselves well to machine learning, because theyre relatively simple and dont require vast amounts of computational power to process complicated decision-making.

Among some of the more complex uses of machine learning are computer vision, such as facial recognition, where technology can be used to recognise people in crowded areas. Handwriting recognition, too, can be used to identify an individual from documents that are scanned en masse. This would apply, for example, to academic examinations, police records, and so on. Speech recognition, meanwhile, such as those used in voice assistants are another application of machine learning.

Because of the nature of deep learning, on the other hand, this technology allows for far more complex decision-making, and near-fully autonomous systems, including robotics and autonomous vehicles.

Deep learning also has its uses in image recognition, where massive amounts of data is ingested and used to help the model tag, index, and annotate images. Such models are currently in use for generating art, in systems like DALLE. Similarly to machine learning, deep learning can be used in virtual assistants, in chat bots, and even in image colorisation. Deep learning has also had a particularly exciting impact in the field of medicine, such as in the development of personalised medicines created for somebodys unique genome.

Read more here:
Machine learning vs deep learning vs neural networks: What's the difference? - ITPro