AWS Machine Learning Blog We’re at an exciting inflection point in the widespread adoption of machine learning (ML), and we believe most customer experiences and applications will be reinvented with generative AI. Generative AI can create new content and ideas, including conversations, stories, images, videos, and music. Like most AI, generative AI is powered by ML models—very large models that are trained on vast amounts of data and commonly referred to as foundation models (FMs). FMs are based on transformers. Transformers are slow and memory-hungry on generating long text sequences due to the sheer size of the models. Large language models (LLMs) used to generate text sequences need immense amounts of computing power and have difficulty accessing the available high bandwidth memory (HBM) and compute capacity. This is because a large portion of the available memory bandwidth is consumed by loading the model’s parameters and by the auto-regressive decoding process.As a result, even with massive amounts of compute power, LLMs are limited by memory I/O and computation limits, preventing them from taking full advantage of the available hardware resources. Overall, generative inference of LLMs has three main challenges (according to Pope et al. 2022): A large memory footprint due to massive model parameters and transient state during decoding. The parameters often exceed the memory of a single accelerator chip. Attention key-value caches also require substantial memory. Low parallelizability increases latency, especially with the large memory footprint, requiring substantial data transfers to load parameters and caches into compute cores each step. This results in high total memory bandwidth needs to meet latency targets. Quadratic scaling of attention mechanism compute relative to sequence length compounds the latency and computational challenges. Batching is one of the techniques to address these challenges. Batching refers to the process of sending multiple input sequences together to a LLM...

Human Resources News - Human Resources News Headlines | Bizjournals.com Today’s workforce is laser focused on achieving better balance across their work and personal lives. This is occurring at a time when employers say attracting and retaining talent remains challenging and paramount to their overall business success. To support current and prospective talent, employers will need to create more competitive benefit offerings, enhance the overall workforce experience and find the right balance of flexibility for employees. One place to start is assessing leave management… Go to Source 25/09/2023 - 09:03 /Maria Trapenasso Twitter: @hoffeldtcom

Coronavirus | The Guardian Mental health issues and lack of faith in government support are driving desire to change statusMental distress and financial insecurity has pushed 40% of self-employed workers to say they would switch to a salaried job if they could secure the same income, according to an academic study that has tracked self-employment trends during the Covid-19 pandemic.About one in eight would accept a 20% pay cut to get out of self-employment, such is the damage done to their mental health and the expectation that government support will not be forthcoming should another crisis wreck their business. Continue reading... Go to Source 24/09/2023 - 12:04 /Phillip Inman Twitter: @hoffeldtcom

Human Resources News - Human Resources News Headlines | Bizjournals.com The Federal Emergency Management Agency is sending representatives to a hardware store on Maui to answer questions from residents impacted by the recent wildfires. Go to Source 22/09/2023 - 00:02 /Katie Helland Twitter: @hoffeldtcom

Coronavirus | The Guardian First comparative research of its kind finds those with lots of social interaction and supportive family coped betterSecondary school pupils in the UK experienced significantly higher rates of depression, social, emotional and behavioural difficulties, and overall worse mental wellbeing during the Covid pandemic, research shows.Cases of depression among secondary school pupils aged 11 to 13 rose by 8.5% during the pandemic compared with a 0.3% increase for the same cohort prior to Covid, according to a comparative study by researchers at the University of Oxford’s psychiatry department. Continue reading... Go to Source 21/09/2023 - 18:02 /Rachel Hall Twitter: @hoffeldtcom

The Straits Times Business News September 21, 2023 7:33 AMInterest rate hikes to tame inflation can slow economic growth and reduce oil demand. Go to Source 21/09/2023 - 03:04 / Twitter: @hoffeldtcom

The Straits Times Business News September 21, 2023 4:00 AMNot only did the OCBC Business app help boost his company's earnings during the festive season last year, it also gives him an overview of expenditure, sales and cash flow. Go to Source 21/09/2023 - 00:01 / Twitter: @hoffeldtcom

The Straits Times Business News September 20, 2023 10:43 AMThe Fed decision will be published at 2am on Thursday Singapore time, along with updated economic forecasts. Go to Source 20/09/2023 - 06:04 / Twitter: @hoffeldtcom

AWS Machine Learning Blog Multi-modal data is a valuable component of the financial industry, encompassing market, economic, customer, news and social media, and risk data. Financial organizations generate, collect, and use this data to gain insights into financial operations, make better decisions, and improve performance. However, there are challenges associated with multi-modal data due to the complexity and lack of standardization in financial systems and data formats and quality, as well as the fragmented and unstructured nature of the data. Financial clients have frequently described the operational overhead of gaining financial insights from multi-modal data, which necessitates complex extraction and transformation logic, leading to bloated effort and costs. Technical challenges with multi-modal data further include the complexity of integrating and modeling different data types, the difficulty of combining data from multiple modalities (text, images, audio, video), and the need for advanced computer science skills and sophisticated analysis tools. One of the ways to handle multi-modal data that is gaining popularity is the use of multi-modal agents. Multi-modal agents are AI systems that can understand and analyze data in multiple modalities using the right tools in their toolkit. They are able to connect insights across these diverse data types to gain a more comprehensive understanding and generate appropriate responses. Multi-modal agents, in conjunction with generative AI, are finding a wide spread application in financial markets. The following are a few popular use cases: Smart reporting and market intelligence – AI can analyze various sources of financial information to generate market intelligence reports, aiding analysts, investors, and companies to stay updated on trends. Multi-modal agents can summarize lengthy financial reports quickly, saving analysts significant time and effort. Quantitative modeling and forecasting – Generative models can synthesize large volumes of financial data to train machine learning (ML) models for applications like stock price...

Human Resources News - Human Resources News Headlines | Bizjournals.com For Founder and CEO Mike Rypka, one of the best parts of growing Torchy's Tacos to 115 locations has been creating new opportunities for longtime employees to grow their careers. "I really wanted to provide opportunities for my people. So obviously, the more we grow, the more opportunities there are for folks within the organization to move up and have a damn good career," he said. Torchy's, which does business as Success Foods Management Group LLC, hopes to grow at about 120 restaurants by the… Go to Source 19/09/2023 - 06:03 /Will Anderson Twitter: @hoffeldtcom

15Five Employers are waking up to the benefits of remote work and non-traditional workplace structures. But to lead effectively, a few critical challenges of managing virtual teams should be addressed before issues escalate and business performance suffers.  Not sure if it’s worth learning the challenges of virtual, remote, or hybrid teams? Are you convinced employees will eventually return to the office? Think again.  Only 6% of over 8,000 workers surveyed by Gallup said they want to return to the office full-time. A huge 99% of workers like aspects of working from home that include no commute, better focus, and improved family connection on the list of benefits. 87% of workers offered at least some remote work options would take the opportunity. Let’s look at how leaders can mitigate the challenges of virtual teams and explore actionable solutions that keep employees engaged and meeting performance expectations. Lack of In-Person Connection Humans are social creatures, and remote work can contribute to feelings of loneliness—even before the COVID-19 pandemic. When employees feel like they’re not the best version of themselves, performance suffers.  Building team relationships for remote employees is a challenge. How do you connect virtual teams with different priorities and communication styles in different time zones? It’s not as simple as inviting everyone for a team meal or holiday party. Solutions 1. Organize team retreats Hold virtual team retreats or workshops to provide teams with an opportunity to discuss work and form deeper connections. For example, creator marketing platform ConvertKit holds bi-annual team retreats where their fully remote workforce collaborates and connects in person.  2. Budget for in-person events Look for work-oriented opportunities for groups to meet up and spend time together. For example, is there an industry conference a whole team could practically attend? Or is there an opportunity for the...

The Straits Times Business News September 18, 2023 8:15 AMSINGAPORE – A giant American private equity group will commit up to $1.1 billion for a 20 per cent stake in Singtel’s regional data centre business. Go to Source 18/09/2023 - 03:06 / Twitter: @hoffeldtcom

BBC News - Home Comedian Russell Brand has denied sexual assault allegations, with scrutiny also on the TV business. Go to Source 17/09/2023 - 15:02 / Twitter: @hoffeldtcom

MIT News - Artificial intelligence As 3D printers have become cheaper and more widely accessible, a rapidly growing community of novice makers are fabricating their own objects. To do this, many of these amateur artisans access free, open-source repositories of user-generated 3D models that they download and fabricate on their 3D printer. But adding custom design elements to these models poses a steep challenge for many makers, since it requires the use of complex and expensive computer-aided design (CAD) software, and is especially difficult if the original representation of the model is not available online. Plus, even if a user is able to add personalized elements to an object, ensuring those customizations don’t hurt the object’s functionality requires an additional level of domain expertise that many novice makers lack. To help makers overcome these challenges, MIT researchers developed a generative-AI-driven tool that enables the user to add custom design elements to 3D models without compromising the functionality of the fabricated objects. A designer could utilize this tool, called Style2Fab, to personalize 3D models of objects using only natural language prompts to describe their desired design. The user could then fabricate the objects with a 3D printer. “For someone with less experience, the essential problem they faced has been: Now that they have downloaded a model, as soon as they want to make any changes to it, they are at a loss and don’t know what to do. Style2Fab would make it very easy to stylize and print a 3D model, but also experiment and learn while doing it,” says Faraz Faruqi, a computer science graduate student and lead author of a paper introducing Style2Fab. Style2Fab is driven by deep-learning algorithms that automatically partition the model into aesthetic and functional segments, streamlining the design process. In addition to empowering novice designers...

Human Resources News - Human Resources News Headlines | Bizjournals.com For founder and CEO Mike Rypka, one of the best parts of growing Torchy's Tacos to 115 locations has been creating new opportunities for longtime employees to grow their careers. "I really wanted to provide opportunities for my people. So obviously, the more we grow, the more opportunities there are for folks within the organization to move up and have a damn good career," he said. Torchy's, which does business as Success Foods Management Group LLC, just a few weeks ago opened its 20th restaurant… Go to Source 15/09/2023 - 00:04 /Will Anderson Twitter: @hoffeldtcom

AWS Machine Learning Blog This post takes you through the most common challenges that customers face when searching internal documents, and gives you concrete guidance on how AWS services can be used to create a generative AI conversational bot that makes internal information more useful. Unstructured data accounts for 80% of all the data found within organizations, consisting of repositories of manuals, PDFs, FAQs, emails, and other documents that grows daily. Businesses today rely on continuously growing repositories of internal information, and problems arise when the amount of unstructured data becomes unmanageable. Often, users find themselves reading and checking many different internal sources to find the answers they need. Internal question and answer forums can help users get highly specific answers but also require longer wait times. In the case of company-specific internal FAQs, long wait times result in lower employee productivity. Question and answer forums are difficult to scale as they rely on manually written answers. With generative AI, there is currently a paradigm shift in how users search and find information. The next logical step is to use generative AI to condense large documents into smaller bite sized information for easier user consumption. Instead of spending a long time reading text or waiting for answers, users can generate summaries in real-time based on multiple existing repositories of internal information. Solution overview The solution allows customers to retrieve curated responses to questions asked about internal documents by using a transformer model to generate answers to questions about data that it has not been trained on, a technique known as zero-shot prompting. By adopting this solution, customers can gain the following benefits: Find accurate answers to questions based on existing sources of internal documents Reduce the time users spend searching for answers by using Large Language Models (LLMs) to provide...

MIT News - Artificial intelligence The classic computer science adage “garbage in, garbage out” lacks nuance when it comes to understanding biased medical data, argue computer science and bioethics professors from MIT, Johns Hopkins University, and the Alan Turing Institute in a new opinion piece published in a recent edition of the New England Journal of Medicine (NEJM). The rising popularity of artificial intelligence has brought increased scrutiny to the matter of biased AI models resulting in algorithmic discrimination, which the White House Office of Science and Technology identified as a key issue in their recent Blueprint for an AI Bill of Rights.  When encountering biased data, particularly for AI models used in medical settings, the typical response is to either collect more data from underrepresented groups or generate synthetic data making up for missing parts to ensure that the model performs equally well across an array of patient populations. But the authors argue that this technical approach should be augmented with a sociotechnical perspective that takes both historical and current social factors into account. By doing so, researchers can be more effective in addressing bias in public health.  “The three of us had been discussing the ways in which we often treat issues with data from a machine learning perspective as irritations that need to be managed with a technical solution,” recalls co-author Marzyeh Ghassemi, an assistant professor in electrical engineering and computer science and an affiliate of the Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic), the Computer Science and Artificial Intelligence Laboratory (CSAIL), and Institute of Medical Engineering and Science (IMES). “We had used analogies of data as an artifact that gives a partial view of past practices, or a cracked mirror holding up a reflection. In both cases the information is perhaps not...

NIMH News Feed During the annual meeting, this open session will feature research progress presentations from the Genes to Mental Health Network (G2MH) investigators. Go to Source 13/09/2023 - 09:03 /National Institute of Mental Health Twitter: @hoffeldtcom

PsycPORT™: Psychology Newswire Symptoms such as frightening hallucinations, paranoia, and sudden behavioral changes may signal a psychotic break. Go to Source 13/09/2023 - 06:05 / Twitter: @hoffeldtcom

PsycPORT™: Psychology Newswire Healthy dissociation is your brain’s way of taking a break from triggers like stress or boredom. Go to Source 13/09/2023 - 06:05 / Twitter: @hoffeldtcom

15Five You may have noticed an uptick in news reports and social media posts lately about workers being yelled at, threatened, or even physically assaulted on the job—you may even know someone who has experienced this firsthand. That’s because these incidents are on the rise, and many frontline workers face increased risk to their physical safety and mental health.  Cultural, economic, and political factors have all contributed to a sharp increase in aggression against workers since the pandemic. Employees in industries such as retail, food service, hospitality, healthcare, and travel have come face-to-face with increasingly agitated and confrontational customers.  Increased safety concerns on the frontlines When we talk about workplace safety, we typically think about jobs that involve some level of inherent danger, like construction, certain manufacturing jobs, emergency services, etc. But today, many jobs that weren’t historically considered unsafe now come with added risk. (Even performers have come under fire—literally—as their so-called “fans” hurl objects at them onstage.) Because of the increase in threats against their employees, some companies have taken drastic steps to protect workers from potential harm. For example, Target recently reported that employees were facing threats over LGBTQ+ Pride merchandise in their stores, leading them to pull some items that had caused “significant confrontational behavior” from their shelves.  According to a 2023 Workplace Safety Survey by Verkada, 58% of frontline workers feel their risk of physical harm on the job is rising, and the majority of healthcare and retail workers fear being subject to “erratic or aggressive behavior.” “Retail workers, like everybody else, are living in a highly volatile and politicized environment right now,” Stuart Appelbaum, president of the Retail, Wholesale and Department Store Union recently told the Washington Post. “They’re seen too often as being invisible and disposable and not as people who should be...

Coronavirus | The Guardian Moderna and Pfizer shots approved with vaccinations potentially beginning as soon as this weekThe US has approved a series of Covid-19 booster vaccines amid rising cases of coronavirus around the country, the Food and Drug Administration (FDA) said on Monday.The FDA said it had approved Moderna and Pfizer-BioNTech vaccines, which can be administered even to people who never previously received a Covid-19 vaccination. Continue reading... Go to Source 12/09/2023 - 00:03 /Adam Gabbatt Twitter: @hoffeldtcom

Coronavirus | The Guardian TV presenter says she is ‘battling the system’ to get support for her husband, who has been severely ill since getting Covid in 2020Social care campaigners have backed TV presenter Kate Garraway after she revealed she is “battling the system” to secure essential care for her severely ill husband, Derek Draper.The Good Morning Britain presenter said at the weekend that she fears her husband could lose the support he needs, and that she is getting just four hours sleep a night. Continue reading... Go to Source 11/09/2023 - 18:08 /Robert Booth Social affairs editor Twitter: @hoffeldtcom

MIT News - Artificial intelligence “As a child, I wished for a robot that would explain others’ emotions to me” says Sharifa Alghowinem, a research scientist in the Media Lab’s Personal Robots Group (PRG). Growing up in Saudi Arabia, Alghowinem says she dreamed of coming to MIT one day to develop Arabic-based technologies, and of creating a robot that could help herself and others navigate a complex world. In her early life, Alghowinem faced difficulties with understanding social cues and never scored well on standardized tests, but her dreams carried her through. She earned an undergraduate degree in computing before leaving home to pursue graduate education in Australia. At the Australian National University, she discovered affective computing for the first time and began working to help AI detect human emotions and moods, but it wasn’t until she came to MIT as a postdoc with the Ibn Khaldun Fellowship for Saudi Arabian Women, which is housed in the MIT Department of Mechanical Engineering, that she was finally able to work on a technology with the potential to explain others’ emotions in English and Arabic. Today, she says her work is so fun that she calls the lab “my playground.”  Alghowinem can’t say no to an exciting project. She found one with great potential to make robots more helpful to people by working with Jibo, a friendly robot companion developed by the founder of the Personal Robots Group (PRG) and the social robot startup Jibo Inc., MIT Professor and Dean for Digital Learning Cynthia Breazeal. Breazeal's research explores the potential for companion robots to go far beyond assistants who obey transactional commands, like requests for the daily weather, adding items to shopping lists, or controlling lighting. At the MIT Media Lab, the PRG team designs Jibo to make him an insightful coach...

MIT News - Artificial intelligence Smart thermostats have changed the way many people heat and cool their homes by using machine learning to respond to occupancy patterns and preferences, resulting in a lower energy draw. This technology — which can collect and synthesize data — generally focuses on single-dwelling use, but what if this type of artificial intelligence could dynamically manage the heating and cooling of an entire campus? That’s the idea behind a cross-departmental effort working to reduce campus energy use through AI building controls that respond in real-time to internal and external factors.  Understanding the challenge Heating and cooling can be an energy challenge for campuses like MIT, where existing building management systems (BMS) can’t respond quickly to internal factors like occupancy fluctuations or external factors such as forecast weather or the carbon intensity of the grid. This results in using more energy than needed to heat and cool spaces, often to sub-optimal levels. By engaging AI, researchers have begun to establish a framework to understand and predict optimal temperature set points (the temperature at which a thermostat has been set to maintain) at the individual room level and take into consideration a host of factors, allowing the existing systems to heat and cool more efficiently, all without manual intervention.  “It’s not that different from what folks are doing in houses,” explains Les Norford, a professor of architecture at MIT, whose work in energy studies, controls, and ventilation connected him with the effort. “Except we have to think about things like how long a classroom may be used in a day, weather predictions, time needed to heat and cool a room, the effect of the heat from the sun coming in the window, and how the classroom next door might impact all of this.” These factors are at...

US Top News and Analysis A year ago, rents were posting 11% annual growth. Now they look like they're about to go negative year over year. Go to Source 08/09/2023 - 21:02 / Twitter: @hoffeldtcom

Coronavirus | The Guardian Pirola variant has prompted concern among scientists because of high number of mutations it carriesCare home residents and people who are housebound will be offered Covid vaccines from Monday, with over-65s and other vulnerable groups to be called for their jabs from the week after.The NHS will kick off its autumn programme of Covid vaccines from next week, having moved the date forward by a month in response to the spread of a new variant nicknamed Pirola. Continue reading... Go to Source 08/09/2023 - 18:03 /Rowena Mason and Hannah Devlin Twitter: @hoffeldtcom

The Straits Times Business News September 08, 2023 12:57 PMTikTok’s Shop marketplace, its biggest bet for new revenue growth, has gone live for some users in the US. Go to Source 08/09/2023 - 09:03 / Twitter: @hoffeldtcom

AWS Machine Learning Blog Amazon SageMaker Pipelines is a fully managed AWS service for building and orchestrating machine learning (ML) workflows. SageMaker Pipelines offers ML application developers the ability to orchestrate different steps of the ML workflow, including data loading, data transformation, training, tuning, and deployment. You can use SageMaker Pipelines to orchestrate ML jobs in SageMaker, and its integration with the larger AWS ecosystem also allows you to use resources like AWS Lambda functions, Amazon EMR jobs, and more. This enables you to build a customized and reproducible pipeline for specific requirements in your ML workflows. In this post, we provide some best practices to maximize the value of SageMaker Pipelines and make the development experience seamless. We also discuss some common design scenarios and patterns when building SageMaker Pipelines and provide examples for addressing them. Best practices for SageMaker Pipelines In this section, we discuss some best practices that can be followed while designing workflows using SageMaker Pipelines. Adopting them can improve the development process and streamline the operational management of SageMaker Pipelines. Use Pipeline Session for lazy loading of the pipeline Pipeline Session enables lazy initialization of pipeline resources (the jobs are not started until pipeline runtime). The PipelineSession context inherits the SageMaker Session and implements convenient methods for interacting with other SageMaker entities and resources, such as training jobs, endpoints, input datasets in Amazon Simple Storage Service (Amazon S3), and so on. When defining SageMaker Pipelines, you should use PipelineSession over the regular SageMaker Session: from sagemaker.workflow.pipeline_context import PipelineSession from sagemaker.sklearn.processing import SKLearnProcessor role = sagemaker.get_execution_role() pipeline_session = PipelineSession() sklearn_processor = SKLearnProcessor( framework_version=’0.20.0’, instance_type=’ml.m5.xlarge’, instance_count=1, base_job_name="sklearn-abalone-process", role=role, sagemaker_session=pipeline_session, ) Run pipelines in local mode for cost-effective and quick iterations during development You can run a pipeline in local mode using the LocalPipelineSession context. In this mode,...

The Straits Times Business News September 07, 2023 8:16 PMThis is the third time the Monetary Policy Committee’ has decided to keep theovernight policy rate (OPR) at 3 per cent, after hiking the rate by 25 basis points in May this year. Go to Source 07/09/2023 - 15:02 / Twitter: @hoffeldtcom

The Straits Times Business News September 07, 2023 7:45 AMWeWork warned in August that there was “substantial doubt” about its ability to stay in business. Go to Source 07/09/2023 - 03:02 / Twitter: @hoffeldtcom

AWS Machine Learning Blog Amazon Kendra is an intelligent search service powered by machine learning (ML). With Amazon Kendra, you can easily aggregate content from a variety of content repositories into an index that lets you quickly search all your enterprise data and find the most accurate answer. Adobe Experience Manager (AEM) is a content management system that’s used for creating website or mobile app content. Many organizations use Adobe Experience Manager (On-Premise) or Adobe Experience Manager (Cloud Service) as their content management platform. Enterprise users need to be able to search for accurate answers easily and securely across content from multiple data sources in the enterprise, including AEM, from content such as assets and pages. Amazon Kendra customers can now use the Amazon Kendra AEM connector to index pages and assets from AEM. Amazon Kendra supports AEM as a Cloud Service author instances and AEM On-Premise author and publish instances. You can index AEM content and filter the types of content you want to index with the Amazon Kendra AEM On-Premise or Cloud Service connector, and search your data from AEM with Amazon Kendra intelligent search. This post shows you how to configure the Amazon Kendra AEM connector to index your content and search your AEM assets and pages. The connector also ingests the access control list (ACL) information for each document. The ACL information is used to show search results filtered by what a user has access to. Solution overview In our solution, we configure AEM as a data source for an Amazon Kendra search index using the Amazon Kendra AEM connector. Based on the configuration, when the data source is synchronized, the connector crawls and indexes all the content from AEM that was created on or before a specific date. The connector also indexes the Access Control...

The Straits Times Business News September 06, 2023 10:59 AMData from the Australian Bureau of Statistics on Wednesday showed real GDP rose 0.4 per cent in the second quarter. Go to Source 06/09/2023 - 06:04 / Twitter: @hoffeldtcom

AWS Machine Learning Blog The success of generative AI applications across a wide range of industries has attracted the attention and interest of companies worldwide who are looking to reproduce and surpass the achievements of competitors or solve new and exciting use cases. These customers are looking into foundation models, such as TII Falcon, Stable Diffusion XL, or OpenAI’s GPT-3.5, as the engines that power the generative AI innovation. Foundation models are a class of generative AI models that are capable of understanding and generating human-like content, thanks to the vast amounts of unstructured data they have been trained on. These models have revolutionized various computer vision (CV) and natural language processing (NLP) tasks, including image generation, translation, and question answering. They serve as the building blocks for many AI applications and have become a crucial component in the development of advanced intelligent systems. However, the deployment of foundation models can come with significant challenges, particularly in terms of cost and resource requirements. These models are known for their size, often ranging from hundreds of millions to billions of parameters. Their large size demands extensive computational resources, including powerful hardware and significant memory capacity. In fact, deploying foundation models usually requires at least one (often more) GPUs to handle the computational load efficiently. For example, the TII Falcon-40B Instruct model requires at least an ml.g5.12xlarge instance to be loaded into memory successfully, but performs best with bigger instances. As a result, the return on investment (ROI) of deploying and maintaining these models can be too low to prove business value, especially during development cycles or for spiky workloads. This is due to the running costs of having GPU-powered instances for long sessions, potentially 24/7. Earlier this year, we announced Amazon Bedrock, a serverless API to access foundation models from Amazon...