Technology
The BenQ X300G 4K gaming projector is $500 off for Prime
When it comes to some of the best Prime Big Deal Days discounts, expensive gear is at the top of the list. Why? Because it’s one of the few times you can find a great price. Take, for example, BenQ’s X300G portable 4K gaming projector which is normally $1,500. That’s a hefty chunk of change, but for the event it’s $500 off, down to $1,299. That’s a fantastic discount and a great price for a capable smart projector with a lot of excellent features.
Why shop this BenQ X300G 4K gaming projector deal for Prime Big Deal Days
Let’s start with the visuals. This projector offers a stunning 4K ultra-HD resolution and works with various devices, but it has been designed for gaming. At full HD 1080P — yes a lower resolution — it offers a 4ms response time and 240Hz refresh rate. Now, that’s also while projecting up a 100-inch super large screen. Plus, you get immersive audio thanks to two built-in 8-watt speakers powered by BenQ’s CinematicSound technology. It makes it all feel like you’re in a movie theater.
The projector also acts as an all-in-one smart entertainment hub. The cast media from Android and iOS devices, via Chromecast or AirPlay, but it also has the Android TV streaming platform built-in. That allows you to stream all your favorite apps and services right away. If you plug into consoles, like a Switch, it uses one USB Type-C cable to display and charge. Moreover, it has several ports like USB, HDMI with audio return, and USB Type-C DP-enabled.
Either way, it’s a great projector to set up at home or bring to a friend’s house when you’re having an impromptu gaming session. But don’t take that to mean it’s for games only. It plays a variety of cinematic content too and would do just great as a movie theater-style projector.
Normally $1,500, this BenQ X300G 4K gaming projector is only $1,299 during Prime Big Deal Days — saving you $500.
Technology
Google Photos will soon help you identify AI-generated images
In the world of artificial intelligence-powered tools, it keeps getting harder and harder to differentiate real and AI-generated images. No one can easily identify an AI-created photo at the first glance. However, Google Photos could soon help you identify AI-generated images. Notably, folks over at Android Authority have uncovered this ability in the APK code of the Google Photos app.
Soon, you will be easily able to identify AI-created images using Google Photos
The source has found clues in the Google Photos app’s version 7.3 regarding the ability to identify AI-generated images. This ability will allow you to find out whether a photo is created using an artificial intelligence tool. One of the layout files in the APK of Google Photos v7.3 has identifiers for AI-generated images in the XML code. The source has uncovered three ID strings namely “@id/ai_info”, “@id/credit”, and “@id/digital_source_type”, inside the code.
Furthermore, the report suggests that the “@id/credit” ID could likely display the photo’s credit tag. If the photo is made using Google’s Gemini, then Google Photos can identify its “Made with Google AI” credit tag. It will allow Google Photos to identify AI-generated images quite easily.
Also, the “@id/digital_source_type” ID could refer to the source type field. This will showcase the media source from where the AI photo was created. There’s no word as to what the “@id/ai_info” ID in the XML code refers to.
Google Photos will use these identifiers of an image to tell if it is AI-generated
Notably, the report also mentions that it’s likely all the aforementioned information will be displayed in the image details section. The IPTC metadata will allow Google Photos to easily find out if an image is made using an AI generator. That said, soon it will be very easy to identify AI-created images using the Google Photos app.
As of now, this feature isn’t live on Google Photos. However, we can expect Google to roll out the new functionality as soon as possible as it’s already inside Google Photos.
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Technology
Snowflake the engine for fintech firm’s AI transformation
When TS Imagine first started using Snowflake, it was simply seeking a means to manage its data. Three years later, Snowflake is providing the fuel for the fintech company’s transformation into an enterprise powered by AI.
Based in Bozeman, Mont., but with no central headquarters, Snowflake is a data cloud vendor whose platform enables customers to store and analyze data. In addition, over the past couple of years, the vendor has made AI a focal point, developing an environment where customers can develop, deploy and manage AI, machine learning models and applications.
TS Imagine, meanwhile, is a SaaS-based financial services vendor headquartered in New York City that provides front-office trading, portfolio management and financial risk assessment capabilities. The company was formed in 2021 after the merger of TradingScreen and Imagine Software.
Following the merger, TS Imagine needed a way to integrate and organize years of data from TradingScreen, founded in 1999, and Imagine Software, founded in 1993.
Snowflake was that way. Now, however, as Snowflake has evolved beyond a platform for data management into an environment for AI, TS Imagine has evolved with it and is using Snowflake’s platform to power its metamorphosis.
“We are a cloud-first company and a Snowflake-first company,” said Thomas Bodenski, TS Imagine’s COO and chief data and analytics officer. “Now there’s a third one: AI-first.”
Using Snowflake, TS Imagine is accessing data previously unavailable to inform decisions. It’s using AI to manage certain processes, and it’s reaping financial benefits.
First, however, it just needed to get organized.
Starting with Snowflake
When TradingScreen and Imagine Software merged in May 2021, the newly formed company faced challenges. Both TradingScreen and Imagine Software brought with them well over two decades of data. In addition, the newly formed company had two data teams; two technology stacks; and plans to expand into new areas, such as fixed-income securities trading.
TS Imagine needed a way to unify that data, and it needed to do so in a single system that would help its expansion.
“We very quickly identified the data as the area where we needed to focus,” Bodenski said. “We knew, strategically, that we had to do something. We have to have data ready at any time because it’s used for trading and for risk management. We need to solve problems that our clients are never meant to see.”
TS Imagine manages over 20 million financial instruments — assets such as stocks, bonds, loans, funds and certificates of deposit that can be traded or exchanged. Each, including the client that owns the instrument, generates data, meaning that TS Imagine needs to manage massive amounts of data to serve the needs of its customers.
It, therefore, needed a data management platform that was simple enough to enable users to easily access data when needed and could also handle scale.
One option included the platforms previously used by TradingScreen and Imagine Technologies. Others included platforms such as Markit EDM and GoldenSource geared specifically for reference data used to categorize financial transactions and for enabling semantic modeling for financial instruments.
Ultimately, TS Imagine chose Snowflake.
Timeliness was a key factor in the TS Imagine’s decision given that it needs to access data in near real time to inform and execute trades, according to Bodenski. So were the breadth and depth — the scale — of Snowflake.
Finally, simplicity played a significant role.
Snowflake understands Python and SQL code. If TS Imagine had chosen a platform that required Java or C++, for example, few of its developers would have had the requisite skills to use the platform. But because Python and SQL can be used in Snowflake, 54 data scientists, engineers and other data experts already had the needed skills.
“We felt that with Snowflake, we had a platform that could empower us,” Bodenski said. “We were able to grow and scale overnight from a small team to a large organization.”
Now, TS Imagine stores all its data in Snowflake and runs all its data management processes, such as data quality monitoring, pipeline monitoring and automated regression testing, in Snowflake.
Adding AI
About a year after TS Imagine got its data in order on Snowflake, OpenAI launched ChatGPT.
Released in November 2022, ChatGPT was a significant improvement in generative AI capabilities over what was previously available. Of particular interest to many organizations were its natural language processing (NLP) and automation capabilities.
Enterprises quickly recognized that if they could combine those capabilities with proprietary data to understand an organization’s operations, they could reap significant benefits such as more widespread use of analytics due to NLP and efficiency gains due to process automation.
Among the organizations that saw the possibilities of generative AI in the enterprise was TS Imagine.
“When the hype around ChatGPT started, we got very excited,” Bodenski said. “All of my executive peers are into data as well, and ChatGPT was part of every meeting.”
TS Imagine had already experimented with NLP and machine learning to automate tasks, such as data classification and cataloging. However, converting unstructured data to structured data to inform models and applications had proven to be difficult.
Unstructured data, such as text, images and audio files, is estimated to make up well over three-quarters of all data. Tapping into unstructured data is critical to gaining a full understanding of an organization.
With Snowflake still focused largely on data management at the time, TS Imagine viewed ChatGPT’s generative AI capabilities as a way to finally gain access to its unstructured data, particularly text in emails and PDF documents.
“We needed to make it more actionable by converting it into structured content,” Bodenski said.
TS Imagine developed an AI engineering team that worked with its data experts to use the data it had stored in Snowflake to train ChatGPT to analyze text.
It created an AI pipeline using open source database ChromaDB to vectorize unstructured data to give it structure, LangChain to develop a retrieval-augmented generation (RAG) pipeline to discover the relevant data required to train its models, and containers from Google Cloud to run its generative AI workloads.
The result was models that delivered precise, accurate outputs when analyzing text from over 500 clients, according to Bodenski.
“It had an unbelievably high provision rate to the point where we could rely on it,” he said.
Nevertheless, TS Imagine did not automate final decisions based on generative AI outputs. It still put a human in place to check the outputs for accuracy and make any final decisions.
For the next year, TS Imagine continued to use ChatGPT to underpin its generative AI development and analysis. That was until Snowflake began developing its own environment for generative AI.
Snowflake for everything AI
Enterprises like TS Imagine weren’t the only ones who recognized the potential value of generative AI following the release of ChatGPT.
With data serving as the underlying engine for AI — the information used to train and inform AI models and applications — analytics and data management vendors, from specialists such as MicroStrategy and Monte Carlo to tech giants such as AWS, Google Cloud and Microsoft, all made generative AI a focal point of their product development plans.
Snowflake rival Databricks was especially aggressive in building up an environment for customers to create AI models and applications. After a slow start, Snowflake followed suit.
Thomas BodenskiCOO and chief data and analytics officer, TS Imagine
In May 2023, Snowflake acquired Neeva, a search engine specialist, to procure generative capabilities. Five months later, the vendor introduced Cortex, an environment for AI development that includes access to LLMs and vector search capabilities, among other capabilities. Since then, Snowflake has continued to add tools aimed at enabling AI and machine learning development, including its own LLM and a chatbot development framework.
With Snowflake — via Cortex — providing the same capabilities TS Imagine was piecing together with ChatGPT, ChromaDB, LangChain and Google Cloud, the financial services specialist decided to migrate its AI operations to Snowflake.
The process was simple, taking one engineer one week to complete the entire undertaking, according to Bodenski.
“Everything AI runs exclusively on Snowflake now,” he said.
Immediately following the migration, simply by eliminating the cost of using various platforms to create an AI pipeline and instead using the tools provided by Snowflake, TS Imagine saw a 30% reduction in spending related to training and managing its generative AI capabilities.
“That was significant for us,” Bodenski said. “It’s a one-stop shop for us. We can build the entire AI pipeline on the technology we are all familiar with.”
With its data already residing in Snowflake, TS Imagine simply builds an AI pipeline on top of that data without needing to move the data to another system where it might get accidentally exposed. In addition, with all the required pieces of an AI pipeline in one environment, it takes just a few days to develop a new model or application.
Results
In the year since the migration, after developing text analysis capabilities using ChatGPT for generative AI capabilities and moving that to Snowflake, TS Imagine has developed five other generative AI pipelines for different applications.
Beyond analyzing emails and PDFs, one of the key applications of generative AI is to monitor customer service. TS Imagine receives an average of 5,000 inquiries per month. Fully understanding everything related to customer service is challenging.
“If you are the global head of customer service, it’s not easy to get that overview,” Bodenski said. “And if you are a regional manager, it’s hard to know everything that is going on.”
With its customer service application, TS Imagine can now classify each customer service incident, automatically assigning sensitivity ratings as well as understanding the sentiment, urgency and complexity of the request.
“Those are all steps that would have had to have been done manually,” Bodenski said.
Tangibly, by developing and deploying generative AI tools using Snowflake, TS Imagine has saved thousands of hours of work — including 4,000 that would have been devoted just to analyzing emails –that otherwise would have been done manually, he continued.
“It allows us to utilize people to do work that is more analytical, more knowledge-oriented,” Bodenski said. “We can use people to be more productive on other tasks.”
Despite all its benefits, like most enterprises using generative AI to improve operations, TS Imagine is working through some problems.
While using Snowflake to develop generative AI tools has been a smooth process, getting models and applications to consistently deliver outputs that can be trusted remains a concern, according to Bodenski.
“There is still a challenge with what large language models produce,” he said.
Accuracy has been a problem for generative AI. Even when trained using high-quality data, models and applications sometimes still deliver incorrect and even bizarre outputs called hallucinations.
To combat those inaccuracies, TS Imagine runs its RAG pipelines multiple times for each query to try to weed out any outliers. Still, however, the company makes sure there is always a person in place to take any action rather than trust the model or application to automatically go from output to action on its own.
“We need to constantly look at the results,” Bodenski said. “And you really need to find the right use cases. This stuff doesn’t solve everything. You need to find the right use cases, and that’s when you get high precision rates. Even still, the outputs are sometimes very strange.”
Future plans
With six RAG pipelines running after one year using Snowflake for its AI development and deployment, TS Imagine has plans to add more AI applications, according to Bodenski.
To date, what the company has done with generative AI is to automate processes to make workers more efficient. It hasn’t yet developed AI assistants that enable business users to query and analyze data using natural language.
TS Imagine has used Snowflake to develop applications its customers can use to analyze data. But those applications are traditional analytics applications rather than AI-powered applications.
The next step is to add generative AI to those applications to enable clients to broaden their use of BI beyond data experts as they analyze financial transactions and strategies.
“What we aim for is self-service analytics,” Bodenski said. “There is a lot of data involved in financial transactions, and with this data available, our clients can self-service themselves. We want to bring AI through our products to our clients. That’s the final objective.”
Eric Avidon is a senior news writer for TechTarget Editorial and a journalist with more than 25 years of experience. He covers analytics and data management.
Servers computers
#Assembling the second #StarTech.com 42U 19" #OpenFrame #Server #Rack
Ensembling the second StarTech.com 42U 19″ #OpenFrame #Server #Rack
Thanks, Gioacchino Tricarico for helping me this time. I really appreciate it. FGL .
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Technology
GM is ditching its one-size-fits-all Ultium battery system and adopting other cell formats
is charting a course away from its . The company is dropping that standardized approach in favor of a wider range of battery cell chemistries and physical formats.
The automaker had hoped that, by adopting a unified system across all of its EVs as well as , it would be able to reduce costs and ship them faster. The plan was to pack the flat pouch-style Ultium cells into a variety of modules depending on what was needed for each EV.
Things haven’t gone smoothly, as notes. Among other things, COVID-19 slowed down the company’s EV roadmap and with the robots that assembled the modules.
“It now makes business sense to transition from one-size-fits-all to new program-specific batteries,” Kurt Kelty, GM’s vice president of batteries, said at an investor event. The automaker hopes that switching from Ultium’s nickel cobalt manganese chemistry to lithium iron phosphate (LFP) battery tech will lower the cost of its EVs by as much as $6,000. As notes, Tesla and Ford are among those that use LFP cells, which are said to be cheaper and less complicated to manufacture. The , which is slated to arrive in late 2025, will use such batteries.
GM plans to build a new battery research facility at the Warren Tech Center in Michigan. The team there will explore cylindrical and prismatic cells in addition to the pouch format. Researchers will also look into alternative battery chemistries.
The shift in battery strategy comes as GM chases profitability in its EV division. The company said it’s getting close to that point. It’s on track to build and sell around 200,000 EVs this year. GM now claims to be the number two EV seller in North America behind Tesla.
Servers computers
Rackmount Solutions: How To Assemble the Cruxial™ Wall Mount Rack
This video will help you assemble Rackmount Solutions’ Cruxial ™ WM series wallmount racks. These self-squaring racks are available in 8u, 12u, 15u and 18u with depths of 13″ and 19″. Assembles in less than 15 minutes.
A relay rack made from U.S. American Finished Steel, the WM Series is built tough in order to handle your server, data center, networking needs.
Rackmount Solutions is an industry leader in supplying server racks, server cabinets, wallmount racks, network racks, LAN racks, portable rackmount cases and accessory products for the IT/Network professional. We deliver rackmount storage server rack solutions for 19″, 23″, 24″ and 28″ wide equipment, as well as PCI Data Security Storage compliant cabinets and racks. We pride ourselves in providing quality customer service. Please call us toll free at 1-800-352-6631 and let us know how we can solve your rackmount needs.
http://www.rackmountsolutions.net/Wall_Mount_Rack_Cabinet/Wall_mount_Rack_Cabinet_Entry.asp .
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