Our solar system has a wide variety of planet types, from tiny rocky Mercury to huge puffy gas giant Jupiter to distant ice giant Uranus. But beyond our own system, there are even more types of exoplanet out there, including water worlds covered in ocean and where life could potentially thrive. Now, researchers using the James Webb Space Telescope have identified a new and exotic type of planet called a steam world, which has an atmosphere almost entirely composed of water vapor.
The planet, called GJ 9827 d, was examined by the Hubble Space Telescope earlier this year and had researchers so intrigued that they wanted to go back for a closer look using Webb. They found that the planet, which is around twice the size of Earth, had a very different atmosphere from the typical hydrogen and helium that is usually seen. Instead, it was full of hot steam.
“This is the first time we’re ever seeing something like this,” said researcher Eshan Raul of the University of Michigan in a statement. “To be clear, this planet isn’t hospitable to at least the types of life that we’re familiar with on Earth. The planet appears to be made mostly of hot water vapor, making it something we’re calling a ‘steam world.’”
To look at the planet’s atmosphere, the researchers used Webb’s Near Infrared Imager and Slitless Spectrograph (NIRISS) instrument that can split light into different wavelengths to see what something is composed of in a technique called transmission spectroscopy. This is easier to do with lighter elements like hydrogen and helium, so being able to use this technique for a heavier element like water means scientists can now start to investigate more diverse planetary atmospheres.
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“Now we’re finally pushing down into what these mysterious worlds with sizes between Earth and Neptune, for which we don’t have an example in our own solar system, are actually made of,” said fellow researcher Ryan MacDonald. “This is a crucial proving step towards detecting atmospheres on habitable exoplanets in the years to come.”
As this is such a new area of research, the discovery required new software written by the team so Raul, who is an undergraduate student, was the first person to see direct evidence that steam worlds existed.
“It was a very surreal moment,” said Raul, now a doctoral candidate at the University of Wisconsin-Madison. “We were searching specifically for water worlds because it was hypothesized that they could exist. If these are real, it really makes you wonder what else could be out there.”
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.
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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.
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.
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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.
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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.
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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.
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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.
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Enterprises might receive these seven benefits when using generative AI.
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.”
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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.
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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.
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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.
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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.
We are a cloud-first company and a Snowflake-first company. Now there’s a third one: AI-first. 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.
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“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.
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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.
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“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.
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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.
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“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.
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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.
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.
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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.
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