Liquid Web has unveiled the launch of a new GPU hosting service designed to keep pace with growing high-performance computing (HPC) requirements.

The new offering will harness Nvidia GPUs and is catered specifically toward developers focused on AI and machine learning tasks, the company confirmed.

NASA astronaut Sunita Williams just turned 59 in space on Thursday. She celebrated her milestone birthday aboard the International Space Station (ISS), which is around 400 kilometres above Earth, for the second time. 

Earlier her birthday celebration took place during a 2012 mission. 

Since June 6, Sunita Williams along with NASA astronaut Barry ‘Butch’ Wilmore has been aboard the ISS as part of the Boeing Crew Flight Test mission. Due to technical issues with the Boeing Starliner spacecraft, their stay has been unexpectedly extended. 

They are expected to return in February 2025. 

On her special day, Williams took up the task of maintaining the space laboratory. 

Reportedly, Williams celebrated her birthday by replacing filters in the waste and hygiene compartment. She also performed the essential task with the help of NASA astronaut Don Pettit to ensure safe and healthy living conditions on the ISS. 

Williams participated in a conference with Mission Control in Houston, Texas. Williams also engaged in discussions with flight directors in Houston, collaborating with astronauts Wilmore and Frank Rubio to outline mission objectives and upcoming tasks. 

Sunita Williams also received birthday wishes from Bollywood stars along with loved ones and family. 

Saregama Official shared a heartwarming video on Instagram that featured a compilation video of famous Indian stars singing Happy Birthday in Hindi to the astronaut. 

The video began with filmmaker Karan Johar sending birthday wishes to Williams, followed by singers, Hariharan, Sonu Nigam, Neeti Mohan and Shaan Mukherji.

In 1998, after joining NASA’s astronaut program, Williams launched into space for the first time on December 9, 2006, during the STS-116 mission. 

As a flight engineer for Expeditions 14 and 15, Williams set multiple records, including over 29 hours of spacewalks and more than 195 days in orbit.

By piloting Boeing’s CST-100 Starliner’s first crewed test flight, Williams made history by successfully docking with the ISS despite facing technical challenges. 

It’s been four years since the robot wizards at Boston Dynamics declared its dog-like Spot robot ready for the workplace.

In that time, the quadruped robot has been trialed in various roles at a number of firms, including for factory mapping at Ford, safety inspections at a Kia auto plant, and radiation surveys for Dominion Energy.

Its latest gig is at a Michelin facility in Lexington, South Carolina, which manufactures tires and light trucks. A video (top) released by Boston Dynamics on Wednesday shows Spot making its way around the site, carrying out various tasks as part of a pilot program.

“We were like kids at Christmas when we first got Spot,” said Wayne Pender, a reliability manager at Michelin whose job it is to ensure that all of the facility’s equipment is running at optimal efficiency.

Ryan Burns, also a reliability manager, said it’s important to get ahead of equipment failures in order to avoid a plant shutdown. Spot helps out by scanning 350 locations with a thermal camera to see if any parts are overheating or performing differently in some other way. Using specially designed software called Orbit, Spot then processes the data and sends it to to its operators for final analysis. If an anomaly is spotted, a human technician is sent out to review the situation before a final decision is made on how to respond.

“From a technician standpoint, Spot going out and doing these routes eliminates a mundane task that humans are doing,” Burns said. “By Spot finding these anomalies and these issues, it gives the technician more time to go out and plan and schedule how they’re going to fix the problem versus going out, identifying, then trying to plan and schedule everything.”

Burns added that it would be ideal to have more Spots at the facility so that the company can improve its inspection procedures, leading to enhanced efficiency and greater output.

Boston Dynamics is continuing to develop Spot and refine its capabilities through various pilot programs and partnerships in the U.S. and beyond.

It’s getting harder to tell what’s been AI-generated on the internet, and that goes especially for AI-generated text. It’s much easier for AI to fake text than it is for audio, images, or videos. As such, watermarking said content seems like an impossible task. However, it seems that Google has a solution in the form of the SynthID Text tool.

Since AI is so convincing, it’s important to have tools to help people identify if a research paper was spat out by ChatGPT. While cheating on your college report is bad, it’s far from the most harmful thing you can do with AI-generated text. A major issue is the spread of misinformation and other harmful content.

This is where Google SynthID Text comes in

The companies giving us the most powerful AI chatbots are also trying to give us tools to help us identify when something was created by those chatbots. OpenAI developed and tested tools to help identify when something was created by ChatGPT, but the company hasn’t seen fit to release it.

Google, on the other hand, has blessed us with a watermarking tool. As the name suggests, this is a tool that people will be able to use to identify if a section of text is AI-generated. SynthID Text is freely available to developers and businesses starting today. We’re not sure if Google is going to release a user-facing tool for casual people to check if text is AI-generated.

Watermarking text?

This seems like something that should be pretty impossible to do. It’s easier to understand watermarking AI-generated images. However, text is much easier to edit. You can easily edit or paraphrase what text a chatbot produces. Google managed to find a way, but it’s not perfect.

This method has to do with what are called Tokens. If you’ve been around AI tools, then you’ve probably seen this term tossed around. When you use an AI tool, you’re inputting data and getting data as an output. For example, typing a prompt “write a story about a rabbit” into a chatbot and getting a 100-word story as a response.

Well, the text in your prompt is divided into what are called tokens. These are sections of words or entire words that you enter into a model to be broken down and analyzed. Your response is also made up of tokens.

Well, according to Google, when a model generates text, it gives each token a score based on how likely it is that it’ll be used in the response. What SynthID Text does is insert additional information into each token by “modulating the likelihood of tokens being generated.” Then, Google compares the score from the original model’s output to the adjusted score. The final pattern of these scores is then “compared with the expected pattern of scores for watermarked and unwatermarked text, helping SynthID detect if an AI tool generated the text or if it might come from other sources,” says Google.

Limitations

It’s a lot to take in, but the important thing to note is that it’s a pretty effective tool. The only thing is that this isn’t a watertight solution. SynthID Text isn’t as accurate when it comes to shorter bits of text. So, you’ll have more luck if someone wants to generate a novel or a college report, however, you’ll have trouble if it’s a piece of advertising copy.

Also, this tool will struggle with text that was translated from another language or rewritten. This makes sense, as this would basically change all of the tokens of the original text.

Along with that, responses to factual questions are also an issue for SynthID Text. This is because it’s hard to adjust the token scores without changing the actual factual information in the response. If you’re talking about the natural habitat of a certain bird, there’s very little that you can change in your response before you start changing actual facts.

In a bit of a surprising announcement, Google stated that this tool was integrated into Gemini months ago, and most of us didn’t even know. Hopefully, this tool will lead the way for other tools that will help us detect AI-generated content.

Arm has taken its feud with Qualcomm to the next level, two years after filing a lawsuit against its former close partner. According to Bloomberg, the British semiconductor company has canceled the architecture license allowing Qualcomm to use its intellectual property and standards for chip design. As the news organization notes, Qualcomm, like many other chipmakers, uses Arm’s computer code that chips need to run software, such as operating systems. Arm has reportedly sent Qualcomm a 60-day notice of cancelation — if they don’t get to an agreement by then, it could have a huge impact on both companies’ finances and on Qualcomm’s operations.

The SoftBank-backed chipmaker sued Qualcomm in 2022 after the latter purchased a company called Nuvia, which is one of its other licensees. Arm argued that the US company didn’t obtain the necessary permits to transfer Nuvia’s licenses. As such, Nuvia breached their contract and it had terminated its licenses, Arm explained in its lawsuit. Qualcomm has been using Nuvia-developed technology in the chips designed for AI PCs, such as those from Microsoft and HP. But Arm wants the company to stop using Nuvia-developed tech and to destroy any Arm-based technology developed prior to the acquisition.

Qualcomm will have to stop selling most of the chips that account for its $39 billion in revenue, Bloomberg says, if the companies don’t resolve the issue within the next 60 days. It seems the US chipmaker believes this is a tactic by Arm to threaten its business and to get higher royalties, because its spokesperson told Bloomberg and the Financial Times: “This is more of the same from Arm — more unfounded threats designed to strong-arm a longtime partner, interfere with our performance-leading CPUs, and increase royalty rates regardless of the broad rights under our architecture license.” Qualcomm also accused Arm of attempting to disrupt the legal process, called its grounds for licensing termination “completely baseless” and said that it’s confident its “rights under its agreement with Arm will be affirmed.”

Meanwhile, an Arm spokesperson told us: “Following Qualcomm’s repeated material breaches of Arm’s license agreement, Arm is left with no choice but to take formal action requiring Qualcomm to remedy its breach or face termination of the agreement. This is necessary to protect the unparalleled ecosystem that Arm and its highly valued partners have built over more than 30 years. Arm is fully prepared for the trial in December and remains confident that the Court will find in Arm’s favor.”

Update, October 23, 2024, 11:33PM ET: This story has been updated to add Arm’s statement.

Model merging is a fundamental AI process that enables organizations to reuse and combine existing trained models to achieve specific goals.

There are various ways that enterprises can use model merging today, but many approaches are complex. A new approach known as Differentiable Adaptive Merging (DAM) could be the answer, providing a solution to the current challenges of model merging. DAM offers an innovative solution to combining AI models while potentially reducing computational costs.

Arcee AI, a company focusing on efficient, specialized small language models, is leading the charge on DAM research. The company, which raised funding in May 2024, has evolved from providing model training tools to becoming a full-fledged model delivery platform with both open-source and commercial offerings.

How DAM creates a new path forward for model merging

Merging can help companies combine models specialized in different areas to create a new model capable in both areas.

The basic concept of merging data is very well understood with structured data and databases. However, merging models is more abstract than merging structured data, as the internal representations of the models are not as interpretable.

Thomas Gauthier-Caron, research engineer at Arcee AI and one of the authors of the DAM research explained to VentureBeat that traditional model merging has often relied on evolutionary algorithms. That approach can potentially be slow and unpredictable. DAM takes a different approach by leveraging established machine learning (ML) optimization techniques.

Gauthier-Caron explained that DAM aims to solve the problem of complexity in the model merging process. The company’s existing library, MergeKit, is useful for merging different models, but it is complex due to the various methods and parameters involved.

“We were wondering, can we make this easier, can we get the machine to optimize this for us, instead of us being in the weeds tweaking all of these parameters?” Gauthier-Caron said.

Instead of just mixing the models directly, DAM adjusts based on how much each model contributes. DAM uses scaling coefficients for each column in the models’ weight matrices. It automatically learns the best settings for these coefficients by testing how well the combined model performs, comparing the output with the original models and then adjusting the coefficients to get better results.

According to the research, DAM performs competitively with or better than existing methods like evolutionary merging, DARE-TIES and Model Soups. The technology represents a significant departure from existing approaches, according to Gauthier-Caron. He described evolutionary merging as a slow process, where it’s not entirely clear up front how good the result will be or how long the merge process should run.

Merging is not an Mixture of Experts approach

Data scientists combine models in many different ways. Among the increasingly popular approaches is the Mixture of Experts (MoE).

Gauthier-Caron emphasized model merging with DAM is something very different from MoE. He explained that MoE is a specific architecture that can be used to train language models. 

The basic concept behind model merging is that it starts from the point where the organization already has trained models. Training these models usually costs a lot of money, so engineers aim to reuse existing trained models.

Practical applications and benefits of DAM for enterprise AI

One of DAM’s key advantages is its ability to combine specialized models efficiently. 

One such example provided by Gauthier-Caron is if an organization wanted to combine a Japanese model with a math model. The goal of that combination is to make a model that’s good at math in Japanese, without the need to retrain. That’s one area where DAM can potentially excel.

The technology is particularly relevant for enterprise adoption of generative AI, where efficiency and cost considerations are paramount. Helping to create more efficient ways of operating at reduced cost is a key goal for Arcee overall. That’s why DAM research is important to both the company and ultimately its users too.

“Enterprise adoption of gen AI boils down to efficiency, availability, scalability and cost,” Mark McQuade, co-founder and CEO of Arcee AI told VentureBeat.