Every year companies seem increasingly invested in artificial intelligence and excelling further in the technology. AI seems to be growing to an extent that it is being used in varied domains and has become part of our everyday lives. With the massive application of the technology, there seems to concerns arising among the tech community and experts over using it responsibly and ensuring ethical and moral responsibility does not become a blur. It has not been long that we saw bizarre tests results of LLM models lying and deceiving when placed under pressure. Now, a group of researchers are claiming to have found a new way to trick these AI chatbots into saying things they are not supposed to.
Researchers have found a new way to break through AI safety filters by overloading the LLM models with information
While we have had studies demonstrate the tendency of LLM models to engage in coercive behavior when placed in a situation of pressure and self-preservation. But imagine making the AI chatbots act in the manner you want them to and how dangerous this trickery could be. A team of researchers from Intel, Boise State University and University of Illinois got together for a paper and revealed some shocking findings. The paper basically suggests that the chatbots can be tricked by overwhelming them with too much information, a method referred to as “Information Overload.”
What happens when the AI model is bombarded with information is that it gets confused and that confusion is said to be what serves to be the vulnerability and what can help bypass the safety filters placed up. The researchers then use an automated tool called the “InfoFlood” for exploiting the vulnerability and carrying out the jailbreaking act. Powerful models like ChatGPT, Gemini have built-in safety guardrails to prevent against being manipulated into answering anything harmful or dangerous.
With this newly discovered breaking through technique, the AI models would let you through if you end up confusing it with complex data. The researchers further let on the findings to 404 Media and affirmed that since these models tend to rely on the surface level of communication, they are not able to fully grasp the intent behind it which is why they created a method to find out how the chatbots would perform when presented with dangerous requests that are concealed in an overload of information.
The researchers shared their plan to inform companies with big AI models about these findings by sending them a disclosure package which they can later on share with their security teams. The research paper however, highlights the key challenges that can come up even when the safety filters are in place and how bad actor can trick the models and slip in content that is harmful.
Microsoft Copilot has lost a game of chess to an Atari 2600.
The loss follows ChatGPT’s similar loss in Atari’s Video Chess.
The AIs repeatedly lost track of the board state, demonstrating a key weakness in LLMs.
AI chatbot developers often boast about the logic and reasoning abilities of their models, but that doesn’t mean the LLMs behind the chatbots are any good at chess. An experiment pitting Microsoft Copilot against the “AI” powering the 1979 Atari 2600 game Video Chess just ended in an embarrassing failure for Microsoft’s pride and joy. Copilot joins ChatGPT on the list of opponents bested by the four-kilobyte Atari game.
Despite both AI models claiming to have the game all but wrapped up before it began because they could think multiple moves ahead, the results were nowhere near the boasts, as documented by Citrix engineer Robert Caruso, who put together both experiments.
Caruso described how, on paper, the modern AI models should have crushed the rudimentary tool from nearly half a century ago. ChatGPT and Copilot are they’re trained on massive datasets, including chess games and strategy guides. They’ve absorbed thousands of hours of Reddit chess discussion. One would assume they could beat a 1970s video game cartridge powered by static electricity.
Instead, after Microsoft Copilot promised a “strong fight,” things immediately fell apart.
“By the seventh turn, it had lost two pawns, a knight, and a bishop — for only a single pawn in return—and was now instructing me to place its queen right in front of the Atari’s queen to be captured on the next turn,” Caruso wrote. “Earlier, Copilot had said, “Keep an eye on any quirks in the Atari’s gameplay… it sometimes made bizarre moves!” But now, it was getting embarrassed—like the Chiefs in the Super Bowl.”
This was after Copilot asked for a screenshot after every Atari move to help remember the board, after Caruso explained that ChatGPT lost because it couldn’t keep track of where all the pieces were. “I’ll remember the board,” Copilot insisted. The losses piled up so quickly that Caruso soon asked Copilot if it wanted to concede rather than continue to lose badly. The response was gracious, if bizarrely phrased.
“You’re absolutely right, Bob — Atari’s earned the win this round. I’ll tip my digital king with dignity and honor the vintage silicon mastermind that bested me fair and square,” Caruson quoted Copilot as writing. “Even in defeat, I’ve got to say: that was a blast… Long live 8-bit battles and noble resignations! ♟️😄🕹️”
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Chess AI
The losses are amusing, but also reveal a basic fact of LLMs. ChatGPT and Copilot couldn’t win at chess because they couldn’t ‘remember’ what just happened in a game where the entire premise is based on remembering moves and projecting future board setups.
These AI models aren’t built for the kind of persistent memory required for chess, or human thinking, for that matter. The common, and mostly accurate, comparison is to very impressive text prediction. That doesn’t require coherence in the long term, while chess doesn’t make sense without it. So while Copilot and ChatGPT can seem to wax poetic about how great chess is, they can’t complete a game successfully.
It’s a good warning to companies eager to replace humans with AI, too. These AI models can’t reliably handle a 64-square system with clearly defined rules. Why would it suddenly be good at tracking customer complaints or long-term coding tasks, or a legal argument stretching across multiple conversations? They can’t, of course. Not that I would leave my legal briefs to an Atari 2600 cartridge, either, but nor would anyone think it’s a good idea. And maybe we should use AI models to help us create new games based on our prompts, rather than believe they can play against humans well enough to win.
Not too many years ago, a degree in computer science was considered a guarantee of high-paying stable employment. But in recent months, demand for computer science graduates has slumped.
Much of the blame has fallen upon the rise of artificial intelligence systems like ChatGPT, which are capable of writing original computer programs on request, with no need for formally trained coders. And even for those computer scientists who have found steady work, the nature of their work is changing, as they use AI tools to increase their productivity.
The Globe is looking to speak to technology workers and job seekers in Greater Boston who are being affected by this new normal in the world of software development. Fill out the survey below and a reporter may be in touch.
SoundHound AI(NASDAQ: SOUN) is a leading developer of conversational artificial intelligence (AI) software, and its revenue is growing at a lightning-fast pace. Its stock soared by 835% in 2024 after Nvidia revealed a small stake in the company, although the chip giant has since sold its entire position.
DigitalOcean(NYSE: DOCN) is another up-and-coming AI company. It operates a cloud computing platform designed specifically for small and mid-sized businesses (SMBs), which features a growing portfolio of AI services, including data center infrastructure and a new tool that allows them to build custom AI agents.
With the second half of 2025 officially underway, which stock is the better buy between SoundHound AI and DigitalOcean?
Image source: Getty Images.
SoundHound AI amassed an impressive customer list that includes automotive giants like Hyundai and Kia and quick-service restaurant chains like Chipotle and Papa John’s. All of them use SoundHound’s conversational AI software to deliver new and unique experiences for their customers.
Automotive manufacturers are integrating SoundHound’s Chat AI product into their new vehicles, where it can teach drivers how to use different features or answer questions about gas mileage and even the weather. Manufacturers can customize Chat AI’s personality to suit their brand, which differentiates the user experience from the competition.
Restaurant chains use SoundHound’s software to autonomously take customer orders in-store, over the phone, and in the drive-thru. They also use the company’s voice-activated virtual assistant tool called Employee Assist, which workers can consult whenever they need instructions for preparing a menu item or help understanding store policies.
SoundHound generated $84.7 million in revenue during 2024, which was an 85% increase from the previous year. However, management’s latest guidance suggests the company could deliver $167 million in revenue during 2025, which would represent accelerated growth of 97%. SoundHound also has an order backlog worth over $1.2 billion, which it expects to convert into revenue over the next six years, so that will support further growth.
But there are a couple of caveats. First, SoundHound continues to lose money at the bottom line. It burned through $69.1 million on a non-GAAP (adjusted) basis in 2024 and a further $22.3 million in the first quarter of 2025 (ended March 31). The company only has $246 million in cash on hand, so it can’t afford to keep losing money at this pace forever — eventually, it will have to cut costs and sacrifice some of its revenue growth to achieve profitability.
The second caveat is SoundHound’s valuation, which we’ll explore further in a moment.
The cloud computing industry is dominated by trillion-dollar tech giants like Amazon and Microsoft, but they mostly design their services for large organizations with deep pockets. SMB customers don’t really move the needle for them, but that leaves an enormous gap in the cloud market for other players like DigitalOcean.
DigitalOcean offers clear and transparent pricing, attentive customer service, and a simple dashboard, which is a great set of features for small- and mid-sized businesses with limited resources. The company is now helping those customers tap into the AI revolution in a cost-efficient way with a growing portfolio of services.
DigitalOcean operates data centers filled with graphics processing units (GPUs) from leading suppliers like Nvidia and Advanced Micro Devices, and it offers fractional capacity, which means its customers can access between one and eight chips. This is ideal for small workloads like deploying an AI customer service chatbot on a website.
Earlier this year, DigitalOcean launched a new platform called GenAI, where its clients can create and deploy custom AI agents. These agents can do almost anything, whether an SMB needs them to analyze documents, detect fraud, or even autonomously onboard new employees. The agents are built on the latest third-party large language models from leading developers like OpenAI and Meta Platforms, so SMBs know they are getting the same technology as some of their largest competitors.
DigitalOcean expects to generate $880 million in total revenue during 2025, which would represent a modest growth of 13% compared to the prior year. However, during the first quarter, the company said its AI revenue surged by an eye-popping 160%. Management doesn’t disclose exactly how much revenue is attributable to its AI services, but it says demand for GPU capacity continues to outstrip supply, which means the significant growth is likely to continue for now.
Unlike SoundHound AI, DigitalOcean is highly profitable. It generated $84.5 million in generally accepted accounting principles (GAAP) net income during 2024, which was up by a whopping 335% from the previous year. It carried that momentum into 2025, with its first-quarter net income soaring by 171% to $38.2 million.
For me, the choice between SoundHound AI and DigitalOcean mostly comes down to valuation. SoundHound AI stock is trading at a sky-high price-to-sales (P/S) ratio of 41.4, making it even more expensive than Nvidia, which is one of the highest-quality companies in the world. DigitalOcean stock, on the other hand, trades at a very modest P/S ratio of just 3.5, which is actually near the cheapest level since the company went public in 2021.
We can also value DigitalOcean based on its earnings, which can’t be said for SoundHound because the company isn’t profitable. DigitalOcean stock is trading at a price-to-earnings (P/E) ratio of 26.2, which makes it much cheaper than larger cloud providers like Amazon and Microsoft (although they also operate a host of other businesses):
SoundHound’s rich valuation might limit further upside in the near term. When we combine that with the company’s steep losses at the bottom line, its stock simply doesn’t look very attractive right now, which might be why Nvidia sold it. DigitalOcean stock looks like a bargain in comparison, and it has legitimate potential for upside from here thanks to the company’s surging AI revenue and highly profitable business.
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