By Deepti Pathak Creating a hanging indent in Google Docs is a simple yet helpful formatting tool, especially when…
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Chatbots quickly surpassed human physicians in diagnostic reasoning — the crucial first step in clinical care — according to a new study published in the journal Nature Medicine.

The study suggests physicians who have access to large language models (LLMs), which underpin generative AI (genAI) chatbots, demonstrate improved performance on several patient care tasks compared to colleagues without access to the technology.

The study also found that physicians using chatbots spent more time on patient cases and made safer decisions than those without access to the genAI tools.

The research, undertaken by more than a dozen physicians at Beth Israel Deaconess Medical Center (BIDMC), showed genAI has promise as an “open-ended decision-making” physician partner.

“However, this will require rigorous validation to realize LLMs’ potential for enhancing patient care,” said Dr. Adam Rodman, director of AI Programs at BIDMC. “Unlike diagnostic reasoning, a task often with a single right answer, which LLMs excel at, management reasoning may have no right answer and involves weighing trade-offs between inherently risky courses of action.”

The conclusions were based on evaluations about the decision-making capabilities of 92 physicians as they worked through five hypothetical patient cases. They focused on the physicians’ management reasoning, which includes decisions on testing, treatment, patient preferences, social factors, costs, and risks.

When responses to their hypothetical patient cases were scored, the physicians using a chatbot scored significantly higher than those using conventional resources only. Chatbot users also spent more time per case — by nearly two minutes — and they had a lower risk of mild-to-moderate harm compared to those using conventional resources (3.7% vs. 5.3%). Severe harm ratings, however, were similar between groups.

“My theory,” Rodman said, “[is] the AI improved management reasoning in patient communication and patient factors domains; it did not affect things like recognizing complications or medication decisions. We used a high standard for harm — immediate harm — and poor communication is unlikely to cause immediate harm.”

An earlier 2023 study by Rodman and his colleagues yielded promising, yet cautious, conclusions about the role of genAI technology. They found it was “capable of showing the equivalent or better reasoning than people throughout the evolution of clinical case.”

That data, published in Journal of the American Medical Association (JAMA), used a common testing tool used to assess physicians’ clinical reasoning. The researchers recruited 21 attending physicians and 18 residents, who worked through 20 archived (not new) clinical cases in four stages of diagnostic reasoning, writing and justifying their differential diagnoses at each stage.

The researchers then performed the same tests using ChatGPT based on the GPT-4 LLM. The chatbot followed the same instructions and used the same clinical cases. The results were both promising and concerning.

The chatbot scored highest in some measures on the testing tool, with a median score of 10/10, compared to 9/10 for attending physicians and 8/10 for residents. While diagnostic accuracy and reasoning were similar between humans and the bot, the chatbot had more instances of incorrect reasoning. “This highlights that AI is likely best used to augment, not replace, human reasoning,” the study concluded.

Simply put, in some cases “the bots were also just plain wrong,” the report said.

Rodman said he isn’t sure why the genAI study pointed to more errors in the earlier study. “The checkpoint is different [in the new study], so hallucinations might have improved, but they also vary by task,” he said. “ Our original study focused on diagnostic reasoning, a classification task with clear right and wrong answers. Management reasoning, on the other hand, is highly context-specific and has a range of acceptable answers.”

A key difference from the original study is the researchers are now comparing two groups of humans — one using AI and one not — while the original work compared AI to humans directly. “We did collect a small AI-only baseline, but the comparison was done with a multi-effects model. So, in this case, everything is mediated through people,” Rodman said.

Researcher and lead study author Dr. Stephanie Cabral, a third-year internal medicine resident at BIDMC, said more research is needed on how LLMs can fit into clinical practice, “but they could already serve as a useful checkpoint to prevent oversight.

“My ultimate hope is that AI will improve the patient-physician interaction by reducing some of the inefficiencies we currently have and allow us to focus more on the conversation we’re having with our patients,” she said.

The latest study involved a newer, upgraded version of GPT-4, which could explain some of the variations in results.

To date, AI in healthcare has mainly focused on tasks such as portal messaging, according to Rodman. But chatbots could enhance human decision-making, especially in complex tasks.

“Our findings show promise, but rigorous validation is needed to fully unlock their potential for improving patient care,” he said. “This suggests a future use for LLMs as a helpful adjunct to clinical judgment. Further exploration into whether the LLM is merely encouraging users to slow down and reflect more deeply, or whether it is actively augmenting the reasoning process would be valuable.”

The chatbot testing will now enter the next of two follow-on phases, the first of which has already produced new raw data to be analyzed by the researchers, Rodman said. The researchers will begin looking at varying user interaction, where they study different types of chatbots, different user interfaces, and doctor education about using LLMs (such as more specific prompt design) in controlled environments to see how performance is affected.The second phase will also involve real-time patient data, not archived patient cases.

“We are also studying [human computer interaction] using secure LLMs — so [it’s] HIPAA complaint — to see how these effects hold in the real world,” he said.

Source:: Computer World

By Hisan Kidwai Blox Fruits is one of the most popular games on Roblox, and for good reason. Inspired…
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By Thomas Macaulay AI took another step into Hollywood today with the launch of a new filmmaking tool from showbiz startup Flawless. The product — named DeepEditor — promises cinematic wizardry for the digital age. For movie makers, the tool offers photorealistic edits without a costly return to set. Flawless has showcased several use cases. One transfers an actor’s performance from one shot to another. Another adds new dialogue while keeping the original scene. The character’s lip movements are synchronised with the updated words. Users can also trim lines, insert pauses, and re-time delivery. Every edit is delivered in 4K resolution. The results…This story continues at The Next Web

Source:: The Next Web

A US judge has ruled in favor of Thomson Reuters in a AI training fight against Ross Intelligence, a legal AI startup, according to The Verge. Thomson Reuters sued Ross Intelligence in 2020 for using the company’s legal research platform Westlaw to train Ross Intelligence’s AI without permission. Westlaw indexes large amounts of non-copyrighted material, but mixes it with its own content.

Ross Intelligence argued that the training should be classified under “fair use” practices, but the judge disagreed. Instead, the court held that Ross Intelligence’s use of the copyrighted material affected its original value because the company intended to develop a direct competitor.

The ruling is significant because it could have implications for future cases where copyrighted material is used for AI training. One wrinkle: this particular case concerned non-generative AI, which is not the same as generative AI used in large language models to create new material based on previous training data.

Source:: Computer World

It’s already known that today’s generative AI (genAI) tools often have trouble with basic facts. Now, it’s clear they don’t well with current events either.

That’s the upshot of a test by the BBC, which asked ChatGPT, Copilot, Gemini and Perplexity to answer 100 questions using BBC articles as a source; more than half of the answers (51%) were wrong.

One in five answers (19%) were based on directly incorrect facts — and 13% of quotes had been modified from the source.cFor example, the AI tools believe that Rishi Sunak is still the UK’s Prime Minister, and they gave the wrong death date for TV personality Michael Mosley.

“The price of AI’s extraordinary benefits must not be a world where people searching for answers are served distorted, faulty content that appears to be fact,” Deborah Turness, managing director of BBC News, wrote. “In what can feel like a chaotic world, it really can’t be right that consumers seeking clarity are met with yet more confusion.”

Source:: Computer World

By Hisan Kidwai Nintendo has been the king of the handheld console market, with the Switch selling over 140…
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By Deepti Pathak If you’ve ever checked an NBA injury report, you might have encountered the term “GTD” next…
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By Thomas Macaulay Stalling growth in the Dutch tech sector has sparked urgent calls for fresh funding streams. New data released today reveals the number of new startups in the Netherlands is declining. The country is also suffering from a severe lack of local investors.  The findings emerged in the State of Dutch Tech report by Techleap, a non-profit that supports startups and scaleups in the Netherlands.  The report raises concerns about the nation’s funding landscape. In 2024, only 104 startups raised over €100,000 — a 23% decline over the previous year. The number of deals, meanwhile, dropped by 20%. Myrthe Hooijman, Techleap’s…This story continues at The Next Web

Source:: The Next Web

By Thomas Macaulay Deepfakes have become alarmingly difficult to detect. So difficult, that only 0.1% of people today can identify them. That’s according to iProov, a British biometric authentication firm. The company tested the public’s AI detective skills by showing 2,000 UK and US consumers a collection of both genuine and synthetic content. Sadly, the budding sleuths overwhelmingly failed in their investigations. A woeful 99.9% of them couldn’t distinguish between the real and the deepfake. Think you can do better, Sherlock? You’re not the only one. In iProov’s study, over 60% of the participants were confident in their AI detection skills — regardless…This story continues at The Next Web

Source:: The Next Web

The escalating electricity demands of artificial intelligence systems are raising concerns about the technology’s sustainability — but that’s apparently of little concern to the governments of the US and the UK.

They were among the invitees at the Paris AI Action Summit that refused to sign the “Statement on Inclusive and Sustainable Artificial Intelligence for People and the Planet,” the summit’s final declaration. The statement did win the approval of 58 countries, including China and India, and two supranational groups, the 27-member European Union (EU) and the 55-member African Union.

That’s more than signed the Bletchley Declaration by countries attending the AI Safety Summit organized by the UK in November 2023. The US and UK did sign that, as did the EU, China, and India, among others.

Signatories of the Paris summit statement agreed on six priorities:

Promoting AI accessibility to reduce digital divides

Ensuring AI is open, inclusive, transparent, ethical, safe, secure, and trustworthy, taking into account international frameworks for all

Making innovation in AI thrive by enabling conditions for its development and avoiding market concentration driving industrial recovery and development

Encouraging AI deployment that positively shapes the future of work and labor markets and delivers opportunity for sustainable growth

Making AI sustainable for people and the planet

Reinforcing international cooperation to promote coordination in international governance

Inclusion excluded

The US refusal to sign was likely triggered by the second priority of making AI inclusive: President Trump has ordered his administration to eliminate any reference to diversity, equity, and inclusion (DEI) from government websites.

But safety and sustainability are also not acceptable goals for the US, according to Vice President JD Vance, who addressed the summit on Tuesday morning.

“We stand now at the frontier of an AI industry that is hungry for reliable power and high-quality semiconductors,” Vance said. “If too many of our friends are deindustrializing on the one hand and chasing reliable power out of their nations and off their grids with the other, the AI future is not going to be won by handwringing about safety.”

Vance’s remarks about chasing out reliable power are likely a reference to moves in Europe to reduce reliance on electricity generated by burning oil and gas, European supplies of which have been disrupted by Russia’s invasion of Ukraine, in favor of renewable but weather-dependent sources such as solar- or wind-powered systems.

Coordination in AI governance is also going to be a point of contention. Even as the EU AI Act’s provisions begin to enter force, Vance warned summit attendees that “Excessive regulation in the AI sector could kill a transformative industry just as it’s taking off. The US, he said, “will make every effort to encourage pro-growth AI policies, and I’d like to see that deregulatory flavor making its way into a lot of the conversations at this conference.”

According to the BBC, the UK government also cited “global governance,” along with national security concerns, as reasons it refused to sign the Paris summit’s declaration.

America first

Vance was clear that his top priority is not accessibility or inclusion, but the US.

“This administration will ensure that American AI technology continues to be the gold standard worldwide, and that we are the partner of choice for others, foreign countries and certainly businesses as they expand their own use of AI,” he said.

But access to that technology will not be open to all.

“Some authoritarian regimes have stolen and used AI to strengthen their military, intelligence, and surveillance capabilities; capture foreign data; and create propaganda to undermine other nations’ national security,” Vance told summit attendees, adding, “This administration will block such efforts. We will safeguard American AI and chip technologies from theft and misuse, work with our allies and partners to strengthen and extend these protections, and close pathways to adversaries attaining AI capabilities that threaten all of our people.”

Billions in funding

Shortly after Trump’s inauguration, he announced that US AI companies would invest $500 billion in Project Stargate, designed to ramp up AI infrastructure in the US — although even with support from investors in Japan and the United Arab Emirates, barely a quarter of that sum is committed so far.

Vance predicted that investment would continue apace: “Of the $700 billion, give or take, that is estimated to be spent on AI in 2028, over half of it will likely be invested in the US,” he said.

But the US doesn’t have a monopoly on big projects. At the Paris summit, European Commission President Ursula Von der Leyen announced the EU’s intention to mobilize €200 billion ($207 billion) in investment in AI.

There’s some sleight of hand going on there too: While Von der Leyen talks of “mobilizing” €200 billion, only €20 billion of that is public money, and she’s expecting private enterprise to make up the rest.

Source:: Computer World

Capital One has launched an AI agent designed to help customers with one of the more difficult and confusing purchase decisions: buying a car.

The new chatbot, called Chat Concierge, will help customers with everything from researching vehicles and scheduling test drives, to exploring financing options. The generative AI-powered assistant, one of many such projects at the financial institution, simplifies car buying by answering basic questions online with no dealership visit needed. It then directs them to existing online services.

Although Capital One’s auto loans are its smallest loan business, they still account for about 28% of its business, or $75 billion.

Chat Concierge is considered a customer service chatbot — a generative AI (genAI) automation tool that can handle simple user questions. The new service stands in contrast to Capital One’s own study last fall that found the in-person dealership experience remains vital for car buyers, even when they use digital tools to streamline early stages of the process. The report showed 88% of car buyers conduct at least half of the car buying process in person; 60% of buyers said sales reps contribute to trust.

“Car buyers’ trust in dealers is a key indicator of how transparent they perceive the car buying process — even with access to digital tools to complete key elements of their purchase,” the study concluded.

Even so, Sanjiv Yajnik, president of Financial Services at Capital One, said Chat Concierge will drive the future of car buying. “By leveraging our own internally developed AI tools to provide personalized, efficient, and transparent interactions, Capital One is reimagining car buying and setting a new standard for customer experience in the automotive industry,” Yajnik said in a statement.

Capital One’s AI assistant is part of a larger trend of companies deploying AI agents to tackle tasks often performed by entry-level employees, or to create efficiencies for high-level workers.

In the simplest sense, an AI agent is the combination of a large language model (LLM) and a traditional software application that can act independently to complete a task. The most basic AI agents include Chatbots such as OpenAI’s ChatGPT, Microsoft’s CoPilot, and Google Bard; they can answer user questions on a myriad of topics. AI agents can also act as spam filters, such as email spam detectors that use keyword matching and smart devices such as Thermostats that can follow set rules for raising or lowering temperature based on environmental conditions.

As AI-powered agents improve, they enable more personalized and effective customer service than early chatbots could deliver. Banks are using the genAI tools to resolve complex issues, setting new standards for efficiency. By leveraging customer data, AI assistants provide 24/7 support, handling thousands of inquiries at once, according to Arthur O’Connor, academic director of data science at the City University of New York (CUNY) School of Professional Studies.

“One of the most interesting developments is emotion recognition (ER), an emerging technology enabling chat bots to detect and respond to customer emotions, allowing for more empathetic and effective interactions, and thus engender customer satisfaction and loyalty,” O’Connor said.

Last month, Google DeepMind announced Project Astra, a research initiative aimed at developing a universal AI assistant that can process text, images, video, and audio inputs, enabling more natural and context-aware interactions. A key feature of Project Astra is its multimodal capabilities, allowing users to engage through various means such as speaking, showing images, or sharing videos. The assistant can remember details from past conversations and utilize tools such as Google Search, Maps, and Lens to provide informed responses.

The US Airforce recently announced it’s experimenting with a chatbot called NIPRGPT that will allow service members to engage in human-like conversations to complete various tasks, including drafting correspondence, preparing background papers, and assisting with coding.

Many AI agents will be integrated into existing software applications without users even knowing it. For example, Google Maps Navigation uses an AI model combined with traffic data and predicted conditions to provide the best route for drivers. Virtual Personal Assistants, such as Apple’s Siri, Amazon’s Alexa, or Google Assistant, use agents to predict user needs.

There are also learning AI agents whose algorithms are sophisticated enough to improve performance based on past experiences. Those systems include consumer recommendation services used on Netflix, Spotify, and YouTube, which all rely on AI to learn user preferences.

Agents that can become “smarter” include DeepMind’s AlphaGo, which learns and adapts to play the boardgame Go at a superhuman level.

Capital One’s Chat Concierge uses multiple AI agents that collaborate to mimic human reasoning. Instead of just providing information, the agents take action based on the user’s requests. They understand natural language, create action plans, validate them to avoid mistakes, and explain everything to the user, according to the bank.

For example, if a buyer asks for a list of trucks and then requests a test drive of the least expensive option, Chat Concierge can handle both tasks seamlessly. Concierge will also:

Simulate and validate plans to ensure they meet the car buyer’s needs and business policies.

Generate and deliver clear, natural language explanations of all the steps to the car buyer.

Let car buyers explore financing without leaving the dealer’s website.

Connect buyers directly to dealers through dealer websites, a navigator platform, and customer relationship management (CRM) apps, integrating customer info into the dealer’s CRM.

Work seamlessly with both Capital One and non-Capital One products.

“Capital One has a long history of using data, technology, and analytics to deliver superior financial services products and services for millions of customers,” said Prem Natarajan, chief scientist and head of enterprise AI at Capital One. “The launch of Chat Concierge is a key milestone in our customer-centered AI journey as we continue to focus on solving some of the most challenging problems in finance with technology.”

Source:: Computer World

By Hisan Kidwai It’s no secret that most of us have downloaded a file and forgotten where it went….
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By Thomas Macaulay A Ukrainian drone tech firm has unveiled an alternative to GPS navigation. Sine.Engineering built the system to counter Russia’s electronic warfare, which has wreaked havoc on GPS signals.  To dodge the interference, Sine invented a satellite-free replacement. The approach is inspired by time-of-flight (ToF) methods, which began tracking aircraft long before the advent of GPS.   Unlike GPS, ToF systems don’t rely on satellites. Instead, they measure the time it takes a signal to between a transmitter and a target. In Sine’s framework, the calculations come from a communication module for drones.  Smaller than a playing card, the module shares signals with a…This story continues at The Next Web

Source:: The Next Web

Every enterprise CIO knows they cannot — and should not — ever trust a vendor’s policy position. Whether that’s because a vendor might not strictly adhere to its policies or can change policies anytime  without notice, it doesn’t matter.

Google’s move last week to back away from assurances  it would not help make weapons or engage in surveillance was utterly unsurprising. Companies are motivated by revenue, profits and market share and if corporate leaders can improve any of those financial metrics by helping to make weapons of mass destruction — or helping a government poison its people — that’s what can happen.

But enterprise CIOs are the customers— customers with big budgets that give them major clout. If companies want your dollars, they must agree to whatever you have in your RFP and your contract.

Why would these massive vendors agree? Because they fear that one of their competitors will do so if they don’t. That could cost them market share and revenue. 

Suddenly, you have their C-suite’s rapt attention.

As for Google in this case, what was the original language the company felt it needed to avoid? Last year’s statement gave a list of “AI applications we will not pursue.” 

This is part of that list: “Technologies that cause or are likely to cause overall harm. Where there is a material risk of harm, we will proceed only where we believe that the benefits substantially outweigh the risks, and will incorporate appropriate safety constraints. Weapons or other technologies whose principal purpose or implementation is to cause or directly facilitate injury to people. Technologies that gather or use information for surveillance violating internationally accepted norms. Technologies whose purpose contravenes widely accepted principles of international law and human rights.”

Then, in an eerily predictive point, it added: “As our experience in this space deepens, this list may evolve.” 

It did evolve. It got a lot shorter.

If a lot of money can be made doing those things, Google now says, in effect, “Human suffering and death and maiming can be trumped by higher profits and marketshare. Ethics, morality and humanity don’t keep the lights on, buddy!”

You’ll also notice that the company has bagged its “Don’t be evil” tagline; Google apparently ditched it 10 years ago. Maybe they could update it now to something like this: “Google. Where we never let avoiding evil stand in the way of making a profit.”

I was recently discussing this issue with two executives at Phoenix Technologies, a Swiss cloud provider. They made the argument that enterprise CIOs shouldn’t rely on vendor promises, especially for large language model (LLM) making, including how they’re trained and used.

“If you are reliant on the model makers and their terms and conditions state that they can service anybody, you have to be willing to deal with the fallout,” said Peter DeMeo, the Phoenix group chief product officer. “You really can’t trust the model makers,” especially when they need revenue from government contracts.

His colleague, Phoenix group CTO Nunez Mencias, applauded Google for removing the restriction, given that it was unlikely it could ever be relied on. “The model makers “can always change their policies, their rules.”

But there’s a big difference between being unable to rely on a vendor’s self-stated rules and being powerless to discourage AI use in areas your company might not be comfortable with.

Just remember: Entities out there doing things you don’t like are always going to be able to get generative AI (genAI) services and tools from somebody. You think large terrorist cells can’t use their money to pay somebody to craft LLMs for them? 

Even the most powerful enterprises can’t stop it from happening. But, that may not be the point. Walmart, ExxonMobil, Amazon, Chase, Hilton, Pfizer and Toyota and the rest of those heavy-hitters merely want to pick and choose where their monies are spent. 

Big enterprises can’t stop AI from being used to do things they don’t like, but they can make sure none of it is being funded with their money. 

If they add a clause to every RFP that they will only work with model-makers that agree to not do X, Y, or Z, that will get a lot of attention. The contract would have to be realistic, though. It might say, for instance, “If the model-maker later chooses to accept payments for the above-described prohibited acts, they must reimburse all of the dollars we have already paid and must also give us 18 months notice so that we can replace the vendor with a company that will respect the terms of our contracts.”

From the perspective of Google, along with Microsoft, OpenAI, IBM, AWS and others, the idea is to take enterprise dollars on top of government contracts. If they were to believe that’s suddenly an either/or scenario, they might suddenly reconsider. 

Given that Google has decided that revenue is more important than morality, the answer is not to appeal to their morality. If money is all they care about, speak that language. 

Fortunately for enterprises, there are plenty of large companies willing to handle your genAI needs. Perhaps now is the time to use your buying power to influence who else they work with and limit what they do.

Source:: Computer World

The US Treasury Department’s payment servers hold the tax returns, social security data and bank account numbers of every adult citizen of the United States.

They are, one would assume, among the most highly secured servers on earth and yet it seems that all the employees of Elon Musk’s Department of Government Efficiency (DOGE) needed to do to access these systems after January 20 was to walk into Treasury Department offices and demand access to the servers’ credentials.

We learn of these extraordinary if still hazy and unconfirmed events by reading between the lines of a weekend ruling by US District Judge Paul Engelmayer in response to a suit brought by 19 states against the actions of the DOGE team.

In the ruling, Engelmayer blocked access by DOGE staff to the Treasury’s payment servers for the time being and ordered that any data downloaded to date by team members should immediately be deleted.

Allowing DOGE access in its current form violated the Administrative Procedure Act (APA), a statutory requirement, as well as the doctrine of the separation of powers and the Take Care Clause of the US Constitution, he ruled.

Further access for unauthorized DOGE staff risked “irreparable damage,” a technical term for serious consequences which can’t be easily remedied through subsequent legal action.

“That is both because of the risk that the new policy presents of the disclosure of sensitive and confidential information and the heightened risk that the systems in question will be more vulnerable than before to hacking,” the ruling continued.

In short, allowing unauthorized personnel to access these servers without monitoring risked data disclosure, also known as a data breach.

“Utterly insane”

The ruling traces the outline of an unexpected fault line that has appeared since President Trump’s inauguration: how far should Presidential appointees be allowed to go when executing executive orders if that risks breaking existing laws and rules around security?

Engelmayer’s answer, for now at least, is not far at all: only staff within the Treasury with the correct security clearance should be granted access to servers containing sensitive citizen and personal data.

Not surprisingly, as it continues its campaign to refashion and downsize the federal workforce, the White House was derisive of the ruling and the legal suit that precipitated it.

“Grandstanding government efficiency speaks volumes about those who’d rather delay much-needed change with legal shenanigans than work with the Trump Administration of ridding the government of waste, fraud, and abuse,” White House spokesperson Harrison Fields said in a statement released to media outlets.

Musk, meanwhile, took to his personal mouthpiece, X, to condemn at length the financial waste he claimed the DOGE access had uncovered within the system.

 “Yesterday, I was told that there are currently over $100B/year of entitlement payments to individuals with no SSN or even a temporary ID number. If accurate, this is extremely suspicious,” he tweeted. “This is utterly insane and must be addressed immediately.”

The counter-argument to this is that it’s not the intention behind the access that’s at issue so much as the principle that security clearance should still apply to people tasked with investigating alleged waste.

Fact vacuum

As is often the case, the ruling doesn’t reveal the full context of what occurred. According to Michel Chamberland, founder of IT services and consulting company IntegSec, this made it hard to judge how far security was bent for the sake of convenience.

“We do not have exact details of what systems were accessed, what specific data they have access to and what level of access they were provided. I think when we hear people’s social security numbers may have been compromised by the DOGE team, it is complete speculation,” he told Computerworld.

One remedy would be for DOGE to explain the nature of their access more clearly:

“I think the first thing they could do is provide more transparency as to what exactly they access, how they do it and the level of access provided,” said Chamberland.

“We also need to hear about the classification of these systems. Not all systems within a government agency will be highly classified. It is possible DOGE was able to do most or all their work without accessing systems that do require a security clearance,” he said.

However, Chamberland agreed that background checks for staff were essential.

“DOGE sharing this information with the public could go a long way to reduce security concerns.”

This is not the first time Musk’s DOGE has upset people enough to provoke legal action. Two weeks ago, a private class action alleged that his team sent emails to the federal workforce from the Office of Personnel Management (OPM) in a way that broke the E-Government Act of 2002 and was insecure.

Source:: Computer World

By Thomas Macaulay European tech leaders Helsing and Mistral have formed a pact to build new military AI systems. The partnership brings together two of Europe’s top startups. Helsing, a defence tech firm based in Germany, was valued at €5bn last year. Founded in 2021, the company develops software for weapons, vehicles, and military strategy. Its systems have been deployed in battlefield simulations, fighter jets, and drones in Ukraine. Mistral, meanwhile, is widely considered Europe’s closest competitor to OpenAI. The French startup has also become a favourite of investors, raising at €600mn at a valuation of €5.8bn last year. The partners announced their…This story continues at The Next Web

Source:: The Next Web

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