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Machine learning (ML) is now mission critical in every industry. Business leaders are urging their technical teams to accelerate ML adoption across the enterprise to fuel innovation and long-term growth. But there is a disconnect between business leaders’ expectations for wide-scale ML deployment and the reality of what engineers and data scientists can actually build and deliver on time and at scale.

In a Forrester study launched today and commissioned by Capital One, the majority of business leaders expressed excitement at deploying ML across the enterprise, but data scientist team members said they didn’t yet have all the necessary tools to develop ML solutions at scale. Business leaders would love to leverage ML as a plug-and-play opportunity: “just input data into a black box and valuable learnings emerge.” The engineers who wrangle company data to build ML models know it’s far more complex than that. Data may be unstructured or poor quality, and there are compliance, regulatory, and security parameters to meet.

LIVERMORE, Calif. — Blueshift Optics, owned by former Sandia employee Joey Carlson, is working to shift the way radioactive materials are detected, using technology that he helped create at Sandia National Laboratories.

Radiation detection has long been a critical aspect of national security and efforts to make the world safer.

“Agencies are trying to cast this wide net to catch nuclear smuggling, and this is one aspect of that effort,” said Sandia materials scientist Patrick Feng. “You could use this technology at a border crossing, in a handheld detector as someone enters a facility or fly it on a drone to map an area.”

OpenAI, the company behind ChatGPT and DALL·E 3, held its first developer conference yesterday in San Francisco. In addition to revealing new products and model upgrades, CEO Sam Altman hinted at something much greater that may be arriving soon.

Credit: Tada Images.

Following the launch of ChatGPT in November 2022, OpenAI released a more powerful version based on GPT-4 in March 2023. This featured many improvements such as a larger text input length, more creative and nuanced responses, and improved safety and security.

The drones utilized large language models to engage with each other and their operator.

Marking a significant leap concerning drone technology, researchers in China have enabled unmanned aerial vehicles (UAVs) to engage in “group chats” to discuss and assign work to one another, much like human teams.

The research work accessed by South China Morning Post (SCMP) was done by a team led by Li Xuelong at the School of Artificial Intelligence, Optics and Electronics at Northwestern Polytechnical University in China. According to them, the technology might improve security patrols, disaster relief, and aerial logistics.

BRUSSELS (AP) — NATO member countries that signed a key Cold War-era security treaty froze their participation in the pact on Tuesday just hours after Russia pulled out, raising fresh questions about the future of arms control agreements in Europe. Many of NATO’s 31 allies are parties to the Treaty of Conventional Armed Forces in Europe, which was aimed at preventing Cold War rivals from massing forces at or near their mutual borders. The CFE…

Scientists showcased the application of machine learning in the sodium-cooled fast reactor (SFR).

Machine learning technology has the potential to transform nuclear reactor operations, according to a team of experts from the US Department of Energy’s Argonne National Laboratory, who demonstrated how it may improve security and efficiency.

They showcased the application of machine learning in the sodium-cooled fast reactor (SFR), a specialized cutting-edge nuclear reactor.

In recent years, the field of artificial intelligence has witnessed remarkable advancements, with researchers exploring innovative ways to utilize existing technology in groundbreaking applications. One such intriguing concept is the use of WiFi routers as virtual cameras to map a home and detect the presence and locations of individuals, akin to an MRI machine. This revolutionary technology harnesses the power of AI algorithms and WiFi signals to create a unique, non-intrusive way of monitoring human presence within indoor spaces. In this article, we will delve into the workings of this technology, its potential capabilities, and the implications it may have on the future of smart homes and security.

The Foundation of WiFi Imaging: WiFi imaging, also known as radio frequency (RF) sensing, revolves around leveraging the signals emitted by WiFi routers. These signals interact with the surrounding environment, reflecting off objects and people within their range. AI algorithms then process the alterations in these signals to form an image of the indoor space, thus providing a representation of the occupants and their movements. Unlike traditional cameras, WiFi imaging is capable of penetrating walls and obstructions, making it particularly valuable for monitoring people without compromising their privacy.

AI Algorithms in WiFi Imaging: The heart of this technology lies in the powerful AI algorithms that interpret the fluctuations in WiFi signals and translate them into meaningful data. Machine learning techniques, such as neural networks, play a pivotal role in recognizing patterns, identifying individuals, and discerning between static objects and moving entities. As the AI model continuously learns from the WiFi data, it enhances its accuracy and adaptability, making it more proficient in detecting and tracking people over time.

A scientist claims to have developed an inexpensive system for using quantum computing to crack RSA, which is the world’s most commonly used public key algorithm.

See Also: Live Webinar | Generative AI: Myths, Realities and Practical Use Cases

The response from multiple cryptographers and security experts is: Sounds great if true, but can you prove it? “I would be very surprised if RSA-2048 had been broken,” Alan Woodward, a professor of computer science at England’s University of Surrey, told me.

Atlassian has discovered yet another critical vulnerability in its Confluence Data Center and Server collaboration and project management platform, and it’s urging customers to patch the problem immediately. The latest advisory by Atlassian describes CVE-2023–22518 as an improper authorization vulnerability that affects all versions of the on-premises versions of Confluence.

It is the second critical vulnerability reported by Atlassian in a month, tied to its widely used Confluence Data Center and Server platform and among numerous security issues from the company during the past year. The previous bulletin (CVE-2023–22515) revealed a vulnerability that could allow an attacker to create unauthorized Confluence administrator accounts, thereby gaining access to instances. That vulnerability had a severity level of 10 and was discovered initially by some customers who reported they may have been breached by it.

To date, Atlassian is not aware of any active exploits of the newest vulnerability, which has a severity level of 9.1., though the company issued a statement encouraging customers to apply the patch. “We have discovered that Confluence Data Center and Server customers are vulnerable to significant data loss if exploited by an unauthenticated attacker,” Atlassian CISO Bala Sathiamurthy warned in a statement. “Customers must take immediate action to protect their instances.”

An international team of scientists has proposed a new remote monitoring method of nuclear stockpiles using mirrors and radio waves.

An international team of scientists has devised an innovative method of using radio waves to monitor a nation’s nuclear stockpile remotely. Conducted by a team of IT security experts from Germany and the United States, it could be used to build trust between nuclear powers to ensure rivals are keeping their promises when it comes to agreed nuclear disarmament treaties. It could also be used to give a “heads up” if one particular nuclear power removes stored nuclear warheads, which could be an indication of intended use.


Johannes Tobisch et al 2023.

Remote nuke monitoring.