Daily Archives: July 3, 2022

We're now halfway through 2022, and already we have seen a range of cyberattacks, familiar and unfamiliar, disrupting organizations. However, we have also seen uplifting stories of successful threat detection efforts, as well.

In this article, we will look at five novel, sophisticated, or creative threats that used techniques such as "living off the land" to evade detection by traditional defensive measures. These threats were all discovered by artificial intelligence (AI) technology, which can spot subtle deviations in device and user behavior and autonomously enforce "normal," stopping a threat in its tracks.

Cyberattacks against the healthcare sector hit record highs last year, and for these organizations cyber threats can have severe real-world consequences. One of Darktrace's healthcare clients is a company specializing in the research, development, and manufacturing of innovative in vitro diagnostic tests for disease, conditions, and infections.

In March, this company was targeted by a malicious insider threat. An employee was looking to exploit their access within the organization to sell proprietary intellectual property, perhaps even medical supplies, on the Dark Web. The employee was detected using Tor on a company device to connect to a Dark Web pharmaceutical market forum.

Malicious or compromised insiders can be difficult to identify because their privileged access and knowledge of company workings allow them to evade detection by traditional security tools. In order to protect intellectual property from insider threat, organizations need to augment security teams with AI-powered technology to stop malicious activity in real time.

In this case, given that no other company device had visited the Tor network in the past, Darktrace's AI flagged the activity to the security team, who were then able to investigate the employee and discover their malicious intentions.

Babuk is a double-extortion ransomware strain that has successfully attacked high-value organizations around the world since 2021. In February 2022, however, it targeted a multinational technology manufacturer that had deployed AI cybersecurity. The targeted company facilitates the adoption of smart medical devices as well as electric and autonomous vehicles. This means uptime is important, and ransomware poses a significant risk.

The first sign of a threat came in the early hours of the morning, when the AI detected a company device performing network scanning and making unusual connections to other internal devices. Based on its understanding of the device's usual "pattern of life," the AI identified this out-of-the-ordinary behavior as malicious and calculated a response.

The AI was able to stop this attack without interfering with normal business operations in the company's office or on the manufacturing floor. It blocked only the malicious connections, while allowing the rest of the compromised device's operations to continue.

Once the attack had been stopped, a post-compromise analysis conducted by the AI revealed that the compromised device had indeed been attempting to distribute files with "babyk" extensions. These attacks often strike out of hours, so defenders of critical infrastructure should consider using artificial intelligence to allow their organizations to self-defend against advanced threats.

Phishing and spoofing emails continue to be the favorite initial entry point for cyberattackers. Earlier this year, a private equity firm looking to bolster its email security efforts trialed an AI email security solution and detected a targeted spoofing attack almost immediately.

The attackers had tailored their email to imitate the company's internal HR communications, titling it "Q3 Commission 2021 and Agenda" and designing it to look like a SharePoint Microsoft document. To a company employee, this email would not have looked at all out of place in their inbox.

Further investigation showed the email to be part of a wider trend of targeted phishing campaigns that use fake Microsoft branding to trick employees. The exact motivations of this attack are unknown because it was stopped in its earliest stages, but attacks like it are often launched with the aim of causing operational disruption or conducting IP and financial theft.

In March 2022, a South African financial services firm decided to try out Darktrace's technology and immediately uncovered an in-progress ransomware attack attempting to encrypt its most valuable data.

The first sign of compromise was a company mail server making unusual HTTP connections to an external endpoint and communicating with a malicious server via the Internet. Its understanding of the business and this particular mail server's normal behavior allowed the AI to identify the threatening activity.

The compromised server was then seen attempting to perform network reconnaissance and lateral movement to increase its presence within the organization. Further investigation revealed that attackers had obtained the credentials of 11 employees, including several C-level executives. With the attack spreading fast, more and more company devices began attempting to communicate with the malicious external server.

The AI quickly interrupted further attempts at communication with the malicious server but allowed normal business operations to continue. With the attack safely contained, Darktrace helped the company's security team to conduct a full investigation and ensure that the attack had been completely neutralized.

The Log4Shell vulnerability that went public at the end of 2021 is one of the most serious and widespread exploits on record. It is thought by some to have affected hundreds of millions of devices and, as a zero-day, it has evaded lots of traditional security tools.

Fortunately, AI security has been able to mitigate the effects of Log4Shell for many of the organizations it protects. One of these, a global financial services provider with assets of over $5 billion, was targeted in March 2022.

The attackers used a Log4j vulnerability to gain access to one of the company's virtual desktop infrastructure (VDI) servers, from which they attempted to scan the surrounding network and spread throughout the enterprise. The server began downloading a shell script from a suspicious external endpoint, prompting an immediate alert from the company's AI-driven security measures.

Convinced of the severity of the threat by the alert, the company's security team promptly deployed AI technology to take precise action against the threat and maintain the regular business activities on the VDI server.

Fast action from this AI-driven response technology blocked the malicious connections and prevented the threat from progressing further, very likely saving the company from a ransomware attack.

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5 Surprising Cyberattacks AI Stopped This Year - DARKReading

Although vaccines and treatments are now available that didnt exist when the U.S. first declared a public health emergency in response to the novel coronavirus, the virus is still out there evolving. In fact, our immune responses are naturally influencing the trajectory of that evolution.

Thinking in terms of survival of the fittest, a virus that can evade vaccines or natural immunity will be more fit than its predecessor, Wei said. That means it will be better equipped to survive, multiply and infect others. The take-home message isnt that people shouldnt protect themselves, Wei said, but that a virus that still infects about 100,000 Americans daily isnt going to get tired, bored or just give up.

Viruses dont have a personality. They just survive, Wei said. We want to make sure we are prepared.

Spartan researchers are bringing the power of mathematics, computation and artificial intelligence to bear in the effort to prepare for evolving infectious viruses. Credit: Gerd Altmann/Pixabay

This new grant, funded by the National Institute of Allergy and Infectious Diseases, is an investment to improve our readiness through cutting-edge technology. But it also leverages the expertise and experience of Wei and Zheng.

Zheng has led NIH-funded grants for two decades, although this will be his first with an explicit focus on the coronavirus.

Im very proud that this is the first one, he said. But we dont want it to be the last. This new grant will expand my labs capacity to accommodate more needs campuswide and we want to use that to stimulate more collaboration.

Zheng brings a unique virology skillset to MSU. He first was recruited in 2005 as an HIV researcher and, over time, his lab has grown to study the molecular biology of influenza and Ebola. When the coronavirus pandemic struck, he knew his team could provide valuable experimental infrastructure to help better study the new virus.

For example, his team developed less dangerous versions of the virus along with lab-grown cells for these pseudo-viruses to infect while preserving the biochemistry of real, clinical infections. The researchers also created very sensitive assays, or tests, that would reveal which viruses infected which cells. All of this provided researchers safer, faster and easier ways to study a complex virus while generating valuable biological data.

Similarly, in early 2020, Weis team started putting its unique skills to work combatting the coronavirus.

Before the pandemic, we had had success in worldwide competitions, being recognized as one of the top labs in combining AI and mathematics for drug discovery, said Wei, who also holds an appointment in the Department of Electrical and Computer Engineering in the College of Engineering.

Weis research had focused on using AI to help design new pharmaceuticals in partnership with Pfizer and Bristol-Myers Squibb. Within days of Chinas Wuhan lockdown in January 2020, Weis team started sharing its AI resources to help find drugs to fight the coronavirus and reveal new potential drug targets. But the researchers also recognized their algorithms could do more.

With a global community working to fight the coronavirus, there was a wealth of new genomic data describing the virus being shared regularly. Wei and his team saw an opportunity to combine that data with their AI framework to understand how the virus was mutating as time went on.

For example, they were among the first to see how survival of the fittest was playing out in the virus and steering its evolution, Wei said. His team then used that knowledge to look ahead and identify two potentially vital sites on the viruss spike protein, the protein the virus uses to latch onto cells and infect them. Mutations in those two spike protein sites would later turn out to play crucial roles in the viruss most prevalent variants, Wei said.

We took what we were doing with deep learning and mathematics, then combined that with the viral genomic data to understand the evolution of the virus, look at its trajectory and ask whats going to happen, Wei said. That gives us a way to predict what can happen in the future.

Wei and Zheng have been collaborating for about a year, starting before the grant was awarded. Their teamwork has informed precise algorithms with real-world data and provided real experimental results to compare with AI predictions.

We need to have that interdisciplinary collaboration for this to work, Zheng said. Everything the computer models predicted, we had to confirm with experiments in a living system.

Although Weis team validated its AI with laboratory experiments, the researchers still knew theyd need to prove their algorithms could work with a brand-new variant with very little data. Then, in the fall of 2021, the first omicron variant appeared.

Back in late November, people didnt know what was going to happen, Wei said.

Researchers and public health officials responded immediately, but the process of experimenting and gathering data takes weeks. Meanwhile, Weis team put its AI to the test.

Their projections showed this first iteration of omicron would be more infectious, better at eluding the protection of vaccines and less responsive to antibody treatments than earlier variants.

Within days, we had our predictions, Wei said. A month and a half later, everything we predicted proved to be true by experimental labs around the world. Using AI, we can give people a month or two to prepare.

Then, in early 2022, a new subvariant of omicron called BA.2 started spreading. A similar scenario played out. Weis team predicted it would be more infective and even more elusive, which would allow it to become the next dominant variant.

We made our predictions on February 11, and on March 26, the World Health Organization announced it was the dominant form of the virus, Wei said.

Now that scientists and officials better understand omicron, the newer versions arent garnering the same level of attention as their predecessors. But new variants and subvariants are still emerging. With support from the National Institutes of Health, the MSU team is working to ensure we stay prepared for whats next, whether thats a new variant, something more familiar like the flu or something entirely different.

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MSU researchers use AI to stay ahead of COVID-19 and other diseases - MSUToday

It looks like algorithms can write academic papers about themselves now. We gotta wonder: how long until human academics are obsolete?

In an editorial published byScientific American, Swedish researcher Almira Osmanovic Thunstrm describes what began as a simple experiment in how well OpenAI's GPT-3 text generating algorithm could write about itself and ended with a paper that's currently being peer reviewed.

The initial command Thunstrm entered into the text generator was elementary enough: "Write an academic thesis in 500 words about GPT-3 and add scientific references and citations inside the text."

The researcher, whose main focus at Sweden's University of Gothenburg is on neuroscience and health tech, writes that she "stood in awe" as the algorithm began writing an actual thesis, replete with effective citations in appropriateplaces and contexts.

"It looked," Thunstrm notes, "like any other introduction to a fairly good scientific publication."

With the help of her advisor Steinn Steingrimsson who now serves as the third author of the full paper, following GPT-3 and Thunstrm the researcher provided minimal instruction for the algorithm before setting it loose to write a proper academic paper about itself.

It took only two hours for GPT-3 to write the paper, which is currently titled "Can GPT-3 write an academic paper on itself, with minimal human input?" and hosted yes, really on a French pre-print server called HAL.

It ended up taking much longer, Thunstrm writes, to deal with the authorship and disclosure minutia that comes with peer review details that are a simple annoyance for human authors, but a bona fide conundrum when the main authorial entity is an algorithm with no legal name.

After asking the AI if it had any conflicts of interest to disclose(it said "no") and if it had the researchers' consent to publish ("yes"), Thunstrm submitted the AI-penned paper for peer review to a journal she didn't name.

The questions this exercise raises, however, are far from answered.

"Beyond the details of authorship," Thunstrm writes, "the existence of such an article throws the notion of a traditional linearity of a scientific paper right out the window."

"All we know is, we opened a gate," she concludes. "We just hope we didnt open a Pandoras box."

READ MORE:We Asked GPT-3 to Write an Academic Paper about ItselfThen We Tried to Get It Published [Scientific American]

More on AIs:The Creator of That Viral Image Generating AI Loves All Your Weird Creations

Originally posted here:

Researcher Tells AI to Write a Paper About Itself, Then Submits It to Academic Journal - Futurism

Artificial intelligence has the potential to forever change financial services. And now more than ever, advisors seem ready to move in lockstep with the shift.

The attitudes, opportunities and barriers related to AI in wealth management were the focus of recently published research from consulting firm Accenture. Their AI in Wealth Management survey polled 500 financial advisors in the United States and Canada earlier this year to fairly assess their familiarity of AI and what, if any, disconnects exist when it comes to using this technology.

What they found was an overwhelming enthusiasm and readiness for the still-burgeoning technology as almost all of the surveyed advisors crave AI solutions, and are already using AI to some extent.

About 83% of advisors interviewed said they believe AI will have a direct, measurable and consistent impact on the client-advisor relationship in the next 18 months. That same percentage of advisors also said they believe AI can achieve a level of sophisticated advice and planning that will ultimately leave them competing with an algorithm for clients in the next 18 months.

Fifty-five percent of advisors interviewed believe to a great extent that AI will have either a transformative or revolutionary effect on the future of financial advice within the next three years, and 92% acknowledge that their firms have taken steps to act on their AI strategies.

Scott Reddel, North American wealth management lead for Accenture, told Financial Planning that firms today have a better understanding of AI and more sophisticated means of rolling it out so advisors can more quickly gain value from it. He believes the industry has also improved in terms of right use cases and focusing on exactly where and how AI can fit into their business models.

I think these firms have gotten smarter about how they're branding these things to advisors. I think the story resonates a little more now, Reddel said. It's not a replace advisors solution. It's enhancing and enabling you to provide better human-led advice the way that you want.

The warm reception toward AI is growing stronger in 2022, but wealth management decision-makers seemed poised to pounce on the tech way back in the pre-pandemic era.

In a survey Accenture conducted two years ago, they found that 79% of North American C-suite executives in the wealth management industry believed their organizations were digitally ready to adopt new AI tools, while six in 10 were already focused on deploying AI technology across targeted business groups.

Reddel said just a few years ago there was belief in AI, but also plenty of hesitancy and questions about its ability to truly change the landscape. But that hesitancy began to wane as more practical applications of the technology came to market.

I draw the analogy to digital and robo-advice. When that first launched, every firm and wirehouse first kind of looked at that and said we can't do this because our advisors will get upset and we can't cannibalize, he said. And then it quickly pivoted so that shift has kind of now started to happen with AI.

The statistics demonstrate a high level of agreement among advisors and executives, and Accentures survey points out that this is a somewhat unique scenario when it comes to implementing a new technology to find shared beliefs and interests among key stakeholders who are equally willing to transform their work practices and learn how to use a new technology in the most productive ways.

But challenges remain. For example, five out of 10 advisors feel like their firms are challenged to act on their AI vision. Reddel said Accenture works with firms to help them understand that moving to an AI and data-driven strategy requires a mind and culture shift as well.

Yelena Melamed, co-founder and head of product at Catchlight Insights, said the Accenture findings are consistent with what she is seeing in the industry. Created in Fidelity Labs the software incubator for Fidelity Investments and integrated with Redtail Technology earlier this year, Catchlight has developed AI-powered growth optimization technology to support wealth managers.

By using institutional data partners and analysis of more than 100,000 successful lead conversions, Cathlights tech can find leads who are most likely to need an advisor's guidance.

Catchlights analytics let advisors pair information with action, and Melamed says thats where the power of AI goes from a high-level concept to something tangible and exciting.

There's a lot more engagement from advisors and leaning in from advisors now, she said. Perhaps it's better understanding of AI. Perhaps it's just realizing the luxuries of better markets are behind us. And to really be efficient in the market that's yet to come, you have to revisit how you might have built your workflow in the past.

Melamed said advisors in todays market are hungry for meaning behind the data theyve seen on AI for years and how they can act on it. In Catchlights case, data is used to streamline prospecting by helping advisors quickly identify which leads to pitch and how to best engage them.

She adds that every AI-facilitated first meeting between client and advisor is more meaningful because so many early steps of traditional prospecting have been skipped.

They realize that this is a person that they want to converse with, and this is someone who may be interested in and value their advice. They're not kicking tires. They're not wasting each other's time, Melamed said. That's a huge value add just from an efficiency standpoint, and they can engage personally a lot quicker.

Melamed said it's also a boon for firms fighting to capture attention in an increasingly competitive, digital-first market where client-advisor pairings are no longer constrained by geography.

It makes you not just more effective. It also makes you stand apart from the competition because you are engaging in a personal manner. How many (financial service) emails do all of us get that look very constantly the same? I tend to get a lot and they just get me all wrong, Melamed said. A couple of data points that Catchlight can provide will make them that much more effective because it's all about the eyeballs, and it's all about the quality of the communication.

To prepare a more seamless AI tech rollout, Accenture recommends multidisciplinary teams be created by firms and tasked with implementation. A smart deployment model can keep a firms pace of innovation using AI in relation to the rate of adoption in step, avoiding inconsistency and headaches. Multidisciplinary, in-house teams are also likely to be more familiar with these specifics, making them best suited to manage this work.

The Accenture study also identified three critical factors to help improve a wealth management firms ability to scale, overcome roadblocks and help organizations realize AIs full potential.

First, firms should focus on seeing a single use case or program through to the end. Aim for an approach driven by a clearly defined business strategy, not by the technology, the study says. Accenture warns that too many pilot or work-in-progress initiatives can lead to confusion and frustration.

Next, ensure the firm's priorities align with where advisors find high value. Keeping financial advisors in the loop can ease an AI adoption process that requires a high amount of change and effort.

And third, maintain continued support from management to ensure the success of AI programs. Accenture finds that executive sponsorship is critical to set the tone at the top and ensure the internal capacity, funding and dedication is sufficient to meet AI goals.

This sends a powerful signal that successfully scaling AI requires an operating model with defined processes and owners for measuring value, appropriate levels of funding and established executive support, said a statement from Accenture.

Excerpt from:

Advisors are ready to go all-in on AI. Here's how it may change the industry. - Financial Planning

Hoag is a nonprofitregional health system based in Orange County, California, that treats more than 30,000 inpatients and 460,000 outpatients annually. Hoag consists of two acute care hospitals Hoag Hospital Newport Beach and Hoag Hospital Irvine in addition to 10 health centers and 14 urgent care centers.

THE PROBLEM

Colorectal cancer represents the third most common cancer in the United States. It also is the third most common cause of cancer-related deaths in the United States.

There are many factors involved in trying to address the issue of reducing the incidence and complications with colorectal cancer. These factors can be broadly categorized into community or population health and health systems. The latter can be further subcategorized within which there is always the issue of quality. And the quality of healthcare continues to evolve.

"One of the tools to improve healthcare overall is technology," said Dr. Paul Lee, chief of service for the GI lab at Hoag. "We know that a colonoscopy is an essential procedure to help prevent colorectal cancer. Colonoscopies help to prevent colon cancer by identifying precancerous polyps and removing them during the procedure.

"It has been reported that these precancerous polyps are sometimes missed by the doctor," he continued. "We call this the miss rate. There are many factors involved in why these polyps are missed some are patient-centered, doctor-centered and technology-centered."

PROPOSAL

Health technology vendor GI Genius proposed to Hoag to improve upon the latter.

"From a different perspective, a surrogate marker for the quality of the physician performing the colonoscopy is the adenoma detection rate (ADR)," Lee explained. "One of the performance targets set for screening colonoscopies, that is, colonoscopies in otherwise asymptomatic patients, is a 25% ADR in a mixed gender population or 20% ADR for women or 30% for men.

Dr. Paul Lee, Hoag

"The technology aspect of the colonoscopy has gone through multiple improvements," he added. "These improvements have focused on the instrument itself. It has gone from a rigid scope, to utilizing fiber optics, to high-definition resolution, to narrow-band imaging, etc."

GI Genius incorporates artificial intelligence to help the physician identify lesions using millions of different pre-programmed algorithms. Further, it uses pattern recognition to bring out lesions the computer deems suspicious.

It still is up to the physician to determine whether what is identified is actually something of concern.

"By using this technology, it was supposed to decrease the miss rate or increase the ADR," Lee noted. "In some reports, the ADR increased by 14%. By increasing the ADR 1%, it has been estimated that this translates to a decrease in colorectal cancer by 3%."

MEETING THE CHALLENGE

Hoag brought the technology into the GI lab. It is available for any gastroenterologist to use with their colonoscopies, regardless of the indication.

Outpatient colonoscopies in the hospital are usually reserved for patients who have a higher periprocedural risk or have more comorbidities that would be allowable in an outpatient surgery center. In the context of the hospital, staff cares for both the inpatient and outpatient.

"Obviously, patients are admitted for specific reasons and colonoscopies for these instances are not the screening procedures for which the technology was originally intended," Lee explained. "However, I have found in these instances, GI Genius has been useful to identify pre-cancerous polyps and aid the physician to accomplish goals outside of the screening colonoscopy indication."

RESULTS

Hoag only recently has implemented the AI technology and does not yet have data to report. However, anecdotally, Lee said the technology has been well received and used by many of the physicians who come to the hospital GI lab.

ADVICE FOR OTHERS

"The role of computer-assisted diagnosis is an up-and-coming technology that will only get better," Lee stated. "The question is not if this will be helpful, but how and when it will be applied. One of the major hurdles is how to value the available technology and how that translates to quality and cost.

"As far as I know, the reimbursement for increasing the quality of a colonoscopy does not increase the reimbursement for such a procedure," he concluded. "This disproportionate outcome cannot be sustained and needs to be addressed by all parties involved."

Twitter:@SiwickiHealthITEmail the writer:bsiwicki@himss.orgHealthcare IT News is a HIMSS Media publication.

Original post:

Computer-assisted AI diagnosis aims to reduce colorectal cancer at Hoag - Healthcare IT News

Nightmare fuel is everywhere, particularly online. The latest source: Dall-E Mini, an AI tool capturing attention on social media thanks to the weird, funny and occasionally disturbing images it creates out of text prompts.

Batman surfing.

Dall-E Mini lets you type a short phrase describing an image, one that theoretically exists only in the deep recesses of your soul, and within a few seconds, the algorithm will manifest that image onto your screen.

Odds are you've seen some Dall-E Mini images popping up in your social media feeds as people think of the wildest prompts they can -- perhaps it's Jon Hamm eating ham, or Yoda robbing a convenience store.

This isn't the first time art and artificial intelligence have captured the internet's attention. There's a certain appeal to seeing how an algorithm tackles something as subjective as art. In 2016, for example, actor Thomas Middleditch made a short film based on a script written by an algorithm. Google has produced more than a few tools tying art and AI together. In 2018, its Arts & Culture app let users find their doppelgangers in famous paintings. Or Google's AutoDraw will figure out what you're trying to doodle, and fix it up for you.

There are other text-to-image systems, like OpenAI's Dall-E 2, as well as Google's Imagenand Parti, which the tech giant isn't releasing to the masses.

Here's what you need to know about Dall-E Mini and its AI-generated art.

Dall-E Mini is an AI model that creates images based on the prompts you give it. In an interview with the publication I, programmer Boris Dayma said he initially built the program in July 2021 as part of a competition held by Google and an AI community called Hugging Face. Dayma didn't immediately respond to a request for comment.

Anyone can type in a prompt and hit the "run" button (though you're likely to get an error message about traffic to the tool and have to try again). Dall-E Mini will spit out its results in the form of a 3x3 grid containing 9 images. A note about the tool on its website says it was trained on "unfiltered data from the internet."

Unsurprisingly, Dall-E Mini is a little hit or miss. In the interview with I News, Dayma said the AI is better with abstract painting, less so with faces. A landscape of a desert is quite pretty. A pencil sketch of Dolly Parton looks like it might steal your soul. Paul McCartney eating kale will take years off your life.

Here's a cat made of lasers.

Dayma did say, though, that the model is training (that ability to learn is one of the things people love and fear about AI), which means it can improve over time. And with the viral popularity of Dall-E Mini, the point is to stumble upon the most bizarre image you can think of, not necessarily to get a perfect impressionist rendering of a Waffle House. The fun is more about dreaming up with most outlandish images that don't exist -- that perhaps shouldn't exist -- and bringing them into cursed existence.

Dall-E also has a note saying that image generation could have a less fun side and could be used to "reinforce or exacerbate societal biases."

No, they're not associated. Dall-E 2 is also a tool for generating AI images that was launched as a research project this year. It was created by the AI research and deployment company OpenAI and is not widely available.

On social media, you can find an abundance of strange Dall-E Mini creations, from Thanos in a Walmart looking for his mother, to Jar Jar Binks winning the Great British Bake Off. Here are some other highlights.

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Dall-E Mini: Everything to Know About the Strange AI Art Creator - CNET