Can AI Models Have Abandonment Issues?
August 9, 2024
Gee, it seems the next big thing may now be just … the next thing. Citing research from Gartner, Windows Central asks, “Is GenAI a Dying Fad? A New Study Predicts 30% of Investors Will Jump Ship by 2025 After Proof of Concept.” This is on top of a Reuters Institute report released in May that concluded public “interest” in AI is all hype and very few people are actually using the tools. Writer Kevin Okemwa specifies:
“[Gartner] suggests ‘at least 30% of generative AI (GenAI) projects will be abandoned after proof of concept by the end of 2025.’ The firm attributes its predictions to poor data quality, a lack of guardrails to prevent the technology from spiraling out of control, and high operation costs with no clear path to profitability.”
For example, the article reminds us, generative AI leader OpenAI is rumored to be facing bankruptcy. Gartner Analyst Rita Sallam notes that, while executives are anxious for the promised returns on AI investments, many companies struggle to turn these projects into profits. Okemwa continues:
“Gartner’s report highlights the challenges key players have faced in the AI landscape, including their inability to justify the substantial resources ranging from $5 million to $20 million without a defined path to profitability. ‘Unfortunately, there is no one size fits all with GenAI, and costs aren’t as predictable as other technologies,’ added Sallam. According to Gartner’s report, AI requires ‘a high tolerance for indirect, future financial investment criteria versus immediate return on investment (ROI).’“
That must come as a surprise to those who banked on AI hype and expected massive short-term gains. Oh well. So, what will the next next big thing be?
Cynthia Murrell, August 9, 2024
DeepMind Explains Imagination, Not the Google Olympic Advertisement
August 8, 2024
This essay is the work of a dinobaby. Unlike some folks, no smart software improved my native ineptness.
I admit it. I am suspicious of Google “announcements,” ArXiv papers, and revelations about the quantumly supreme outfit. I keep remembering the Google VP dead on a yacht with a special contract worker. I know about the Googler who tried to kill herself because a dalliance with a Big Time Google executive went off the rails. I know about the baby making among certain Googlers in the legal department. I know about the behaviors which the US Department of Justice described as “monopolistic.”
When I read “What Bosses Miss about AI,” I thought immediately about Google’s recent mass market televised advertisement about uses of Google artificial intelligence. The set up is that a father (obviously interested in his progeny) turned to Google’s generative AI to craft an electronic message to the humanoid. I know “quality time” is often tough to accommodate, but an email?
The Googler who allegedly wrote the cited essay has a different take on how to use smart software. First, most big-time thinkers are content with AI performing cost-reduction activities. AI is less expensive than a humanoid. These entities require health care, retirement, a shoulder upon which to cry (a key function for personnel in the human relations department), and time off.
Another type of big-time thinker grasps the idea that smart software can make processes more efficient. The write up describes this as the “do what we do, just do it better” approach to AI. The assumption is that the process is neutral, and it can be improved. Imagine the value of AI to Vlad the Impaler!
The third category of really Big Thinker is the leader who can use AI for imagination. I like the idea of breaking a chaotic mass of use cases into categories anchored to the Big Thinkers who use the technology.
However, I noted what I think is unintentional irony in the write up. This chart shows the non-AI approach to doing what leadership is supposed to do:
What happens when a really Big Thinker uses AI to zip through this type of process. The acceleration is delivered from AI. In this Googler’s universe, I think one can assume Google’s AI plays a modest role. Here’s the payoff paragraph:
Traditional product development processes are designed based on historical data about how many ideas typically enter the pipeline. If that rate is constant or varies by small amounts (20% or 50% a year), your processes hold. But the moment you 10x or 100x the front of that pipeline because of a new scientific tool like AlphaFold or a generative AI system, the rest of the process clogs up. Stage 1 to Stage 2 might be designed to review 100 items a quarter and pass 5% to Stage 2. But what if you have 100,000 ideas that arrive at Stage 1? Can you even evaluate all of them? Do the criteria used to pass items to Stage 2 even make sense now? Whether it is a product development process or something else, you need to rethink what you are doing and why you are doing it. That takes time, but crucially, it takes imagination.
Let’s think about this advice and consider the imagination component of the Google Olympics’ advertisement.
- Google implemented a process, spent money, did “testing,” ran the advert, and promptly withdrew it. Why? The ad was annoying to humanoids.
- Google’s “imagination” did not work. Perhaps this is a failure of the Google AI and the Google leadership? The advert succeeded in making Google the focal point of some good, old-fashioned, quite humanoid humor. Laughing at Google AI is certainly entertaining, but it appears to have been something that Google’s leadership could not “imagine.”
- The Google AI obviously reflects Google engineering choices. The parent who must turn to Google AI to demonstrate love, parental affection, and support to one’s child is, in my opinion, quite Googley. Whether the action is human or not might be an interesting topics for a coffee shop discussion. For non-Googlers, the idea of talking about what many perceived as stupid, insensitive, and inhumane is probably a non-started. Just post on social media and move on.
Viewed in a larger context, the cited essay makes it clear that Googlers embrace AI. Googlers see others’ reaction to AI as ranging from doltish to informed. Google liked the advertisement well enough to pay other companies to show the message.
I suggest the following: Google leadership should ask several AI systems if proposed advertising copy can be more economical. That’s a Stage 1 AI function. Then Google leadership should ask several AI systems how the process of creating the ideas for an advertisement can be improved. That’s a Stage 2 AI function. And, finally, Google leadership should ask, “What can we do to prevent bonkers problems resulting from trying to pretend we understand people who know nothing and care less about the three “stages” of AI understanding.
Will that help out the Google? I don’t need to ask an AI system. I will go with my instinct. The answer is, “No.”
That’s one of the challenges Google faces. The company seems unable to help itself do anything other than sell ads, promote its AI system, and cruise along in quantumly supremeness.
Stephen E Arnold, August 8, 2024
Thoughts about the Dark Web
August 8, 2024
This essay is the work of a dumb humanoid. No smart software required.
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The Dark Web. Wow. YouTube offers a number of tell-all videos about the Dark Web. Articles explain the topics one can find on certain Dark Web fora. What’s forgotten is that the number of users of the Dark Web has been chugging along, neither gaining tens of millions of users or losing tens of millions of users. Why? Here’s a traffic chart from the outfit that sort of governs The Onion Router:
Source: https://metrics.torproject.org/userstats-relay-country.html
The chart is not the snappiest item on the sprawling Torproject.org Web site, but the message seems to be that TOR has been bouncing around two million users this year. Go back in time and the number has increased, but not much. Online statistics, particularly those associated with obfuscation software, are mushy. Let’s toss in another couple million users to account for alternative obfuscation services. What happens? We are not in the tens of millions.
Our research suggests that the stability of TOR usage is due to several factors:
- The hard core bad actors comprise a small percentage of the TOR usage and probably do more outside of TOR than within it. In September 2024 I will be addressing this topic at a cyber fraud conference.
- The number of entities indexing the “Dark Web” remains relatively stable. Sure, some companies drop out of this data harvesting but the big outfits remain and their software looks a lot like a user, particularly with some of the wonky verification Dark Web sites use to block automated collection of data.
- Regular Internet users don’t pay much attention to TOR, including those with the one-click access browsers like Brave.
- Human investigators are busy looking and interacting, but the numbers of these professionals also remains relatively stable.
To sum up, most people know little about the Dark Web. When these individuals figure out how to access a Web site advertising something exciting like stolen credit cards or other illegal products and services, they are unaware of a simple fact: An investigator from some country maybe operating like a bad actor to find a malefactor. By the way, the Dark Web is not as big as some cyber companies assert. The actual number of truly bad Dark Web sites is fewer than 100, based on what my researchers tell me.
A very “good” person approaches an individual who looks like a very tough dude. The very “good” person has a special job for the touch dude. Surprise! Thanks, MSFT Copilot. Good enough and you should know what certain professionals look like.
I read “Former Pediatrician Stephanie Russell Sentenced in Murder Plot.” The story is surprisingly not that unique. The reason I noted a female pediatrician’s involvement in the Dark Web is that she lives about three miles from my office. The story is that the good doctor visited the Dark Web and hired a hit man to terminate an individual. (Don’t doctors know how to terminate as part of their studies?)
The write up reports:
A Louisville judge sentenced former pediatrician Stephanie Russell to 12 years in prison Wednesday for attempting to hire a hitman to kill her ex-husband multiple times.
I love the somewhat illogical phrase “kill her ex-husband multiple times.”
Russell pleaded guilty April 22, 2024, to stalking her former spouse and trying to have him killed amid a protracted custody battle over their two children. By accepting responsibility and avoiding a trial, Russell would have expected a lighter prison sentence. However, she again tried to find a hitman, this time asking inmates to help with the search, prosecutors alleged in court documents asking for a heftier prison sentence.
One rumor circulating at the pub which is a popular lunch spot near the doctor’s former office is that she used the Dark Web and struck up an online conversation with one of the investigators monitoring such activity.
Net net: The Dark Web is indeed interesting.
Stephen E Arnold, August 8, 2024
Train AI on Repetitive Data? Sure, Cheap, Good Enough, But, But, But
August 8, 2024
We already know that AI algorithms are only as smart as the data that trains them. If the data models are polluted with bias such as racism and sexism, the algorithms will deliver polluted results. We’ve also learned that while some of these models are biased because of innocent ignorance. Nature has revealed that AI algorithms have yet another weakness: “AI Models Collapse When Trained On Recursively Generated Data.”
Generative text AI aka large language models (LLMs) are already changing the global landscape. While generative AI is still in its infancy, AI developers are already designing the next generation. There’s one big problem: LLMs. The first versions of Chat GPT were trained on data models that scrapped content from the Internet. GPT continues to train on models using the same scrapping methods, but it’s creating a problem:
“If the training data of most future models are also scraped from the web, then they will inevitably train on data produced by their predecessors. In this paper, we investigate what happens when text produced by, for example, a version of GPT forms most of the training dataset of following models. What happens to GPT generations GPT-{n} as n increases? We discover that indiscriminately learning from data produced by other models causes ‘model collapse’—a degenerative process whereby, over time, models forget the true underlying data distribution, even in the absence of a shift in the distribution over time.”
The generative AI algorithms are learning from copies of copies. Over time the integrity of the information fails. The research team behind the Nature paper discovered that model collapse is inevitable when with the most ideal conditions. The team did discover two possibilities to explain model collapse: intentional data poisoning and task-free continual learning. Those don’t explain recursive data collapse with models free of those events.
The team concluded that the best way for generative text AI algorithms to learn was continual interaction learning from humans. In other words, the LLMs need constant, new information created by humans to replicate their behavior. It’s simple logic when you think about it.
Whitney Grace, August 8, 2024
The Customer Is Not Right. The Customer Is the Problem!
August 7, 2024
This essay is the work of a dinobaby. Unlike some folks, no smart software improved my native ineptness.
The CrowdStrike misstep (more like a trivial event such as losing the cap to a Bic pen or misplacing an eraser) seems to be morphing into insights about customer problems. I pointed out that CrowdStrike in 2022 suggested it wanted to become a big enterprise player. The company has moved toward that goal, and it has succeeded in capturing considerable free marketing as well.
Two happy high-technology customers learn that they broke their system. The good news is that the savvy vendor will sell them a new one. Thanks, MSFT Copilot. Good enough.
The interesting failure of an estimated 8.5 million customers’ systems made CrowdStrike a household name. Among some airline passengers, creative people added more colorful language. Delta Airlines has retained a big time law firm. The idea is to sue CrowdStrike for a misstep that caused concession sales at many airports to go up. Even Panda Chinese looks quite tasty after hours spent in an airport choked with excited people, screaming babies, and stressed out over achieving business professionals.
“Microsoft Claims Delta Airlines Declined Help in Upgrading Technology After Outage” reports that like CrowdStrike, Microsoft’s attorneys want to make quite clear that Delta Airlines is the problem. Like CrowdStrike, Microsoft tried repeatedly to offer a helping hand to the airline. The airline ignored that meritorious, timely action.
Like CrowdStrike, Delta is the problem, not CrowdStrike or Microsoft whose systems were blindsided by that trivial update issue. The write up reports:
Mark Cheffo, a Dechert partner [another big-time lawfirm] representing Microsoft, told Delta’s attorney in a letter that it was still trying to figure out how other airlines recovered faster than Delta, and accused the company of not updating its systems. “Our preliminary review suggests that Delta, unlike its competitors, apparently has not modernized its IT infrastructure, either for the benefit of its customers or for its pilots and flight attendants,” Cheffo wrote in the letter, NBC News reported. “It is rapidly becoming apparent that Delta likely refused Microsoft’s help because the IT system it was most having trouble restoring — its crew-tracking and scheduling system — was being serviced by other technology providers, such as IBM … and not Microsoft Windows," he added.
The language in the quoted passage, if accurate, is interesting. For instance, there is the comparison of Delta to other airlines which “recovered faster.” Delta was not able to recover faster. One can conclude that Delta’s slowness is the reason the airline was dead on the hot tarmac longer than more technically adept outfits. Among customers grounded by the CrowdStrike misstep, Delta was the problem. Microsoft systems, as outstanding as they are, wants to make darned sure that Delta’s allegations of corporate malfeasance goes nowhere fast oozes from this characterization and comparison.
Also, Microsoft’s big-time attorney has conducted a “preliminary review.” No in-depth study of fouling up the inner workings of Microsoft’s software is needed. The big-time lawyers have determined that “Delta … has not modernized its IT infrastructure.” Okay, that’s good. Attorneys are skillful evaluators of another firm’s technological infrastructure. I did not know big-time attorneys had this capability, but as a dinobaby, I try to learn something new every day.
Plus the quoted passed makes clear that Delta did not want help from either CrowdStrike or Microsoft. But the reason is clear: Delta Airlines relied on other firms like IBM. Imagine. IBM, the mainframe people, the former love buddy of Microsoft in the OS/2 days, and the creator of the TV game show phenomenon Watson.
As interesting as this assertion that Delta is not to blame for making some airports absolute delights during the misstep, it seems to me that CrowdStrike and Microsoft do not want to be in court and having to explain the global impact of misplacing that ballpoint pen cap.
The other interesting facet of the approach is the idea that the best defense is a good offense. I find the approach somewhat amusing. The customer, not the people licensing software, is responsible for its problems. These vendors made an effort to help. The customer who screwed up their own Rube Goldberg machine, did not accept these generous offers for help. Therefore, the customer caused the financial downturn, relying on outfits like the laughable IBM.
Several observations:
- The “customer is at fault” is not surprising. End user licensing agreements protect the software developer, not the outfit who pays to use the software.
- For CrowdStrike and Microsoft, a loss in court to Delta Airlines will stimulate other inept customers to seek redress from these outstanding commercial enterprises. Delta’s litigation must be stopped and quickly using money and legal methods.
- None of the yip-yap about “fault” pays much attention to the people who were directly affected by the trivial misstep. Customers, regardless of the position in the food chain of revenue, are the problem. The vendors are innocent, and they have rights too just like a person.
For anyone looking for a new legal matter to follow, the CrowdStrike Microsoft versus Delta Airlines may be a replacement for assorted murders, sniping among politicians, and disputes about “get out of jail free cards.” The vloggers and the poohbahs have years of interactions to observe and analyze. Great stuff. I like the customer is the problem twist too.
Oh, I must keep in mind that I am at fault when a high-technology outfit delivers low-technology.
Stephen E Arnold, August 7, 2024
Publishers Perplexed with Perplexity
August 7, 2024
In an about-face, reports Engadget, “Perplexity Will Put Ads in it’s AI Search Engine and Share Revenue with Publishers.” The ads part we learned about in April, but this revenue sharing bit is new. Is it a response to recent accusations of unauthorized scraping and plagiarism? Nah, the firm insists, the timing is just a coincidence. While Perplexity won’t reveal how much of the pie they will share with publishers, the company’s chief business officer Dmitry Shevelenko described it as a “meaningful double-digit percentage.” Engadget Senior Editor Pranav Dixit writes:
“‘[Our revenue share] is certainly a lot more than Google’s revenue share with publishers, which is zero,’ Shevelenko said. ‘The idea here is that we’re making a long-term commitment. If we’re successful, publishers will also be able to generate this ancillary revenue stream.’ Perplexity, he pointed out, was the first AI-powered search engine to include citations to sources when it launched in August 2022.”
Defensive much? Dixit reminds us Perplexity redesigned that interface to feature citations more prominently after Forbes criticized it in June.
Several AI companies now have deals to pay major publishers for permission to scrape their data and feed it to their AI models. But Perplexity does not train its own models, so it is taking a piece-work approach. It will also connect advertisements to searches. We learn:
“‘Perplexity’s revenue-sharing program, however, is different: instead of writing publishers large checks, Perplexity plans to share revenue each time the search engine uses their content in one of its AI-generated answers. The search engine has a ‘Related’ section at the bottom of each answer that currently shows follow-up questions that users can ask the engine. When the program rolls out, Perplexity plans to let brands pay to show specific follow-up questions in this section. Shevelenko told Engadget that the company is also exploring more ad formats such as showing a video unit at the top of the page. ‘The core idea is that we run ads for brands that are targeted to certain categories of query,’ he said.”
The write-up points out the firm may have a tough time breaking into an online ad business dominated by Google and Meta. Will publishers hand over their content in the hope Perplexity is on the right track? Launched in 2022, the company is based in San Francisco.
Cynthia Murrell, August 7, 2024
Lark Flies Home with TikTok User Data, DOJ Alleges
August 7, 2024
An Arnold’s Law of Online Content states simply: If something is online, it will be noticed, captured, analyzed, and used to achieve a goal. That is why we are unsurprised to learn, as TechSpot reports, “US Claims TikTok Collected Data on Users, then Sent it to China.” Writer Skye Jacobs reveals:
“In a filing with a federal appeals court, the Department of Justice alleges that TikTok has been collecting sensitive information about user views on socially divisive topics. The DOJ speculated that the Chinese government could use this data to sow disruption in the US and cast suspicion on its democratic processes. TikTok has made several overtures to the US to create trust in its privacy and data controls, but it has also been reported that the service at one time tracked users who watched LGBTQ content. The US Justice Department alleges that TikTok collected sensitive data on US users regarding contentious issues such as abortion, religion and gun control, raising concerns about privacy and potential manipulation by the Chinese government. This information was reportedly gathered through an internal communication tool called Lark.”
Lark is also owned by TikTok parent company ByteDance and is integrated into the app. Alongside its role as a messaging platform, Lark has apparently been collecting a lot of very personal user data and sending it home to Chinese servers. The write-up specifies some of the DOJ’s concerns:
“They warn that the Chinese government could potentially instruct ByteDance to manipulate TikTok’s algorithm to use this data to promote certain narratives or suppress others, in order to influence public opinion on social issues and undermine trust in the US’ democratic processes. Manipulating the algorithm could also be used to amplify content that aligns with Chinese state narratives, or downplay content that contradicts those narratives, thereby shaping the national conversation in a way that serves Chinese interests.”
Perhaps most concerning, the brief warns, China could direct ByteDance to use the data to “undermine trust in US democracy and exacerbate social divisions.” Yes, that tracks. Meanwhile, TikTok insists any steps our government takes against it infringe on US users’ First Amendment rights. Oh, the irony.
In the face of US government’s demand it sell off TikTok or face a ban, ByteDance has offered a couple of measures designed to alleviate concerns. So far, though, the Biden administration is standing firm.
Cynthia Murrell, August 7, 2024
Old Problem, New Consequences: AI and Data Quality
August 6, 2024
This essay is the work of a dinobaby. Unlike some folks, no smart software improved my native ineptness.
Grab a business card from several years ago. Just for laughs send an email to the address on the card or dial one of the numbers printed on it. What happens? Does the email bounce? Does the person you called answer? In my experience, the business cards I have gathered at conferences in 2021 are useless. The number rings in space or a non-human voice says, “The number has been disconnected.” The emails go into a black hole. I would, based on my experience, peg the 100 random cards I had one of my colleagues pull from the files that work at fewer than 30 percent. In 24 months, 70 percent of the data are invalid. An optimist would say, “You have 30 people you can contact.” A pessimist would say, “Wow, you lost 70 contacts.” A 20-something whiz kid at one of the big time AI companies would say, “Good enough.”
An automated data factory purports to manufacture squares. What does it do? Triangles are good enough and close enough for horseshoes. Does the factory call the triangles squares? Of course, it does. Thanks, MSFT Copilot. Security is Job One today I hear.
I read “Data Quality: The Unseen Villain of Machine Learning.” The write up states:
Too often, data scientists are the people hired to “build machine learning models and analyze data,” but bad data prevents them from doing anything of the sort. Organizations put so much effort and attention into getting access to this data, but nobody thinks to check if the data going “in” to the model is usable. If the input data is flawed, the output models and analyses will be too.
Okay, that’s a reasonable statement. But this passage strikes me as a bit orthogonal to the observations I have made:
It is estimated that data scientists spend between 60 and 80 percent of their time ensuring data is cleansed, in order for their project outcomes to be reliable. This cleaning process can involve guessing the meaning of data and inferring gaps, and they may inadvertently discard potentially valuable data from their models. The outcome is frustrating and inefficient as this dirty data prevents data scientists from doing the valuable part of their job: solving business problems. This massive, often invisible cost slows projects and reduces their outcomes.
The painful reality, in my experience, consists of three factors:
- Data quality depends on the knowledge and resources available to a subject matter expert. A data quality expert might define quality as consistent data; that is, the name field has a name. The SME figures out if the data are in line with other data and what’s is off base.
- The time required to “ensure” data quality is rarely available. There are interruptions, Zooms, and automated calendars that ping a person for a meeting. Data quality is easily killed by time suffocation.
- The volume of data begs for automated procedures and, of course, AI. The problem is that the range of errors related to validity is sufficiently broad to allow “flawed” data to enter a systems. Good enough creates interesting consequences.
The write up says:
Data quality shouldn’t be a case of waiting for an issue to occur in production and then scrambling to fix it. Data should be constantly tested, wherever it lives, against an ever-expanding pool of known problems. All stakeholders should contribute and all data must have clear, well-defined data owners. So, when a data scientist is asked what they do, they can finally say: build machine learning models and analyze data.
This statement makes clear why flawed data remain flawed. The fix, according to some, is synthetic data. Are these data of high quality? It depends on what one means by “quality.” Today the benchmark is good enough. Good enough produces outputs that are not. But who knows? Not the harried person looking for something, anything, to put in a presentation, journal article, or podcast.
Stephen E Arnold, August 6, 2024
Agents Are Tracking: Single Web Site Version
August 6, 2024
This essay is the work of a dumb humanoid. No smart software required.
How many software robots are crawling (copying and indexing) a Web site you control now? This question can be answered by a cloud service available from DarkVisitors.com.
The Web site includes a useful list of these software robots (what many people call “agents” which sounds better, right?). You can find the list of about 800 bots as of July 30, 2024) on the DarkVisitors’ Web site at this link. There is a search function so you can look for a bot by name; for example, Omgili (the Israeli data broker Webz.io). Please, note, that the list contains categories of agents; for example, “AI Data Scrapers”, “AI Search Crawlers,” and “Developer Helpers,” among others.
The Web site also includes links to a service called “Set Up Your Robots.txt.” The idea is that one can link a Web site’s robots.txt file to DarkVisitors. Then DarkVisitors will update your Web site automatically to block crawlers, bots, and agents. The specific steps to make this service work are included on the DarkVisitors.com Web site.
The basic service is free. However, if you want analytics and a couple of additional features, the cost as of July 30, 2024, is $10 per month.
An API is also available. Instructions for implementing the service are available as well. Plus, a WordPress plug in is available. The cloud service is provided by Bit Flip LLC.
Stephen E Arnold, August 6, 2024
The US Government Wants Honesty about Security
August 6, 2024
I am not sure what to make of words like “trust,” “honesty,” and “security.”
The United States government doesn’t want opinions from its people. They only want people to vote, pay their taxes, and not cause trouble. In an event rarer than a blue moon, the US Cybersecurity and Infrastructure Security Agency wants to know what it could better. Washington Technology shares the story, “CISA’s New Cybersecurity Official Jeff Greene Wants To Know Where The Agency Can Improve On Collaboration Efforts That Have Been Previously Criticized For Their Misdirection.”
Jeff Greene is the new executive assistant director for the Cybersecurity and Infrastructure Security Agency (CISA). He recently held a meeting at the US Chamber of Commerce and asked the private sector attendees that his agency holding an “open house” discussion. The open house discussion welcomes input from the private sector about how the US government and its industry partners can improve on sharing information about cyber threats.
Why does the government want input?
“The remarks come in the wake of reports from earlier this year that said a slew of private sector players have been pulling back from the Joint Cyber Defense Collaborative — stood up by CISA in 2021 to encourage cyber firms to team up with the government to detect and deter hacking threats — due to various management mishaps, including cases where CISA allegedly did not staff enough technical analysts for the program.”
Greene wants to know what CISA is doing correctly, but also what the agency is doing wrong. He hopes the private sector will give the agency grace as they make changes, because they’re working on numerous projects. Greene said that the private sector is better at detecting threats before the federal government. The 2015 Cybersecurity Information Sharing Act enabled the private sector and US government to collaborate. The act allowed the private sector to bypass obstacles they were otherwise barred from so white hat hackers could stop bad actors.
CISA has a good thing going for it with Greene. Hopefully the rest of the government will listen. It might be useful if cyber security outfits and commercial organizations caught the pods, the vlogs, and the blogs about the issue.
Whitney Grace, August 6, 2024
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