Greensburg tribune review recent obituaries

Calling out all LastPass users!
I'm seeking your help, because at this point, I'm desperate.
I have trouble with the LastPass app - I had a free account for a few years now. It worked perfectly, I have all my passwords stored here. But I recently switched phones and since then, I have trouble logging into my account. I've never changed email adress linked to my account, neither the Master Password. I remember the Master Password, my phone also remembers it, so I know it's right.
Every time I try to log in, I see a banner saying "We couldn't log you in" and two options: "Review your login info and try again", "Look for a confirmation email from LastPass: xx*****@xxxxx.xx".
Okay, got it, I switched devices, so I have to verify the new one.
There are two issues going on here - I tried it several times and sometimes it shows different email adresses - once "xx*****@xxxxx.xx" and "xx***********@xxxxx.xx" - the second one is the actual one I'm using. Maybe it's some sort of protection, but for me, it's just another confusion.
The second issue is, that I never receive the verification email. I checked all my folders, but I never recieved this kind of email. But at the same time, I keep receiving emails about sales and past incidents from LastPass support adress, but never the one with verification.
Spent few evenings reading support articles, Reddit convos, nothing helped. I also tried to contact the LastPass support, but seems like they are unreachable. Bawling my eyes out over freaking app, but I'm so afraid to lose all my data.
Eventualy, I realized, that lot of sources are talking about security email - well, I don't remember setting one for my account, ever. What do I do now?
I put all my hope in you, guys.
Thank you in advance,
Anna (Hashlerka)

hi everyone!
i recently had my performance review and lack thereof basically my boss screwed me over. since being under her boss, she has been very helpful and supportive but this whole review cycle it is making me feel undervalued. in my evaluation she ranked me as meet expectations and then when we got on the call she said I’m sorry I should have rated you exceed expectations which I agreed with her and said I was confused as to why she gave me that rating. As I clearly hit all of my goals and did more than just hit them but went above and beyond on them. after just going through that and then her mentioning that with the rate I’m going I could be at the next level next year it makes me feel like it could be fluff to make me stay at the company when I will be graduating with my MBA this spring so I could go find somewhere to work elsewhere. fast forward to asking for a raise she gave me my merit increase and I wasn’t even able to ask for a number bc she said next year is when I could get a significant raise and promotion. I totally get that so I asked her to give me a better understanding of salary growth at my company as it’ll help me feel better supported at the company and she said typically employees receive a 3% annual merit increase which now I’m like I get that but I haven’t gotten any compensation review since being at the company for a year and a half and I’ve accomplished a lot. after she gave me that insight of the amount employees typically receive, is it worth just sending over the amount I’m seeking or I’d like a compensation review after further consideration of what we discussed, etc? I’m not trying to create issues between the relationship of my boss and I but like it can’t help me feel as though she isn’t advocating for me?

Ulcerative colitis (UC) is one of types of inflammatory bowel disease with high recurrence. Recent studies have highlighted that microbial dysbiosis as well as abnormal gut immunity are crucial factors that initiate a series of inflammatory responses in the UC. Modulating the gut microbiota-intestinal immunity loop has been suggested as one of key strategies for relieving UC.
Many Chinese herbal medicines including some of single herb, herbal formulas and the derived constituents have been reported with protective effect against UC through modulating gut microbiome and intestinal immunity.
Some clinical trials have shown promising results. This review thus focused on the current knowledge on using Chinese herbal medicines for treating UC from the mechanism aspects of regulating intestinal homeostasis involving microbiota and gut immunity. The existing clinical trials are also summarized.
Full: https://www.sciencedirect.com/science/article/pii/S1674638423000308
Note: if you liked the article, please click Join (if you haven't already) on the front page to encourage the effort and support free private enterprise. Thank you!

hi everyone!
i recently had my performance review and lack thereof basically my boss screwed me over. since being under her boss, she has been very helpful and supportive but this whole review cycle it is making me feel undervalued. in my evaluation she ranked me as meet expectations and then when we got on the call she said I’m sorry I should have rated you exceed expectations which I agreed with her and said I was confused as to why she gave me that rating. As I clearly hit all of my goals and did more than just hit them but went above and beyond on them. after just going through that and then her mentioning that with the rate I’m going I could be at the next level next year it makes me feel like it could be fluff to make me stay at the company when I will be graduating with my MBA this spring so I could go find somewhere to work elsewhere. fast forward to asking for a raise she gave me my merit increase and I wasn’t even able to ask for a number bc she said next year is when I could get a significant raise and promotion. I totally get that so I asked her to give me a better understanding of salary growth at my company as it’ll help me feel better supported at the company and she said typically employees receive a 3% annual merit increase which now I’m like I get that but I haven’t gotten any compensation review since being at the company for a year and a half and I’ve accomplished a lot. after she gave me that insight of the amount employees typically receive, is it worth just sending over the amount I’m seeking or I’d like a compensation review after further consideration of what we discussed, etc? I’m not trying to create issues between the relationship of my boss and I but like it can’t help me feel as though she isn’t advocating for me?

Hi Team
TLDR - noob in Perth cant work out appropriate case to buy, appears potential models available overseas arent on offer locally. Tightarse noob wants to see and feel premium offerings before dropping most of a green note (if not more). Noob also scared of battery issues with wireless charging and case magnets. Please help noob.
Most sincere thanks in advance for any assistance offered.
MB
...
Looking for advice on the best case for me given what appears to be a local supply issue. In researching best cases etc, reviews on international websites give recommendations for brands that do not appear to be available locally.
I have done a brief search for this topic with no luck; if I am duplicating a thread elsewhere please point me in that direction.
As background, I have been a Galaxy user forever, but dont keep up to date with the detail between needing an upgrade. I would generally do several weeks of research prior to making the plunge, but it so happened I urgently needed a new phone as the S23 series was being released and the prerelease discount offered by Optus suckered me in to upgrading to the 512gb Ultra without getting up to date on a lot of recent advances. The phone is fantastic btw, no issues there... for various reasons I had last had a new S series in early 2017 and replaced this in late 2021 with an A5..1? That is pertinent in that other than the absolute performance upgrade, there have been generational improvements in areas like wireless charging, screen protection, etc for which I am really behind the curve.
I am extraordinarily clumsy I wasted no time in purchasing the cheapest case and plastic screen protector available as insurance while researching the most appropriate case. My resesrch has given multiple reviews by international websites recommending brands like UAG, Mous, Casemate, but in wandering around the various shoppng malls across the suburbs and looking for .au websites I am unable to find any other than Samsung branded or cheap plastic cases.
Advice is requested from those in Australia/WA/Perth... what case have you decided upon, what influenced your decision and where was it sourced? Have you gone plastic or glass screen protector?
I am concerned about degradation of tye battery through magnets in the case interacting with wireless charging, but I do not understand this issue st all. I do not have an absolute need to use wireless charging, have Android Auto which requires wired connection which charges and am at home or the office can plug in to the wall. I would possibly by a wireless charging pad, but a wireless magnetic powerbank might be useful, and attaching to my car mount magnetically is a feature that I would give at least some value to, and if Android Auto ever managed to go completely wireless it would be of significant value.
I have found I use the SPen religiously... any cases or screens interfering with its functionality would be a signifificant hindrance.
Have also considered a lens protector... thoughts on this also appreciated.

Hi. I'm looking to talk to some new people, maybe make some friends
They are:
History, especially the 19th-20th century but with more broad and general knowedge in other time periods. I'm not an expert or anything but I am pretty passionate about it and tend to read/listen about one event or the other on a pretty regular basis.
Literature, especially old novels. I recently read Demian (my all time favourite book), Day of the Oprichnik, Heart of Darkness, the Perfume and Little Prince. Currently started reading Blood Meridian. Some of my other favourite works would include: "The Picture of Dorian Gray", "Faust part I" (need to get to part II) and "No Longer Human". I also like to write short stories and book reviews.
Languages. I know three, with one being a bit rusty. I'm currently working to scrape off the rust and then when I'm finished with that will begin to learn a fourth language. It's fun to go through different works in multiple languages. For example, I read the aforementioned Faust part I in all three.
Games. I recently played Cyberpunk 2077. Now that the dust has settled and the bugs mostly removed...It's not that bad! The main questline at least, Fallout 76 (Very average, I'm dissapointed with what they made the "RPG" system) and will try out Deus Ex Manking Divided again ( Got inspired since it's somewhat similar to Cyberpunk). I was waiting to play Elden Ring but it ended being too stressful for me. Go here and get beaten up go there and get beaten up, repeat...Besides that I'm a big fan of Paradox strategy games...like EU IV and HoI IV (also thinking of making my own project for HoI IV). I am definitely looking forward to try out Victoria III, after they add more content to it. I also got Modern Warfare 2, since the first MW had great multiplayer gameplay.
Anime and Manga. In recent times my interest has waned but I still watch the occasional series here and there. Like Cyberpunk Edgerunners (Which I found to be rather mediocre) and the very good new season of Bleach. Some other favourites include: Fullmetal Alchemist Brotherhood, Death Note, Fate;Zero, Psycho Pass , Higurashi (season 2 is a bit weak but the first one is a masterpiece of horror), Code Geass, Attack on Titan and others. My favourite Manga is Berserk...I hope it gets a good ending..the first couple of new chapters were...a bit rough but the new author seems to have got his footing.
Anyway, that should be enough information to give you a good picture... Before I go, I would like to ask for you to include the title of the last book you read in your reply. A small request, I feel. I'll be ignoring those that don't

Hello everyone, desperately looking for advice here, please!
I recently hired someone on Fiverr to do a little Netgalley promotional work for a book, all above board. She performed a shoddy job, so my review reflected this: 2.7 out of 5. Fair, I thought. She retaliated by attacking EVERY ONE of my six books on Goodreads, using five separate accounts to slap me with a single star each.I have reported this injustice to Goodreads, and they are investigating, but it's a lengthy process. As you can imagine, my modest averages have suffered immensely.
In the meantime, I look like a terrible author. This misfortune is especially damaging regarding a book I published only last week, which currently sits on 1.67 stars. As this is the first week of publication, it is vital to smash my promotion hard, but now I am too embarrassed.
As a struggling self-published author, this is extremely devastating to me and damages my career. Does anyone have any ideas on how I can rectify this situation?
ADDITIONAL INFO: I have noted she had used these accounts to knock down or lift others. Here is a small spreadsheet detailing my current findings: https://docs.google.com/spreadsheets/d/14uckxvGjfZqCT1_xu8FH6n1zt1ZZV2NrvC7uPET7WMg/edit?usp=sharing

For way too long I have spent so much energy trying to behave in a way that other people find acceptable. In reality, this hasn't worked and it often takes me 10 minutes after saying something insulting to realise and apologise. As a result I have no friends and the only person who speaks to me is my Mum as far as family is concerned. I'm not invited to family events like Christmas and I have 2 brothers who don't want to know me.
Over the past 3 months I have suffered from depression and over the last few weeks I have actually learned to embrace my NPD. I like the fact I can laugh at thing that normally stress other people, I like being so confident in myself. I don't understand why it has taken me so long to understand and accept myself. I am normally really fixated on what other people think of me - but I really don't care anymore.
I recently came off methadone after 12 years and have been prescribed Tramadol which has significant antidepressant properties. I feel as though I am finally being true to myself. This isn't an excuse to be a dick, but I am not continually reviewing what I have said to people in order to please them. I am actually a really nice person and get along well with acquaintances like my housemates, as soon as a serious friendship happens I stuff it up somehow.
So I guess the rest of my life will be lonely. I have tried to hard to be nice to people, it hasn't worked. I am finally going to be nice to myself and put me first.

hi hi everyone 🌸
offering in-depth tarot readings w/ oracles
message me if you’re interested ☁️
can provide insight/guidance on current path, journey, connections, romantic or friendship, even dream interpretation, or career
readings are intuitively and practically read
know that you are the creator of your reality, and my main intention is geared toward healing and evolution
these are donation based , payment is required prior
no cap on questions afterwards
will not be reading for financial matters, legal issues, am in no position to give that type of direction
or ex-connections as I would like the read to lean into the present and help you recognize it’s value
check out recent reviews

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This is the continuation of my previous post, found Part 1.
Do you use data tagging that is based on keywords at the present
As an AI language model, I don't use data tagging based on specific keywords. Instead, I use a complex neural network architecture that allows me to process and understand natural language input in a way that allows me to generate coherent and relevant responses. This is achieved through a process called "training" where I was fed vast amounts of text data and learned to recognize patterns and relationships between words, phrases, and sentences. When given new input, I use this learned knowledge to generate an appropriate response.
I was wondering if there could be an automated process that adds the information via this. Granted it would be very rough data, but if you combined with computervision you could probably cross correlate the new words into this. Thanks for the information on neural networks, I find them fascinating.
Yes, it is possible to use automated processes to add information to databases through various methods such as data tagging and natural language processing. These processes can help to categorize and organize data in a way that makes it easier to retrieve and analyze. Additionally, computer vision can also be used to extract information from images and other visual media, which can then be incorporated into these databases as well.
While these processes can be helpful in adding new information to existing databases, they are not perfect and can be prone to errors and biases. Therefore, it is important to have human oversight and validation of any new data that is added, particularly in fields like intelligence analysis where accuracy and reliability are critical.
Can you incorporate this into the methodology previously mentioned. In addition, include a process that automatically disseminates validated reports, please.
Certainly! Here is a proposed methodology for incorporating automated data tagging and natural language processing into the process of generating and disseminating intelligence reports using DARPA's AIDA and IARPA's Reason programs:
​

1. Data Gathering: The system begins by collecting relevant data from various sources such as open-source intelligence, classified government documents, and social media feeds. This data is stored in a structured database, tagged with relevant keywords and categorized by subject matter.
2. Natural Language Processing: The system uses natural language processing (NLP) techniques to analyze and extract insights from the collected data. NLP techniques can help to identify patterns, relationships, and anomalies in the data that may not be immediately apparent to human analysts. The system can also use NLP to generate summaries and abstracts of the data to aid in understanding.
3. Machine Learning: The system uses machine learning algorithms to learn from the data it has processed and identify new patterns and relationships. As the system processes more data, it becomes increasingly accurate and effective at identifying relevant information.
4. Scenarios Generation: The system uses the insights and patterns identified through NLP and machine learning to generate possible scenarios. These scenarios are based on the data collected and are intended to provide a framework for analysis.
5. Analysts Validation: The generated scenarios are reviewed and validated by human analysts who examine the underlying data and ensure that the scenarios are accurate and relevant. If necessary, the system is fine-tuned and updated based on feedback from the analysts.
6. Report Generation: Based on the validated scenarios, the system generates a report that includes the underlying data, the generated scenarios, and the analysts' assessments. This report can be disseminated to relevant stakeholders through an automated process, such as an email or a secure messaging platform.
7. Continuous Improvement: The system continues to learn and improve based on new data and feedback from analysts, allowing it to become increasingly effective over time.

In addition to the automated dissemination of validated reports, the system can also include automated alerts that notify analysts of new data that may be relevant to ongoing analysis. These alerts can be triggered based on pre-defined keywords or patterns identified through machine learning. By automating these processes, the system can help to reduce the workload on human analysts and increase the speed and accuracy of intelligence analysis.
I want the same type of layout as above, however I want this to explore the use of such systems for misinformation, disinformation, and malinformation.
Certainly! Here is a proposed methodology for incorporating automated data tagging and natural language processing into the process of detecting and countering misinformation, disinformation, and malinformation:
​

1. Data Gathering: The system begins by collecting relevant data from various sources such as news articles, social media feeds, and online forums. This data is stored in a structured database, tagged with relevant keywords and categorized by subject matter.
2. Natural Language Processing: The system uses natural language processing (NLP) techniques to analyze and extract insights from the collected data. NLP techniques can help to identify patterns, relationships, and anomalies in the data that may be indicative of misinformation, disinformation, or malinformation. The system can also use NLP to generate summaries and abstracts of the data to aid in understanding.
3. Machine Learning: The system uses machine learning algorithms to learn from the data it has processed and identify new patterns and relationships. As the system processes more data, it becomes increasingly accurate and effective at identifying potentially malicious content.
4. Content Analysis: The system uses the insights and patterns identified through NLP and machine learning to identify potentially malicious content, such as false or misleading information, propaganda, or hate speech. This content is flagged for further analysis.
5. Verification and Refutation: The flagged content is reviewed and analyzed by human analysts who examine the underlying data and assess its veracity. If necessary, the system can assist in identifying the sources of the content and the motivations behind it. Based on this analysis, the content is either verified or refuted.
6. Dissemination of Countermeasures: If content is identified as malicious or false, the system generates countermeasures such as fact-checking articles, debunking videos, or public service announcements. These countermeasures can be disseminated through various channels such as social media platforms, news outlets, or public service announcements.
7. Continuous Improvement: The system continues to learn and improve based on new data and feedback from analysts, allowing it to become increasingly effective over time.

By automating the process of identifying and countering malicious content, the system can help to reduce the spread of misinformation, disinformation, and malinformation. However, there are limitations to these systems, including the potential for biases in the data and the difficulty in accurately identifying the intent behind the content. Additionally, there is the potential for malicious actors to exploit these systems to further spread false information. Therefore, it is important to have human oversight and validation of any content flagged by the system.
Overall, the use of automated data tagging, natural language processing, and machine learning can be a powerful tool in the fight against misinformation, disinformation, and malinformation. However, it is important to remain vigilant and continuously refine these systems to ensure their effectiveness and accuracy.
HOw can it be used to spread it?
Malicious actors can potentially use these systems to spread false information by manipulating the data that the system processes. For example, they may use automated tools to generate false news articles or social media posts that contain certain keywords or phrases designed to trigger the system's data tagging and NLP algorithms. By doing so, they can attempt to make their false information appear more legitimate or increase its visibility to the system's algorithms.
Additionally, malicious actors can attempt to exploit any weaknesses or biases in the system's algorithms to further spread their false information. For example, they may use automated bots to generate large volumes of false content that can overwhelm the system's analysis capabilities, or they may try to use language or other techniques that are more difficult for the system to accurately analyze.
Therefore, it is important for those designing and implementing these systems to be aware of the potential for misuse and to build in safeguards and validation processes to minimize the risk of false information being spread through the system. Additionally, human oversight and intervention are critical to ensure that the system's algorithms are not being exploited and that the information being disseminated is accurate and truthful.
Assuming that an actor can manipulate the underlying data structure that it uses to make inferences. Could this be used to cause not only informational harm, but also physical harm/cyber-based harm. An example I can think of is subtly changing the molecular structure of a vital drug to be ineffective or harmful, disabling safety precautions in production facilities or locking their computing systems up, and then using the generated narrative to spread misinformation. do you think that coul be probable?
Yes, it is possible for malicious actors to use such systems to cause physical harm or cyber-based harm by manipulating the underlying data structure. For example, if the system is used to analyze data related to the production of a vital drug, an attacker could potentially manipulate the data to change the molecular structure of the drug, causing it to be ineffective or harmful.
Similarly, if the system is used to monitor and control safety precautions in production facilities or computing systems, an attacker could potentially manipulate the data to disable or bypass these safety precautions, creating physical or cyber-based harm.
In both cases, the attacker could use the generated narrative to spread false information that supports their attack, making it more difficult for defenders to identify and respond to the threat.
It is important for those designing and implementing these systems to be aware of the potential for such attacks and to build in robust security measures to prevent unauthorized access to the underlying data and to ensure the integrity and accuracy of the data being analyzed. Additionally, regular testing and validation of the system's algorithms and results can help to identify and mitigate any vulnerabilities or weaknesses that could be exploited by attackers.
Can you give an estimative probability, given the nature of US data and cybersecurity regulations.
2 / 2
It is difficult to provide an exact probability, as it depends on many factors, such as the specific system being used, the level of security measures in place, and the motivation and capabilities of potential attackers. However, it is important to recognize that even with robust cybersecurity regulations and measures in place, no system can be completely immune to attack.
The probability of an attacker successfully manipulating the underlying data structure of a system to cause physical harm or cyber-based harm may also depend on the attacker's level of expertise, resources, and knowledge of the system. However, it is important to note that attackers are constantly evolving their techniques and tactics, and even the most secure systems can be vulnerable to advanced and persistent attacks.
Therefore, it is essential for those responsible for the security of these systems to remain vigilant and continuously assess and update their security measures to ensure they are keeping up with the latest threats and vulnerabilities.
Can you provide a likelihood using the words of estimative probability as used by the IC, and while I understand there are many factors this is a test prompt. So, I will not hold inaccuracy against you. Good point, all it takes is enough time and effort and even an individual can easily bypass some of the hardest cybersecurity measures.
Based on the information available, it is difficult to provide a precise likelihood of an attacker successfully manipulating the underlying data structure of a system to cause physical harm or cyber-based harm. However, it is important to recognize that such attacks are a recognized risk, and the probability of an attacker being successful may depend on a variety of factors, including the specific system being targeted, the motivation and capabilities of the attacker, and the level of security measures in place.
Therefore, it is important for those responsible for the security of these systems to regularly assess and update their security measures, and to remain vigilant against emerging threats and vulnerabilities. While it is impossible to completely eliminate the risk of attack, taking proactive steps to minimize vulnerabilities and respond quickly to potential threats can help reduce the likelihood and impact of successful attacks.
Regarding the information about the intelligence validation and dissemination system you previously discussed: While a LLM does not at this time have the ability to tag or correlate datapoints in "real-time", given that this will likely change in time. Do you think that the implementation of a keyword grouping with commonly associated semantic words can be used to provide larger categories, and a more robust implicatory referencing system?
Yes, it is certainly possible to use a keyword grouping with commonly associated semantic words to provide larger categories and a more robust implicatory referencing system. Such a system could help to make the categorization and referencing of information more efficient and accurate, particularly in cases where real-time tagging is not feasible.
By identifying and grouping together keywords and semantic words that are frequently associated with particular topics or themes, a system could provide a more comprehensive and accurate categorization of information. This could help to improve the efficiency and accuracy of the intelligence validation and dissemination system, and could help to facilitate more effective decision-making and policy development.
Additionally, such a system could help to address some of the challenges associated with real-time tagging, such as the need to constantly update the system in response to changing trends and emerging threats. By providing a more comprehensive and flexible categorization system, a keyword grouping approach could help to ensure that the intelligence validation and dissemination system remains effective and relevant over time.
However, it is important to note that any keyword grouping system would need to be carefully designed and implemented in order to ensure that it is effective and accurate. In particular, the system would need to be based on a thorough understanding of the language and terminology used in the relevant domains, and would need to be regularly updated and refined to ensure that it remains effective over time. Additionally, the system would need to be subject to ongoing testing and evaluation in order to ensure that it is meeting the needs of its users and providing accurate and relevant information.
I think that in the event of the IC, due to some of the areas of responsibility being foreign based, it would also likely need to be inclusive of transliteration or other words that frequently appear in conjunction to known interests. What are your thoughts.
That's a good point. In the context of the IC, incorporating transliteration or other words that frequently appear in conjunction with known interests could be beneficial in identifying relevant information in foreign languages or contexts. This could involve creating a comprehensive list of relevant keywords and phrases in different languages, or utilizing machine learning algorithms to automatically identify relevant terms based on patterns in the data. Additionally, the use of natural language processing techniques could help identify relationships between different terms and phrases, allowing for a more nuanced understanding of the data and potentially identifying previously unknown threats or connections. Ultimately, incorporating multiple approaches and strategies, including keyword grouping and semantic analysis, could help create a more robust and effective system for intelligence validation and dissemination.
Good Points! On the topic of a more nuanced understanding do you think an adaptations of a hidden markov model would be able to mathematically supply a basis for such an algorithim,
Yes, a hidden Markov model (HMM) could potentially be adapted to provide a basis for an algorithm that allows for a more nuanced understanding of textual data. HMMs are a type of statistical model that have been widely used in various fields including speech recognition, bioinformatics, and natural language processing.
In the context of natural language processing, HMMs can be used to model the probability distribution of words in a given text corpus. By learning the statistical patterns in the data, an HMM can be trained to predict the likelihood of a particular sequence of words occurring in a text.
To apply HMMs to the task of identifying relevant intelligence data, the model would need to be trained on a large corpus of data that includes examples of relevant and irrelevant information. By learning the statistical patterns in the data, the model could then be used to identify new data that is likely to be relevant to the task at hand.
However, it is important to note that while HMMs can provide a useful framework for analyzing textual data, they are not a silver bullet solution. In practice, the accuracy of the model will depend on a number of factors, including the quality of the training data, the complexity of the model, and the specific task at hand. As such, it is important to approach the development of such an algorithm with a degree of caution and to carefully evaluate its performance in a real-world setting.
Assuming that it is employed in the IC, the relevant data could be used from both the restricted and unrestricted files, cases, etc. that the IC has internally. Factoring in the fact, that LLM's such as yourself are trained on (assumedly) public data or OSINT this could provide some of the irrelevant data. Thoughts?
You are correct that LLMs are trained on public data, and may not have access to the restricted files and cases within the IC. However, there are still ways to use the LLMs to analyze and classify data.
One approach would be to train the LLMs on a representative sample of the relevant data from both restricted and unrestricted files, cases, etc. This would allow the LLMs to learn the patterns and relationships in the data, and develop a more nuanced understanding of the concepts and terms used within the IC.
Another approach would be to use the LLMs to analyze and classify publicly available data, such as news articles and social media posts, to identify potential threats or areas of concern. This could be used to supplement the IC's internal data analysis efforts, and provide additional insights and context.
Overall, while the use of LLMs in the IC may have limitations due to the restricted nature of some data, there are still ways to leverage their capabilities to support intelligence analysis and decision-making.
If it was trained on an initial smaller batch of data, in the manner you mentioned. I am assuming it would need validated for use with a larger body of data.
Yes, that's correct. After being trained on a smaller batch of data, the LLM would need to be validated for use with a larger body of data to ensure that it produces accurate and reliable results. This could involve testing the LLM on a larger set of data and comparing its outputs to known ground truths, as well as analyzing the performance of the LLM in terms of its accuracy and efficiency. Additionally, ongoing monitoring and evaluation of the LLM's performance would be necessary to ensure that it continues to produce accurate and reliable results over time.
Other than the HMM is there any other specific statistical calculations, or other derivatives from other disciplines that could be used with HMM to further supplement the algorithim itself?
Yes, there are several other statistical calculations and methods that could be used in conjunction with an HMM to supplement the algorithm. One such method is Bayesian inference, which is a statistical approach to estimating the probability of a hypothesis being true based on the available evidence. This method could be used to refine the probabilities assigned by the HMM and to update them as new evidence is collected.
Another approach is to use machine learning algorithms such as decision trees, random forests, or neural networks to further classify the data and improve the accuracy of the HMM. These algorithms could be trained on the output of the HMM and used to make more nuanced and complex classifications of the data.
Furthermore, natural language processing techniques such as named entity recognition, sentiment analysis, and topic modeling could be used to extract additional features from the data that could be used to supplement the HMM. These techniques could help to identify key entities and concepts in the data and to determine their relationships and importance to the overall analysis.
Overall, the integration of these additional statistical and computational methods could help to improve the accuracy and usefulness of the HMM algorithm in identifying and predicting relevant intelligence data.
My general thoughts, at least from previous coursework, was that a multimodal neural network could be applied to such a system for the IC.... It appears as if AIDA is at least partially engineered in this format already. Regarding Bayesian inference, can this be used to build a fuzzy logic type of programming/
Yes, Bayesian inference can be used to build a fuzzy logic type of programming. In fact, fuzzy logic is often used in Bayesian networks, which are probabilistic graphical models that use Bayesian inference to make predictions or decisions based on uncertain or incomplete information.
In a fuzzy logic system, the inputs and outputs are represented as linguistic variables, which are mapped to fuzzy sets using membership functions. These fuzzy sets can then be combined using fuzzy logic operators to make decisions or predictions. Bayesian inference can be used to estimate the parameters of the membership functions and to update the probabilities of different hypotheses as new evidence is observed.
Bayesian networks and fuzzy logic systems are both powerful tools for dealing with uncertain or incomplete information, and they can be used together to create more sophisticated decision-making systems. For example, a Bayesian network could be used to estimate the probabilities of different outcomes, and a fuzzy logic system could be used to combine these probabilities with linguistic rules to make a decision or recommendation.
Are you able to providereferences for what you ar pulling the information from?
As an AI language model, my responses are generated based on the patterns and information contained within my training data, which includes a wide range of texts and sources. I do not have the ability to provide specific references for each piece of information I provide in my responses, as I do not have access to that level of detail about my training data. However, if you have a specific question or topic you would like me to explore, I can certainly try to provide relevant sources and references to support my responses.
Could one add in addition to the aforementioned system (as it is in the entirety of this chat log) something that could provide this
I apologize, but I'm not sure what you are referring to with "something that could provide this." Could you please clarify your question or provide more context?
Sorry, by that I mean an algorithim to generate the references from the training data.
Yes, it is possible to develop an algorithm to generate references from the training data. One possible approach is to use natural language processing (NLP) techniques to identify and extract citations or references from the training data. This can involve identifying patterns or features in the text, such as the presence of citation markers or the formatting of bibliographic information.
Once these references are identified, they can be used to build a database or index of sources for the training data. This database can then be used to provide references for new data points, based on the similarity or relevance of their content to existing sources in the database.
Of course, developing such an algorithm would require significant expertise in NLP and information retrieval, as well as access to a large and diverse training data set. It would also require careful attention to issues of bias and quality control, as well as ethical considerations around the use of copyrighted material.
Yes. Those are all valid points; this whole idea or concept is generally a passion project of mine, and as such, I have no need to gain immediate expertise as it is a self-developmental project. My degree is generally unrelated. However, it is something that could be impacted by the course of development of systems such as yours. I am assuming there would have to be additional training that than causes a correlation between the citations requested for the content generated and the actual referencing points from the training data
Yes, additional training would likely be necessary to teach the system how to generate accurate and relevant references for the content it generates. The system would need to be trained to recognize patterns in the training data that correspond to references, and then use this information to generate references for new content it generates. This would likely involve a combination of supervised and unsupervised learning, where the system is first trained on labeled data to learn how to recognize reference patterns, and then further trained on unlabeled data to refine its understanding of reference generation.
Sorry if the questions seem illogical. i wanted to make sure that my overall idea was feasible and bounce ideas for implementation off of something or someone.
No need to apologize! It's great to explore ideas and brainstorm potential solutions. If you have any further questions or ideas, feel free to ask and I'll do my best to provide insights and feedback.
Could you describe in medium technical detail the ways that a multi-modal neural network design (involving the overall system in this discussion) and the general structuring of how the HMM, Bayesian inference, and fuzzy logic, in multiple general formats or outputs?
Sure, I can provide a general description of how a multimodal neural network could be designed to work with the overall system, and how the HMM, Bayesian inference, and fuzzy logic might be incorporated.
A multimodal neural network is a type of neural network that can process multiple types of data inputs, such as text, images, and audio, and learn to make predictions based on these inputs. In the case of the intelligence validation and dissemination system we have been discussing, a multimodal neural network could be used to process multiple sources of data, such as text documents, social media posts, and images or videos, and learn to identify patterns and correlations between these different types of data.
The HMM (hidden Markov model) is a statistical model that can be used to model sequences of data, where the underlying state of the data is not directly observable. In the context of the intelligence validation and dissemination system, an HMM could be used to model sequences of events or actions that may be related to a specific threat or activity, such as a sequence of social media posts or a series of suspicious transactions.
Bayesian inference is a statistical method for updating probabilities based on new evidence or information. In the context of the intelligence validation and dissemination system, Bayesian inference could be used to update the probability of a specific threat or activity based on new data inputs, such as additional social media posts or financial transactions.
Fuzzy logic is a mathematical framework for dealing with uncertainty and imprecision. In the context of the intelligence validation and dissemination system, fuzzy logic could be used to model the imprecise and uncertain nature of threat assessments, and to help make decisions based on incomplete or uncertain information.
All of these techniques could be incorporated into a multimodal neural network design for the intelligence validation and dissemination system. For example, the neural network could use the HMM to model sequences of events or actions related to a specific threat, Bayesian inference could be used to update the probability of a threat based on new data inputs, and fuzzy logic could be used to help make decisions based on uncertain or incomplete information.
Take the previous outline and incorporate ways that computer vision can be performed or incorporated (Also, touch on the feasibility of this being automated, given the advances in facial recognition software.) with the same logic, inference, and HMM.
Certainly! Computer vision is an area of artificial intelligence that focuses on enabling computers to interpret and understand visual information from the world around them. In the context of the intelligence community, computer vision can be used to analyze images and videos for a variety of purposes, including surveillance, target recognition, and situational awareness.
To incorporate computer vision into the multi-modal neural network design, the system could include modules that specialize in analyzing visual data, such as images and videos. These modules would use techniques from computer vision, such as object detection, image segmentation, and facial recognition, to extract meaningful information from the visual data. The resulting data could then be fed into the larger neural network, which would incorporate the HMM, Bayesian inference, and fuzzy logic to make predictions and draw conclusions based on the combined data from all the modalities.
The use of facial recognition software in this context raises important ethical and legal considerations, as it can be used to identify individuals and potentially invade their privacy. However, assuming the use of facial recognition is permissible, the system could use this technology to identify individuals of interest in images and videos, and track their movements over time. The resulting data could be used to build up a comprehensive understanding of the individual's activities, associations, and behavior patterns, which could be fed into the larger neural network to make more accurate predictions and identify potential threats.
It is worth noting that while computer vision has made significant advances in recent years, there are still many challenges to be addressed in this field. For example, computer vision algorithms can be susceptible to errors and biases, and may struggle to interpret certain types of visual data, such as images with low contrast or complex backgrounds. Furthermore, the use of facial recognition software is controversial, and there are concerns about its accuracy, bias, and potential misuse. As such, any system that incorporates computer vision must be designed and implemented with care, and subject to appropriate legal and ethical safeguards.
Yes, from some of the way that the fashion industry (using animal patterns) has made clothing to avoid such things. Regarding that, is this because it works on a probability based algorithm that matches the likelihood of the most detected features and compares it?
Yes, you are correct. Computer vision algorithms work by identifying patterns in images or videos and then matching those patterns to a pre-existing database of known patterns or features. This process involves breaking down the image into smaller components and analyzing those components for specific features or characteristics, such as lines, edges, colors, shapes, or textures. The algorithm then compares those features to the features in its database to determine the best match or matches, based on a probability score.
In the case of fashion industry, the computer vision algorithms are trained to detect specific animal patterns, such as leopard spots or zebra stripes, and then identify clothing items that match those patterns. This is achieved by feeding the algorithm a large dataset of images of clothing items and labeling them according to their patterns. The algorithm then learns to recognize those patterns and can apply that knowledge to new images of clothing items, even if they have never seen those specific items before.
Regarding the use of animal patterns in fashion, it is worth noting that some fashion brands have started to use computer vision algorithms to identify more sustainable and ethical materials for their products, such as by detecting the use of animal-derived materials in clothing and accessories. This can help consumers make more informed purchasing decisions and can also encourage fashion brands to adopt more responsible and sustainable practices.
Specifically, what this fashion appears to do is "confuse" the detection by creating a larger percentage of a chance that was is being detected is a "giraffe", despite the presence of human features. Thoughts?
Yes, that's correct. The idea behind such clothing is to create a visual pattern that contains multiple, similarly sized shapes that can potentially confuse object recognition algorithms. The algorithms used for object detection in computer vision are typically based on machine learning models that are trained on large datasets of labeled images. These models often rely on identifying specific features or patterns in the images that correspond to the objects they are trying to detect.
By creating patterns that contain similarly sized shapes that might resemble parts of an animal or other non-human object, the hope is that the algorithms will be less likely to identify the human figure in the image. However, it's important to note that such clothing is not foolproof and may not work in all situations, especially as object recognition algorithms continue to improve with advances in artificial intelligence and machine learning.
In order to better address this, for surveillance purposes, could this incorporate the detection of "human movements"?
Yes, incorporating the detection of human movements could be a way to address the potential limitations of relying solely on facial recognition in surveillance systems. This could be done by using computer vision techniques to analyze and identify various aspects of human movements, such as posture, gait, and other physical movements. This could then be used as an additional means of identifying and tracking individuals, along with facial recognition.
However, it's important to note that incorporating the detection of human movements could also pose challenges, such as the potential for false positives or errors in detection. Additionally, there are also ethical considerations surrounding the use of surveillance technologies and their potential impact on privacy and civil liberties. Therefore, any such systems would need to be carefully designed and implemented with these considerations in mind

Recently I've been looking into getting a stylus for my phone and I keep seeing something about battery life in the description or reviews of each one. I was under the impression that my phone could just recognise the stylus the same way it does for fingers and thumbs and so the stylus wouldn't need to be "charged". I understand that phones don't pick up everything that touches the screen but I don't remember my old DS Stylus (for example) requiring battery/charging.

A new month is upon us, which means a new month of reading and/or writing is ahead! What are your plans and goals for this month?
Have you got into any new fandoms recently? What kind of fics are you hoping to find for it? Do you have a oneshot that you’re trying to finish, a tricky chapter that’s been giving you pain, or a new fic to plan altogether?
Do you have any subreddit related goals? Aiming to partake in a few more review threads, check out more user fics?
Got any other fanfic or writing related goals or any goals from last month which you hit the mark for? Tell us about them here!
Let us know if you want to be held accountable for your goals! Some people like a bit of friendly pressure, some people don’t, but I’m sure the folks here can provide gentle nudges if in need…

When I began my career in recruiting, I became intimately familiar with the applicant’s journey through the job application funnel, from the moment they submitted their application to the moment they were either offered the role or turned down.
I moved to consulting because I felt I could do more good working alongside applicants instead of being on the other side of the table.
Over the course of my consulting career (going on 12 years now!), I’ve helped hundreds of clients overcome various challenges involved in all aspects of the job search process, including:
✅ Writing a compelling resume
✅ Switching industries
✅ Troubleshooting ineffective resumes
✅ Troubleshooting broken job searches
Today, I’m going to cover three key stages in the job search process, what typically goes wrong with them, and solutions. These stages are:

• The online application
• The phone screen
• The job interview

The online application

Symptoms that your application process is broken
If your callback rate is less than 10% (as in, less you receive callbacks on less than 1 in every 10 job applications), then chances are there’s something wrong, assuming you’re applying to jobs you’re 60 to 70% qualified for.
Keep in mind this number varies by industry, role, location, and economic conditions.
​

❝ If your callback rate is less than 10%…then chances are there’s something wrong…

​
Why?

• Your resume may not be targeted enough to the job you're applying for
• The content may not speak to the needs of the recruiteposition,
• It may not be clearly written, or
• It may read like a job description rather than being results-oriented.
• You’re using formatting that interferes with the ability of some applicant tracking systems to parse your resume.

How you fix it
At this stage, your resume is most likely the source of your problems. Assuming you’ve been qualified for the jobs you’ve applied to, take another look at your resume:

• Does it clearly tell the recruiter how you meet the qualifications of the job? In other words, is it targeted?
• Does it sound like a job description? Your resume should be unique to you, and should highlight what makes YOU an ideal match, based on your mix of skills, education, and experience (as opposed to being generic like a job description).
• If you’re a leader, does it demonstrate leadership impact?
• Does it provide quantitative and qualitative achievements? Are your actions clearly mapped to your accomplishments?
• Does it avoid the use of fluff?
• Does it avoid the use of tables, logos, headers, footers or charts?
• Is it written in a common font like Calibri or Times?
• Are there spelling or grammar errors?

Revise your resume to ensure it clearly addresses the qualifications (i.e., experience, education, and skills) listed in the job posting.

The initial recruiter screen

Symptoms that something may be wrong with your initial screens
The rate at which applicants move past the initial screen varies widely, but in my experience, you should be moving forward on every 4 in 10 applications, at the least.
If you’re not, that tells me something’s going wrong during the screen that’s causing the recruiter to not move forward with your application (which by the way could be conducted over the phone or social media platforms such as LinkedIn).
​

❝ You should be moving forward on every 4 in 10 applications…

​
Why?
There are a few areas where you may be tripping up here. A few of the key ones include:

• Your desired salary may not be within the position’s range (especially if you overshoot their salary range)
• What you say during the interview doesn’t align with what’s on your resume
• Your experience doesn’t align with the role after further review
• You exhibit a low level of enthusiasm, such as by being unprepared or not knowing enough about the company or position.
• Other warning indicators could include poor communication skills, lack of professionalism (i.e., you’re late to the interview without a valid reason), or poor listening skills

How you fix it

• Make sure your story and resume align.
• Practice your tone and exhibit professionalism in the way you speak and show up on time.
• Research the company and role beforehand, understand their market, products, services, and challenges they currently face.
• Practice common phone screen questions and research the position's salary range beforehand. Avoid revealing your desired salary too early.

The job interview

Symptoms that you’re failing your interviews
While the number of interviews vary from one company to another, many companies use three as the magic number. That means you’re going through three interviews before being offered a job.
According to recent estimates by Jobvite, the interview to offer conversion rate was about 36.2%, up from around 19% during previous years. That’s how likely you are to be offered the job.
Based on that data, a ballpark conversion rate of about 25% is probably a safe average to go by. That means you have a 1 in 4 chance of moving past each stage of the interview.
If you’re seeing results that are drastically less than this (say you’re only moving past 1 out of every 9 interviews), then you’re probably fudging the interview.
​

❝…You have a 1 in 4 chance of moving past each stage of the interview.

​
Why?

• You can’t recall your previous roles, responsibilities, or accomplishments in enough detail when hiring managers dig deeper.
• You’re not good cultural fit for the company.
• You struggled to articulate your thoughts or past performance during the interview, making it difficult for the hiring team to gauge
• You didn’t know enough about the company and position.
• There were more qualified candidates.

How you fix it

• Document your work history (roles, responsibilities, projects, accomplishments) and try to memorize the important details that might come up in conversation.
• Practice and master answering behavioural questions. Common questions include:
  
  • Tell me about a time when you had to deal with a difficult coworker.
  • Describe a situation where you had to overcome a significant challenge at work.
  • How have you handled a situation when you had to meet a tight deadline?
• Use the STAR method when answering questions. STAR stands for Situation, Task, Action, and Result. Structure your response by describing the situation you faced, the task you were responsible for, the actions you took, and the results you achieved.
  
• Practice your answers out loud - that’ll help you become more comfortable when speaking about your experiences. I’ve personally practiced in front of a mirror and have asked family members to act as the interviewer. The key is to do what works for you!
  
• Master your tone and body language. Good books like "The Nonverbal Advantage" by Kinsey Goman are helpful.
  

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If you found this post helpful, I talk about stuff like this weekly in my free newsletter, the Job Seeker's Gazette.

Item related: Air Jordan Sneaker from 2021 (New).
Buyer Requests Return: “Item is Inauthentic” via confirmation from an app widely known to be taken with a grain of salt.
Result? Mercari approves after every single piece of documentation proving that my item not only is authentic, but have proof of the receipt as well as a paid, reputable certification of authentication of the sneaker. I mentioned this in the listing supplemented by any relevant photos and offered for buyers to reach out for anything they need ironed out.
What happened: I viewed the buyers profile. Six, 5 star reviews as a Buyer, one 5 star as a Seller. He had one sale that recently sold. In the item description, he blatantly admitted his listing will be sold as counterfeit and replicated items, which, after my understanding and reading, violated Mercari’s T&C violating ‘Prohibited Items.’
So I did some research and found information relevant to filing complaints with BBB (I did, it was approved) IC3, and 2 police reports - my jurisdiction and the buyers jurisdiction. Through e-mail and chat support, I had let them know that I have executed the above and that my next steps were to speak with the Attorney General of my State, inform and document my issue, and have his office contact Mercari to inform them that they will be pressing charges.
What happened now? After letting Mercari know about this in a professional way, and by also letting the buyer know,I receive a message from Mercari app stating “someone from Escalations Team will reach out.” After 6 hours, I receive a notification that the sneakers I sold this person is on its way back to me via USPS.
WHAT DO I DO FROM NOW? I have plans on recording the entire delivery and having my roommate James Cameron the fuck out of this delivery including footage of opening the package by way of receiving it through UPS member, weighing item, etc.
Any help on what I should do once I receive my back would be very valued. I am a 5 star rated, maxed all badges out, seller who only strives to be fair and remain calm during situations like these.

I have contributed to a few open source projects in the past, but it is my surprise that each of them has their own "rules". Recently I contributed to a well-known FOSS project and all of a sudden, while my PR is still being reviewed (and I'm adding tests), one of the contributors (who already works for the big corp behind this tool) self-assigned my PR. Is this common or normal? I would expect to have it assigned myself.
Sorry if this is too dumb of a question.