Hi, we adopted AI due to its advantages. Responsible AI is the practice of designing, developing and deploying AI with good intention to empower businesses and fairly impact customers and society. But how do we trust and what are the qualities of Responsible AI? We are going to discuss about in this session about trusted and responsible AI with explainability, adversely bias and fairness. Hi, this is Dr. Wamsi Mohan. Yeah, coming to my credentials, I'm working as a chief technology officer for a company called Hub. Hub technology is very limited. Coming to my awards and recognitions, I'm a top 50 global professionals for the year 2022 and best scientist of the year for 2021. And I also got CXO excellence award for 2021 and next 100 CAOs for the year 2020. And I also got recognition as top 50 global thought leaders and influencers for the RPA and data center technologies and cybersecurity as well. Coming to my academics and credentials, I did my PhD in computer science and engineering and holding several patents in data transmission and cybersecurity. I did my post graduation in computer science and engineering and master's degree in management from IIM Ahmedabad. I published 40 place national and international generals and successfully driven several industry academia initiatives with various universities, tier one and tier two technology institutions globally and also an industry speaker in national and international conferences. This agenda is we are going to discuss about the introduction, the principles of responsible AI, which talks about ethics or fairness, as well as accountability, inclusiveness, reliability and safety, transparency, privacy and security. And also we are going to discuss about expandable AI, XAI, which is and why XAI is important in the growing trends of AI and also what is different principles and benefits of XAI. We are going to discuss about it and we are going to talk about adversarial AI. We will be talking about different ML defenses as well as adversarial trainings, switching models and generalized models. And we are going to talk about some of the adversarial attacks such as poisoning attacks, evasion attacks, model stealing attacks, methods of combating attacks as well. And we'll be talking about bias AI, how the bias will be created and what is the intention between these biases and how we can eliminate and how can we create an unbiased system and how to fix the biases in the ML algorithms we are going to talk about. And also we will be talking about the fairness of AI and what are the different metrics for this fairness we'll be discussing about followed by the conclusion. According to the introduction the trusted and responsible AI ensures fairness and benefits for the society is vital for future acceptance. Nowadays we are seeing many threats from the AI systems and because of the no not having a trust people are hesitant to adopt this. For adopting AI it should be a trustworthy as well as it is a responsible and for these factors we need a fairness, interpretability, privacy and security in this AI systems. Also apart from this we need the good governance on the AI systems and the regulatory bodies to audit these AI principles as well when they are building. What is the best way to build fairness, interpretability, privacy and security into the enterprise systems let us let us see how to build a responsible AI. Coming to the AI principles there are six primary principles which we are going to discuss one is inclusiveness, accountability, ethical and fairness, reliability, reliability and safety, transparency, privacy and security. Coming to the ethical fairness basically artificial intelligence is widely popular from the last couple of years and also it inherits risks of relaying AI has emerged across the number of years. AI-powered solutions sometimes we are discriminatory as we are seeing in the in the recent news and are and are cause potential risk to two individuals unable to explain the decisions what is behind the algorithms, privacy given their heavy reliance on the data and this also gives a security threats on the lack of ethical or fairness in the in the systems. For ethical perspective AI should be fair and inclusive in its assertions it should be accountable for the decisions whatever the system has taken. Coming to the accountability who has to take this accountable the AI scientists as well as the builders of the systems are accountable for these AI systems that the accountability comes as an essential pillar for the responsible of AI. The people who design and deploy the AI systems need to be accountable and interface should consider an internal review body that provides oversight insights and the guidance about developing and deploying the AI systems unless a good governance as we discussed in the previous slide it is quite difficult to monitor the AI activities. The guidance might be depending on these guidelines can be flexible or a right guidelines needs to be placed according to the company reason as well as the industry segments such as healthcare education or our financial segments and coming to the inclusiveness it is responsible of the people who build solutions to that AI is inclusive and provides a net positive benefit to society. Inclusive means helping the ecosystem where possible such as I can give you an example of speech to text or a text to speech and visual recognition technologies should should be used to empower people with hearing visual and other impairments. Artificial intelligence when we are talking about the inclusiveness is a mandate and should be considered all human races and experiences and inclusive design practices can help developers to understand and address the potential issues. And reliability and safety AI systems needs to be reliable and safe as we discussed and it should be trustworthy. Its inherent uh uh reliance should resist intended or unintended manipulations. Regress testing and validations should be established for the operating conditions to ensure that the systems are safely built according to the business cases. As well as continuous monitoring and model tracking is mandated to establish uh safe execution else it will create uh uh the biasing as well as uh some threads on the system. Security is one of the uh factor or a principle of uh of responsible AI. Achieving transparency helps AI stakeholders to understand the data and algorithms used to train this model transform transformational logic and its associated assets. This gives uh a great confidence to the stakeholders who are using these AI systems. The information offers the insights about how the model was created which allows it to be reproduced in a transparency transparent way uh comes as part of the principle. And also uh one of the principle is privacy and security. When we are talking about the privacy, privacy means one of the recurrent uh recurrent concern that people have about AI technologies or AI systems. The personal data needs to be secured one and it should be accessed in a way that doesn't compromise an individual privacy. Data privacy is a critical factor when we are talking about uh either either enterprise systems or AI systems. These uh these data privacy is often linked with the AI models based on the consumer data. Interpretable AI or interpretable AI the term defines uh explains or interprets the AI derived solutions understood by the engineers and the scientists. XA is a artificial intelligence framework which the results of the solution can be understood by the human beings. It aligns and supports the white box in machine learning where engineers and scientists can explain why an AI arrived at a specification. XA is implemented as a social right to explanation. XA is relevant even if there is no XA legal right or regulatory requirements. Scientists should be able to explain to the to their stakeholders how they have uh achieved certain levels of accuracy and what influenced the outcome. To comply with this enterprise policies, auditors needs a specific set of right tools to validate these AI models as well as uh uh the business decision makers can take a right decision while incorporating the AI models in their systems. Yeah, we are going to discuss why XA is important. It is crucial for enterprises to learn and understand how these AI decision making processes without blindly depending on the AI systems and also XA helps to understand and explain machine learning algorithms, deep learning and neural networks. And one of the uh one of the critical factor is ML models are like a black boxing though it is quite difficult to understand and interpret uh about the integrities of the system. Neural networks used in deep learning are some of the hardest things to uh for humans to understand. And also the biasing of biasing of the system based on the race, gender or location has been a long standing risk in the training models as well as the AI model performance can drift or a degrade to the production data uh from the training data as the data varies from that uh the training environment to the production environment. This makes it crucial for uh uh businesses to continuously monitor and model to promote AI explainability while measuring the business impact of such algorithms. The principles of uh AI is also uh introduced by national initiative of standards and technology. There are four principles introduced uh the first one is AI systems should provide explanations that are backed by the evidences which uh primarily talks about XAI and also explanations should be meaningful in a way that can be understood by the users of the AI. This talks about the clear definitions of the models and what are the what algorithms they have used and what is the intent of these algorithms has to be explained and documented as part of this process and it should be accurate describing the AI system process as well. This helps the auditors to audit the models and uh follow uh in line with the uh with the corporate governance on the uh on the AI systems. Also AI systems should operate within the limits that they were designed for. Uh this is this talks about the scoping of the AI system uh a particular scoping needs to be applied when designing the AI models and it should be it should work within the uh limits of those uh principles. These four principles capture a a variety of disciplines that contributes to explainable AI uh including uh uh different competencies like uh computer science or healthcare or psychology or engineering. And what are the benefits of explainable AI? The first benefit is operates with AI uh trust and confidence and reduce time to AI results migrate risk and cost of governance models. The operationalize AI uh with the trust and confidence helps to build trust in production systems as well as rapidly bring AI models to the production uh easily. Also interoperability uh and uh explainability of AI models simplify the process of model evaluating while increasing the model's transparency and traceability. This helps to operationalize AI with the trust and confidence uh for the deployment passport as well as uh systematically monitor and uh manage models to optimize the business outcomes and and continuous evaluation as we discussed in the previous slide a monitoring mechanism is needed uh to uh to track and evaluate the improved models. This helps to uh to reduce the uh blind spots as well as increase the performance of the AI systems. And and the third benefit is mitigate risk and cost of the model governances. This helps to keep a models explainable and transparent and uh manage regulatory compliances and uh risk and other requirements helps as per the governance model adversely AI helps in in the cybersecurity uh especially the threat intelligence or vulnerability detection. Machine learning offers many benefits to the companies but it can also enhance threat actors attacking uh progress. Machine learning models are complicated to understand and these poor understanding is exploited by the hackers or attackers uh on these hidden weaknesses. They could trick the model into a making incorrect predictions or give away sensitive information. Fake data could even use to corrupt models without using uh without unknown using the uh the uh data. The field of adversely machine learning aims to address these weaknesses and also adversely learning aims to uh these some of the techniques uh some of the tricks uh machine learning models provided by the defective inputs. These uh these are causing the uh vulnerabilities as well as threats uh such attacks to overcome it adversely learning helps to uh understand and build the models efficiently. The most successful techniques to train AI systems to understand these attacks fall under two classes one is adversely training and the second one is defensive distillation. Second training is a brute force supervised learning method where many adversely examples as possible it will be fed into the system and it automatically whenever the threat comes or a similar kind of uh uh threats comes into the system it detects and uh eliminates from the uh cube. This is similar to the approach most of the anti-virus softwares work on our systems. The second kind of uh the second class of the class of AI systems are defensive distillation. This strategy adds flexibility to the algorithms classification uh so that uh it will be applied on the on the model output of the second model. There is an advantage with this basically uh the advantage of this distillation approach is that adaptable to the unknown threats and it is a progressively increasing however the biggest disadvantage is while the second model has more uh wiggle room to reject input manipulations it is still bound to the general rule of the first model because of that even though it is a progressive model some of the hidden uh hidden insights will be still there in the second model as well with that the efficiency of the second model might reduce because of the first model the drawbacks. Yeah coming to the adversely training the first approach is to train the model to identify adversely examples like uh the image loading to the system uh with the recognition model that classifies uh uh different segments and it would be uh the hope is that by rigorous training of these models it are it effectively it works on the uh incoming uh threats and it it separates from the cube. The problem is that it may be difficult to discover these adversely examples in the first place so research in this area not only aims at defending against them but also automatically discovering. There are uh there are many tools in the in the market on the adversely robustness uh one of the tool uh recently developed by IBM uh which is quite effective uh for the adversely training impacts. Training models protect the system from multiple attacks the approach is to use multiple models within the system the model used to predict uh the the changes randomly this creates moving target as an attacker would not know which model is currently used however this is a costlier effect at the same time they may also to compromise all the models in order for an attack to be successful poisoning our findings adversely examples for multiple models is much harder than for the just ones as i as uh we discussed this is a costlier effect uh for the uh model switching generalized model approach focus on the data and models it is important to remember the models doesn't exist and in an isolation as they are part of the larger systems this means many attackers can avoid it with changes to the system in general for example encrypting and good password practices can protect database making uh poisoning effects less likely uh likewise uh the other example when we are talking about the spam filtering or spam mails coming to the inbox uh when when there is in when the uh mails coming uh coming to the inboxes when when the rejected mails when we are sending to the the sender as a rejected mail it helps the attacker to track and and trace out uh whether whether it is a right format it it sent to the inbox or not this uh helps the attacker to predict in a whether the action he did it in a genuine way or not coming to the uh adversely attacks there are primarily four kinds of different attacks one is uh uh poisoning attacks uh evasion attacks model stealing and methods of combating attacks machine learning can help us automate more complicated tasks the downside is that the model is introduced a new target for attackers to exploit new types of attacks can be used against it systems this primarily used as i told you uh poisoning uh attacks and model stealing attacks are primary in this adversely attacks coming to the poisoning attacks uh it focuses on the data used to train the model attackers will change existing data or mislabel uh the uh data data and input to the system the model trained these data will make incorrect predictions on the correctly labeled data this automatically the entire the system will be misguided uh attack attackers misguide these systems by changing the existing data or existing labels and uh we can we can have an example uh an attacker can change the labels or relabel the existing data and it could uh it could cause frauds even in case of uh autonomous systems or uh autonomous vehicles when these labels are getting changed automatically uh a driverless car uh will will see the signals as a red signal into the green or a green into the uh uh green signal into the red automatically it hampers the entire system evision attacks focus on the model itself they involve modifying data to seem legitimate but leads to the incorrect predictions to be clear the attackers modifies the data used by the model to make predictions and not data used to be trained models like a image recognitions and the pattern findings uh will be uh completely misinterpreted for example recently uh researchers at google showed how introducing specific noise into an image could change the predictions of an image recognition model yeah model stealing attack focus on the stealing the models after it has been trained specifically an attacker wants to learn about the structure of the model which has already trained and it stealing this model helps the attacker to learn about the model and uh gain the uh financial benefit or the data which is trained to use it uh and also the attacker could use this information for further attacks as well for example they they could find exact what words a spam filtering model will flag the attacker could then alter spam phishing emails to uh to ensure these spam mails are delivered to the inboxes coming to the combating attacks the way we defend our ml systems depends on the type of models which we use many problems can be solved simply models like linear regression or logistic regressions more complicated models like neural networks are less interceptable uh this means that we are uh at a poorer understanding of inner workings of the model this leads to the hidden weaknesses and more opportunities for the attacks this is why most research in this field adversely missing machine learning is aimed at combating attacks against these models let's discuss about the biasing how the biases will be happening and we is it intentional or unintentional how the systems the systems are biased with respect to the several factors when coming to the biasing a biases is a anomaly in the output of machine learning algorithms due to the prejudice assumptions made during the algorithm development process are prejudice in the training data uh the biasing happens there are two primarily uh kind of uh biasing one is a cognitive bias and the second one is lack of complete data cognitive bias basically uh it depends on the uh on the stakeholders or the scientists and the engineers who works for this uh AI system uh intentionally they have created some biasing in the system which works according to the uh the inputs given by the engineers designers knowingly or unknowingly introduce uh these biasing to the models and a training data sets uh which includes some of these biases that is why uh with a strong governance model and at regulators it avoids the cognitive biasing uh from the uh uh from being biased AI systems and the second kind of a category is lack of complete data if data is not complete it may not be representative and therefore it may cause uh biases how to make a AI unbiased uh these uh some of some of the recommendations uh is given from the industry is uh if you clean your training data from the conscious and unconscious assumptions like race gender or other ideological concepts uh the system will be clean and it can be far from the biasing systems and and it will be it will create a unbiased data event decisions this this is one of the the cleaning activity uh helps the AI systems to be unbiased as well as uh to minimize the AI bias it can uh possibly be possibly by testing data and algorithms and developing AI systems with the responsible AI principles uh basically the human nature uh creates the biasing when different race or a different category of humans when they are training these models they intentionally they uh make the AI systems biased this can be can be eliminated as we discussed in the uh in the uh previous slides a strong governance mechanism is required one of the question is how to fix uh fix biases in AI and machine learning algorithms and as straightforward uh one of one of the approach is removing the labels from the train data and putting into the system but it may cause uh the entire system uh clumsy as well as uh it the main intention of this AI system uh will will be uh will be questioned let's talk about the fairness of AI uh fairness is a ubiquitous term in artificial intelligence and machine learning fairness is a generic concept not restricted to the AI any decision making system can exhibit biased towards certain factors and thus needs to be evaluated for fairness fairness is tested by verifying if the system is unbiased as per the pre-established ethical principles fairness uh metrics in AI there are many different definitions of fairness they often conflict each other the definitions you can choose depends on the context which the decision making a being made fairness through an unawareness basically it is without intentional or unknowingly uh keeping the factors into the system and expecting the fairness however the uh the blind spots all still exist in the system that needs to be uh come out of the building platforms and the second one is a demographic parity the focus is equalizing the selection rate between privileged and then non-privileged groups there are two metrics commonly used the first one is a disparate impact ratio the second one is a statistical parity differences disparate impact ratio the ratio of rate of a favorable outcome for the unprivileged group to the of the privileged group is a disparate uh disparate impact ratio and statistical parity difference uh is the difference in the rate of favorable outcomes received by the unprivileged group to the privileged group this also create uh fairness in the AI also equal opportunity and equalized odds are uh different factors when achieving the fairness in the uh uh in the in the in the AI systems coming to the conclusion a primary argument supporting a the efficiencies and the capabilities of this technology which surprises human abilities the arguments agnus uncontrolled development of a presented by the technologies specialists and the scientists argue that it is unclear by placing right measures regulations and the governance a will become a boom for the human kind we are we are privileged to have computing machines which we resisted in the era of 70s and 80s hence while artificial intelligence and machine learnings are rapidly changing our world and uh powering the fourth revolution human humanity does not need to be afraid and leverage the uh benefits of AI this hence the session i hope this is uh valuable and useful for you uh thank you very much for being part of this session and thanks to lax foundation and open source summit


Hello and welcome to the Rugged Rockhound. Today I'm actually in Colorado. So here's the Interstate 40. We just came over the border. This is Rabbit Springs. And the reason I'm here is we're just stopping on the way to Grand Junction, Colorado. And apparently in this area you can find some agate, Jasper, and some iron concretions. So I'm just going to take a quick look around because it's right off the Interstate Easy. And just a little bit of a heads up. There's a reason I'm making a visit to Grand Junction. And there's my wife with me. So let's go ahead and take a look around and see what we can find. So I've come this far. So far not really seeing much. Mostly you're seeing some sand and silt and mud layers. The mud layers tend to be kind of this greenish. And not seeing a whole lot in it. Get a few interesting looking things. But no Jasper agate yet that I'm seeing. Directions out here were just, it's along this road. So I don't know if it's below the cliffs or above them. So we'll search down here for a little bit and then I'll go up there if I don't find anything. So yeah we'll just take a look. Still not finding anything in this stuff. But that's a cool balanced rock. It's sitting right there. So in this area there's this like interesting green and whitish unit that might take a polish. I don't know. But some of them are pretty interesting looking. Like these. It might even be a volcanic ash layer. And another reason I'm thinking that is I found this one. And if you look at the texture on the surface it almost looks like wood. I'll have to clean that off to see. But that might actually be a piece of petrified wood. It's possible. Or it might just be the layering I see in like a mudstone or shale or something like that. Okay just found something that I've gotten used to seeing at Cisco and Yellowcat. So here you've got some nice gypsum. It makes these nice crystal growths that make you think it's petrified wood or something like that. When in fact it's crystals of gypsum. Sometimes you get crystals of calcite or barite as well. We're not too far from Cisco and Yellowcat so that is possible. So we might be seeing some of that around. Interesting layering. But yeah I see there's some more of it. Found this. It's kind of cool. Just a basically the sandstone. But it's got iron in it giving it some different colors. So kind of get some cool banding. I actually just searched the very top here. Didn't find anything I felt like filming.  Not terribly spherical but it's kind of cool. So I picked that up and it's more of the green but it's actually solid enough I think you could technically call it a jasper. So kind of a greenish jasper. Okay so that's maybe something. What is this? Oh now that looks like something. That looks like something. Okay I'm gonna have to get this washed off when I get back home. Of course you'll see it really soon. But that looks like something. So I'm gonna take that back and wash that off. Here's this one cut open. Not too bad. Interesting. I found another one I think. Yeah it has this interesting shape on the outside. This might be what it was referring to. It's kind of a greenish color though and it kind of reminds me of those replaced bearite concretions over at Cisco and Yellowcat. I don't know. We'll wash that one off too. They actually seem to be fairly abundant here. All right there. So you can see a little bit going through that. That looks like a little bit of agate going through right there. Huh. Found a big piece. So there you've got you can see some of the agate in there and then the outside jasper is kind of a gray green. Not very pretty but it's still interesting. Here's this one washed off. The agate right here was really nice. Look at that. Hadn't that not much. So I guess out here you kind of want to try and find these veins of agate if you can. All right I'm seeing it all over now. I picked up that piece and that piece and right over here you see that thing. Once again not good quality but I am seeing a lot of it now. Here's this one cut open. All right. Some interesting patterns and swirls in there. I think I see something hiding in the bush here. Yep it's more of that jasper. Here's this one cut open. And here I found one that you got a top to and looking at it it really does look like that replaced barite over at Cisco Nailcat. So I think this is the replaced barite but it's not the pretty red and yellow you get over there. It's kind of a puke green to gray. Not the most beautiful color but there does seem to be a fair amount of it. Just found that one. It's got a little bit of an agate seam in it. Okay and then over here and selling that one. Okay this one's a little bit of cleaned off and oh it has some actual nice patterns in it. That one's actually a nicer piece. Okay now that is much more interesting. I like that. Okay I could definitely use more of those. Here's this one washed off. Nice. Now I found something a little bit more interesting. That's a better looking agate inside there. Can't see the rest. It's super dirty. I'll get it washed off but I can see some agate in there. Now that looks like it might be agate inside. Interesting. I'm gonna take that and cut that one open. Here's this one cut open. The other side fell apart. That's what it looked like. Pretty nice. And I'm back at the car so I didn't have a lot of time to spend today because we're kind of in a hurry today because we've got a lot of stuff going on. So just so you know where it was that we found the stuff. So when I was down at the base of it I wasn't finding much. It's when you get into this kind of central green layer maybe a little higher up. This is where I was finding it right through here. So maybe that zone kind of going like that and like I said it's just off Interstate 40. Rabbit Springs exit.  So of course like always I'll give you some GPS coordinates to where we're parked right here. So you know where I walked immediately and found a few pieces right here. I imagine you should be able to find it all along right here and down there. I just didn't see it at first because I was expecting red, brown, yellow jasper because that's what I'm used to. So kind of refreshing finding some greenish jasper. I just wish it was a prettier green. Well you've already seen everything washed and cut off. I haven't. I don't know how good it is. Based on what I see it it's probably just okay. But maybe it's better than that and if it is maybe it warrants coming out here again because maybe there's some better pieces hiding in here. Maybe some better agate pieces. I don't know. I don't have a lot of time because we're heading to Grand Junction because I just got a job in Grand Junction, Colorado. So we're going down to get some housing. We're going to go find us some housing out there in Grand Junction over that way. So yay! That's where I'm going to be stationed from is Grand Junction, Colorado from now on. Just so you know I got a job in STEM education. So I'm going to be educating and teaching about science, technology, engineering, and math. But the Utah border is just over there. So I'll be doing as much Colorado as I will. I'll be doing as much Utah as I will Colorado probably. So it's actually a really good spot. So we're excited. So look forward to a bunch more videos coming up in the future when I get more time. These first couple weeks transition is going to be busy. But then once things get going, I'll be doing a bunch of rockheading right here and out in this area especially. It's really nice because during the winter months quite often you can still just rockhead. So I'm looking forward. As always, remember, there is treasure everywhere.


Hi, everyone. Welcome to this program sponsored by HPE. I'm your host, Lisa Martin. We're here talking about being confident and trusting your server security with HPE. I have two guests here with me to talk about this important topic. Cole Humphries joins us, Global Server Security Product Manager at HPE and Anne Potten, Trusted Supply Chain Program Lead at HPE. Guys, it's great to have you on the program. Welcome. Hi, thanks. Thank you. It's nice to be here. Anne, let's talk about really what's going on there, some of the trends, some of the threats. There's so much change going on. What is HPE seeing? Yes. Good question. Thank you. Yeah, cybersecurity threats are increasing everywhere and it's causing disruption to businesses and governments alike worldwide. The global pandemic has caused limited employee availability originally. This has led to material shortages and these things opens the door perhaps even wider for more counterfeit parts and products to enter the market. And these are challenges for consumers everywhere. In addition to this, we're seeing the geopolitical environment has changed. We're seeing rogue nation states using cybersecurity warfare tactics to immobilize an entity's ability to operate and perhaps even use their tactics for revenue generation. The Russian invasion of Ukraine is one example. But businesses are also under attack. For example, we saw solar winds, software supply chain was attacked two years ago, which unfortunately went unnoticed for several months. And then this was followed by the colonial pipeline attack and numerous others. It just seems like it's almost a daily occurrence that we hear of a cyber attack on the evening news. And in fact, it's estimated that the cybercrime cost will reach over $10.5 trillion by 2025 and will be even more profitable than the global transfer of all major illegal drugs combined. This is crazy. The macro environment in which companies operate in has changed over the years. And all of these things together and coming from multiple directions presents a cybersecurity challenge for an organization and in particular its supply chain. And this is why HPE is taking proactive steps to mitigate supply chain risk so that we can provide our customers with the most secure products and services. So, Cole, let's bring you into the conversation and did a great job of summarizing the major threats that are going on, the tumultuous landscape. Talk to us, Cole, about the security gap. What is it, what is HPE seeing and why are organizations in this situation? I think, Lisa, you know, what we're seeing is, as the threat landscape increases to, you know, disrupt or attempt to disrupt our customers and our partners and ourselves, right? It's a kind of a double edge, if you will, because you're seeing the increase in attacks. But what you're not seeing is an equal to growth of the skills and the experiences required to address the scale. So it really puts the pressure on companies because you have a skill gap, a talent gap, if you will. There's, you know, for example, there are projected to be three and a half million cyber roles open in the next few years, right? So all this scale is growing and people are just trying to keep up, but the gap is growing, just literally the people to stop the bad actors from attacking the data. And the complicate matters, you're also seeing a dynamic change of the coup and the how the attacks are happening, right?  You'll have, you know, nation states going after commercial entities and, you know, criminal syndicates and alluded to that there's more money in it than the international drug trade. So you can imagine the amount of criminal interest in getting this money. So you put all that together and the increasing of attacks, it just is really pressing down as literally, I mean, the reports we're reading over half of everyone, obviously the most critical infrastructure cares, but even just mainstream computing requirements need to have their data protected, help me protect my workloads and they don't have the people in house, right? So that's where partnership is needed, right? And that's where we believe, you know, our approach with our partner ecosystem, this is not HTE delivering everything ourselves, but all of us in this together is really what we believe the only way we're going to be able to get this done. So Cole, let's double click on that. HPE and its partner ecosystem can provide expertise that companies in every industry are lacking. You're delivering HPE as a 360 degree approach to security. Talk about what that 360 degree approach encompasses. Thank you. It is an approach, right? Because I feel that security is a thread that will go through the entire construct of a technical solution, right? There isn't a, oh, if you just buy this one server with this one feature, you don't have to worry about anything else. It's really, it's everywhere and at least the way we believe it, it's everywhere. And in a 360 degree approach, the way we like to frame it is it's this beginning with our supply chain, right? We take a lot of pride in the designs, you know, the really smart engineering teams, the designer, technology, our awesome world-class global operations team working in concert to deliver some of these technologies into the market.  And then you move in to, you physically deploy that through our global operations. Now you're in an operating environment. That's where automation becomes key, right? We have software innovations in, you know, our ILO product of management inside those single servers. And we have really cool, new GreenLake for compute operations management services out there that give customers more control back and more information to deal with this scaling problem. And then lastly, as you begin to wrap up, you know, the natural lifecycle and you need to move to new platforms and new technologies, right? We think about the exit of that lifecycle and how do we make sure we dispose of the data and move those products into a secondary life cycle so that we can move back into this kind of circular 360 degree approach. We don't want to leave our customers hanging anywhere in this entire journey. That 360 degree approach is so critical, especially given as we've talked about already in this segment, the changes, the dynamics in the environment. And as Cole said, this is this 360 degree approach that HPE is delivering is beginning in the manufacturing supply chain. Seems like the first line of defense against cyber attackers. Talk to us about why that's important and where did the impetus come from? Was that COVID? Was that customer demand? Yep, yep. Yeah, the supply chain is critical. Thank you. So in 2018, we could see all of these cybersecurity issues starting to emerge and predicted that this would be a significant challenge for our industry. So we formed a strategic initiative called the Trusted Supply Chain Program designed to mitigate cybersecurity risk in the supply chain and really starting with the product lifecycle, starting at the product design phase and moving through sourcing and manufacturing, how we deliver products to our customers and ultimately a product's end of life that Cole mentioned. So in doing this, we're able to provide our customers with the most secure products and services, whether they're buying their servers for their data center or using our own GreenLake services. So just to give you some examples, something that is foundational to our Trusted Supply Chain Program, we've built a very robust cybersecurity supply chain risk management program that includes assessing our risk at our all factories and our suppliers. We're also looking at strengthening our software supply chain by developing mechanisms to identify software vulnerabilities and hardening our own software build environments. To protect against counterfeit parts that I mentioned in the beginning from entering our supply chain, we've recently started a blockchain program so that we can identify component provenance and trace parts back to their original manufacturers. So our security efforts continue even after product manufacturing. We offer three different levels of secure delivery services for our customers, including a dedicated truck and driver or perhaps even an exclusive use vehicle. We can tailor our delivery services to whatever the customer needs. And then when a product is at its end of life, products are either recycled or disposed using our approved vendors. So our servers are also equipped with the one button secure erase that erases every byte of data, including firmware data. In talking about products, we've taken additional steps to provide additional security features for our products. Number one, we can provide platform certificates that allow the user to cryptographically verify that their server hasn't been tampered with from the time it left the manufacturing facility to the time that it arrives at the customer's factory facility. In addition to that, we've launched a dedicated line of trusted supply chain servers with additional security features, including secure configuration lock, chassis intrusion detection. And these are assembled at our U.S. factory by U.S. vetted employees. So lots of exciting things happening within the supply chain, not just to shore up our own supply chain risk, but also to provide our customers with the most secure products. And so with that, Cole, do you want to make her big announcement? Alright, thank you. You know, the great setup though, because I think you got to really appreciate the whole effort that we're putting into, you know, bringing these online. But one of the just transparently the gaps we had as we proved this out was, as you heard, this initial proof was delivered with assembly in the U.S. factory. The employees, you know, fantastic program, really successful in all our target industries and even expanding to places we didn't really expect it to, but it's kind of going to the point of security isn't just for one industry or one set of customers, right? We're seeing it in our partners, we're seeing it in different industries than we have in the past. But the challenge was we couldn't get this global right out the gate, right? This has been a really heavy, transparently a U.S. federal activated focus, right? If you've been tracked and what's going on since May of last year, there's been a call to action to improve a nation's cyber security. So we've been all in on that and we have an opinion and we're working hard on that. But we're a global company, right? How can we get this out to the rest of the world? Well, guess what? This month, we figured it out and was taking a lot more than this month. We did a lot of work and we figured it out and we have launched a comparable service globally called server security optimization service, right? HPE, server security optimization service for ProLiant.  Do you want to be clever? HPE SOF that we can now deploy globally. We get that product hardened in the supply chain, right? Because if you take the best of your supply chain and you take your technical innovations that you've innovated into the server, you can deliver a better experience for your customers, right? So the supply chain equals server technology and our awesome, you know, services teams deliver supply chain security at that last mile. And we can deliver it in the European markets and now in the Asia-Pacific markets, right? Now, we could always just, we could ship it from the US to other markets, so we could always fulfill this promise. But I think it's just having that local access into your partner ecosystem and stuff just makes more sense. But it is a big deal for us because now we have activated a meaningful supply chain security benefit for our entire global network of partners and customers. And we're excited about it and we hope our customers are too. That's huge, Cole and Anne, in terms of the significance of the impact that HPE is delivering through its partner ecosystem globally as the supply chain continues to be one of the terms on everyone's lips here. I'm curious, Cole, we just a couple of months ago were at Discover. Can you talk about what HPE is doing here from a security perspective, this global approach that it's taking as it relates to what HPE was talking about at Discover in terms of we want to secure the enterprise to deliver these experiences from edge to cloud? You know, I feel like for me, and I think you look at the shared responsibility models and, you know, other frameworks out there, the way I believe it to be is this is, it's a solution, right? There's not one thing, you know, if you use HPE supply chain at the end or if you buy an HPE Proline at the end, right, it is an integrated connectedness with our as a service platform, our service and support commitments, you know, our extensive partner ecosystem, our alliances, all of that comes together to ultimately offer that assurance to a customer. And I think these are specific meaningful proof points in that chain of custody, right? That chain of trust, if you will, because as the world becomes more zero trust, we are going to have to prove ourselves more, right? And these are those kind of technical credentials and identities and, you know, capabilities that a modern approach to security needs. Excellent. Great work there. And let's go ahead and take us home, take the audience through what you think ultimately, what HPE is doing, really infusing security at that 360 degree approach level that we talked about. What are some of the key takeaways that you want the audience that's watching here today to walk away with? Right, right. Thank you. Yeah, you know, with the increase in cyber security threats everywhere, affecting all businesses globally, it's going to require everyone in our industry to continue to evolve in our supply chain security and our product security in order to protect our customers and our business continuity. Protecting our supply chain is something that HPE is very committed to and takes very seriously. So, you know, I think regardless of whether our customers are looking for an on-prem solution or a green lake service, you know, HPE is proactively looking for and mitigating any security risk in the supply chain so that we can provide our customers with the most secure products and services. Awesome. Ann and Cole, thank you so much for joining me today talking about what HPE is doing here and why it's important, as our program is called, to be confident and trust your server security with HPE and how HPE is doing that. Appreciate your insights on your time. Thank you so much for having us. Thank you, Lisa. For Cole Humphries and Ann Potten. I'm Lisa Martin. We want to thank you for watching this segment in our series, Be Confident and Trust Your Server Security with HPE. We'll see you soon.


Dibrio perihemolyticus is a bacterium commonly found in seafood and can cause acute gastroenteritis in humans. A new detection method has been developed combining multiplex polymerase chain reaction, PCR, and a lateral flow dipstick, LFD. This combination allows for rapid and accurate identification of pathogenic V. Perihemolyticus, from seafood samples. The method was optimized and evaluated using agarose gel electrophoresis and a universal LFD strip. The results showed no nonspecific amplifications and a limit of detection of 0.78-ing DNA slash reaction. Additionally, the method was tested on artificially contaminated shrimps and successfully detected the presence of pathogenic V, perihemolyticus at concentrations as low as 10 CFU slash 10 G of sample. This method may prove useful in developing a rapid and reliable detection kit for use in the food safety sector. This article was authored by Jirikrit Seytang, Fatapong Sukapat, Surya Palami, and others. We are article.tv, links in the description below.


Welcome, folks. This is Tom O'Brien of TFNN. We have five days a week. We go seven hours a day. We go 24 hours a day on the internet at tfnn.com. Always remember, folks, whatever you think about, you bring about whatever you focus on, grow, so everyone's having a great day, safe day. Let's make it a great night, folks. Don't take anything personally. Transform your life. When you refuse to take things personally, you avoid many upsets in your life. Your feelings of anger, jealousy, and even sadness will simply disappear if you don't take them personally. Knock it wise! Let's take a look at it out here. We have the Dow Industrial's up 201, NASDAQ up 141, S&P's up 38, Gold Contract up $3.40 traded 18.79 notes. We had Silver Dow 12 cents, 23 dollars, 54 cents, and ounce. Lates we crude up $2.45, $77.57 a barrel, notes and bonds. A 10-year note. Up 13 ticks, trading 1, 14, 13, to 30, or up 26 ticks, at 129.01, and $Kingdala. $Kingdala flat, 103, 240, Euro, 107, yen, 132.48, and the British Pound is at 1 to 21 to 1 US dollar. Our phone number's<PHONE_NUMBER>. Give us a call, folks. Want to know what's going on in your world and the world of the S&Ps? Let's take a look at them. Okay, so we take a look at this S&P out here. We have no volume in this market today at all, folks. I mean, this is really light volume. So what you have there is your swing that we're going into. So what happened, you know, bottom line on Friday went higher. You had 73 million shares there. You had a high that was established at 393.70. Okay? So bottom line yesterday, what you do yesterday, where do we go? We rejected lower price. You only had 65 million shares. You're going into 104. Now, and this is how it goes with time in the trade, folks. It just goes back and forth, back and forth on a continual basis when the market does that. Well, the market was, the market's doing out here today is that now it's coming into the swing point and you have a contraction of volume. That's amazing, actually. So now the real kicker here is going to be, do you get a rejection of higher price? Because if you don't get a rejection of higher price, bottom line, market can go higher tomorrow. As we come into this close, if this closes under 393.70, a probability goes much higher that you are going to go south in about two seconds at 8.30 tomorrow morning. So let's go take a look at this. We put this up. Let's see what we have here. So here's the number you want to keep your eye on. There's two different numbers, actually. The first one is right there. We're at 10 cents away from it. The first one, see that bar right there? The bar that you want to keep your eye on is the bar from 11, no, it said, $1.10, $1.13, $1.13, $1.13, $1.10, Eastern time, right? See the top of that bar? The top of that bar is $3.94, $2.28. We're $3.94, $4.47 right now. Number one, if it gets into that bar, then it may go to the bottom of it. And you know, when we bump the futures, that is a high-volume bar. And you can see even in the context of the spy, you know, compared to the rest of these bars, that's a high-volume bar. We did 1.47 million on that 10-minute bar. So that's the number you want to keep an eye on. We go to the NDX100. We take a look at the NDX100. Same type of setup inside the NDX, the 3Qs. What we have with the 3Qs is that that bar there, $2.75, $2.29. Now with the Qs, we're $1.20 over that. That volume there is $45 million. We came down yesterday with $35 million. You rejected lower price. Now the problem is that, well, it's going to be interesting, actually, because the Qs could do $35 million. We're at $33 million right now. So that's the, that's, first, we'll keep an eye on both the price and the volume. We go to the gold contract. We take a look at the gold contract. What do we have inside the gold contract? Gold contract out here, got another sideways move. You know, you went up to a high of $18.90. You went down to a low of $18.70 and you're trading out of $18.80. Now it has volume, $198,000 is good volume. And then we go to the good old dollar. So we take a look at the dollar and the bottom line is that the dollar is trying to claw its way back into the aspect of this $103.586. It's not there though. That's the bottom line. It is not there. And then, yeah, let's go to Amazon because we talked about the Amazon. That very well, you know, one of our tigers saying, hey man, this, this could be a bottom. And what I brought up is that the aspect is that Amazon did bottom three to four months ahead of everything in the market in 2008. So what you have here is that four days ago, Amazon was $81.94. We put this in a weekly. And let's see what we have on a weekly. So, hey, well, you broke the downtrend, man. That's the bottom line.  You broke the downtrend. Now, let's go to the bottom market because what is happening out here, folks, and did happen on the last CPI number is that someone got the CPI number first and they got it and they traded it in the bond market. It's amazing that the SEC still hasn't got, you know, anything on this. But the bottom line is that this 10-year is important to look at because on the last CPI number, yeah, so this is pushing high with late volume. On the last CPI number, check this out. Let me get this. We remember the whole deal. I mean, yeah, here it is right here. So the last CPI number by within, okay, so 60 seconds before it, trading volume in the 10-year future, soared, reaching three times the level seen a minute before the release of the last 24 CPI reports. Bottom line, someone got the number. That's the real bottom line. So we'll see how this baby shakes out tomorrow. You know, how we close today, I think it's going to be really important as to, are we going to spike higher? How's the number going to come in? The number's going to come in hot. The real key there is that how does the market react to it? You know, that's the bottom line because the argument out here is that, and you see Gunlap is making this argument yesterday, last night, he's making the argument that, hey, listen, the Fed's wrong and the bond market's right. And if that's the case, that means that the Fed is going to come down on rates before the mantra inside of the market is going to. Now, my take is that we're going to have rates for longer. They don't have to go down, but I think we're going to be laying out this for a good year. Stay right there, folks. Come right back.


In this turning video, we're going to look at conditional form-marking to form-mark by some text, in this case the product category, and then sorting by color. So first I'm going to highlight the products in the product category. Okay, conditional form-marking, how to tell a rule. I only want to give an example of text that contains, we're going to say we're doing well in machine products. So we're going to say machine, and we're going to say in color category, they're doing well first. We're going to go with a green color. So I'm going to say they're doing really well first. So we're going to color code them. I'm going to go back to the conditional form-marking, and let's tell a rule. That's the contains in this time on S24. Last one, we're going to say excessively red. So we're going to start typing a little bit. You get what we want. You can see that the type of everything. Now I'm going to sort by color. I'm going to highlight all the head-ins. Then highlight the data. I think the actual control is shifting down a little. Do this, or highlight them. Now I'm going to go to the data tab. Sort the sort by color category. Not on cell values, but cell color. We're going to take a traffic light, so red on top. Add a level for the category again. Cell color again. And I want green on bottom. With three colors, I can form a wave looking. So we're trying to form a top effect in this case, where the amber green. And this will do it. Okay. Now we've sorted all the reds. All the amber, all the greens. We now know where we're going. Well, not so well, and we're going to need it. So this completes the training video. We're going to start by using conditional formatting with packs. And then color in the information. And then we also looked at sorting the information via the data sort. And in this case, we needed a sort by two levels. And we sorted by cell color.