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From natural disasters like hurricanes and earthquakes to pandemics, cyberattacks, and labor strikes, companies have to navigate so many complexities to get goods where they need to go.
What's their secret weapon to operating within the unknown?
It’s the people.
Welcome to Supply Chain Champions, the show that showcases the stories of those who keep supply chains running smoothly. We're here to highlight their untold stories and share lessons they’ve learned along the way.
Join us as we peel back the curtain on the people who make supply chains work and enhance your own career in the process.
Tune in. Get smart. Move forward.
Amanda Cameron [00:00:00]:
Not everything is an AI problem. Sometimes it's more of a conditional logic, human reasoning function and sometimes I am a little fatigued by automation everywhere. But I think it's an important ideal to strive to because ultimately we want that efficiency, but not at the cost of, you know, human reasoning and humans working together collectively to solve problems.
Eric Fullerton [00:00:25]:
Welcome to Supply Chain Champions, the show brought to you by Project44, where we're talking to the people who make supply chains work. Hello, and thank you for tuning in to Supply Chain Champions. I am your host, Eric Fullerton. Today on Supply Chain Champions, we are taking on the biggest buzzword in the technology and really supply chain industry. That's right. Today we are talking AI and to do that, I am very fortunate to have a true expert in the AI space. Joining us today, Amanda Cameron. Amanda is an AI and Machine Learning Customer Engineer at Google.
Eric Fullerton [00:01:14]:
She's really focused today on supply chain use cases and she's going to help us demystify AI a bit, get a little bit specific and tangible and hopefully, like, maybe we won't get too technical. So, Amanda, thank you very much for being here.
Amanda Cameron [00:01:33]:
Thank you. I'm really happy to be here. Thanks for having me.
Eric Fullerton [00:01:36]:
Sure. So let's get started with a question. I kind of want to start at the top, which is there is a lot of terminology today and there is misconceptions and a lack of real understanding almost at the baseline level in terms of what these things actually mean. So if we start at the top and get some clarification, there are three terms, three words, three phrases, however you want to think about it, that are often used interchangeably, but they're different things. So we have AI, we have machine learning, and we have data science. And what I'm hoping as we get started is can you just at the top level, clarify what is the difference between those things, how are they different and what do they, what do they need?
Amanda Cameron [00:02:30]:
It's something I actually get a lot with my customers, like no matter what level of the business that they work at. And I see artificial intelligence as really that top-level layer. It covers a lot of areas, you know, in, in decision science, not just data science. We'll keep it simple for now and go from there. At a high level, like, artificial intelligence just refers to this capability to mimic human decision-making. So that can be in really effective artificial intelligence systems, you know, or can just be, if this, then you take this action. Right. It doesn't necessarily need to be this super complicated deep learning algorithm.
Amanda Cameron [00:03:09]:
And when I think about machine learning, we're starting to go down the AI value chain. From my perspective, we're making an inference off of something that happened in the past. So we're talking about techniques and methodologies such as classification or regression. And then the data scientist, you know, the AI is one tool in their tool belt, but they're also concerned with the data as a whole, making decisions. What insights do I need to surface to tell a certain story to the business, to prove value to the business based off of this initiative that's happening?
Eric Fullerton [00:03:44]:
Okay, that makes sense. I go to a lot of conferences and I feel like every conference that I've been to in the past three years has had a significant focus on AI, whether it's the keynotes or the topics where they have an AI track. And I guess what I've found time and time again is that these forums result in a conversation that is too high level, which is most often right, which is, oh, we need to use AI. Yeah, we're going to use AI to fix that. And that's like the most often. But then there are some instances when things get very technical very quickly. And I think it can be challenging to kind of find that middle ground. And what I think that middle ground is, and we chatted about this a little bit, is the use cases and practical application.
Eric Fullerton [00:04:41]:
So what I wanted to start with, and we'll go broader. Can you give me one very practical use case for AI and supply chain? Ideally, you know, it's something that you've worked on.
Amanda Cameron [00:04:54]:
Yeah, no, I'd really love to. And a lot of what I've been working on in the past year of supply chain has actually been around, you know, making sure critical commodities like strawberries, blueberries, and you know, other commodities associated to a cold chain and a supply chain. How do we deliver something that's super fresh to the store so the consumer has a good experience. Where it gets really interesting and complex very quickly is in supply chains you have a lot of regulations and laws that are instantiated that you want to make sure that you're compliant with. And one such example of this is going to be the PACA Act. It's the Perishable Agricultural Commodities Act. It was actually passed in 1930. So this is where fame and AI gets really interesting from my perspective is you have this old law that's still in practice today.
Amanda Cameron [00:05:44]:
And now we get to use new technology to be compliant for that law more efficiently and also more importantly in a more transparent way. And so at a high level, like why PACA is important is it protects growers, it ensures, you know, farmers and sellers get a fair payment for the perishable goods. And it also just promotes fair trade in general. And the USDA, you know, as a organization, it provides like arbitration and mediation services. Any business that's buying or selling produce or participating in interstate or foreign commerce, they have to obtain this PACA license. And so essentially what that means from an AI perspective and how generative AI specifically can help is AI can make sure that, you know, we're analyzing data to ensure at the distribution center specifically that we're visually inspecting produce to ensure and proactively identifying food safety risks. So if we're delivering on this concept and promise to consumers, if I'm a business and I want to deliver the freshest commodities at the store, then I'm going to be beholden to PACA. And I'm going to want to understand how technology can help me deliver that at the nexus of regulation and innovation.
Eric Fullerton [00:06:58]:
So that makes sense in terms of the use case. And now I'm thinking next time someone asks me about how within AI use case, I'll just say strawberries. But how would you actually use AI to help maintain that compliance?
Amanda Cameron [00:07:14]:
Yes. So, for example, I'd want like two essential capabilities. This concept of predictive modeling, right. Leveraging traditional machine learning workflows to do classification, analyzing historical data that's in the WMS so we can identify patterns. So, for example, high-risk distribution centers, whether it's their location or maybe the quality analysts that are inspecting the produce as it comes off the truck, maybe they're not as tenured as quality analysts at a distribution center. So now you have this gap and know humans making decisions and doing the best that they can. And if I'm someone in the business, I want to make sure that no matter what distribution center I'm in, that there is a unifying standard of what freshness is. And we want to be able to take action on that.
Amanda Cameron [00:08:04]:
And artificial intelligence is one tool that can help us out with that. And so specifically with image recognition and quality control, right. We start to go from, if we do have to fill out this manual defect report based on the quality of strawberries in a distribution center, can we start thinking about it in terms of where does automation start to weave in to help improve this process? And on top of automation, we really want that real-time monitoring, that data fabric from the sensor data is on the truck. And so now we're starting to develop this workflow of if I'm on a farm, if I'm a grower, that's selling my, you know, strawberries to a specific business we're all functioning under. And we're beholden to PACA in this specific way. But we want to make sure that there's that traceability, this almost ledger that we're building of the health of my commodities from the farm to the store, with the distribution stops in between.
Eric Fullerton [00:09:03]:
I like the specificity because I think that's. That's one of the things we're missing. So what I want to do now is I kind of want to. I want to zoom out. We're going to talk a little bit about you and your career. Starting off kind of around data architecture, cybersecurity space. We go into AI and machine learning, engineering, and then there's the Google component. And now kind of really keying in on retail supply chain space.
Eric Fullerton [00:09:35]:
And that's actually a very short version of your career and you can certainly extend it. But how does that happen?
Amanda Cameron [00:09:42]:
I've been actually asking myself a lot because I've been in the corporate world for the past eight years since I graduated college. And you know, I never really thought I would be here. I actually wanted to get a PhD in systems biology and kind of look at CRISPR and you know, I won't go into that too much, but basically mathematically modeling a biological process. And I was never really a math nerd. I was never someone who traditionally was always very good at math. I actually wanted to be an English major when I joined college. But I think it's important. And actually my love of reading has really served me well in my career and what I tell folks.
Amanda Cameron [00:10:19]:
And I've had the opportunity to go back to my alma mater and connect with so many other folks in networking contexts. And what I really tell people is you just gotta really find where the need is and follow your curiosity. And so like when I first started my career in cybersecurity, I thought it was something that was a really cool adventure and a way for me to find meaning. And you know, the globalization and what was happening in the larger world and starting to kind of understand is going from that perspective of I'm a consumer, I'm going to businesses to buy these specific services to. I'm actually in the business and I'm learning how businesses deliver solutions to consumers. That was something. Making things real was something that really has always driven my curiosity. And so kind of moving up a little bit at AT&T, I really cut my teeth on.
Amanda Cameron [00:11:10]:
I started out as a business intelligence analyst analyzing software security information. There's a Software supply chain, right. So while I really been working in retail for the past two and a half years, I've been kind of carving out this specialization in back-office transformation. You know, not just supply chain, but like HR processes where we can apply generative AI, financial processes where we can apply generative A.I. to accelerate solutions in that space. So it just kind of felt very natural in my career too. Right. Or whether you're solving a problem or if we're talking about my career, you really start with that core data foundation.
Amanda Cameron [00:11:47]:
You need to understand how systems work in a business. And then I kind of went from there to like, well, how do we build solutions with data? And AI is a very natural part of that.
Eric Fullerton [00:11:57]:
That's awesome. I think one of the things as we were getting to know each other a little bit, you said something really interesting. But I will also say that it's not necessarily uncommon. And when you said, when I talked to people in this space, which was I haven't always been working in supply chain, but I kind of always have been working in supply chain. Right. So when you think about that and we talk about how software has a supply chain too, I think that kind of mindset we're seeing more and more. I want to shift talking about mindsets though, because one of the other things that I think is interesting for you in your position now and probably even historically throughout your career is there's this kind of old guard, new guard component where you have these very tenured supply chain professionals with super valuable and tremendous experience and then you have these kind of newer technologists, systems AI engineering mindset. So can you talk a little bit about that? How does that play itself out for you?
Amanda Cameron [00:13:12]:
When I was at AT and T, we were always very interested in the inventory of inventories. Right. That was kind of our North Star. Basically what we were really interested in fundamentally was we were applying this old lens of how much inventory. How many infrastructure assets do I have? Well, that's an easy question when you're not working with cloud-centric assets. But when you're working in the cloud, you have to change that. We had to change our paradigm. And our reporting model of this infrastructure asset is ephemeral.
Amanda Cameron [00:13:44]:
It doesn't live forever, you know, and we're not afraid to turn it off. Right. And so that's one example of in bringing it back to supply chain is when in working with, going back to commodities specifically, you have heavily tenured quality analysts who spent decades building specifications between what and analyzing what the USDA specification is talking to business Folks understanding what their standard for fresh is, correlating that, building new specs, evolving specs, right? So now you're sitting on all of this unstructured data built from years of experience that's extremely difficult to scale, but at the same time that is incredibly valuable for a business. So if I'm a business executive, I want to understand how do I tap into the brightest minds in my organization and how do I ensure that logic is running automated processes in my organization.
Eric Fullerton [00:14:40]:
Okay, thank you. So that makes sense. I think. One thing I think would be a good transition is that when you think about many of the processes and the systems that are required, or at least maybe not required, but used for supply chains to function today, many of them are still older. We have legacy technology and even beyond that, I mean there are paper processes that are still being used all across the supply chain ecosystem. So when you think about bringing in net new very cutting-edge technology like AI, it can be a little bit of a challenge. So what I wanted to get your take on is what are some of those viable use cases for AI in supply chain today?
Amanda Cameron [00:15:35]:
I think this is such an interesting area and I think when you're using and applying AI to use cases, you really need to meet companies where they're at, right? And so there's still a space and I don't see some paper processes going away in the next year or so just because generative AI is very good at developing unstructured reports that paper processes are attached to. But if we're building and talking about net new capabilities, my take and kind of taking that spirit of meeting companies where they're at is like if I am building a defect report, right? At Google we have like document processing with document AI. Like that's a really good example where we can take these unstructured data blobs, whether they're images, correlate them to a defect report and then, you know, feed mine these reports for detail that we can apply to a predictive analytics, you know, a solution. So for example, if I'm doing a demand forecast and it's been a very difficult season for this particular batch of commodities, so we're still talking strawberries, right? And I want to understand, you know, historically, do I have more defects at this specific season than the last one or. And now we can start to really build out our data foundation here because it's not just the timing of the year and the season, right. It's the weather patterns associated to that region. All right. That's going to inform our demand Forecasts.
Amanda Cameron [00:17:05]:
What about, you know, sales data? That's happened historically. That's another thing. Market trends. I don't really see social media sentiment as, you know, something that's driving the consumption of things like strawberries. But at the same time, right at the crux of it, we're taking a lot of data and ultimately we're trying to optimize production planning and inventory management and distribution strategies as a whole. When we're talking about a demand forecast right now, we start to get into how interconnected supply chain is in general. So this is connected to an inventory optimization. So this is more decision science, less data science.
Amanda Cameron [00:17:42]:
And now we start to really build out this framework of I'm taking this AI solution, this predictive forecast that I ran of how many strawberries do I need at my distribution center? But now as a business, I want to start making decisions more deterministically about, well, how many pallets do I need at this distribution center? So now jumping from data science to decision optimization, we can start to kind of see and build our AI strategy in the sense of we can understand and intuit whether you know blatantly or latently, that there is almost the order of operations like a PEMDAS for some of these AI analytical techniques. Because before we know that, you know, to surface, just to bring it all together to surface a business problem. A lot of business executives are using KPI reporting. This is analytical AI. We use that predictive AI to curate a demand forecast. Now we're imputing the output of my demand forecast into my mathematical optimization function. Now I can start to provide back and refeed this into the business. You were starting to build out this concept of a self-healing supply chain and chaining multiple methodologies together to solve this very simple problem.
Eric Fullerton [00:18:57]:
When you were talking, it reminded me of a recent guest who came on and he's got like 35 years in supply chain space, right? So I asked him what's the most overhyped and underhyped tech in the industry today? He says AI and AI, the thinking is when you focus on a problem which some of the things you were just talking about, like the demand forecasting problem, and then you identify ways to use AI to solve or enhance it in a unique way, then the actual value of that is underhyped. But when you look at a business and say we need to use AI, where can we put it in? Then it almost becomes a solution looking for a problem and becomes almost overhyped. Have you seen that as well? I mean, actually, let's get Controversial. Like, do you think he's right?
Amanda Cameron [00:19:53]:
Yeah, I was going to say I thought you were going to ask me that question. And I was like, oh, he kind of stole my answer. That's a really solid answer, you know, but if I take it a step further, ultimately, you know, we're getting a little bit more specific about generative AI, about how it is generative AI in a hype cycle or not. And I think if we were just talking about creating content or having models writing songs or poems, right. This is very cool still. And it's powerful technology and it delights consumers. But when we get across workflows and methodologies and decision science and data science, I almost see generative AI as an orchestration-level layer to jump between like for example, imputing the output of my demand forecast into mathematical optimization. Generative AI with the LangChain framework can start to now connect the outputs of these functions to other tools.
Amanda Cameron [00:20:47]:
And so from my perspective, that feels very real and tangible to me. That doesn't feel very hyped. But where I start to kind of lose maybe faith and you know, people saying that they have an AI solution that can help them is, well, are you associated to a line of business use case? Like, how can you measure? Truly we're getting into this concept of applied AIs. I think the business and business executives in general that I work with are very hungry to know how AI can work for them and how can we measure that. And I think the line of business is the first place to start with that.
Eric Fullerton [00:21:22]:
So I wanted to ask a little bit about specific industries or sectors from your purview. Obviously where you've seen AI have a disproportionate impact within the supply chain operations.
Amanda Cameron [00:21:38]:
Where I kind of see AI as having a disproportionate effect on those industries related to supply chain cybersecurity really comes up as a natural example in my mind because, you know, first off, from a people perspective, there's a talent gap. And so if I'm in a supply chain and I'm trying to run a process that it needs to be table stakes, that the systems I'm working in are already secure. And so I want to make sure, like in using generative AI as an example, that I have agents built into my solution that can start to help automate some of those functionalities of cybersecurity. So to get really specific, right. If I'm concerned about data privacy and regulation, right. It's not just are we talking about data privacy of strawberries or commodities in general, it's. We want to make sure that the business specification, our performance, that we're working with, resilient systems that will not fail. And so when I think about where traditional reporting meets generative AI and where that reporting kind of comes into, you have two paradigms, right? Your reactive paradigm and your proactive paradigm.
Amanda Cameron [00:22:48]:
And for reactive reporting, you're really talking about known issue remediation, you know, at the table. What keeps the lights on in a business, just in general, where AI can help with that, is kind of synthesizing and providing a layer of explainability on top of those KPIs for people who don't live within those KPIs day to day. And then what we're getting into this concept and a lot of the businesses and customers I work with is reactive reporting is table stakes. There's lots of dashboards everywhere. Where businesses want to take this is really this concept of what actions do I need to take next and in what area what is hurting my bottom line, right? So ultimately tying down to the financial side of things, right? And when we're talking about this proactive insight is. And this is where generative AI is really the accelerator and the bridge here is it's not just providing that layer of explainability that's super important, but now you can start to get inferences across your dashboard saying, and you know, even beyond your dashboards, well, this percentage of commodities had this issues and they are associated to this region. You need to take a look at that. And so we can kind of build these solutions that are centered around human in the loop, where humans know what actions to take based on what AI is telling them.
Amanda Cameron [00:24:10]:
And there are going to be conflicts with the outputs of AI models and what human judgment says and where I'm kind of working and with consumers now thinking through in my own framework is where are those touch points where the model's perspective wins, so to speak, versus where are those touch points where the human judgment should be always prioritized? And I think this is now we're starting to get in the concept of superintelligence, right? And so we could kind of riff off it a little bit.
Eric Fullerton [00:24:40]:
It's actually a great segue because another thing I wanted to get your take on is the way in which the AI boom, which we are currently in, I don't think you can argue with that in terms of the focus and attention on it across industries in media, et cetera, how that will impact the supply chain, jobs and then the skills. I think I'm more interested in the skills needed in the industry. Right. So there is a potential impact to supply chain jobs. But I think what are the skills that are now maybe more important than ever as a result of this?
Amanda Cameron [00:25:22]:
Where things get interesting is okay. If we are expecting efficiency impacts, what do humans do with that extra time? And that really goes into the segue of what skills do I need for the supply chain of the future. And I think we're still figuring that out. As an industry where I've kind of landed in my humble opinion is, you know, non-technical, is like follow the curiosity and you know, from a technical perspective, it's really about looking for ways proactively is where are those spaces and touch points where the model quite isn't there yet.
Eric Fullerton [00:25:58]:
Let's imagine that there are some students out there and they want to be just like Amanda, they want to work in AI big tech company solving big problems. You're thinking about the global supply chain from your perspective. What would be some advice in terms of hey, these are the skills or these are tools that someone should develop to work in the AI space and work in supply chain.
Amanda Cameron [00:26:26]:
We're all a part of a supply chain, right? So in supply chain is so broad. So my first thought is think about what aspect of the supply chain excites you and just think about it from a grassroots level. Like when you're at a farmer's market and you're holding all this fresh fruit in your hands, Like I personally think back to like what farmer grew this or if this is from a different farm compared to what I saw at another booth, like what were those factors that led to it. And I feel like having that data-driven perspective or that I can quantify and I have a framework now that know whatever problem I'm facing, I have a way of thinking about like how do I measure that, how do I take action on that? And honestly, like my mathematics undergraduate really prepared me for that. This was kind of a turning point in my career when I was at AT&T. I'm the only one on the patent, so I'm very proud of that. But I passed into this inventory management solution for software-defined networking. And so basically it was the same concept of how do we measure ephemeral resources and the patents, essentially about tying it back to that orchestrator.
Amanda Cameron [00:27:32]:
But going through that intellectual property process at a corporation, and I believe I was 24 at the time, I felt like a little bit over my head and I was swimming in all those details. But that concept and I feel like that's defined my Whole career is taking those challenges and big steps and just raising your hand and saying, I want to try this, even though I'm not completely sure if it's going to work out. And having good managers who support that curiosity, who support chances that you can take to learn new things. Like, I've been so blessed in my life to have wonderful mentors who believed in me, even if I wasn't having that, like, mentorship need met necessarily my workplace. I've always felt like I'm growing. And one of my mottos is don't make the same mistake twice. And I feel like I've done a really good job with that as well.
Eric Fullerton [00:28:25]:
All right, so I'm going to take us in a totally different direction. Now. I know you said earlier you kind of slipped it in there. You're an avid reader. So I'm looking for some recommendations. And I'm curious, what is the best book that you've read in the past year?
Amanda Cameron [00:28:41]:
Yeah, well, this year's been really transformative for me in a lot of ways, you know, personally and professionally, just in all the ways. And I feel like what kickstarted, that is last September, around this time last year, I read for the first time, No Surrender, a Field Manual for Creating Work with Heart by Paul Wagner. And what it's really about is each of us is taking this idea that's taking form over the course of our lives. And we're in this massive cosmic ecosystem on a rock that's spinning around the universe. Our internal system biologically is changing, our world is changing. And just having that reckoning of how do I follow my intuition while also balancing, like, what delivers practical results in my life very quickly? So it's a book about discipline, but it's also tied to what makes you passionate about life.
Eric Fullerton [00:29:32]:
Okay, so I want to talk a little bit about the future and where we're headed, but I think it's important, whenever we're talking about the topic of AI to be specific, is there one specific use case that maybe isn't viable today from a tech perspective, but in the near future, six months, a year, maybe a little bit more will be viable that you think is really exciting or is going to have significant impact?
Amanda Cameron [00:30:03]:
Yes. And this is where I think where hype meets actuality. Right. Imagine combining generative AI with blockchain for, like, supply chain management. And where I think this can be really powerful is bringing it back to the PACA act is blockchain can create this immutable record for every transaction in the supply chain from the moment that the commodity is harvested to its final delivery in the store. And so we don't just have traceability now we have this universal ledger of what was the health of the strawberry at the distribution center versus at the store. And then I imagine with generative AI, we would get this kind of solution to tie the ledger of the transaction to the actual image of the strawberry. So we get those almost touch points of health of all commodities, not strawberries.
Amanda Cameron [00:30:53]:
And ultimately. Right. This reduces fraud. We can understand very in this universal ledger, right.
Amanda Cameron [00:30:59]:
That the USDA in this ideal, would own. And now we have transparent information about origin of the product, quality of the product, why a product was, you know, accepted or rejected at the store distribution center. And ultimately, I think this is what consumers are really hungry for, is that transparency and their knowledge that in this hypothetical future, if I'm a consumer and there's a QR code on my clamshell strawberries, what if I could see the lifetime of that strawberry, all those in the clamshell, right? And maybe the consumer doesn't need to know all that. And so I just kind of wonder, where are those other ways? And like with quantum computing, right, we can start to weed these technologies of the future with the technologies of the present. And I see generative AI as a vehicle for transporting that, that information across systems, processes, and people.
Eric Fullerton [00:31:52]:
Awesome. So before we close out, you know, we are a podcast, so maybe we're trying to go a little bit viral. I won't ask you anything too controversial, but one thing I like to ask all of our guests is, what's your hot take on the supply chain industry today?
Amanda Cameron [00:32:10]:
I would say in with the new doesn't mean out with the old. And sometimes not everything is an AI problem. Sometimes it's more of a conditional logic, human reasoning function. And sometimes I am a little fatigued by automation everywhere. But I think it's an important ideal to strive to because ultimately we want that efficiency, but not at the cost of, you know, human reasoning and humans working together collectively to solve problems. We still need each other. And, you know, I think especially when we're talking about labor workforce that's under a lot of transformation right now, not just, you know, stepping outside of supply chain. Right.
Amanda Cameron [00:32:49]:
I was reading this article that this younger generation is considering options alternative to college. And I think that's a wonderful thing to get to experiment with different life paths. Right. Because we're all on our own journey with things. And I want to make sure that the future we have those frameworks of we're also not enforcing old standards for you need a master's degree to do this job right. We need to be more flexible that there's multiple ways of learning and coming at a problem.
Eric Fullerton [00:33:16]:
I learned a lot. I know our listeners will too. I think about there was a lot of really amazing detail that we dug into today. So I just want to really thank you for your time and for spending it with us. We've talked a lot about strawberries, which is great because I think it carried what is often a very complex thing to wrap your head around. Carried it through with a lot of those examples. He talked about never making the same mistake twice, which is your motto. And then thinking about for the future this idea that in with the new doesn't mean out with the old.
Eric Fullerton [00:33:56]:
So outside of a lot of the awesome detail and nuance we got, like, I think those things will stick with me. But most importantly, Amanda, thank you for spending time with us. Thank you for being here. Thank you for being a Supply Chain Champion.
Amanda Cameron [00:34:09]:
Thank you so much for the opportunity. I'm super grateful to have met you and I hope you get to collaborate in the future with additional use cases.
Eric Fullerton [00:34:19]:
Thank you for listening to Supply Chain Champions. To get connected and learn more, visit project44.com and click the link in the comments to subscribe to Project44's newsletter. Tune in, get smart and move forward.