Eros Marcello a software engineer/ developer and architect specializing in human interfacing artificial intelligence, with a special focus on conversational AI systems, voice assistance, chat bots and ambient computing.
Eros has been doing this since 2015 and even though today for the rest of us laymen in the industry we’re hearing about AI everywhere, for Eros this has been something he’s been passionately working in for quite a few years.
Super excited to have him here to talk to us about artificial intelligence and help demystify some of the terminology that you all may be hearing out there.
I’m so excited to welcome Eros Marcello to this conversation to learn a little bit more about AI. He is so fully well versed in it and has been working in AI at since 2015, when it was just not even a glimmer in my eyes so I’m so glad that to have somebody here who’s an expert in that space.
Eros glad to have you here I would love to just jump into the conversation with you. For many of us this this buzz that we’re hearing everywhere sounds new, as if it’s just suddenly come to fruition. But that is clearly not the case, as it’s been around for a long time, and you’ve been involved in it for a long time.
Can you take us to as a creative, as an artist, as an architect, as an engineer take us through your genesis and how did you get involved and how did you get started. Let’s just start at the beginning.
The beginning could be charted back sequentially working in large format facilities, as surprise surprise the music industry, which you know was the initial interest and was on the decline. You’d have this kind of alternate audio projects, sound design projects that would come into these the last remaining, especially on the East and West, Northeast and So-cal areas, the last era of large format analog-based facilities with large recording consoles and hardware and tape machines.
I got to experience that, which was a great primer for AI for many reasons, we’ll get more into that later.
So what happened was that you’d have voiceover coming in for telephony systems, and they would record these sterile, high-fidelity captures of voice that would become the UI sound banks, or used for speech synthesis engines for call centers.
That was the exposure to what was to come with voice tech folks in that space, the call center world, that really started shifting my gears into what AI machine learning was and how I may fit into it.
Fast forward, I got into digital signal processing and analog emulation, so making high caliber tools for Pro Tools, Logic, Cubase , Mac and PC for sound production and music production. specifically analog circuitry emulation and magnetic tape emulation “in the box” as it’s called that gave me my design and engineering acumen.
Come 2015/2016, Samsung came along and said you’ve done voice-over, know NLP, machine learning, and AI, because I studied it and acquired the theoretical knowledge and had an understanding of the fundamentals. I didn’t know where I fit yet, and then they’re like so you know about, plus you’re into voice, plus you have design background with the software that you worked on. I worked on the first touchscreen recording console called the Raven MTX for a company called Slate Digital. So I accidentally created the trifecta that was required to create what they wanted to do which was Bigxby which was Samsung’s iteration of the series for the Galaxy S8 and they wanted me to design the persona… and that as they say is history.
Samsung Research America, became my playground they moved me up from LA to the Bay Area and that was it.
It hasn’t really stopped since it’s been a meteoric ascension upward. They didn’t even know what to call it back then, they called it a UX writing position, but UX writers don’t generate large textual datasets and annotate data and then batch and live test neural networks. Because that’s what I was doing, so I was essentially doing computational linguistics on the fly.
And on top of it in my free time I ingratiated myself with a gentleman by the name of Gus who was head of deep learning research there and because I just happened to know all of these areas that fascinated me in the machine learning space, and because I was a native English speaker, I found a niche where they allowed me to not only join the meetings, but help them prepare formalized research and presentations which only expanded my knowledge base.
I mean we’re looking into really cutting-edge stuff at the time, AutoML, Hyperparameter tuning and Param ILS and things in the realms of generative adversarial neural networks which turned me on to the work of Ian Goodfellow, who was until I got there was an Apple employee and now it’s gone back to Google Deep Mind.
He’s the father of Generative Adversarial Neural Networks, he’s called the GANfather and that’s really it the rest is history. I got into Forbes when I was at Samsung and my Hyperloop team got picked to compete at SpaceX, so it was a lot that happened in a space of maybe 90 days.
You were at the right place at the right time, but you were certainly there at a time where opportunities that exist today didn’t exist then and you were able to forge that. I also can see that there are jobs that will be coming up in AI that don’t exist today. It’s just such an exciting time to be in this space and really forge forward and craft a path based on passion and yours clearly was there.
So you’ve used a lot of words that are regular nomenclature for you, but I think for some of the audience may not be can you take us through…adversarial I don’t even know what you said adversarial … Yes Generative Adversarial Neural Networks.
A neural network is the foundational machine learning technique, where you provide curated samples of data, be it images or text, to a machine learning algorithm neural network which is trained, as it’s called, on these samples so that when it’s deployed in the real world it can do things like image recognition, facial recognition, natural language processing, and understanding.
It does it by showing it, it’s called supervised learning, so it’s explicitly hand-labeled data, you know, this picture is of a dog versus this is a picture of a cat, and then when you deploy that system in production or in a real-world environment it does its best to assign confidence scores or domain accuracy to you know whether it’s a cat or a dog.
You take generative adversarial neural networks and that is the precipice of what we see today is the core of MidJourney and Stable Diffusion and image-to-image generation when we’re seeing prompts to image tools.
Suffice it to say generative adversarial networks are what is creating a lot of these images or, still image to 3D tools, you have one sample of data and then you have this sort of discriminator and there’s a waiting process that occurs and that’s how a new image is produced. because the pixel density and tis diffused, it’s dispersed by you know by brightness and contrasts across the image and that can actually generate new images.
So for example if an artist is just dabbling with Dall-E, let’s say, and they put in the prompt so they need to put in to create something, that’s really where it’s coming from, it’s all the data that is already been fed into the system.
Right, like Transformers which again are the type of neural network that’s used in ChatGPT or Claude, there are really advanced recurrent neural networks. And current neural networks were used a lot for you know NLP and language understanding systems and language generation and text generation systems. Prior, they had a very hard ceiling and floor, and Transformers are the next step.
But yeah more or less prompt to image. Again tons of training that assigns, that parses the semantics and assigns that to certain images and then to create that image there’s sequence to sequence processes going on. Everyone’s using something different, there’s different techniques and approaches but more or less you have Transformers.
Your key buzzwords are Transformers, Large Language models, Generative AI, and Generative neural networks. It’s in that microcosm of topics that we’re seeing a lot of this explode and yes they have existed for a while.
Where should somebody start? Let’s say you have a traditional digital designer who doesn’t really come from an engineering or math background like you didn’t and they can see that this is impacting or creating opportunities within their space– where should they start?
First and foremost leveling up what they can do. Again, that fundamental understanding, that initial due diligence, I think sets the tone and stage for success or failure, in any regard, but especially with this. Because you’re dealing with double exponential growth and democratization to the tune where like we’re not even it’s not even the SotA state-of-the-art models, large language models that are the most astounding.
If you see in the news Open AI is and looking at certain economic realities of maintaining. What is really eclipsing everything is and what’s unique to this boom over like the.com bubble or even the initial AI bubble is the amount of Open Source effort being apportioned and that is you know genie out of the bottle for sure when it comes to something of this where you can now automate automation just certain degrees. So we’re going to be seeing very aggressive advancement and that’s why people are actually overwhelmed by everything. I mean there’s a new thing that comes out not even by the day but seemingly by the minute.
I’m exploring for black AI hallucinations, which for the uninitiated hallucinations are the industry term they decided to go with for erroneous or left field output from these large language models. I’m exploring different approaches to actually leverage that as an ideation feature, so the sky is the limit when it comes to what you can do with these things and the different ways people are going to use it.
Just because it’s existed it’s not like it’s necessarily old news as much as it’s fermented into this highly productized, commoditized thing now which is innovation in it and of itself.
So where they would start is really leveling up, and identifying what these things can do. And not trying to do with them on their own battlefield. So low hanging fruit you have to leverage these tools to handle that and quadruple down on your high caliber skill set on your on what makes you unique, on your specific brand, even though that word makes me cringe a little bit sometimes, but on your on your strengths, on what a machine can’t do and what’s not conducive to make a machine do and it’s does boil down to common sense.
Especially if you’re a subject matter expert in your domain, a digital designer will know OK well Dall-E obviously struggles here and there, you know it can make a logo but can it make you know this 3D scene to the exact specifications that I can?
I mean there’s still a lot of headroom that is so hyper-specific it would never be economically, or financially conducive to get that specific with this kind of tools that handle generalized tasks.
What we’re vying for artificial general intelligence so we’re going to kind of see a reversal where it’s that narrow skill set that is going to be, I think, ultimately important. Where you start is what are you already good at and make sure you level up your skills by tenfold. People who are just getting by, who dabble or who are just so so, they’re going to be displaced.
I would say they start by embracing the challenge, not looking at it as a threat, but as an opportunity, and again hyper-focusing on what they can do that’s technical, that’s complex, quadrupling on that hyper-focusing on it, highlighting and marketing on that point and then automating a lot of that lower tier work that comes with it, with these tools where and when appropriate.
I would imagine just from a thinking standpoint and a strategy standpoint and the creative process that one needs to go through, that’s going to be even more important than before, because in order to be able to give the prompts to AI, you have to really have to strategize where you want to take it, what you want to do with it, otherwise it’s information in and you’re going to get garbage out.
Right absolutely. And it depends on the tool, it depends on the approach of the company and manufacturer, creators of the tool. You know Midjourney, their story is really interesting. The gentleman who found that originally founded Leap Motion, which was in the 2010s that gesture-based platform that had minor success. He ended up finding Midjourney and denying Apple two acquisition attempts, and like we’re using Discord as a means for deployment and many other things simultaneously and to great effect.
So it’s the Wild West right now but it’s an exciting time to be involved because it’s kind of like when Auto-tune got re-popularized. For example it all kind of comes back to that music audio background because Autotune was originally a hardware box. That’s what Cher used on her song and then you have folks that you know in the 2010s T-Pain and Little Wayne and everybody came along it became a plug-in, a software plug-in, and all of a sudden it was on everything and now it’s had its day, it had 15 minutes again, and then it kind of dialed back to where it’s used for vocal correction. It’s used as a utility now rather than a kind of a buzzy effect.
Another thing to demystify.. Deep fake—what is that?
Yes deep fake, can be voice cloning, which is neural speech synthesis and then you have deep fakes that are visual, so you have you know face swapping, as it’s called.
You have very convincing deep fakes speeches, and you have voice clones that that more or less if you’re not paying attention can sound and they’re getting better again by the day.
What are the IP implications of that even with the content that’s created on some of these other sources?
The IP implications in Japan passed that the data used that’s you know regenerated, it kind of goes back I mean it’s not if you alter something enough, a patent or intellectual property laws don’t cover it because it’s altered, and to prove it becomes an arbitrary task for it has an arbitrary result that’s subjective.
You are the founder and chief product architect of BlackDream.ai. Tell us a little bit more about that what the core focus?
So initially again it was conceived to research computer vision systems, adversarial machine intelligence. There’s adversarial prompt injection, where you can make a prompt to go haywire if you kind of understand the idiosyncrasies of the specific model dealing with, or if you in construction of the model, found a way to cause perturbations in the data set, like basically dilute or compromise the data that it’s being trained on with malice. To really kind of study those effects, how to create playbooks against them, how to make you know you know zero trust fault tolerant playbooks, and methodologies to that was the ultimate idea.
There’s a couple moving parts to it, it’s part consultancy to establish market fit so on the point now where again, Sandhill Road has been calling, but I’ve bootstrapped and consulted as a means of revenue first to establish market fit.
So I’ve worked for companies and with companies, consulted for defense initiatives, for SAIC and partnering with some others. I have some other strategic partnerships that are currently in play. We have two offices, a main office at NASA/Ames, our headquarters is that is a live work situation, at NASA Ames / Moffett field in Mountain View CA so we are in the heart of Silicon Valley and then a satellite office at NASA Kennedy Space Center ,at the in the astronauts memorial building, the longevity of that which you know it’s just a nice to have at this point because we are Silicon Valley-based for many reasons, but it’s good to be present on both coasts.
So there’s an offensive cyber security element that’s being explored, but predominantly what we’re working on and it’s myself as the sole proprietor with some third party resources, more or less friends from my SpaceX /Hyperloop team and some folks that I’ve brokered relationships with along the way at companies I’ve contracted with or consulted for.
I’ve made sure to kind of be vigilant for anyone who’s, without an agenda, just to make sure that I maintain relationships with high performers and radically awesome and talented people which I think is I’ve been successful in doing. So I have a small crew of nonpareil, second to none talent, in the realm of deep learning, GPU acceleration, offensive cyber security, and even social robotics, human interfacing AI as I like to call it.
So that’s where Blackdream.ai is focusing on: adversarial machine intelligence research and development for the federal government and defense and militaristic sort of applications
This image of an iceberg comes to mind that we only see in the tip of it over the water you know with the fun everybody’s having with the Dall-Es and the ChatGPT’s but just the implication of it, what is happening with the depth of it ….fascinating!!
Thank you you for being with us and just allowing us to kind of just maybe dip our toe a little bit under the water and to just see a little bit of what’s going on there. I don’t know if I’m clearer about it or if it was just a lot more research needs to be now done on my part to even learn further about it.
But I really want to thank you for coming here. I know you’re very active in the space and you speak constantly on about AI and you’re coming up soon on “Voice and AI”.
And where can people find you if they wanted to reach out and talk to you some more about this or have some interest in learning more about Blackdream.ai?
The websites about to be launched Blackdream.AI. On Linkedin I think only Eros Marcello around and www.theotheeros.com, the website was sort of a portfolio. Don’t judge me I’m not a web designer but I did my best. It came out OK and then you have LinkedIn, Instagram its Eros Marcello on Twitter/X its ErosX Marcello.
I try to make sure that I’m always up to something cool so I’m not an influencer by any stretch or a thought-leader, but I certainly am always getting into some interesting stuff, be it offices at NASA Kennedy Space Center, or stranded in Puerto Rico…. you never know. It’s all a little bit of reality television sprinkled into the tech.
Before I let you go what’s the last message you want to leave the audience with?
Basically like you know I was I grew up playing in hardcore punk bands and you know. Pharma and Defense, AI for government and Apple AI engineer, none of that was necessarily in the cards for me, I didn’t assume. So my whole premise is, I know I may be speaking about some on higher levels things or in dealing more in the technicalities than the seemingly, the whole premise is that you have to identify as a creative that this is a technical space and the technical is ultimately going to inform the design.
And I didn’t come out of the womb or hail from you know parents who are AI engineers. This isn’t like a talent, this is an obsession. So if I can learn this type of knowledge and apply it, especially in this rather succinct amount of time I have, that means anyone can. I mean it’s not some secret sauce or method to it, it’s watch YouTube videos or read papers, you know tutorials, tutorials, tutorials.
Anyone can get this type of knowledge, and I think it’s requisite that they do to bolster and support and scale their creative efforts. So this is gonna be a unique situation in space and time where that you know the more technical you can get, or understand or at least grasp the better output creatively the right it will directly enrich and benefit your creative output and I think that’s a very kind of rare symmetry that isn’t really inherent in a lot of other things but if I can do it anyone.
I love it thank you for this peek into what’s going on the defense component of it, the cyber security component of it, the IP component of it… there just so many implications that are things we need to talk about and think about, so thank you for starting that conversation.
Absolutely pleasure I appreciate you having me on hopefully we do this again soon.