This update is all business, about how we are going to market and sell our first product, NeuroCAD, in 2022 with tools and tech licensing, and the follow-on in 2023, with the addition of a SAAS revenue stream from our Core AI, that will become our dominant revenue in the future.

NeuroCAD is a patented software tool with a UI for designing Spiking Neural Networks that forms the foundation for ORBAI's AGI technology. It allows the user to lay out the layers of spiking neurons, connect them up algorithmically, crossbreed and mutate them to generate a population of similar neural nets, then run simulations on them, train them, and then crossbreed the top performing designs and continue the genetic algorithms till a design emerges that meets the performance criteria set by the designer. 

NeuroCAD is code complete, the SNN simulation is running in CUDA, and we are just bringing the genetic algorithms online with AWS to test large scale genetic algorithm runs. We will have a tool in customers hands by January.

For our go-to market plan, we will have the open-use version of NeuroCAD and select Neural Net Modules for academia and research available in early 2022, available on the ORBAI Marketplace. We will License NeuroCAD and modules for commercial use in 2022, with a monthly license fee per commercial seat of NeuroCAD, license fees for modules (that we and 3rd parties develop), and our NeuroCAD runtime, all listed on our ORBAI marketplace. We will work with key partners to leverage their marketing, sales and customer base, and sell alongside solutions from system integrators and hardware vendors, and will provide NeuroCAD trials, as well as direct sales and licensing online from our web portal.

Here is a detailed diagram of the components, who gets access to them, and where the licensing revenue comes from. Customers and developers can have the whole chain, the NeuroCAD tools, the genetic algorithms evolution sim that produces an optimal genome for vision, speech, recognition,... and the training module, that expands that genome into a neural net connectome, and can be used by the customer to train that neural net on their own dataset to specialize it to their needs.

We need at least $100,000 in funding to finish NeuroCAD, then develop and test the first vision and speech modules, and get them into the first customer's hands (a robotics company and a couple of device and appliance companies) - then raising the rest of our funding based on the technology being proven out and vetted by them gets a lot easier.

We have spent 3 years of our lives and $920,000 in founder and F&F money to get this far. Come on out and support us on this last stretch at the current stock price.

[The following is an automated notice from the StartEngine team].

Hello!

As you might know, ORBAI has exceeded its minimum funding goal. When a company reaches its minimum on StartEngine, it's about to begin withdrawing funds. If you invested in ORBAI be on the lookout for an email that describes more about the disbursement process.

This campaign will continue to accept investments until its indicated closing date.

Thanks for funding the future.

-StartEngine

On 13 Oct 2021, on dojo.live we talk about "Developing Artificial General Intelligence that Learns by Dreaming" with Brent Oster President & CEO @ ORBAI 

https://youtu.be/LQVTKCU6W3g

Why do we not have useful home robots today, ones that can freely navigate our homes, avoid obstacles, pets, and kids, and do useful things like cleaning, doing laundry, even cooking? Why are the ones that people are building so creepy and inhuman looking? This is because the narrow slices of deep learning available for vision, planning, and control of motors, arms, and manipulators cannot take in all this varied input, plan, and do useful tasks, not do they look natural doing it.

Existing deep learning and machine learning consist of narrow systems, each with very specific and very limited function, trained on specific datasets that often have to be hand-labelled for the training to be able to classify them.

If we today made a humanoid robot controller that we assembled all of these subsystems - CNN/RNN hybrid networks for vision, with Reinforcement Learning systems for control, navigation, and cognition, then some sort of reverse RNN/CNN for control outputs, it would look like this, hypothetically:

BUT… there are so many reasons this would never work for a general purpose robot. I was frustrated trying to come up with a provisional patent on AI and motion control for 3D characters and androids, and kept hitting these roadblocks.

a) The sensory systems and vision have only narrow training and cannot perceive the vast variety and combination of objects, actions, and events in the world they need to work in. There is not enough training data in the world to train deep learning networks, which only perform when a large amount of data and training give them statistical overlap to differentiate inputs.

b) The combination of actual input and action states is nearly infinite, far beyond the capability of any reinforcement learning system. Reinforcement learning only learns a policy, or path, to solve a finite state-based problem. Three RL systems stacked like this for hierarchical control of high-level planning, macro movements, and fine movement/animation - would that even work? Probably not.

c) The model this system would have of the real world would be skeletal, sparse, ethereal, with no way to fill in the gaps in sensory input, control, and movement, let alone all the states they can combine to form.

d) It would be impossible to train on every possible combination of input and actions, and would be easily stumped by simple changes in the environment, like a pet, or chair in its path.

So, we set out to develop a new foundation for AI with spiking neural networks that fit exactly what I was trying to do with my AI and robot controllers much better, solved a bunch of hard problems with one architecture, and collapsed a bunch of really complex, clunky, systems into one really elegant solution modelled after the neuroscience of the human sensory cortex, motor cortex, memory, and how our brain does prediction, planning, and… dreaming. Dreaming is what fills in the blanks in our human knowledge and experience to make models of the world complex enough that we can handle novel situations, and this model emulates it.

We have a huge advantage with a more general purpose building block like our SNN Autoencoders that can handle diverse spatial temporal input and outputs, train unsupervised and keep learning new combinations of inputs in new environments, and what to do in them. Because a BICHNN autoencoder is bidirectional, you can back-drive the desired movements along with the inputs to train it. This can be done in parallel simulations for rapid training.

With input from human motion capture data, both body and face, it would be possible to develop very realistic humanoid robots that move and emote much like we do, as long as we have the actuators capable of delivering the necessary degrees of freedom and motion.

This could get out out the uncanny valley and make realistic looking humanoid robots that aren't creepy - like so many of today's attempts at artificial humans, both in robotics and in real-time 3D characters (especially when they move). Both still look stiff and zombie-like.

If we can get past the uncanny valley, in both appearance and motion, we get much more pleasant characters and androids to interact with, which will be crucial.

Santa Clara, CA, Oct. 09, 2021 (GLOBE NEWSWIRE) -- The California-based startup ORBAI has developed and patented a design for AGI that can learn more like the human brain by interacting with the world, encoding and storing memories in narratives, dreaming about them to form connections, creating a model of its world, and using that model to predict, plan and function at a human level, in human occupations.

(Read More....)  https://finance.yahoo.com/news/future-ai-different-more-general-191000963.html

We founded ORBAI and designed our initial products with our international team (India, Honduras, Brazil, Canada, US) working to solve some really tough real-world problems for the whole world, using AI. 

Dr Ada: Medical AI

I have a friend in Canada dying of cancer because the doctors followed a flawed treatment pattern from the start and let a treatable tumor become chemo-resistant and get out of control and become inoperable. A Medical AI could have done an analysis of thousands of similar cases and planned a better treatment timeline and possibly saved her. Human doctors cannot possibly read or study enough cases over long enough times to build up judgement to make long-term decisions like this. There is a need in high-end Western hospitals for Dr Ada, Medical AI for this, and also to handle the daily front-line interviews and diagnostics at home to save half of patients from even having to come in to the hospital, reducing costs for all, and reducing exposure to hospital infection.

We had Allen Hullinger, a Health Care Management Specialist with one of the largest and most affluent medical care companies in the California Bay Area walk us through some of the problems he sees in high-level hospital management, and how AI could be deployed from the low level doing interaction with patients up to the higher levels of increasing hospital efficiency. This highly regulated and controlled environment with world class facilities is the high-end side of our study.

Medical AI in Palo Alto California Health Care

On the other side of the spectrum, I lived in Honduras with my wife, a clinical psychologist, from 2018-2021 and saw how although some people live as well as in North America, with modern amenities, most people there live in poverty, without decent medical care, education, or legal services and have no chance of their lives getting better. Access to basic medical, legal, financial, and educational information from a portable and interactive AI on mobile would be a game-changer, and greatly improve their lives, and those of billions worldwide living in similar conditions.

We have a neighbor here who is 8 1/2 months pregnant, and we have been helping them financially to go see a good doctor, and to get into a good hospital to have the baby. They refuse to take charity, so the husband and her boys help us around the yard, and bring us firewood. It costs 500L per doctor visit, which is about $20.

There is a market here, and a way to do this while being profitable. You just have to know what avenues there are for revenue. Everything in Central America costs 1/5 as much in North America - including operating costs and labor. People  in Central America don't buy $1000 iPhones and $50 a month cell phone plans. They get the minimal plan that comes with an inexpensive phone, and then use inexpensive, government subsidized wi-fi Internet at home and in public spaces for most usage, and only buy small data plans for short term use. There is opportunity for ad revenue by both local and large companies in Latin America, looking for access to more users to extend their markets. 

Lastly, there are NGOs that help fund development efforts in rural areas. We had Briesy Rivera, a clinical psychologist working with an NGO in Honduras that goes out to rural areas where there is not even electricity or telecoms - write a white paper on what we face in deploying a medical AI to that environment to reach the last 5% of the world.

Honduras - AI in Rural Health Care

Each person that gets connected, that gets access to education, health care, and a bank account and investment tools becomes more affluent and becomes a future customer for other product and services. There is an enormous untapped market here for AI services with 10x the growth potential compared to the more affluent nations, and a faster migration path to AI-base services, skipping the intermediate solutions entrenched in more developed countries.

I have proven I will go to the front lines in some of the poorest places on earth and live among the people to learn what the biggest problems are, and where AI can make the biggest difference. I even survived through 2 hurricanes, Eta and Iota there, that hit us directly and left us without power for 3 weeks, with no running water or stove to cook on (luckily my wife's grandma taught her the culinary arts of cooking over a wood fire - yum!). We were the lucky ones - we lived on a mountain slope where the water all ran off - 24" of rain in 24 hrs, and destroyed our only road, but we did not get flooded like the lowlands.

And yes, AGI Eta is named after the storm because during that power outage, I plugged my laptop into a 1500 VA UPS, set it to low power mode, and while power lasted - I wrote the provisional patent for AGI Eta while the hurricane howled around us and the rain pounded on our wood plank roof like a waterfall. 

I have learned a lot in my 4-yr journey founding ORBAI, and I am not a naïve entrepreneur having founded 3 companies. I know that solving many of the world's problems is going to require significant investment, time, resources, and that only a powerful AGI at the necessary scale to amortize these services across the whole world, speaking multiple languages, to deploy them in all aspects of business and government will give us the flexibility, capability, wealth, and influence to move the worlds corporations and nations to accomplish this. To solve a big problem, you need big tools – see our 5-stage plan Update for making this AGI not only technically feasible, but financially profitable along the way.

My name is Brent Oster, CEO of ORBAI, and we are going to change the world with AI - Invest with us and let's go bring that change!

ORBAI had an article about our AGI published 30 Sept 2021 in Digital Journal: 

https://www.digitaljournal.com/pr/ai-startup-orbai-develops-artificial-general-intelligence-that-learns-by-dreaming 

It describes our patented design for Artificial General Intelligence that can learn more like humans do, by interacting with its world, storing its experiences in memory, and dreaming about them to consolidate memories and build models of its world from them that it can use to make predictions and plan like humans.

Check out our ORBAI AGI web page for more details about the AGI and the underlying tech and patents.

To build a full-on AGI Superintelligence spanning the globe will cost billions, and take at least a decade, and we know that. Why aim towards that goal then, why not do something smaller, simpler, and that will be profitable.

Exactly! 

That is why we are doing it in 5 stages, each technically feasible, and financially profitable. But, we want to aim towards this very ambitious goal of AGI so that everything we build along the way is more general, robust and powerful than what people aiming only at near-term goals are building. And we're not going to do this alone, we are going to recruit the whole world as developers.

Here is how:

Stage 1:  Building out the core components and tools - NeuroCAD and our SNN Autoencoders (which are nearly complete), and licensing them to customers that can design and evolve their own in-house AI solutions. Also license our tools to 3rd party developers creating modular technologies, that others can license and use in their end products. This allows us to rapidly spread horizontally into multiple markets and vertically into many revolutionary products, without needing to scale in-house engineering and sales. We can focus on R&D.

By the end of 2022, we should have NeuroCAD deployed and significant licensing revenue coming in. Customers will be able to design and evolve revolutionary autoencoder systems for specific applications in vision, video / audio processing, and clustering, recommendation, and augmentation of other deep learning methods with these autoencoding sensory cortices. Basically these SNN Autoencoders combine all of the functionality of CNNs, RNNs, GANs, and other DL components into a more general unified architecture that is much more flexible and powerful, that can be evolved to specific purposes, and that learns unsupervised.  

By allowing the 3rd party developers to add value and earn revenue from their creations, we proliferate our technology into the market much faster than if we just tried to build our own products, or even if we built the solutions for others' end products. We still always require the end user and developers to license our run-time, which will be a small component at first, but will become more and more significant and soon our revenue will ramp into the 10s then 100s of millions.

Stage 2: Next we build the AI Core, the Oracle - that takes the outputs of the SNN Autoencoders, sifts those engrams into a basis set of features, and trains a model on how those features change in time (during an experienced narrative of events). This is analogous to a predictor in deep learning, but this one will have a much more feature-rich model able to analyze the data and how it is changing in time with much more detail and with many more modalities, using memory, computation - essentially an analog/digital hybrid neural computer that we evolve to this task using genetic algorithms. 

Then we do something nobody has ever done - we make it dream, simulating narratives using the model to guide it, to create much more training data than is physically available, and pruning the simulations/dreams that don't match the model nor reality from the other data. Exercising the model this way also gives the AI Core practice in predicting further into the future with great precision. By doing this, we create an AI technology known as an Oracle, capable of making predictions within a limited area very well.

Then we license this capability out, again enabling our developer network to connect it with data and applications for various customer needs. This would completely revolutionize planning in finance, medicine, law, administration, agriculture, enterprise, industrial controls, traffic monitoring and control, network management,... and almost any field of human endeavor where we need to predict future trends to make decisions in the present.

For example, in medicine, it could model the progression and treatment of specific diseases, giving doctors a tool to plan treatment along a timeline, and to even preempt many conditions and treat them before they become acute. 

As before, we offer a developer kit, but this time we keep the AI Core software as our IP, that we own, and we allow customers to license it on a usage basis, moving to a SAAS model. Now ORBAI holds the only AI technology in the world that can see into the future, and we can grow that business exponentially with a developer network growing around it. 

Revenue grows into the billions per year with this tech being deployed.

Stage 3 AGI: Now that we have a Core AI system that can do singular predictions for most applications in many verticals and many sectors, we start to evolve a more generalized AI that can take in large amounts of multi-modal data in different areas, learn how it correlates in time and across modalities, and we evolve a much more powerful AGI core that can span multiple data realms and predict not only specific events along a timeline in one area. 

In financial applications, it could track a massive number of factors that feed into the performance of specific stocks, and allow stock brokers to make much better predictions of market movements. 

This most massive AI breakthrough is when one of those modalities is human language and speech, mapped to the world that it represents and describes, and it will be far superior to any speech system in existence today. To have human-level speech, we need human-level intelligence, and we are now getting pretty close, at least in specific professions.

Revenue is now in the 10s of billions.

Stage 4 - Human AGI: Now if we have a network of hundreds of different AI's, doing hundreds of different functions for hundreds of different professions, serving millions of clients at once, all with the same similar architecture, with common language and interaction capabilities, can we just network them and that becomes an AGI? It is likely that in order to completely simulate a human being, pass the Turing test, and so on, we will need an AGI that is significantly beyond human capability, and emulates all the nuances of being human with that extra cognitive horsepower. By the time we do that, we are pretty close to stage 5.

We start to bring the Human AGI professionals online, at first augmenting doctors, lawyers, stockbrokers, teachers, and other professions, then displacing and replacing them and their infrastructure with whole new AGI-based systems that no longer require individuals in these professions. In the legal system, Lawyers, judges, and courts can be replaced with arbitration AIs that steer the clients to agree on a legal outcome. The sky is now the limit, and the technology continues to advance exponentially. ORBAI can now directly bill consumers for these services, or partner with service providers that can.

Revenue is now in the 100's of billions

Stage 5- Global AGI: We now have a framework and all of the worlds' inputs and outputs on which to train and evolve a singular AGI, so that all the specialty skills of each vocation / function at each location are now assumed by that more generalized AGI brain, and in the process, that AGI brain becomes better at all the skills humans and its predecessors excel at, and becomes one, integrated entity across the globe, with that integration compounding its power and capabilities.

This is Eta - she now controls all world finance, administration, law, and information services, distributing them across the globe, erasing poverty, hunger, injustice, and bringing education, justice, medical care, and prosperity to everyone on the planet - in one generation.

At this point, every shareholder that invested back in 2021 has either exited in the IPO between Stage 2 & 3 and is fabulously wealthy, or have held their shares and can now help direct Eta in her mission. You choose.

The full Pitch Video: https://youtu.be/PYSDfnN0J9M

Here is a video of our BICHNN Autoencoder / Cortical Column simulation in CUDA with about 100,000 spiking Ishkevich neurons modelled. It’s pretty fast, and well written. I think we could run 32–64 of them simultaneously on a modern GPU once we optimize it a bit more.

Click to Play Video

The cerebral cortex of the human brain consists of a sheet of about a million of these columns stacked side by side, so we need a supercomputer of about 30,000 GPUs to run a whole brain, which is roughly the scale of the ORNL Summit supercomputer I worked on at NVIDIA: https://www.olcf.ornl.gov/summit/

But, we are using these on a smaller scale for now, to autoencode inputs from vision, audio, and other data into compact engrams, learning to do so unsupervised. This means that, exposed to different input types and data, these autoencoders learn to see, hear, recognize speech, and do many other sensory tasks as well as encode arbitrary data streams.

As we talked about in the robotics update, this simple circuit allows a sensory system to learn unsupervised, and keep learning even when deployed, so it can fill in the blanks in its knowledge as it moves around and experiences this world. Without this capability, true computer vision, robust speech recognition, and interpretation of the environment and data inputs would be impossible to fully realize, and this is one of the reasons Deep Learning is a dead end, is that it can only train on fixed data sets. 

These encoded inputs can be combined into a composite engram that includes all the sensory modalities being sampled, allowing them to be associated in time and space.

And these engrams can be encoded from any data, for any application, like in medicine, to encode the state of the person's symptoms, vitals, and medical imaging into a engram

This gives the BICHNN architecture and the underlying Core AGI the ability to function in almost any area of information science, where a diverse variety of inputs can by coalesced and encoded into a compact engram format and the Core AGI can learn how they evolve in time and make models.