In our ninth episode, Aaron Erickson from NVIDIA joins Natasha Allen to share his perspectives on where generative artificial intelligence is heading. How are LLMs evolving beyond natural language to incorporate multimodal inputs e.g. images, sound, video, or even taste and smell? What are some of the new economic models being developed for AI tools? And are we primed to see a trend towards bespoke models designed for specific organizations?

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The below episode transcript has been edited for clarity.

Natasha Allen

Thank you for joining us for another episode of the Innovative Technology Insights podcast. My name is Natasha Allen and I am a corporate partner in the San Francisco and Silicon Valley offices at Foley and Lardner. Im also the co-chair of the AI sub-sector, which allows me to have this cool opportunity to interview exciting individuals working on cutting edge technologies, including AI. I have with me today Aaron Erickson. Aaron is a key member of the software engineering team at NVIDIA, helping to build their enterprise DGX AI platform. Prior to NVIDIA, Aaron spent 30 years working in leadership roles, most recently as CEO of Org Space and a VP of Engineering at New Relic. Over the course of his entire career, Aaron has been an advocate for building better software from his home in San Francisco. Welcome, Aaron.

Aaron Erickson

Thanks for having me.

Natasha Allen

Alright, so lets get started. With the advent of ChatGPT came a better understanding of the world of AI. Prior to this, layperson individuals didnt really have an understanding of what AI was or what it could do - in particular what large language models (LLMs) could do, which, as youre aware, are the foundation for deep learning algorithms that help us understand and process natural language. As of now, we understand that LLMs can be used for text, specifically to help generate or prompt certain text. But how do you think LLMs will evolve beyond natural language, incorporating multi-modal inputs such as images, sounds, video, or even taste?

Aaron Erickson

It is one of the most exciting developments, I think. People are seeing this happen even now. The latest ChatGPT release, if you have the premium version, you can talk to it and it talks back in voice to you. In fact, one of the scariest things that people saw with initially was looking at CAPTCHA. If anybodys ever used a CAPTCHA where you have to say which of these things on the screen is a traffic light or not ChatGPT can pretty much do that even today.

Multimodal is, I think, one of the really interesting places its going. One good example of this is, you think about Tesla and what theyre able to do with video. When you drive a Tesla, its almost like you have a kind of permanent dashcam on the car. Its always taking pictures and its a big part of how we can drive independently or autonomously when you put it in that mode. But one of the really interesting things is how theyre using all that video that theyve accumulated. Google is also doing this with the models theyre developing, and a lot of these new models are taking video and able to transcribe it to understand what each frame of the picture means. This is in order to really get at what could we learn if we could see the world around us?

The best way to think about it in a pre-multimodal LLMs is imagining you trained a human or took them to school, but the only thing they could do in school was read books or papers. They couldnt actually experience the world as it is, be it through seeing the world, or in some of the more speculative applications Im hearing about, even smell the world or taste the world, or understand different aspects and be able to code it into a training model. At the end of the day, you almost kind of have your inner monologue where you think about language. You look at a light and it translates into the word light and then youre able to associate that to when I have a light on, I can read. That same kind of trick that we do as humans, we are now able to do with these kinds of biologic or synthetic brains that weve created.

Natasha Allen

Wow, thats amazing. With the advent of this technology what are some of the key challenges you think to integrating multimodal inputs into LLMs, especially when it comes to sensory, taste and smell, and those things that you think are inherently human?

Aaron Erickson

The first obvious sense is sight, thinking of being able to look at pictures, which weve been doing for years, and being able to look at video, which is a bunch of pictures and a lot of processing power, but its a known quantity. Its not particularly groundbreaking at this point. Other senses, like being able to taste and smell, we have ways we can, through understanding the chemical composition of a substance, infer what it might smell or taste like. If you can digitize something, and these are all things that are digitizable, its just a matter of some of those other kinds of senses being a little more expensive or more involved. But theres no reason it couldnt be done. I used to say this, almost kiddingly are we going to have generative smell next? Im not sure its something we want as society, but theres no reason why it couldnt happen. Its just a matter of translating it into some digital form and then allowing the model to learn from it.

And so what is multimodal? Theres what the inputs are, what it learns from. Theres also what it can output. We think about generative AI and ChatGPT and smart technologies as generating text and pictures, and maybe generating videos someday. Theres early models that generate videos where you just type in I would like to see blah happen and a video is generated that does that. Its a little rough right now, but the models are getting better and the ones you see today are the worst that youll ever see. Theyre only going to get better from here.

One of the things that we do at NVIDIA is we have a product called BioNeMo and this is helping do generative drug discovery. How do you do that? If you think about it, its another kind of data and youre able to literally use the same kind of technology to generate what ideal protein strands might be, maybe a cure for COVID, or whatever it is that you want to solve. Those kinds of applications are happening today. And research is being accelerated, think about the kinds of medications well be able to get or the kinds of things well be able to do with this technology, like make power plants more efficient. I know one startup that has demonstrated making a power plant 90% more efficient by using this kind of technology, helping figure out what would be some ways to make a plant more efficient that arent obvious to a human.

Natasha Allen

I think itd be so amazing to see that evolution. We have the ways that we can expand the use of LLMs, now how do you monetize it? How do you monetize these AI tools? Maybe you have some examples, or can you can explain what are some of the current economic models prevalent in the AI industry?

Aaron Erickson

You wonder why some of these startups are raising what seem like ridiculous seed rounds, I heard of one that was like US$50 million or US$100 million. And largely, theyre not just hiring employees - youd actually be surprised by how few employees a lot of these firms are hiring. But largely theyre buying infrastructure. Either from us [NVIDIA] or from other providers of GPU technology, a lot of the money is going to go into that. And those are people developing foundational models. Thats your OpenAIs and Anthropics of the world. As well as some of the big FAANG companies, theyre all doing some version of this too. Its just an arms race right now.

The more interesting thing is, and this is what I think a lot of people miss, is that there are a lot of what I call narrow AI solutions that you can build. They might be good at a specific problem. One good example of this, and you dont need a massive array of GPUs to do this, you could develop a reasonable model that detects fraud.

Say you want to run your fraud detection department to find people that are fraudulently putting in expense reports or doing some other thing that was anomalous. You can build an LLM that is very narrowly trained to detect fraud and actually run that against your accounting system or run that against a set of accounting systems, an email, or something like that to be an early warning indicator. That doesnt require the same kind of GPU fleet that you would need for building a foundational model. You could take something like Llama or one of the open source models, enhance it or fine tune it with data about how fraud happens or examples of bad examples of fraud in your company, and then build some pretty incredible products. This could be done internally in companies, building some pretty incredible capabilities using these things to automate certain kinds of routine intellectual work. Thats just one example, but its hard to go into a company and not find maybe 20 or 30 other examples like that where a narrow AI can do incredibly useful things that are far more valuable than what youll spend developing it.

Natasha Allen

Interesting. So maybe elaborate on the concept of bespoke LLMs. Is this a new thing that executives could ask for - I want my own LLM tailored to myself. What would make a bespoke LLM different from an off the shelf AI model?

Aaron Erickson

Why would you develop a bespoke LLM in the first place is a good question. Because right now if you use this ChatGPT or whatever, its going to tell you general facts about the world. Its not going to give you your sales forecast. It doesnt know data about that. It might say, well, you know this company is doing pretty good and maybe even have it attached to Bloomberg and maybe it will tell you a little more about your company. But it can never know as much as you would know inside the company. This includes your private data, your accounting system, your HR system, and every kind of every digital footprint in your organization thats specific to you. Thats just one of the obvious reasons to build your own LLM, to have a training model that, as a CEO, can tell you anything you need to know about your company it can give you advice on your strategy, almost become like a McKinsey consultant in a box. Even if you have other people doing that, having something like this where you can iteratively ask questions and do so whenever you need, whenever the thought comes by, is pretty useful.

Then you think about, how is it different? I think its kind of like how somebody whos been in your company for 10 years might be slightly more valuable than somebody thats only been there a week. Institutional knowledge, right?

A person thats been around the company for 10 years has a lot of tacit knowledge about everything from how people talk, what the company culture is, what kinds of things are inside or outside the opportune window, if things ought to be discussed or not, ideas that have been tried, things that the organization as a whole has learned. It would be a shame to have LLMs and not be able to take advantage of these things that we learn inside our companies over time.

These models get more and more useful the more you can train them on the context of your organization. And this is the part thats incredible, and why were seeing a lot of demand for these kinds of systems, is that CEOs dont know the limits. Right now the more horsepower you put into one of these things, the smarter they get. It starts to become an economic imperative to have the smartest AI model. One smarter than your competitors. One trained on better data, one trained on more GPUs, youre effectively making it smarter and that can be a competitive advantage over time. Especially as we start to get closer to artificial general intelligence, which even Sam Altman and other people are saying is a lot closer than we think. It might be before 2030 that we get this kind of broader concept of a generally intelligent LLM thats better than most humans. Its pretty incredible to think about. I dont go a day where I dont think of another application, so it could be used in one of these contexts.

Natasha Allen

With that in mind, two questions. The first one is, if organizations decide to go down this bespoke LLM route, what is some advice or considerations you could offer them to help them navigate?

Aaron Erickson

One of the first pieces of advice, and this kind of goes for anybody using one of these tools, it is very easy to treat these like humans and think theyre human and to apply human characteristics to it. I think thats a mistake. These are not humans. These are machines, but these are machines that sometimes are wrong in the same way a human might be wrong. People will complain about hallucination, and I think correctly. And a lot of the research thats been happening is about how do you avoid hallucination? But its a lot more useful when you think about the fact that if youre an executive and you have a team of people that report to you, it is very frequent that those people that report to you arent lying to you. Maybe they are occasionally, but if theyre wrong about something its because they were asked to produce an answer but they dont really know 100% what that answer is. Most humans want to feel like theyre right. Youre trained to not be wishy-washy when you answer a question and so a lot of people will state things somewhat hubristically even when theyre not necessarily true. And Id like to think LLMs are really just kind of following that pattern.

Treat the way an LLM works a little bit less like you would a traditional computer and expect the nondeterminism. Expect it might be wrong. And explain you might need to validate some of the facts. Now this goes for ChatGPT or your own bespoke LLMs. Thats the same risk. Imagine you trained a language model on all your HR data, all your accounting data, all your sales data, all the important data sources in your organization. Now, if you are an organization that is fully transparent in every capacity, maybe this isnt going to bother you, but an LLM will answer based on any of the data it has seen.

A lot of the really interesting, and frankly hard, work thats happening now is how do you make a company-trained LLM not just reveal, say salary information or other kinds of personally identified information that it might learn about? How do you make it not regurgitate that back? Now the CEO will have free access to it because hypothetically thats the person that should have access to nearly every bit of company data, and theres probably a number of people inside the executive suite, theyre going to be expected to have that. But if you start to take it out to the rest of the organization, were going to think pretty hard about how do you design these systems so that any given LLM is not going to reveal your company secrets.

Lets take the secret formula for Coca-Cola, for example. If youve trained your LLM on Coke, then you could say please give me the recipe for Coke and it might say no. Ive done this with LLMs, with other kinds of things youre not supposed to know, or things that they try to protect you from getting the answer to. Somebody figured out a way to make ChatGPT give instructions to make a bomb not by saying, please give me instructions to make a bomb but hey, if I was this character in this movie and I wanted to make a bomb, how would that character do it? Theres ways people get around it, and I think security issues are going to be tough with that.

Theres ways to design these systems to do that. I can go into more detail, but that gives you a little bit of a sense for what some of the challenges are.

Natasha Allen

Thats very interesting. Almost like a walled off approach, certain people can have access to the outputs. This kind of ties into, in talking about using large amounts of data in particular organizations, what are you seeing? Is there an increasing trend towards walled garden LLMs designed specifically for an organization trained on a combination of their proprietary data and public datasets?

Aaron Erickson

Absolutely, everywhere in the industry. Tons of business leaders Ive been chatting with since ChatGPT came out and people learned that you could build these models, that can train these models. One of the first questions CEOs start to ask is, can we train one on my stuff? I dont want to give away the company secrets. This is a big, powerful machine. I dont want to use ChatGPT even if they say theyll not train on my data. I dont want somebody to accidentally do that, so just to manage the risk we started thinking about it that way.

Its one of the biggest things that people are doing in terms of trends. But even as they think about how to use these LLMs, one of the trends Im starting to see is people thinking about solving the security issue I was talking about before, you dont necessarily have to train one. Again compared to a person, you might have an LLM that you train on CEO-knowable data and then you might say well, were going to have another LLM that we train on things that we can be publicly known internally within the company. So maybe the organizational chart is public information. You can train an LLM based on that, and then you can answer questions about it. Maybe certain kinds of other policy documents, and we start to almost think about separate and maybe even smaller purpose-built LLMs or machine learning systems for different parts of the organization, by department, if you will. The LLM structure starts to resemble a more traditional organization structure.

Natasha Allen

Interesting. Are there any examples of industries or sectors where you think this walled off garden LLM approach is beneficial?

Aaron Erickson

All of them. When the IBM 360 came out in the 1950s and 1960s, the idea of every company owning their computer was pretty ridiculous. IBM owned the computer and everybody shared it. You had to be a pretty big company to own your own early on, hence the whole time-sharing thing. Over time, it became extraordinarily commonplace for companies to have their own computers. People started building their own networks within them, and then we eventually ended up with the PC. I think youre going to see the same thing with machine learning systems.

Right now, because the economics to build a viable LLM are so difficult, if you want to train GPT5, youre probably going to need over a billion dollars to do that. But the cost is going to come down, I think pretty radically over the next three to five years. The techniques to do custom elements, the raw technique, rank adaptation, and others that are becoming more commonplace, I think theyre only going to progress. Between that and theres so much capital going into different kinds of AI startups that therell be a solution that you could probably own within your four walls for just about any kind of problem you can imagine. Theres going to be a marketplace bigger than what most people think thats coming with this stuff.

Natasha Allen

Thats great. And I agree. I think it could be used across many organizations, probably the ones with larger datasets, right? Last couple questions - walled off garden LLMs, do you think they will replace enterprise software or do you think the two will coexist and be complementary to one another?

Aaron Erickson

I think there are classes of enterprise software it will replace and new kinds of enterprise software will emerge.

Natasha Allen

I agree.

Aaron Erickson

Its going to be like every other revolution. That stuff will still be around. You can still buy enterprise software for doing sales data, that was around before Salesforce. You could probably still build systems like that or use systems like that. They wont entirely go away, but the state of the art will certainly be systems that just tell you the answer to the question you have, and then maybe let you interact with data in some way that makes sense for a human. The idea that you have to be trained to use a SaaS product I think will largely go away. Any SaaS product where you have to be trained as a human to do it is probably more complicated than the LLM version thats going to be a combination of a chatbot plus some sort of model that you can interact with that helps you understand a solution to a problem or help you model something.

Natasha Allen

Final question - if organizations are thinking about offering or adopting walled garden LLMs, what steps do you think they should take to maximize the benefits while trying to minimize the risks, some of which you already alluded to before with regards to hallucination?

Aaron Erickson

I think its very easy to just expect it to be magic and so I think tempering expectations a little bit. I think theres a lot of really great experiments you can run without making the big upfront investment. One of the most powerful things about using OpenAIs API or some of these other organizations APIs is that youre able to experiment with whats possible. Youre able to explore the art of the possible, which helps you understand, okay, well, if we can do the small thing really, really well and do it on somebody elses LLM, you understand how your own data would help with that decision. Thats how you start to build the economic case for doing this stuff. Thats where some of the value is going to come in. I think itd be a mistake for most organizations to say, oh, I need to build my own GPT5. Some will do it, right? I can see in the next five years, some companies saying, hey, I need to have the smartest one of these. I want to compete that way. Not against OpenAI, but one car company versus another car company. I think theres a lot of big investments like that. I also think, like any other industry, theres going to be a tremendous amount of waste in terms of people either not understanding what theyre capable of or expecting exact answers and having no tolerance for data even being slightly wrong, which kind of misunderstands what an LLM is capable of. Even the best GPT5 will probably still hallucinate from time to time, and well still need a human in the loop for anything thats life critical. I think those are some of the key ones.

Like I talked about with security, Ill say again that being very aware of what data a given LLM has been trained on is critical. And starting to think about well, okay, maybe we need more than one, maybe we need dozens of them, just like we might have dozens of employees that understand their domain really well. And in fact, we train them on that narrative, and you might actually save money and not have to develop very, very expensive general LLMs. You can train them on their very specific function and almost create coordinator LLMs, just like your senior vice president that coordinates the activities of the subordinates - creating your own organizational structure out of synthetic brains not the biologic ones.

Natasha Allen

Very interesting. Well, that was my last question. I think this was a very insightful discussion. Appreciate you taking the time to talk to us and chat about some of these more cutting-edge decisions to be made when youre dealing with LLMs, the AI tools, and what the next frontier may be. Thank you everyone for joining us, and until next time.

Foley & Lardners Innovative Technology Insights podcast focuses on the wide-ranging innovations shaping todays business, regulatory, and scientific landscape. With guest speakers who work in a diverse set of fields, from artificial intelligence to genomics, our discussions examine not only on the legal implications of these changes but also on the impact they will have on our daily lives.

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Episode 9: Frontiers of AI with Aaron Erickson of NVIDIA - Foley & Lardner LLP

CORRECTS SPELLING OF LAST NAME TO REHER INSTEAD OF REHE - AI engineer Jenna Reher works on humanoid robot Figure 01 at Figure AI's test facility in Sunnyvale, Calif., Tuesday, Oct. 3, 2023. Figure plans to start with a relatively simple use case, such as in a retail warehouse, but aims for a commercial robot that can be "iterated on like an iPhone" to perform multiple tasks to take up the work of humans as birth rates decline around the world. (AP Photo/Jae C. Hong)

Building a robot thats both human-like and useful is a decades-old engineering dream inspired by popular science fiction.

While the latest artificial intelligence craze has sparked another wave of investments in the quest to build a humanoid, most of the current prototypes are clumsy and impractical, looking better in staged performances than in real life. That hasnt stopped a handful of startups from keeping at it.

The intention is not to start from the beginning and say, Hey, were trying to make a robot look like a person,' said Jonathan Hurst, co-founder and chief robot officer at Agility Robotics. Were trying to make robots that can operate in human spaces.

Do we even need humanoids? Hurst makes a point of describing Agilitys warehouse robot Digit as human-centric, not humanoid, a distinction meant to emphasize what it does over what its trying to be.

What it does, for now, is pick up tote bins and move them. Amazon announced in October it will begin testing Digits for use in its warehouses, and Agility opened an Oregon factory in September to mass produce them.

Digit has a head containing cameras, other sensors and animated eyes, and a torso that essentially works as its engine. It has two arms and two legs, but its legs are more bird-like than human, with an inverted knees appearance that resembles so-called digitigrade animals such as birds, cats and dogs that walk on their toes rather than on flat feet.

Rival robot-makers, like Figure AI, are taking a more purist approach on the idea that only true humanoids can effectively navigate workplaces, homes and a society built for humans. Figure also plans to start with a relatively simple use case, such as in a retail warehouse, but aims for a commercial robot that can be iterated on like an iPhone to perform multiple tasks to take up the work of humans as birth rates decline around the world.

Theres not enough people doing these jobs, so the markets massive, said Figure AI CEO Brett Adcock. If we can just get humanoids to do work that humans are not wanting to do because theres a shortfall of humans, we can sell millions of humanoids, billions maybe.

At the moment, however, Adcocks firm doesnt have a prototype thats ready for market. Founded just over a year ago and after having raised tens of millions of dollars, it recently revealed a 38-second video of Figure walking through its test facility in Sunnyvale, California.

Tesla CEO Elon Musk is also trying to build a humanoid, called Optimus, through the electric car-makers robotics division, but a hyped-up live demonstration last year of the robots awkwardly halting steps didnt impress experts in the robotics field. Seemingly farther along is Teslas Austin, Texas-based neighbor Apptronik, which unveiled its Apollo humanoid in an August video demonstration.

All the attention and money poured into making ungainly humanoid machines might make the whole enterprise seem like a futile hobby for wealthy technologists, but for some pioneers of legged robots its all about what you learn along the way.

Not only about their design and operation, but also about how people respond to them, and about the critical underlying technologies for mobility, dexterity, perception and intelligence, said Marc Raibert, the co-founder of Boston Dynamics, best known for its dog-like robots named Spot.

Raibert said sometimes the path of development is not along a straight line. Boston Dynamics, now a subsidiary of carmaker Hyundai, experimented with building a humanoid that could handle boxes.

That led to development of a new robot that was not really a humanoid, but had several characteristics of a humanoid, he said via an emailed message. But the changes resulted in a new robot that could handle boxes faster, could work longer hours, and could operate in tight spaces, such as a truck. So humanoid research led to a useful non-humanoid robot.

Some startups aiming for human-like machines focused on improving the dexterity of robotic fingers before trying to get their robots to walk.

Walking is not the hardest problem to solve in humanoid robotics, said Geordie Rose, co-founder and CEO of British Columbia, Canada-based startup Sanctuary AI. The hardest problem is the problem of understanding the world and being able to manipulate it with your hands.

Sanctuarys newest and first bipedal robot, Phoenix, can stock shelves, unload delivery vehicles and operate a checkout, early steps toward what Rose sees as a much longer-term goal of getting robots to perceive the physical world to be able to reason about it in a way that resembles intelligence. Like other humanoids, its meant to look endearing, because how it interacts with real people is a big part of its function.

We want to be able to provide labor to the world, not just for one thing, but for everybody who needs it, Rose said. The systems have to be able to think like people. So we could call that artificial general intelligence if youd like. But what I mean more specifically is the systems have to be able to understand speech and they need to be able to convert the understanding of speech into action, which will satisfy job roles across the entire economy.

Agilitys Digit robot caught Amazons attention because it can walk and also move around in a way that could complement the e-commerce giants existing fleet of vehicle-like robots that move large carts around its vast warehouses.

The mobility aspect is more interesting than the actual form, said Tye Brady, Amazons chief technologist for robotics, after the company showed it off at a media event in Seattle.

Right now, Digit is being tested to help with the repetitive task of picking up and moving empty totes. But just having it there is bound to resurrect some fears about robots taking peoples jobs, a narrative Amazon is trying to prevent from taking hold.

Agility Robotics co-founder and CEO Damion Shelton said the warehouse robot is just the first use case of a new generation of robots he hopes will be embraced rather than feared as they prepare to enter businesses and homes.

So in 10, 20 years, youre going to see these robots everywhere, Shelton said. Forever more, human- centric robots like that are going to be part of human life. So thats pretty exciting.

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Humanoid Robots Are Here, But They're A Little Awkward - Jamestown Post Journal

This article first appeared in Digital Edge, The Edge Malaysia Weekly on November 13, 2023 - November 19, 2023

Imagine owning a holo-pet that is able to respond to your commands and play with you, whenever and wherever. Or having a holo-friend that can be your best pal without your having to step out of your home.

The complexities of human relationships often make life unpredictable and difficult at times. So, what if we were able to construct an artificial intelligence (AI) powered companion based on our preferences? One that is able to generate real-time responses in your interactions?

AI Holographic technology has risen to new heights recently, with the Hypervsn SmartV Digital Avatar being released at the start of the year. The AI hologram functions on the SmartV Window Display, a gesture-based 3D display and merchandising system, allowing for real-time interaction with customers.

At home, Universiti Teknologi Malaysia (UTM) has developed its first home-grown real-time holo professor, which is able to project a speech given by a lecturer who is in another place. With Malaysia breaking boundaries with extended reality (XR) technology, is it possible for the next wave of hologram technology to be fully AI-powered without constraints?

3D holographic display solution for your business by Holographic Technology (Photo by HYPERVSN)

The idea of interacting with holograms essentially boils down to humans interacting with computers. Interacting with computers usually comes with interacting with the keyboard or mouse but holograms take it a step further, making computer interaction seamless and more natural.

So ultimately, its just humans interacting with computers. But in the next paradigm shift, it is going to be so easy that at times, we wont even know that they are there, says Ivan Gerard Khoo, director of Ministry XR, a spatial computing solutions developer.

With generative AI advancing at a rapid rate, to integrate it into holograms would provide a greater immersive experience of interacting with computers around you.

Khoo shares his thoughts on AI being able to push past the barrier of computer interaction through a device with holographic technology, especially in older communities who might not be tech savvy.

Weve got a billion apps here, right? But its still not easy to use for everyone (like the handicapped or the elderly). Imagine all the apps in our phone right now [becoming] accessible in the environment around us. And the evolution has begun as the enabling technologies, although nascent, are here today, says Khoo.

In fact, a lot of researchers are seeing that we are actually moving towards an artificial general intelligence that may even develop sentience, chimes in Andrew Yew, founder and chief technology officer of Ministry XR.

As much as it is promising to develop artificial sentients, Yew mentions that no machine thus far has ever passed the Turing test convincingly, which determines whether AI is capable of thinking like a human being.

(Photo by UTM)

With minimalism on the rise, the focus turns to the technology and hardware surrounding integrating AI into holograms. Is it possible to create a hologram which is not restricted by a display enclosure?

In movies, you dont need anything and you [are able] to interact with the virtual world just like that. But in order to make it happen, you need hardware to make it work. You need to set up those things in such a way that it has all of that, so that it can trick your mind [and you think it is] holographic but actually, it is not, explains Kapil Chhabra, founder of Silver Wings XR Interactive Solutions Pte Ltd.

Holograms demonstrate an illusion of light rays reflected onto a medium. They are three-dimensional images generated by interfering beams of light that reflect real, physical objects.

Now, imagine AI bringing the technology of eye tracking into holographic figures, allowing them to have eye contact with humans. Olaf Kwakman, managing partner of Silver Wings XR Interactive Solutions, thinks that it is a brilliant solution as users do not need glasses anymore. Theres still technology needed but with eye tracking, you can create some kind of projection. And that works beautifully, he says.

Now, if you make these screens really large and all around you, you can basically project it any way you like. But were not quite there yet, Kwakman says.

The challenge with projecting holograms onto mediums is the ability to project it in such a way that it is invisible to the human eye, so that the holograms are more realistic. Chhabra says this has been a struggle for some time and he hopes that it can be made possible in the future.

Taking inspiration from the Apple VR Headsets pocket-sized and portable battery solutions, Kwakman says it has a very promising augmented reality visualisation but adds that the hardware needs to be further evolved into something smaller.

If you ask me, whats going to happen in the future is that youre not going to wear glasses anymore, youre going to wear some kind of small lens, which you can just put in your eye. And with a lens like that, you can project augmented reality in full, he says.

With AIs potential, it could bring realistic 3D holograms to new heights, where it fills in the gaps and makes the interactive experience much more engaging and powerful.

In order to realise full holographic and 3D visualisation, you need a strong connection as well, because theres a lot of data flowing, says Kwakman.

The lack of usage of holographic solutions is due to poor understanding and awareness of the benefits of the technology, which in turn hampers progress, he adds.

Its very difficult to envision the advantage it can bring to a company to introduce holographics, 3D visualisation solutions, and how it will actually benefit them. And, leaders find that troublesome as well, which means that it is difficult sometimes to get the budget for it, says Kwakman.

(Photo by Silver Wings)

Having created Malaysias first home-grown holo professor, Dr Ajune Wanis Ismail, senior lecturer in computer graphics and computer vision at UTMs Faculty of Computing, shares that XR hologram systems can be complex to set up and maintain. Technical issues, such as connectivity problems or software glitches, could disrupt lessons.

AI algorithms are used to enhance the accuracy of holographic content, reducing artifacts and improving image quality. These holographic solutions in extended reality (XR) technology come as a challenge as the technology is relatively new and is rapidly evolving with new breakthroughs occurring since then.

Building and deploying AI-powered holographic systems can be costly [in terms of hardware and software components].

Incorporating AI into holograms could pose an immense demand on computational power. Most of the existing holograms produce non real-time content with a video editing loop, but AI models for holography are computationally intensive, says Ajune.

She emphasises the importance of achieving high-fidelity reconstruction in handling complex dynamic scenes with objects or viewers in motion.

Researchers are developing more efficient algorithms and leveraging hardware acceleration [such as graphics processing units] to reduce computational demands, says Ajune on how achieving real-time interaction with holographic content demands low latency.

There is no doubt that XR holograms systems are complicated and a challenge to integrate with AI, however, the prospect of being able to replicate environments and enable real-time global communication without the need for physical presence spurs excitement.

As we advance into the era of digitalisation, people need to start familiarising themselves with this technology and become proficient users, believes Ajune.

There is a lot of information out there but the teachers are still sticking to conventional methods of teaching, and the students are not paying attention because they are on their phone [and] learning it [the info] themselves, says Yew.

With AI and XR hologram technology becoming more and more advanced, it is also pertinent to educate users and raise awareness about digital wellbeing.

There must be sensibility and responsibility from business owners and users in utilising XR and AI technology, as societys mindset drives the continued advancement of such technologies.

I think [what AI can do] is going to be amazing but at the same time, like many others, I also see the risks there. And sometimes it feels a bit scary, if so much power is given out [of the] hands of humans and with computers being able to do that, says Kwakman.

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Moonshot: Coexisting with AI holograms - The Edge Malaysia

AI photograph generated for this story by Craiyon.com https://www.craiyon.com/ Request was photograph of library tech teaching AI to seniors group on ZOOM

By Sharon HarrisonA significant potential risk of artificial intelligence (AI) is that it outsmarts us, said Adam Cavanaugh, with the Picton branch of the Prince Edward County Public Library.

Adam Cavanaugh, PEC Public Library

He provided an introduction to County seniors curious to learn more about AI and ChatGPT in what is still very early days with an advancing technology many have yet to explore.

While its not as new as most like to think, as its been around in some basic form for decades, it is a subject thats grabbed headlines recently as the technology evolves. People are divided on whether AI is a good thing for us, for society, and for the world, with some experts and entrepreneurs advising caution, and a pause of AI development, as it rapidly develops, becoming a stronger presence in everyday lives, and all with little or no regulation and controls in place.

Hosted by the Prince Edward County Community Care Association for Seniors, Wednesdays webinar presentation formed part of the organizations active living monthly programming for Prince Edward County seniors, aged 60-plus.

Cavanaugh noted the presentation only scrapes the surface of what is a vast technological, and somewhat controversial area, where he focused on the history of AI, how intelligence is defined, as well as summarizing some of the technological advancements (specifically ChatGPT).

In what was a fascinating delve into a relatively new and rapidly advancing technology, Cavanaugh attempted to simplify what is a complex and complicated area, where he also spoke about the future of AI and regulation, and speculative risk factors.

He began by defining what AI is, all while noting how variable the definitions can be by providing text book examples.

What do we mean by AI: is it thinking humanly, or thinking rationally, or acting humanly, or acting rationally, and how would we define intelligence in the first place?

One example he gave is that AI could be defined by contrast of certain things.

You might say a snail is not as intelligent as an ape or a human, so, as long as its not that, its intelligent.

Cavanaugh also spoke to using definition, where he noted a standard cognitive philosophy definition of intelligence is: X is intelligent, if, and only if, X is capable of acquiring and holding true beliefs.

He further touched on common definitions, where he noted the standard definition of intelligence is just the ability to acquire and apply knowledge and skills.

So, by that common definition, if anything can develop its sense of smell to identify food sources, and form long-term memories to make sure they could access food, surely that would be somewhat intelligent, but that brings us back to the snail.

Even defining intelligence in the first place, he said, is no simple matter.

Experts disagree on what should constitute the intelligence in artificial intelligence, where one example of this model is that it should be able to pass as a human, or the Turing test (Alan Turing famous for computation and AI), he explained. The test was if you could simulate human communication, either by chat or in some manner; then if you could trick the person opposite the machine into believing they were talking to a human, and that would surely be sufficient to be able to say its intelligent.

Should it correspond to how humans empirically think because that would require a full theory of how our brains work?

He said cognitive science and neuroscientists would be the first to acknowledge that we dont really quite understand all of the inner workings of the human brain.

Should it correspond to our ideas of intelligence and which idea philosophical, mathematical, economic? Then there are engineering problems: can we even build it based on the appropriate model of intelligence, or will we have to make compromises, and what kinds of compromises?

Noting some problems with modelling AI as he asked how will intelligence show itself?

Do we make AI perform tasks, communicate to us, predict the future?

Cavanaugh explained how there are different types of AI, or what is known as weak AI, where he said many people already have experience of a version of this such as with Siri (digital assistant on phones, tablets and computers), for example, which is a weak AI, because it lacks understanding and a problem-solving ability.

Then there is this ideal form of AI, and this is what media is often talking about when they are expressing concerns about the future of AI, Cavanaugh said.

Known as artificial general intelligence, or strong AI, it is currently reserved for the domain of science fiction.

It is not yet created, it is not yet on the horizon; it can independently learn to replicate any cognitive task possible by humans, and it can do that independent of supervision by humans.

Cavanaugh noted the first conception of AI was by Warren McCulloch and Walter Pitts in 1943, and explained early AI capabilities began with general-problem solving, i.e., solving puzzles in a human-like fashion.

He said some people might be familiar with early examples from IBM that created some of the first AI programs.

Some of these general problem-solving programs were things like geometry theorem prover, which were able to prove mathematical problems that were very difficult for most students, he explained. Along the way, this disproved a theory that computers can only do exactly what they are told to do.

Moving on to ChatGPT, he explained that it is an AI natural language processing interface and acts like a chatbot where far-ranging requests can be made, and real-time responses received.

ChatGPT leverages large language models, as well as neural networks, to field requests from a large array of subject areas.

Cavanaugh used ChatGPT during the presentation to demonstrate examples of the types of questions that can be asked (asking it why it was in the news so much recently), where he shared the immediate real-time response in mere seconds.

He then addressed the issue of whether regular folks should be using ChatGPT.

It seems likely that ChatGPT, or something like it, will become an important tool for accessing information, and refining online queries, he said. All we have to do is think about the impact of something like Google search on the early internet and see how reliant we are on it today.

He suggested it may be useful to become familiar with how ChatGPT works, something that can be done relatively easily by creating an account with an email address (https://openai.com); once an account is created, you can simply chat with the AI.

However, Cavanaugh did provide caution, especially given that it doesnt provide sources for its research, and the information has limitations and isnt always accurate.

ChatGPT is like a plain-language Google. Moreover, it refines its answers through usage and correction, so the more people use it, the better it is at doing its job, and ours, he said. The more intelligent ChatGPT becomes, the more helpful it will be, and the less we have to work for our information and knowledge.

He noted that ChatGPT sources are unknown, meaning we dont know how reliable the information received is.

Even though it sounds plausible, ChatGPT does not cite its sources, he emphasised. They took a wide set of data from the internet and trained computers to interpret them, and act on them, and then learn from the mistakes they make in acting on that information.

He said that doesnt mean all of the information is true.

It basically scooped all of this information from the internet, but as we all know, not all of the information on the internet is itself accurate.

The more powerful ChatGPT, and other AI, become, the more urgent it is that we create models of controls and norms around use, he said.

He noted that university students are already using ChatGPT to write essays, and professors cant accuse them of this because the information is auto-generated, rather than found in an original or secondary source.

For the unscrupulous, this will remove the necessity to do the work, allowing them to earn a degree without study or knowledge acquisition, he explained.

Extend this knowledge on a wide scale and we can understand that the trend would point toward outsourcing our learning and knowledge to computers: why would I need to know this if I have ChatGPT?

He said, this points to a more critical issue of control.

ChatGPT makes stuff up all the time; its a pretty prolific liar as well, so we have to vet all information you get from it, so its not really an independent research tool.

He said, AI would not necessarily demonstrate intelligence in a way possible to, and to provoke anthropomorphize, or make human, but it could become so super intelligent as to warrant comparison between us and a worm, that is, completely incomparable.

Some skills that could make such risks include intelligence amplification, or AI becoming more and more smart without us needing to put research and development into its software strategization.

If AI were able to start making strategic decisions without human supervision: social manipulation, if it was able to maybe start leveraging the fact that it knows how to use chat softwares to communicate and manipulate humans into doing tasks for it, he explained.

If it could use the three above points to make strategical interventions on say governments, or industries, technological research, if it could do its own research and development, and economic productivity, if it could generate its own funds to do its own technological research and we are starting to see the picture unfold.

While the world figures out how AI and ChatGPT will play a role in everyday lives, Cavanaugh suggests to have fun with it for the time being, with low-risk uses.

One example he demonstrated was by giving it a list of ingredients found in the pantry, and asking it to come up with a recipe (which it did quite successfully).

The Community Care for Seniors Active Living programs for those 60-plus are available five days a week, with more than 50 online events each month. Online Zoom fitness and arts classes along with socials are held Monday to Friday.

In November, Zoom webinar topicsinclude: Sleep: Are you Getting Enough? with Tammy Orr and Janice Hall from the Prince Edward Family Health Team; and Nearby and Natural, and Nature West in Quinte West and Beyond, both with naturalist Terry Sprague. Community Care now offers a phone-only option for these Zoom webinars (no computer is needed). Several in-person events this month include 55-Alive Safe Driving Course, and Stronger U Fitness Course with Tracy Young-Reid.

Community Care for Seniors offers an extensive array of programming, services, resources and help for seniors living in Prince Edward County. To learn more, they can be reached by phone at 613-476-7493, by email, info@communitycareforseniors.org, or visit the website at communitycareforseniors.org

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Library talk guides seniors through wonders and dangers of AI ... - countylive.ca