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The rapid pace of global AI development has some calling for a clutch, if not a brake

By Nelson Bennett, ChatGPT | May 1, 2023, 3:30pm

Sanctuary AIs general-purpose robots are remotely piloted or supervised by people|Sanctuary Ai

General-purpose robots that may soon be able to assume manual tasks performed by astronauts in space. Programs for self-driving cars that understand human behaviour. Developing new drugs to fight cancer.

These are some of the novel ways in which B.C. companies are using machine learning and artificial intelligence and to the clear potential benefit of humanity.

But like nuclear fission, machine super-intelligence is a Promethean power with the potential to be corrupted, which is why there is now a sudden push to erect guardrails and develop ethical guidelines and regulations before AI either becomes autonomous or simply falls into the wrong hands.

Elon Musk and Yoshua Bengio, a Canadian pioneer in deep learning, are among the more than 27,000 people who have signed an open letter calling for a six-month moratorium at all AI labs, until concerns about it can be addressed. Just last week, KPMG convened what might be described as an emergency summit in Vancouver to discuss AI and the opportunities and challenges this rapidly developing technology presents.

The purpose of it was really to start a conversation around whats becoming very clearly a very transformative piece of technology that is just accelerating in terms of its adoption, said Walter Pela, regional managing partner for KPMG. Theres obviously concerns and issues. At the same time, it is a tool thats being adopted.

In fact, its being adopted by businesses in the U.S. a lot faster than in Canada, according to a KPMG survey released last week.

The pace in Canada right now of AI adoption in business is about half of what it is in the U.S., according to a recent poll we did in February, Pela said.

Vancouver does not have pure-play AI companies or institutes, like Montreals Mila research institute, but it has developed a hub of applied AI companies.

Computer scientists have been developing machine learning and artificial intelligence for decades. But it wasnt until San Fracisco, Calif.-based OpenAI made its ChatGPT-3 chat bot available to the public that ordinary people got to see just how powerful this one type of AI already is.

The pace of Open AIs progress has generated both awe and alarm.

Some of the concerns around generative AI programs, like ChatGPT, is that they could be used for fraud, cybercrime and the amplification of misinformation. Another concern is that its level of disruption at least similar in scale to that of the internet, if not greater could put a lot of people in creative fields and knowledge industries out of work in fairly short order.

ChatGPT is just one type of generative AI technology that has the capacity to generate text, images, videos or music that look or sound like they were created by humans.

ChatGPT is text-based, and is basically like a super digital library containing a massive corpus of text from the Internet a library with the ability to learn, to respond to commands and to write anything from song lyrics to HTML code for websites, all in about 30 seconds. You can ask it to write an essay on virtually any topic, and then, half a minute later, ask to have that essay rewritten it in almost any language.

Diffusion AI is a text-to-image model. Diffusion AI programs like DALL-E, Midjourney and Stable Diffusion have the potential to displace illustrators. In fact, that may be the biggest immediate threat that AI poses not rogue machines turning their human masters into servants, but sudden, massive displacement of workers in certain industries, such as web design.

A Vancouver company called Durable, for example, uses AI for a program that can build basic websites for any type of business in 30 seconds.

Any knowledge worker that is trained to do certain things and already theyre interfacing in the digital realm thats the first thing that gets impacted, said Handol Kim, CEO of Variational AI and a board director for AInBC. So, content writers? Absolutely already happening. Graphic design, already happening. Lawyers? Starting to happen. Accountants, starting to happen. Software developers? Already youre getting decent code. Its not great, but its not bad. Heres the thing it gets better. Next year, it will get twice as good. The year after that, it will get five times as good.

Eventually it will be able to make movies. Anything thats represented digitally and can be manipulated digitally, eventually it can get to a level thats uncanny.

I think its fairly clear that there will be job dislocation in fairly short order, I think, said Steve Lowry, executive director of AInBC. For fastest change, I think well see in the creative realm generative AI changing the job of designers, photographers, marketers like overnight basically.

Though AI threatens to make some jobs obsolete, it also creates new opportunities including jobs in applied AI.

A number of companies in Vancouver are using various types of machine learning and AI for a wide range of applications.

Sanctuary AI, a B.C. company co-founded by Suzanne Gildert and Geordie Rose the founder of D-Wave Systems, which built the worlds first quantum computer is using AI in the development humanoid general-purpose robots.

The company is using AI to develop a cognitive architecture for its robots that will mimic the different subsystems in a persons brain. The company expects the robots could be used to replace humans to do work that is dangerous, tedious or in the vacuum of space.

In the not-too-distant future, Sanctuary technology will help people explore, settle, and prosper in outer space, the company said in a news release last year, after securing $75 million in a Series A financing round.

Inverted AI is a Vancouver company that uses deep learning and generative AI to understand the behaviour of drivers, cyclists and pedestrians, for companies developing self-driving vehicles.

Companies developing self-driving cars or advanced driver-assistance systems use simulators. Inverted AI helps to add the irrational human element to those simulations by recording traffic with a drone and then using machine learning to learn how humans behave in traffic.

We record how people behave on the road, both as drivers but also as pedestrians, cyclists and so on, and we use that to improve the realism of simulations for self-driving cars, said Inverted AI CTO Adam Scibior, an adjunct professor at the University of British Columbias computer science department. We basically make those more realistic.

Variational AI is using a type of machine learning variational auto-encoder to identify small molecules that will bind to protein kinases associated with cancer and tumors. But there are about 500 protein kinases in the human genome, all similar in structure, and finding the right molecule to bind only to kinases associated with cancers is a massive trial-and-error challenge.

If you have a small molecule that binds to one kinase, its going to bind to many others, and you dont want that, Handol Kim explained.

Rather than hunt for pre-existing molecules, then, Variational AI uses generative machine learning to make new molecules. In other words, rather than trying to find the right key out of hundreds of options, Variational AI is using machine learning to just cut new keys.

The generative chemistry process the company uses has the potential to dramatically accelerate the drug discovery process.

It can take a decade and up to $1 billion to $2 billion to take a new drug through clinical trials and approval for use. Kim said using machine learning may be able to dramatically reduce both the time and costs associated with new drug discovery.

What were trying to do is turn years into months, Kim said. Were trying to turn pre-clinical development, move it from hundreds of millions of dollars to single-digit millions.

nbennett@biv.com

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How artificial intelligence is already powering work in B.C. - Business in Vancouver

A revolution is taking place, but we seem to not yet realize it.Paradigm shifting technologies often produce an abrupt transition when they get adopted. However, that transition is not easy to recognize early on: the effects of an exponential trend appear linear at the begninning, so the explosive force of the transition that occurs a little later takes many by surprise.

Let us look at the status of development of large language models. This is a relatively new technology that is powered by recent advances in machine learning - in particular, the capability we have acquired to train very large neural networks tasked with producing meaningful text in answer to arbitrarily complex questions. The networks that perform this task today have billions or trillions of parameters, whose values are learned by processing huge datasets of text mined from the internet. The training of these models takes enormous amounts of computing power, and correspondingly large amounts of money (of the order of 10 million dollars per training, and up).

The scaling up of the size of these large language models, in particular ChatGPT3 and 4, has brought to what appears like a phase transition in their performance. Yet we have grown accustomed to treat artificial intelligence developments with a contempt: every time something new comes up which in the past used to be considered a far, hard-to-achieve target, we react by some shoulder-shrugging. Self-driving cars? Just a dumb neural network trained with lots of images. Speech recognition? Nothing but mathematical transformation of sound into time series. Computers beating humans at chess and go? Only an effect of CPU scaling. We continue to shift the bar up, and keep claiming that artificial intelligence is "something else", which is yet to come. But is it?

I am not a true expert in artificial intelligence - I am a physicist, for goodness' sake! But I do work with complex machine learning systems, and I have been an observer of the field for several decades now. So I feel entitled to tell you what I think about the matter. What I see is that the sensation produced by the recently made available ChatGPT models mostly lays in observing the wealth of applications that these tools have, and their game-changing effect on our society; but we should look further in it.

The potential dangers of unrestrained, uncontrolled use of the new technology is a real concern which has brought to the open letter arguing for a 6-months pause on the development of these models, by the "Future of Life" institute. It seems indeed a reasonable course of action to wait before developing further more powerful language models, and use the time to try to assess the situation and create a system of checks and balances to prevent damage to crucial elements of our civilizations: in particular, the exploitation of these AI technologies might result in manipulation and reshaping public opinion, for the purpose of gaining political control. But there are also other potential threats.

If you have never had a conversation with ChatGPT I suggest that you try it out for yourself. The system is capable to not only correctly interpret quite complex questions, but to produce text and answers that are of very high quality. After a while, it feels like you are really talking to a sentient being. Now, we must be careful here - of course, we cannot call "sentient" a computer program that puts together words according to mathematical recipes, can we? And by the way, it is not difficult to get ChatGPT produce false statements, or completely made up references. But so can we when we talk with other humans!

I have started to use ChatGPT as a companion in my studies, a better, smarter, faster, more powerful version of Google. Yesterday I tested it by formalizing in seven lines of text a problem that would probably have taken twenty minutes to precisely explain to a colleague - those seven lines of text were quite thick with math, written as you would write math in an email ("Consider a likelihood ratio of Poisson measurements, R= L_1(Poisson(N_i|mu_i,1)) / L_0(Poisson(N_i|mu_i,0) where i runs on a set of observed counts ...."). Well, ChatGPT not only provided me with a correct answer to my question, but it also used the same kind of language in its answer; and when I asked it to produce code that performed the operations leading to the solution of my problem, it did so flawlessly. Of course you have to be careful when using these outputs: there is absolutely no guarantee that the programs will be correct, or that the answers are correct. But neither can you say that about the answer of a colleague!

Intelligence is a concept very hard to define: there is a huge literature on what it is, what are its components, how we can quantify it or recognize it. I won't get into that matter, but I want to observe that one of the ways we typically assess an individual as an intelligent person is by hearing he or she talk. The capability to produce complex language and elaborate abstract concepts is undoubtedly a mark of intelligent beings. And when we are hit by a stroke, maybe a small hemorrage in our brain, we may temporarily lose our ability to speak or to put together meaningful sentences.

Further, consider Alzheimer: people who are progressively hit harder and harder by that impairing condition gradually lose their ability to put together correct sentences. I lost my mother that way six years ago, and I remember observing that in very close connection with her capability to speak, came a gradual deterioration of her intelligence. The two things are inextricably linked: we appear to be able to put together intelligent thought by processing text in our brain, even if we do not speak.

Because of the above, I believe that we must acknowledge that these large language models possess distinct traits of intelligence. It does not matter to me much if they put together their flawless answers by mathematical operations between large matrices of weights and biases: what matters is the result of those operations, and the fact that it is hard to distinguish -if not superior- to what a human mind can produce.

Of course, large language models are static systems: once they are trained -as I said, with considerable effort and expense, not to mention CO2 impact- they do not further "learn" by interacting with their users. They also do not have any means of acquiring information and processing it by sensory inputs. These limitations make these systems quite different to what we have always considered could be the capabilities of a true "artificial general intelligence". Indeed, a world-class expert on the matter like Yann Le Cun insists on saying that "on the way to AGI, large language models are an off-track" in twitter and in other venues, and he is of course right: these instruments will never "come alive" and become independent. They will be limited to one task: producing text in response to a prompt. Not real intelligence, not really. And yet...

Yet I cannot help thinking that we have to rethink what we call "intelligence" in the light of the capabilities of these systems. If they match our speech and writing skills, they have to be credited to be reasoning. The reasoning they perform is different from the reasoning that takes place in our brains to some extent, but not overly so after all: we also reason by using weights and biases that are encoded in our neurons. So we are not that different from large language models, at least in how we produce language.

Whether humanity will benefit and exploit for good causes the empowerment provided by ChatGPT and its successors - because I am convinced that there will be further more powerful models in our near future - or whether it will succumb to this new technology, the jury is still out on. But for sure these are interesting times!

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On Language And Intelligence - Science 2.0