Marcus founded RobustAI and Geometric Intelligence (acquired by Uber), is the Professor Emeritus of Psychology and Neural Science at NYU, and authored Rebooting.AI. His views on AGI are worth listening to.

AGI is the kind of artificial intelligence depicted in movies like Space Odyssey (‘HAL’) and Iron Man (‘J.A.R.V.I.S’). Unlike current AIs that are trained for a specific task, AGIs are more like the human brain and can learn how to do tasks.

Most experts believe AGI will take decades to achieve, while some even think it will never be possible. In a survey of leading experts in the field, the average estimate was there is a 50 percent chance AGI will be developed by 2099.

Elon Musk is far more optimistic:

2029 feels like a pivotal year. I’d be surprised if we don’t have AGI by then. Hopefully, people on Mars too.

— Elon Musk (@elonmusk) May 30, 2022

Musk’s tweet received a response from Marcus in which he challenged the SpaceX and Tesla founder to a $100,000 bet that he’s wrong about the timing of AGI.

AI expert Melanie Mitchell from the Santa Fe Institute suggested the bets are placed on longbets.org. Marcus says he’s up for the bet on the platform – where the loser donates the money to a philanthropic effort – but he’s yet to receive a response from Musk.

In a post on his Substack, Marcus explained why he’s calling Musk out on his prediction.

“Your track record on betting on precise timelines for things is, well, spotty,” wrote Marcus. “You said, for instance in 2015, that (truly) self-driving cars were two years away; you’ve pretty much said the same thing every year since. It still hasn’t happened.”

Marcus argues that pronouncements like Musk is famous for can be dangerous and take attention away from the kind of questions that first need answering. 

“People are very excited about the big data and what it’s giving them right now, but I’m not sure it’s taking us closer to the deeper questions in artificial intelligence, like how we understand language or how we reason about the world,” said Marcus in 2016 in an Edge.org interview.

An incident in April, where a Tesla on Autopilot crashed into a $3 million private jet in a mostly empty airport, is pointed to as an example of why the focus needs to be on solving serious issues with AI systems before rushing to AGI:

“It’s easy to convince yourself that AI problems are much easier than they are actually are, because of the long tail problem,” argues Marcus.

“For everyday stuff, we get tons and tons of data that current techniques readily handle, leading to a misleading impression; for rare events, we get very little data, and current techniques struggle there.”

Marcus says that he can guarantee Musk won’t be shipping fully-autonomous ‘Level 5’ cars this year or next, despite what Musk said at TED2022. Unexpected outlier circumstances, like the appearance of a private jet in the way of a car, will continue to pose a problem to AI for the foreseeable future.

“Seven years is a long time, but the field is going to need to invest in other ideas if we are going to get to AGI before the end of the decade,” explains Marcus. “Or else outliers alone might be enough to keep us from getting there.”

Marcus believes outliers aren’t an unsolvable problem, but there’s currently no known solution. Making any predictions about AGI being achievable by the end of the decade before that issue is anywhere near solved is premature.

Along those same lines, Marcus points at how deep learning is “pretty decent” at recognising objects is but nowhere near as adept at human brain-like activities such as planning, reading, or language comprehension.

Here’s a pie chart used by Marcus of the kind of things that an AGI would need to achieve:

Marcus points out that he’s been using the chart for around five years and the situation has barely changed, we “still don’t have anything like stable or trustworthy solutions for common sense, reasoning, language, or analogy.”

Tesla is currently building a robot that claims to be able to perform mundane tasks around the home. Marcus is sceptical given the problems that Tesla is having with its cars on the roads.

“The AGI that you would need for a general-purpose domestic robot (where every home is different, and each poses its own safety risks) is way beyond what you would need for a car that drives on roads that are more or less engineered the same way from one town to the next,” he reasons.

Because AGI is still a somewhat vague term that’s open to interpretation, Marcus makes his own five predictions that AI will not be able to do by Musk’s 2029 prediction that AGI will be achieved:

Well then, Musk—do you accept Marcus’ challenge? Can’t say I would, even if I had anywhere near Musk’s disposable income.

(Photo by Kenny Eliason on Unsplash)

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Musk has been among the most vocal prominent figures in warning about the dangers of artificial intelligence. In 2018, for example, Musk famously warned that AI could become “an immortal dictator from which we would never escape” and that the technology is more dangerous than nuclear weapons.

Speaking in a New York Times interview, Musk said that current trends suggest AI could overtake humans by 2025. However, Musk adds “that doesn’t mean that everything goes to hell in five years. It just means that things get unstable or weird.”

If correct, the latest prediction from Musk would mean the so-called technological singularity – when machine intelligence overtakes human – is set to happen much sooner than other experts predict. Ray Kurzweil, a respected futurist, has previously estimated the aforementioned singularity to occur around 2045.

As the founder of Tesla, SpaceX, and Neuralink – three companies which use AI far more than most – Musk isn’t against the technology, but has called for it to be regulated.

Musk also founded OpenAI back in 2015 with the goal of researching and promoting ethical artificial intelligence. Following disagreements with the company’s direction, Musk left OpenAI in 2018.

Back in February, Musk responded to an MIT Technology Review profile of OpenAI saying that it “should be more open” and that all organisations “developing advanced AI should be regulated, including Tesla.”

Last year, OpenAI decided not to release a text generator which it believed to have dangerous implications in a world already struggling with fake news and disinformation campaigns.

Two graduates later recreated and released a similar generator to OpenAI’s, with one saying that it “allows everyone to have an important conversation about security, and researchers to help secure against future potential abuses.”

OpenAI has since provided select researchers access to their powerful text generator. The latest version, GPT-3, has been making headlines in recent weeks for the incredible things it can achieve with limited input.

GPT-3 offers 175 billion parameters compared to GTP-2’s 1.5 billion parameters – which shows the rapid pace of AI advancements. However, Musk’s prediction of the singularity happening within five years perhaps needs to be taken with a pinch of salt.

(Image Credit: Elon Musk by JD Lasica under CC BY 2.0 license)

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The panelists included Newcastle FC Sports Scientist John Fitzpatrick, Sport Lisboa e Benfica CIO Joao Copeto, and Sport Lisboa e Benfica Head of Analysis Nuno Maurício.

Fitzpatrick has been with Newcastle United for around five years and has witnessed the increasing demand for IoT and AI technologies. The football club had around five GPS units when he joined to analyse players’ movements, but this number has now swelled to over 100.

“Every player, from the first team down to the under 15s will be analysed in every training session,” says Fitzpatrick. ”Now it’s about talking about what we did in training and be more proactive and predictive with what we’re doing with the data.”

Protecting the personal data of athletes while collecting this data is a necessity. Certainly in Europe, with GDPR, it will soon be enshrined in law with severe penalties for non-compliance. Explicit consent for collecting and sharing player data must be received.

“Not only are we protecting the intellectual property of the club, but we’re working with data that is personal,” comments Copeto. “We need to be able to anonymise the data so we can share it without giving too much information.”

In many cases, like when sharing data with universities for their research, this data can be completely anonymised. Some of the most exciting possibilities, however, can only be unlocked by personally identifying players which may pose a problem.

Everyone knows and has an opinion on the huge salaries footballers get paid. They are costly enough to a club at the best of times, but an injury which puts a player out of action for say nine months provides even less value.

By using AI and predictive modelling, clubs may be able to predict injuries to certain players and their recovery time. Managers can then either take action to avoid the injury or make preparations for continuing without them.

“What we want to do is predict, this is what we’re looking for, everything else we can do,” explains Copeto. “The answer to AI in sport is prediction.”

To keep costs down whilst ensuring a team is as competitive as can be, AI could even be used for scouting talent. Players which could have the biggest impact can be identified earlier on in their careers when their salaries are less demanding than seasoned professionals.

“Can we perform tests on young players? Try and understand their trajectory as they grow older and whether they’ll become premier league players,” muses Fitzpatrick. “That would save us a lot of money if we can produce our own players rather than buying them in.”

Even more exciting for a club is to implement this knowledge into whether particular strategies are likely to result in a win. Much like a business, this is not information you want your competitors to know and therefore security must be a priority. In fact, rather than rely on an off-the-shelf cloud solution, Copeto iterates the need to build your own layer.

What data to share, and what not to, has long plagued technology companies. AI is a great example of where the technology advances quickest when data is open sourced and knowledge shared.

“Closed models are not successful, you need a very well defined model that’s open to changes,” comments Mauricio. “Then, it’s a question of communication inside.”

As alluded by Mauricio, sporting clubs often face the problem of how to even get internal teams to share. Sports performance experts, coaches, dieticians, science, doctors, and IT can all have trouble communicating, and their data can end up existing within their own silos.

Data has been collected from around the past ten years which can already recount things such as every touch of the ball a player has. Bringing all of this data together and using AI to make sense of it is what’s going to unlock the most, quite literally, game-changing advancements.

What are your thoughts on the use of AI in sport? Let us know in the comments.

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