The legal battle, unfolding in the Superior Court of California for the County of San Francisco, revolves around OpenAI’s departure from its foundational mission of advancing open-source artificial general intelligence (AGI) for the betterment of humanity.

Musk was a co-founder and early backer of OpenAI. According to Musk, Altman and Greg Brockman (another co-founder and current president of OpenAI) convinced him to bankroll the startup in 2015 on promises that it would remain a nonprofit.

In his legal challenge, Musk accuses OpenAI of straying from its principles through a collaboration with Microsoft—alleging that the partnership prioritises proprietary technology over the original ethos of open-source advancement.

Musk’s grievances include claims of contract breach, violation of fiduciary duty, and unfair business practices. He calls upon OpenAI to realign with its nonprofit objectives and seeks an injunction to halt the commercial exploitation of AGI technology.

At the heart of the dispute is OpenAI’s recent launch of GPT-4 in March 2023. Musk contends that unlike its predecessors, GPT-4 represents a shift towards closed-source models—a move he believes favours Microsoft’s financial interests at the expense of OpenAI’s altruistic mission.

Founded in 2015 as a nonprofit AI research lab, OpenAI transitioned into a commercial entity in 2020. OpenAI has now adopted a profit-driven approach, with revenues reportedly surpassing $2 billion annually.

Musk, who has long voiced concerns about the risks posed by AI, has called for robust government regulation and responsible AI development. He questions the technical expertise of OpenAI’s current board and highlights the removal and subsequent reinstatement of Altman in November 2023 as evidence of a profit-oriented agenda aligned with Microsoft’s interests.

See also: Mistral AI unveils LLM rivalling major players

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Their new ‘SELF-DISCOVER’ prompting framework – published this week on arXiV and Hugging Face – represents a significant leap beyond existing techniques, potentially revolutionising the performance of leading models such as OpenAI’s GPT-4 and Google’s PaLM 2.

The framework promises substantial enhancements in tackling challenging reasoning tasks. It demonstrates remarkable improvements, boasting up to a 32% performance increase compared to traditional methods like Chain of Thought (CoT). This novel approach revolves around LLMs autonomously uncovering task-intrinsic reasoning structures to navigate complex problems.

At its core, the framework empowers LLMs to self-discover and utilise various atomic reasoning modules – such as critical thinking and step-by-step analysis – to construct explicit reasoning structures.

By mimicking human problem-solving strategies, the framework operates in two stages:

• Stage one involves composing a coherent reasoning structure intrinsic to the task, leveraging a set of atomic reasoning modules and task examples.
• During decoding, LLMs then follow this self-discovered structure to arrive at the final solution.

In extensive testing across various reasoning tasks – including Big-Bench Hard, Thinking for Doing, and Math – the self-discover approach consistently outperformed traditional methods. Notably, it achieved an accuracy of 81%, 85%, and 73% across the three tasks with GPT-4, surpassing chain-of-thought and plan-and-solve techniques.

However, the implications of this research extend far beyond mere performance gains.

By equipping LLMs with enhanced reasoning capabilities, the framework paves the way for tackling more challenging problems and brings AI closer to achieving general intelligence. Transferability studies conducted by the researchers further highlight the universal applicability of the composed reasoning structures, aligning with human reasoning patterns.

As the landscape evolves, breakthroughs like the SELF-DISCOVER prompting framework represent crucial milestones in advancing the capabilities of language models and offering a glimpse into the future of AI.

(Photo by Victor on Unsplash)

See also: The UK is outpacing the US for AI hiring

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The strategic move demonstrates OpenAI’s commitment to expanding its operations, embracing diverse perspectives, and accelerating its mission of ensuring that artificial general intelligence (AGI) benefits all of humanity.

London, renowned for its exceptional talent pool, was chosen as the ideal location for OpenAI’s international office. The city’s vibrant technology ecosystem, welcoming regulatory environment, and thriving community of innovators make it the perfect hub for OpenAI to advance its cutting-edge research and engineering capabilities.

The London teams will work closely with local communities and policymakers, fostering collaboration on OpenAI’s mission to create and promote safe AGI.

“We are thrilled to extend our research and development footprint into London,” said Diane Yoon, OpenAI’s VP of People.

“We are eager to build dynamic teams in Research, Engineering, and Go-to-Market functions, as well as other areas, to reinforce our efforts in creating and promoting safe AGI.”

OpenAI has been at the forefront of AI research, creating breakthroughs in natural language processing, reinforcement learning, and other areas. With the establishment of its international office, OpenAI intends to tap into the diverse expertise and perspectives available in London, further bolstering its capabilities and amplifying its impact.

Sam Altman, CEO of OpenAI, also shared his excitement about the future prospects of the London office.

“We see this expansion as an opportunity to attract world-class talent and drive innovation in AGI development and policy,” Altman stated.

“We’re excited about what the future holds and to see the contributions our London office will make towards building and deploying safe AI.”

By establishing a physical presence in London, OpenAI can forge closer partnerships with local institutions, universities, and industry experts, fostering a collaborative environment that propels AI innovation forward.

(Photo by Andrew Neel on Unsplash)

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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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SingularityNET is a decentralised AI marketplace which has the ultimate goal of forming the basis for the emergence of the world’s first true Artificial General Intelligence (AGI).

One of the brightest and most respected minds in AI leads the SingularityNET project, Dr Ben Goertzel.

“Current speed and cost issues with the Ethereum blockchain have increased the urgency of exploring alternatives for SingluarityNET’s blockchain underpinning,” says Goertzel.

“The ambitious Ethereum 2.0 design holds promise but the timing of rollout of different aspects of this next-generation Ethereum remains unclear, along with many of the practical particulars.“

SingularityNET claims that Cardano has now reached a level of maturity which makes it possible to port such a complex project to the new blockchain.

Back in August, Goertzel gave a talk at the Cardano Summit on why functional programming – enabled by Cardano’s Haskell and new Plutus languages – is invaluable for blockchain-based AI:

Current AIs in the SingularityNET marketplace are designed for specific, relatively straightforward tasks like image/language processing, time series analysis, and genomics data analysis. The project’s Android SDK has been used for tasks like separating vocals from music in the SongSplitter app.

While useful, these AIs show the current limitations of the technology today. SingularityNET, true to its name, has far more ambitious plans.

The broader vision of SingularityNET is to decentralise AI away from “big tech” and prevent AIs from being siloed so that one AI can outsource work to others and use their specific expertise to solve problems. This will ultimately bring us a step closer to AGI which acts more like a human by seeking help where needed.

In a blog post, SingularityNET provides further technical details about its decision to switch to Cardano:

“There may also be synergies between SingularityNET-Cardano integration and the OpenCog Hyperon initiative, which is focused on creating a more scalable, flexible and usable successor to the current OpenCog AGI R&D platform (which underlies a handful of specialized AI agents currently running on the SingularityNET network).

The OpenCog AGI design involves a metagraph knowledge store called the Atomspace, concurrently and cooperatively acted on by a number of different cognitive processes representing different learning and reasoning methods such as probabilistic logic, evolutionary learning, pattern mining and neural pattern recognition. Currently, to integrate OpenCog into SingularityNET, one creates a SingularityNET agent wrapping a whole OpenCog system with its own internal Atomspace and AI-process population.

However, in a SingularityNET-on-Cardano approach, it may eventually be possible to take a more decentralized approach in which the Hyperon Atomspace is provided as a service to any SingularityNET agent who needs it, and many of the cognitive processes involved in the Hyperon design are represented as SingularityNET agents that interact with Atomspace via channels set up via SingularityNET protocols. Such an approach would exploit the deep commonalities between the new version of OpenCog’s Atomese language being created for Hyperon and the dependent type based API of APIs under current exploration. The result would be a more fundamentally decentralized approach to AGI design.”

A fascinating interview between Cardano founder Charles Hoskinson and Dr Goertzel can be viewed here:

Cardano vs Ethereum

Ethereum is currently the world’s largest decentralised platform but suffers from slow speeds and increasing transaction costs. A switch to a more efficient and environmentally-friendly Proof-of-Stake consensus is underway which – along with new scaling innovations – should address Ethereum’s issues. However, it’s expected to be several years before Ethereum 2.0 is fully rolled out.

Cardano has observed Ethereum’s problems and is taking its time to address them with a scientific and peer-reviewed approach; which brings legitimacy to the project that will be needed for enterprise adoption.

While it could appear from the outside like Cardano has been lazy and is far behind other projects – after all, it’s yet to even support smart contracts – this is far from the case. Cardano is often ranked top of all blockchain projects for development activity and has continued signing large partnerships.

“Cardano has gone from strength to strength this year, and having the backing of such a prominent organisation only reaffirms this,” comments Hoskinson. 

“SingularityNET is a project we’ve followed for a long time, and we’re excited to see how the Cardano blockchain can help SingularityNET realise its ambitious goals.”   

Smart contracts are due to launch on Cardano in the coming months as part of what it calls its ‘Goguen’ phase. Unlike Ethereum, Cardano is using Proof-of-Stake from the start and won’t have the speed, cost, and scalability problems of the current decentralised platform leader.

Cardano will even become the most decentralised network in the space following the recent successful launch of its ‘Shelley’ upgrade.

On its website, Cardano explains:

“We expect Cardano to be 50-100 times more decentralized than other large blockchain networks, with the incentives scheme designed to reach equilibrium around 1,000 stake pools.

Current prominent blockchain networks are often controlled by less than 10 mining pools, exposing them to serious risk of compromise by malicious behavior – something which Cardano avoids with a system inherently designed to encourage greater decentralization.”

We’re already seeing groundbreaking projects like SingularityNET beginning to shift over to Cardano. While it may appear that Cardano has a long way to catch up with Ethereum, it’s worth remembering that – of Ethereum’s close to 3,000 apps – only a minority carry significant value or have many active users.

There is currently around $11 billion “locked up” in Ethereum’s much-vaunted DeFi projects. That’s nothing to be sniffed at, but it only takes some big projects to move to Cardano to put a big dent in Ethereum’s primary use case. It’s also worth remembering the whole space is very young with plenty of growth potential—the global legacy financial system is worth hundreds of trillions of dollars.

While multiple blockchain projects will likely co-exist, Cardano’s ability to “flip” Ethereum is looking more possible than ever.

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If you’ve spent your life binging on sci-fi flicks and shows, today’s AI is far from the likes of HAL and Jarvis. For those kinds of experiences, AGI is needed.

Sam Altman, CEO of OpenAI, said:

“The creation of AGI will be the most important technological development in human history, with the potential to shape the trajectory of humanity. Our mission is to ensure that AGI technology benefits all of humanity, and we’re working with Microsoft to build the supercomputing foundation on which we’ll build AGI.

We believe it’s crucial that AGI is deployed safely and securely and that its economic benefits are widely distributed. We are excited about how deeply Microsoft shares this vision.”

Whereas AI is essentially pre-programmed to perform set tasks, the promise of AGI is to act more like a human brain. Indeed, this is where the excitement of assistants like the aforementioned turn into concerns of a Skynet-like AI as portrayed in Terminator.

Elon Musk founded OpenAI in part due to his concerns that AGI could pose an existential risk if developed carelessly. OpenAI most recently made headlines for developing essentially a fake news generator that, for obvious reasons, it deemed too dangerous to release.

Musk departed OpenAI in February last year over disagreement’s with the company’s development, but the San Francisco-based firm continues to pursue AGI.

Microsoft is clearly on-board with OpenAI’s mission. The $1 billion investment ensures Microsoft becomes OpenAI’s exclusive provider of cloud computing services to OpenAI, and the pair will build new Azure AI supercomputing technologies.

Satya Nadella, CEO of Microsoft, commented:

“AI is one of the most transformative technologies of our time and has the potential to help solve many of our world’s most pressing challenges.

By bringing together OpenAI’s breakthrough technology with new Azure AI supercomputing technologies, our ambition is to democratise AI — while always keeping AI safety front and centre — so everyone can benefit.”

Both companies promise developments will adhere to their ‘shared principles on ethics and trust,’ and will ‘create the foundation for advancements in AI to be implemented in a safe, secure, and trustworthy way’.

The goal of Microsoft and OpenAI is a lofty one that may not even be achieved in many of our lifetimes. In a recent survey of some leading experts in the field, the average estimate was there is a 50 percent chance AGI will be developed by 2099.

Interested in hearing industry leaders discuss subjects like this and their use cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, Blockchain Expo, and Cyber Security & Cloud Expo.

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