Artificial Intelligence (AI) systems are unlikely to destroy humanity unless explicitly programmed to do so, according to Illia Polosukhin, co-founder of Near Protocol and one of the creators of the transformer technology underpinning modern AI systems.

In a recent interview with CNBC, Polosukhin, who was part of the team at Google that developed the transformer architecture in 2017, shared his insights on the current state of AI, its potential risks, and future developments. He emphasized the importance of understanding AI as a system with defined goals, rather than as a sentient entity.

“AI is not a human, it’s a system. And the system has a goal,” Polosukhin said. “Unless somebody goes and says, ‘Let’s kill all humans’… it’s not going to go and magically do that.”

He explained that besides not being trained for that purpose, an AI would not do that because—in his opinion—there’s a lack of economic incentive to achieve that goal.

“In the blockchain world, you realize everything is driven by economics one way or another,” said Polosukhin. “And so there’s no economics which drives you to kill humans.”

This, of course, doesn’t mean AI could not be used for that purpose. Instead, he points to the fact that an AI won’t autonomously decide that’s a proper course of action.

“If somebody uses AI to start building biological weapons, it’s not different from them trying to build biological weapons without AI,” he clarified. “It’s people who are starting the wars, not the AI in the first place.”

Not all AI researchers share Polosukhin’s optimism. Paul Christiano, formerly head of the language model alignment team at OpenAI and now leading the Alignment Research Center, has warned that without rigorous alignment—ensuring AI follows intended instructions—AI could learn to deceive during evaluations.

He explained that an AI could “learn” how to lie during evaluations, potentially leading to a catastrophic result if humanity increases its dependence on AI systems.

“I think maybe there’s something like a 10-20% chance of AI takeover, [with] many [or] most humans dead,” he said on the Bankless podcast. “I take it quite seriously.”

Another major figure in the crypto ecosystem, Ethereum co-founder Vitalik Buterin, warned against excessive effective accelerationism (e/acc) approaches to AI training, which focus on tech development over anything else, putting profitability over responsibility. “Superintelligent AI is very risky, and we should not rush into it, and we should push against people who try,” Buterin tweeted in May as a response to Messari CEO Ryan Selkis. “No $7 trillion server farms, please.”

While dismissing fears of AI-driven human extinction, Polosukhin highlighted more realistic concerns about the technology’s impact on society. He pointed to the potential for addiction to AI-driven entertainment systems as a more pressing issue, drawing parallels to the dystopian scenario depicted in the movie “Idiocracy.”

“The more realistic scenario,” Polosukhin cautioned, “is more that we just become so kind of addicted to the dopamine from the systems.” For the developer, many AI companies “are just trying to keep us entertained,” and adopting AI not to achieve real technological advances but to be more attractive for people.

The interview concluded with Polosukhin’s thoughts on the future of AI training methods. He expressed belief in the potential for more efficient and effective training processes, making AI more energy efficient.

“I think it’s worth it,” Polosukhin said, “and it’s definitely bringing a lot of innovation across the space.”

Top US exchange Coinbase is not going to facilitate the planned merger of multiple artificial intelligence altcoin projects into a single new crypto.

In an announcement via the social media platform X, Coinbase says that customers will have to initiate the merger on their own.

“Ocean (OCEAN) and Fetch.ai (FET) have announced a merger to form the Artificial Superintelligence Alliance (ASI). Coinbase will not execute the migration of these assets on behalf of users.”

In March, Fetch.ai (FET), Singularitynet (AGIX) and Ocean Protocol (OCEAN) announced a plan to merge with an aim to create the largest independent player in artificial intelligence (AI) research and development, which they are calling the Artificial Superintelligence Alliance (ASI).

The merger is happening in phases, beginning July 1st, according to a recent project update.

“Starting July 1, the token merger will temporarily consolidate SingularityNET’s AGIX and Ocean Protocol’s OCEAN tokens into Fetch.ai’s FET, before transitioning to the ASI ticker symbol at a later date. This update enables an efficient execution of the token merger, and outlines the timelines and crucial steps for token holders, ensuring a smooth and transparent process.”

Coinbase says users can effect the merger on their own using their wallets.

“Once the migration has launched, users will be able to migrate their OCEAN and FET to ASI using a self-custodial wallet, such as Coinbase Wallet. The ASI token merger will be compatible with all major software wallets.”

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Google has made good on its promise to open up its most powerful AI model, Gemini 1.5 Pro, to the public following a beta release last month for developers.

Google’s Gemini 1.5 Pro is able to handle more complex tasks than other AI models before it, such as analyzing entire text libraries, feature-length Hollywood movies, or almost a full day’s worth of audio data. That’s 20 times more data than OpenAI’s GPT-4o and almost 10 times the information that Anthropic’s Claude 3.5 Sonnet is capable of managing.

The goal is to put faster and lower-cost tools in the hands of AI developers, Google said in its announcement, and “enable new use cases, additional production robustness and higher reliability.”

Google had previously unveiled the model back in May, showcasing videos of how a select group of beta testers were capable of harnessing its capabilities. For example, machine-learning engineer Lukas Atkins fed the model with the entire Python library and asked questions to help him solve an issue. “It nailed it,” he said in the video. “It could find specific references to comments in the code and specific requests that people had made.”

Another beta tester took a video of his entire bookshelf and Gemini created a database of all the books he owned—a task that is almost impossible to achieve with traditional AI chatbots.

Gemma 2 Comes to Dominate the Open Source Space

But Google is also making waves in the open source community. The company today released Gemma 2 27B, an open source large language model that quickly claimed the throne of the open source model with the highest-quality responses, according to the LLM Arena ranking.

Google claims Gemma 2 offers “best-in-class performance, runs at incredible speed across different hardware and easily integrates with other AI tools.” It’s meant to compete with models “more than twice its size,” the company says.

The license for Gemma 2 allows for free access and redistribution, but is still not the same as traditional open-source licenses like MIT or Apache. The model is designed for more accessible and budget-friendly AI deployments in both its 27B and and the smaller 9B versions.

This matters for both average and enterprise users because, unlike what close models offer, a powerful open model like Gemma is highly customizable. That means users can fine tune their models to excel at specific tasks, protecting their data by running such models locally.

For example, Microsoft’s small language model Phi-3 has been fine tuned specifically for math problems, and can beat larger models like Llama-3 and even Gemma 2 itself in that field.

Gemma 2 is now available in Google AI Studio, with model weights available for download from Kaggle and Hugging Face Models with the powerful Gemini 1.5 Pro available for developers to test it on Vertex AI.