Latest news with #DeepThink
Hans India
4 days ago
- Business
- Hans India
Google DeepMind CEO: AGI Still Years Away as AI Struggles with Simple Mistakes
Big Tech giants like Google, Meta, and OpenAI are locked in a high-stakes race to develop artificial general intelligence (AGI) — AI systems capable of thinking, planning, and adapting on par with humans. But according to Google DeepMind CEO Demis Hassabis, that goal remains distant, as current AI still makes surprisingly simple errors despite impressive achievements. Speaking on the Google for Developers podcast, Hassabis described today's AI as having 'jagged intelligence' — excelling in certain domains but stumbling in basic ones. He cited Google's latest Gemini model, enhanced with DeepThink reasoning technology, which has reached gold-medal-level performance in the International Mathematical Olympiad — one of the toughest math competitions worldwide. Yet, that same model can still make avoidable mistakes in high school-level math or fail at simple games. 'It shouldn't be that easy for the average person to just find a trivial flaw in the system,' Hassabis remarked. This inconsistency, he explained, is a sign that AI is far from human-level intelligence. He argued that simply scaling up models with more data and computing power will not bridge the gap to AGI. Instead, fundamental capabilities like reasoning, planning, and memory — areas still underdeveloped in even the most advanced AI — must be strengthened. Another challenge, Hassabis noted, is the lack of rigorous testing. Many standard AI benchmarks are already saturated, creating the illusion of near-perfect performance while masking weaknesses. For example, Gemini models recently scored 99.2% on the AIME mathematics benchmark, leaving minimal room for measurable improvement. However, these results don't necessarily mean the model is flawless. To overcome this, Hassabis called for 'new, harder benchmarks' that go beyond academic problem-solving to include intuitive physics, real-world reasoning, and 'physical intelligence' — the ability to understand and interact with the physical world as humans do. He also stressed the need for robust safety benchmarks capable of detecting risks such as deceptive behavior in AI systems. 'We're in need of new, harder benchmarks, but also broader ones, in my opinion — understanding world physics and intuitive physics and other things that we take for granted as humans,' he said. While Hassabis has previously suggested AGI might arrive within five to ten years, he now emphasizes caution. He believes AI companies should first focus on perfecting existing models before chasing full AGI. The path ahead, he implied, is less about winning a race and more about ensuring AI's capabilities are reliable, safe, and truly intelligent across the board. For now, despite breakthroughs in reasoning and problem-solving, the dream of AI that matches human intelligence remains a work in progress — and one that may take longer than the industry's most optimistic predictions.
India Today
4 days ago
- Business
- India Today
AGI when? Google DeepMind CEO says AI still makes simple mistakes despite big wins in elite math
Big Tech giants like Meta, OpenAI, and Google are racing to build artificial general intelligence (AGI). It's the AI systems capable of thinking, planning, and adapting like humans. These companies are pouring billions into research and aggressively recruiting, even poaching, top talent to assemble the best teams. But Google DeepMind CEO Demis Hassabis believes true AGI is still years away, as the AI industry remains far from perfecting current models, let alone achieving human-level on the latest episode of the Google for Developers podcast, Hassabis said even the most advanced AI models today display 'jagged intelligence'. Meaning that, while they excel in some areas, they still stumble on basic tasks. He cited examples from Google's Gemini models, highlighting that Google's latest and most powerful Gemini AI model, which incorporates the company's DeepThink reasoning technique, can achieve gold-medal-level performance at the International Mathematical Olympiad, one of the toughest competitions in the world. Yet, he noted, those same models can still make avoidable errors in high school-level mathematics or fail at simple games.'It shouldn't be that easy for the average person to just find a trivial flaw in the system,' Hassabis said. Hassabis argued that bridging the gap to AGI will require more than simply scaling up models with additional data and computing power. In his view, companies will need to focus on fundamental capabilities, particularly reasoning, planning, and memory, which remain underdeveloped in current AI missing piece, he added, is the lack of robust testing. While many standard benchmarks are already saturated, giving the impression of near-perfect performance, he suggested they often fail to expose weaknesses. For example, Hassabis noted that Gemini models recently scored 99.2 per cent on the AIME mathematics benchmark, leaving little room for measurable improvement, even though the model still has address this, Hassabis said companies need 'new, harder benchmarks' — not only in academic problem-solving, but also in areas such as intuitive physics, real-world reasoning, and 'physical intelligence'. He also emphasised the importance of safety benchmarks to detect traits such as deception. 'We're in need of new, harder benchmarks, but also broader ones, in my opinion — understanding world physics and intuitive physics and other things that we take for granted as humans,' he Hassabis has previously predicted that AGI could arrive within five to ten years, but cautioned that current systems, from Gemini to OpenAI's latest GPT-5, still lack critical capabilities. He stressed that the focus of AI companies should first be on perfecting today's AI models before pursuing full AGI.- Ends
Business Insider
5 days ago
- Business
- Business Insider
The CEO of Google DeepMind says one flaw is holding AI back from reaching full AGI
The one thing keeping AI from full AGI? Consistency. On an episode of the "Google for Developers" podcast published Tuesday, Google DeepMind CEO Demis Hassabis said that advanced models like Google's Gemini still stumble over problems most schoolkids could solve. "It shouldn't be that easy for the average person to just find a trivial flaw in the system," he said. He pointed to Gemini models enhanced with DeepThink — a reasoning-boosting technique — that can win gold medals at the International Mathematical Olympiad, the world's most prestigious math competition. But those same systems can "still make simple mistakes in high school maths," he said, calling them "uneven intelligences" or "jagged intelligences." "Some dimensions, they're really good; other dimensions, their weaknesses can be exposed quite easily," he added. Hassabis's position aligns with Google CEO Sundar Pichai, who has dubbed the current stage of development "AJI" — artificial jagged intelligence. Pichai used the term on an episode of Lex Fridman's podcast that aired in June to describe systems that excel in some areas but fail in others. Hassabis said solving AI's issues with inconsistency will take more than scaling up data and computing. "Some missing capabilities in reasoning and planning in memory" still need to be cracked, he added. He said the industry also needs better testing and "new, harder benchmarks" to determine precisely what the models excel at, and what they don't. Hassabis and Google did not respond to a request for comment from Business Insider. Big Tech hasn't cracked AGI Big Tech players like Google and OpenAI are working toward achieving AGI, a theoretical threshold where AI can reason like humans. Hassabis said in April that AGI will arrive "in the next five to 10 years." AI systems remain prone to hallucinations, misinformation, and basic errors. OpenAI CEO Sam Altman had a similar take ahead of the launch of GPT-5 last week. While calling his firm's model a significant advancement, he told reporters it still falls short of true AGI. "This is clearly a model that is generally intelligent, although I think in the way that most of us define AGI, we're still missing something quite important, or many things quite important," Altman said during a press call. Altman added that one of those missing elements is the model's ability to learn independently. "One big one is, you know, this is not a model that continuously learns as it's deployed from the new things it finds, which is something that to me feels like AGI. But the level of intelligence here, the level of capability, it feels like a huge improvement," he said.
Yahoo
5 days ago
- Business
- Yahoo
Google DeepMind CEO says one flaw is holding AI back from reaching full AGI
AI's next step toward AGI hinges on one key fix: consistency Google DeepMind CEO said AI can win elite math contests but still flub school-level problems. "Some missing capabilities in reasoning and planning in memory" need to be cracked, said Demis Hassabis. The one thing keeping AI from full AGI? Consistency, said Google DeepMind CEO Demis Hassabis. Hassabis said on an episode of the "Google for Developers" podcast published Tuesday that advanced models like Google's Gemini still stumble over problems most schoolkids could solve. "It shouldn't be that easy for the average person to just find a trivial flaw in the system," he said. He pointed to Gemini models enhanced with DeepThink — a reasoning-boosting technique — that can win gold medals at the International Mathematical Olympiad, the world's most prestigious math competition. But those same systems can "still make simple mistakes in high school maths," he said, calling them "uneven intelligences" or "jagged intelligences." "Some dimensions, they're really good; other dimensions, their weaknesses can be exposed quite easily," he added. Hassabis's position aligns with Google CEO Sundar Pichai, who has dubbed the current stage of development "AJI" — artificial jagged intelligence. Pichai used this term on an episode of Lex Fridman's podcast that aired in June to describe systems that excel in some areas but fail in others. Hassabis said solving AI's issues with inconsistency will take more than scaling up data and computing. "Some missing capabilities in reasoning and planning in memory" still need to be cracked, he added. He said the industry also needs better testing and "new, harder benchmarks" to determine precisely what the models excel at, and what they don't. Hassabis and Google did not respond to a request for comment from Business Insider. Big Tech hasn't cracked AGI Big Tech players like Google and OpenAI are working toward achieving AGI, a theoretical threshold where AI can reason like humans. Hassabis said in April that AGI will arrive "in the next five to 10 years." AI systems remain prone to hallucinations, misinformation, and basic errors. OpenAI CEO Sam Altman had a similar take ahead of last week's launch of GPT-5. While calling his firm's model a significant advancement, he told reporters it still falls short of true AGI. "This is clearly a model that is generally intelligent, although I think in the way that most of us define AGI, we're still missing something quite important, or many things quite important," Altman said during a press call on Wednesday before the release of GPT-5. Altman added that one of those missing elements is the model's ability to learn independently. "One big one is, you know, this is not a model that continuously learns as it's deployed from the new things it finds, which is something that to me feels like AGI. But the level of intelligence here, the level of capability, it feels like a huge improvement," he said. Read the original article on Business Insider
Yahoo
5 days ago
- Business
- Yahoo
Google DeepMind CEO says one flaw is holding AI back from reaching full AGI
AI's next step toward AGI hinges on one key fix: consistency Google DeepMind CEO said AI can win elite math contests but still flub school-level problems. "Some missing capabilities in reasoning and planning in memory" need to be cracked, said Demis Hassabis. The one thing keeping AI from full AGI? Consistency, said Google DeepMind CEO Demis Hassabis. Hassabis said on an episode of the "Google for Developers" podcast published Tuesday that advanced models like Google's Gemini still stumble over problems most schoolkids could solve. "It shouldn't be that easy for the average person to just find a trivial flaw in the system," he said. He pointed to Gemini models enhanced with DeepThink — a reasoning-boosting technique — that can win gold medals at the International Mathematical Olympiad, the world's most prestigious math competition. But those same systems can "still make simple mistakes in high school maths," he said, calling them "uneven intelligences" or "jagged intelligences." "Some dimensions, they're really good; other dimensions, their weaknesses can be exposed quite easily," he added. Hassabis's position aligns with Google CEO Sundar Pichai, who has dubbed the current stage of development "AJI" — artificial jagged intelligence. Pichai used this term on an episode of Lex Fridman's podcast that aired in June to describe systems that excel in some areas but fail in others. Hassabis said solving AI's issues with inconsistency will take more than scaling up data and computing. "Some missing capabilities in reasoning and planning in memory" still need to be cracked, he added. He said the industry also needs better testing and "new, harder benchmarks" to determine precisely what the models excel at, and what they don't. Hassabis and Google did not respond to a request for comment from Business Insider. Big Tech hasn't cracked AGI Big Tech players like Google and OpenAI are working toward achieving AGI, a theoretical threshold where AI can reason like humans. Hassabis said in April that AGI will arrive "in the next five to 10 years." AI systems remain prone to hallucinations, misinformation, and basic errors. OpenAI CEO Sam Altman had a similar take ahead of last week's launch of GPT-5. While calling his firm's model a significant advancement, he told reporters it still falls short of true AGI. "This is clearly a model that is generally intelligent, although I think in the way that most of us define AGI, we're still missing something quite important, or many things quite important," Altman said during a press call on Wednesday before the release of GPT-5. Altman added that one of those missing elements is the model's ability to learn independently. "One big one is, you know, this is not a model that continuously learns as it's deployed from the new things it finds, which is something that to me feels like AGI. But the level of intelligence here, the level of capability, it feels like a huge improvement," he said. Read the original article on Business Insider
