Millimeter-wave and terahertz integrated circuits and chips are expected to serve as the backbone for future wireless networks and high resolution sensing. However, design of these integrated circuits and chips can be quite complex, requiring years of human expertise, careful tailoring of hand crafted circuit topologies and co-design with parameterized and pre-selected templates of electromagnetic structures. These structures (radiative and non-radiative, single-port and multi-ports) are subsequently optimized through ad-hoc methods and parameter sweeps. Such bottom-up approaches with pre-selected regular topologies also fundamentally limit the design space. Here, we demonstrate a universal inverse design approach for arbitrary-shaped complex multi-port electromagnetic structures with designer radiative and scattering properties, co-designed with active circuits. To allow such universalization, we employ deep learning based models, and demonstrate synthesis with several examples of complex mm-Wave passive structures and end-to-end integrated mm-Wave broadband circuits. The presented inverse design methodology, that produces the designs in minutes, can be transformative in opening up a new, previously inaccessible design space. — Read More

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Nobel laureate and Google DeepMind CEO Demis Hassabis said Tuesday (Jan. 21) that he expects to see pharmaceutical drugs designed by artificial intelligence (AI) to be in clinical trials by the end of the year.

During a fireside chat at the World Economic Forum in Davos, Switzerland, Hassabis said these drugs are being developed at Isomorphic Labs, a for-profit venture created by Google parent firm Alphabet in 2021 that was tasked to reinvent the entire drug discovery process based on first principles and led by AI.

“That’s the plan,” Hassabis said. — Read More

• Historically, general approaches always win in AI.
• Founders in AI application space now repeat the mistakes AI researchers made in the past.
• Better AI models will enable general purpose AI applications. At the same time, the added value of the software around the AI model will diminish.

Recent AI progress has enabled new products that solve a broad range of problems. I saw this firsthand watching over 100 pitches during YC alumni Demo Day. These problems share a common thread – they’re simple enough to be solved with constrained AI. Yet the real power of AI lies in its flexibility. While products with fewer constraints generally work better, current AI models aren’t reliable enough to build such products at scale. We’ve been here before with AI, many times. Each time, the winning move has been the same. AI founders need to learn this history, or I fear they’ll discover these lessons the hard way. — Read More

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Sam Altman started 2025 with a bold declaration: OpenAI has figured out how to create artificial general intelligence (AGI), a term commonly understood as the point where AI systems can comprehend, learn, and perform any intellectual task that a human can.

In a reflective blog post published over the weekend, he also said the first wave of AI agents could join the workforce this year, marking what he describes as a pivotal moment in technological history. — Read More

The world may have changed, and we might not have realized it yet.

Yesterday, OpenAI shocked (and this is not hyperbole) everyone with the announcement of OpenAI o3 and o3-mini, the brand new models of the ‘o’ family (they skipped ‘o2’ due to trademark reasons).

o3 results are so astonishing that some people are actually convinced that it is AGI, as it destroys some of the so-called ‘impossible’ benchmarks for current models. — Read More

In a very recent interview, Satya Nadella, Microsoft’s CEO, claimed that current business applications will “collapse in the agent era.” Notably, he is referring to the very same apps his company is currently selling. Thus, he is predicting the death of its own current business model in favor of AI agents.

But this vision implies a much more powerful change that Satya is less keen on mentioning because it directly impacts Microsoft’s raison d’être: the introduction of AI as a structural part of general-purpose computing, the end game of ChatGPT: the LLM Operating System, or LLM OS.

This vision is so powerful that it is unequivocally OpenAI’s grand plan. Today, we are distilling their vision into simple words. I believe this is one of my most didactic articles on the future of AI. — Read More

Recent advancements in large language models (LLMs) have sparked optimism about their potential to accelerate scientific discovery, with a growing number of works proposing research agents that autonomously generate and validate new ideas. Despite this, no evaluations have shown that LLM systems can take the very first step of producing novel, expert-level ideas, let alone perform the entire research process. We address this by establishing an experimental design that evaluates research idea generation while controlling for confounders and performs the first head-to-head comparison between expert NLP researchers and an LLM ideation agent. By recruiting over 100 NLP researchers to write novel ideas and blind reviews of both LLM and human ideas, we obtain the first statistically significant conclusion on current LLM capabilities for research ideation: we find LLM-generated ideas are judged as more novel (p < 0.05) than human expert ideas while being judged slightly weaker on feasibility. Studying our agent baselines closely, we identify open problems in building and evaluating research agents, including failures of LLM self-evaluation and their lack of diversity in generation. Finally, we acknowledge that human judgements of novelty can be difficult, even by experts, and propose an end-to-end study design which recruits researchers to execute these ideas into full projects, enabling us to study whether these novelty and feasibility judgements result in meaningful differences in research outcome. — Read More

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At the end of last month, I attended an inaugural conference in Berkeley named the Curve. The idea was to bring together engineers at big tech companies, independent safety researchers, academics, nonprofit leaders, and people who have worked in government to discuss the biggest questions of the day in artificial intelligence:

Does AI pose an existential threat? How should we weigh the risks and benefits of open weights? When, if ever, should AI be regulated? How? Should AI development be slowed down or accelerated? Should AI be handled as an issue of national security? When should we expect AGI?

If the idea was to produce thoughtful collisions between e/accs and decels, the Curve came up a bit short: the conference was long on existential dread, and I don’t think I heard anyone say that AI development should speed up. 

… At the moment, no one knows for sure whether the large language models that are now under development will achieve superintelligence and transform the world. And in that uncertainty, two primary camps of criticism have emerged. 

The first camp, which I associate with the external critics, holds that AI is fake and sucks. The second camp, which I associate more with the internal critics, believes that AI is real and dangerous. — Read More

Have you seen a thought leadership LinkedIn post and wondered if it was AI-generated or human-written? In this study, we looked at the impact of ChatGPT and generative AI tools on the volume of AI content that is being published on LinkedIn.

We have likely all experienced the same feeling on LinkedIn within the last couple of years… seeing a long-form post and suspecting it of being AI-generated but the author is passing it off as their own thought leadership. 

In this study, we look at the impact of ChatGPT and other generative AI tools on the volume of AI content that is being published on LinkedIn.   — Read More

It’s been the biggest year for elections in human history: 2024 is a “super-cycle” year in which 3.7 billion eligible voters in 72 countries had the chance to go the polls. These are also the first AI elections, where many feared that deepfakes and artificial intelligence-generated misinformation would overwhelm the democratic processes. As 2024 draws to a close, it’s instructive to take stock of how democracy did.

In a Pew survey of Americans from earlier this fall, nearly eight times as many respondents expected AI to be used for mostly bad purposes in the 2024 election as those who thought it would be used mostly for good. There are real concerns and risks in using AI in electoral politics, but it definitely has not been all bad.

The dreaded “death of truth” has not materialized—at least, not due to AI. And candidates are eagerly adopting AI in many places where it can be constructive, if used responsibly. But because this all happens inside a campaign, and largely in secret, the public often doesn’t see all the details. — Read More