Daily AI Newsletter: First Quantum Computing Chip, Majorana 1

In today’s email:

🐞 AI cracks superbug problem in two days that took scientists years
🔮 Can AI Predict the Next Big IPO? Crunchbase Thinks So.
👀 Inside the Humane acquisition: HP offers big raises to some, others immediately laid off
🧰 12 new AI-powered tools and resources. Make sure to check the online version for the full list of tools.

Microsoft announces quantum computing breakthrough with new Majorana 1 chip

Microsoft has unveiled a major breakthrough in quantum computing with its new Majorana 1 processor. After 17 years of research, the company’s scientists have created a “topoconductor” — a novel material that lets Microsoft leverage Majorana particles instead of electrons to form topological qubits. This approach could help address the fragility and noise sensitivity that have long bedeviled quantum computing, creating a clearer path to large-scale quantum machines. Majorana 1 itself contains eight qubits on a chip small enough to fit in the palm of a hand, and Microsoft believes it can ultimately scale to a million qubits on a single processor.

At the heart of this advancement is Microsoft’s ability to observe and control Majorana particles using a specially engineered compound made from indium arsenide and aluminum. Compared to existing quantum computing methods, these topological qubits offer a more stable and error-resistant computing environment. This not only improves performance and reliability but also accelerates progress toward quantum computers that can tackle real-world problems in industries like material science, pharmaceuticals, and climate research.

Microsoft’s long-term vision is to build a fault-tolerant quantum computer within years, not decades. Recently, the US Defense Advanced Research Projects Agency (DARPA) selected Microsoft as one of two organizations to move forward with its Underexplored Systems for Utility-Scale Quantum Computing (US2QC) program. Now, with the proven foundational technology in place, Microsoft is preparing to deliver a prototype that could handle industrial-level workloads, aiming to unlock scientific discovery on a scale previously unattainable with classical computing.

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Figure’s humanoid robot takes voice orders to help around the house

Figure’s new Helix model represents a significant step in humanoid robotics by combining vision, language, and action. This Vision-Language-Action (VLA) model allows a robot to understand its surroundings visually, receive natural language prompts, and then carry out tasks in real time. According to Figure, Helix can pick up novel objects it has never encountered before, relying on generalization capabilities rather than hours of hand-coded programming or thousands of demonstrations.

The technology is being showcased with Figure’s 02 humanoid robot performing household tasks, which is notoriously complex given the varied and unpredictable nature of home environments. With Helix, two robots can collaborate on tasks, such as handing off items and placing objects in drawers, all based on straightforward spoken commands like “Receive the bag of cookies from the robot on your left and place it in the open drawer.” This marks a new wave of practical applications for humanoid robots, as systems like Helix strive to replicate the flexibility and adaptability of human workers.

Despite these promising developments, Helix remains at an early stage, requiring substantial work to reliably tackle tasks in real-world environments. While factories and warehouses have long been the focus of many robotics companies, Figure’s latest announcement underscores the importance of eventually bringing robots into the home. Challenging and time-intensive as it may be, training robots to execute complex tasks in settings such as kitchens will pave the way for broad adoption and help build more advanced and cost-effective systems for everyday use.

AI cracks superbug problem in two days that took scientists years

A research team at Imperial College London, led by Professor José R Penadés, spent nearly a decade uncovering how certain dangerous superbugs gain immunity to antibiotics. They discovered these bacteria could acquire “tails” from different viruses, allowing them to transfer between species and spread resistance. To test an experimental AI tool by Google, Prof Penadés provided a brief prompt about the problem—without divulging his published or unpublished findings—only to find that in two days, the AI arrived at the same hypothesis they had painstakingly confirmed over many years.

Prof Penadés was stunned the AI independently suggested that superbugs borrow viral tails, effectively granting them “keys” to move from one “home” to another (or from one species to another). Beyond replicating his team’s core theory, the system proposed four other plausible explanations, one of which the researchers had never considered. That new line of inquiry is now guiding further study in the lab, showcasing how AI could accelerate scientific breakthroughs by generating creative, viable ideas.

While some worry that AI tools may one day replace certain jobs, Prof Penadés views it as an “extremely powerful tool” that promises to transform scientific research. Thanks to such AI-driven insights, tasks that once took years could be compressed into days or weeks, freeing scientists to devote more time to testing and confirming discoveries. It marks an exciting step forward, hinting at a future where human ingenuity, bolstered by AI’s problem-solving capabilities, could propel science to greater heights.