Robotics/AI

IT’S IMPOSSIBLE TO OVERSTATE DEFLATIONARY PRESSURES:

Machine learning powered protein engineering lab could accelerate biotech innovation (Dr. Tim Sandle, August 7, 2026, Digital Journal)

Artificial intelligence is rapidly reshaping scientific research, from drug discovery to materials science. Now, a major new investment aims to push that transformation even further through the creation of the country’s first publicly accessible, AI-powered cloud laboratory dedicated to protein engineering.

Northwestern University has been awarded $20 million over four years to establish the AI-Driven, Rapid, Experimental Automation Machine (DREAM) Cloud Lab, a facility designed to accelerate the design, construction, and testing of engineered proteins using AI, robotics, and laboratory automation. While the initiative is based in Illinois, its significance extends well beyond the United States. For Canada’s growing biotechnology sector, the project offers a glimpse into how the future of life sciences may operate: cloud-connected, highly automated, AI-driven, and increasingly collaborative.

ABOVE AVERAGE IS OVER:

AI is Rewriting Mathematics: The beginning of the beginning. (Nofil, Aug 02, 2026, Nofil’s Substack)

August 1
OpenAI announces their next major model, Astra, solved 10 major open problems in mathematics, quantum complexity and theoretical computer science. The cost of generating proofs for all 10 of these combined was less than $2,000 at Sol API prices.

To give you an idea of just how absurd this is, every single one of these is a decades-old open problem, with some being considered as flagship problems of the entire field.

This isn’t AI assisting or solving one problem. This is a system conducting original research across entire fields and rewriting our understanding of them. If a single person produced all 10 of these proofs, it would be unprecedented; it would be like an entire generations worth of research across several fields being compressed into a single person’s output.

IT’S IMPOSSIBLE TO OVERSTATE DEFLATIONARY PRESSURES:

Powerful AI models are being given away for free. It was inevitable.: They aren’t a security threat — they’re what competition looks like. (Bill Gurley, July 20, 2026, Washington Post)

There is a name for what those challengers did. Economist Michael E. Porter called it cost leadership and placed it among a handful of fundamental business strategies. Cost leadership, he argued, is the surest route to a market position a rival cannot easily attack. Walmart, Dell, Southwest — none of them won by building the fanciest product. They won by delivering a comparable one at a structurally lower cost, and letting price do the rest. Open-source software is the purest form of software cost leadership ever devised.

The classical economists Adam Smith and David Ricardo would not have found any of this surprising. In a competitive market, they observed, price is driven toward the cost of producing one more unit. For most goods, such as steel, wheat or labor, that floor is stubbornly high. For software, it is almost nothing: Once the first copy exists, the millionth costs essentially zero to make. A price that falls toward zero is therefore not a market failing. It is a market working exactly as the classical economists said it would.

MAC MAKE BIG MAC:

Generative artificial intelligence creates delicious, sustainable, and nutritious burgers (Vahidullah Tac, Christopher D. Gardner & Ellen Kuhl, 2026, Science of Food)

Using burgers as a model system, the generative AI rediscovers the classic Big Mac without explicit supervision and generates novel burgers optimized for deliciousness, sustainability, or nutrition. Compared to the Big Mac, its delicious burgers score the same or better in overall liking, flavor, and texture in a blinded sensory evaluation conducted in a restaurant setting with 101 participants; its mushroom burger achieves an environmental impact score more than an order of magnitude lower; and its bean burger attains nearly twice the nutritional score. Together, these results establish generative AI as a quantitative framework for learning human taste and navigating complex trade-offs in principled food design.

SPEAKING AS A wasp…:

New humanoid robot built for companionship with 90% accuracy in recognizing emotions (Jijo Malayil, Jul 01, 2026, Interesting Engineering)

The full-size humanoid features 88 degrees of freedom and a dual-pivot biomimetic cervical spine that enables it to reproduce up to 90 percent of fundamental human movements. It is powered by what UBTech describes as the world’s first emotion-aware LLM for long-term companionship, capable of identifying more than 20 fine-grained emotional states with over 90 percent accuracy.

…there’s no way there are that many emotions.

PRETTY FANCY SHOWER CURTAIN RINGS:

Counter-drone tech is gaining major traction (Stephen Bryen, June 15, 2026, Asia Times)

The Ukrainian solution that has gained the most attention is called Sting. It was developed in Ukraine by a volunteer engineering group called Wild Hornets.

It is a bullet-shaped 3-D printed aerodynamic frame featuring a pursuit speed of 213 mph (343 km/h) and a service ceiling of around 10,000 feet (3,000 meters). Its operational radius is about 20 kilometers (12.4 miles). It is equipped with a home-built thermal imaging camera so it can take down enemy drones at night.

The Sting interceptor costs US$2,100. Ukraine has developed several specialized interceptor brigades (e.g., the 412th Nemesis Brigade), which claim a 95% success rate. The system is operated by a pilot, but the latest versions feature autonomous interception using an automation layer in the Sting software.

The AI computer vision module can lock onto the silhouette of an enemy drone, such as the Geran 2, and navigate autonomously relieving the pilot of target responsibility.

Ukraine uses a system called Hornet Vision, a remote control architecture, meaning the pilot/operators can be far away from the front line, so the Russians cannot target them easily.

Ukraine has a number of advantages over the Russians. It has remote operators/pilots who are good at what they do. It can locally source important drone components, especially thermal imagers. It has talented software engineers with high motivation. And it has considerable battlefield experience.

PAYING NO COST TO BE THE BOSS:

Autonomous Corsair maritime drone rescues US military pilots after crash near Oman (Brandi Vincent, June 9, 2026, Defense Scoop)

“The surface drone that assisted in last night’s rescue of the Apache crew off the coast of Oman was a U.S. Navy Corsair unmanned surface vessel operated by U.S. 5th Fleet’s Task Force 59,” Hawkins said.

In that rescue operation, he told DefenseScoop, the maritime drone picked the two pilots up “and transported them to another location on the water where they were then hoisted up to a helicopter for further transport.”

The Corsair is a 24-foot autonomous surface vessel (ASV) that’s designed for rugged, long-duration missions. The drones can operate at speeds greater than 35 knots and carry up to 1,000 lbs over 1,000 nautical miles, according to Saronic’s product specifications.

ABOVE AVERAGE IS OVER:

A Florida hospital is using Palantir to catch sepsis earlier, it’s saved 866 lives so far (Skye Jacobs, June 10, 2026, Techspot)

The system is estimated to have helped save 886 lives since August 2022. Sepsis is notoriously difficult to catch early. It can begin with small shifts in vital signs that do not immediately stand out – slight increases in heart rate, minor temperature changes, subtle indicators that can easily be lost in the noise of a busy hospital floor. Once it takes hold, though, it can escalate quickly, triggering organ failure and, in many cases, death. Roughly one in five patients diagnosed with sepsis does not survive.

The approach at Tampa General is built around catching early signals before they develop into a crisis. The hospital partnered with Palantir to use its Foundry platform alongside existing clinical systems.

The result is a continuous stream of aggregated data pulled from electronic health records, lab results, clinician notes and bedside monitors. Instead of sitting in separate systems, that data is unified and presented in real time through a centralized dashboard that tracks roughly 1,000 patients at once.

ABOVE AVERAGE IS OVER:

LLMs Reproduce Human Purchase Intent via Semantic Similarity Elicitation of Likert Ratings (Benjamin F. Maier, Ulf Aslak, Luca Fiaschi, Nina Rismal, Kemble Fletcher, Christian C. Luhmann, Robbie Dow, Kli Pappas, Thomas V. Wiecki, 10/27/25, arxiv)

Consumer research costs companies billions annually yet suffers from panel biases and limited scale. Large language models (LLMs) offer an alternative by simulating synthetic consumers, but produce unrealistic response distributions when asked directly for numerical ratings. We present semantic similarity rating (SSR), a method that elicits textual responses from LLMs and maps these to Likert distributions using embedding similarity to reference statements. Testing on an extensive dataset comprising 57 personal care product surveys conducted by a leading corporation in that market (9,300 human responses), SSR achieves 90% of human test-retest reliability while maintaining realistic response distributions (KS similarity > 0.85). Additionally, these synthetic respondents provide rich qualitative feedback explaining their ratings. This framework enables scalable consumer research simulations while preserving traditional survey metrics and interpretability.

It’s not that AI, robotics, etc. will replace a few jobs but that it will make entire classes of employment superfluous.

IT’S IMPOSSIBLE TO OVERSTATE DEFLATIONARY PRESSURES:

75% of C-suite leaders say agentic AI lives up to the hype or is underestimated. 48% still plan to cut headcount. (Ana Maria Constantin, June 10, 2026, TNW)

The headline figure is bullish: 50% said the hype around agentic AI is justified, 25% said it is under-hyped, and only 25% called it over-hyped.