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1) Nanofabrication of Micro-Robots for Health Monitoring: An Army of Microscopic Robots Is Ready to Patrol Your Body (Singularity Hub)

“Because the actuators and the bodies are both made using standard lithography, the robots can be easily fabricated in parallel: up to one million bots on a four-inch silicon wafer. This en masse approach means that they’re extremely cheap to make: roughly one-tenth of a penny ($0.001). As microfabrication techniques continue to mature, the cost will likely drop even further.”

“At just 40 μm wide and 70 μm long — smaller than a single-celled algae, the width of the average human hair and on par with a grain of salt — the robots are the smallest microbots with onboard electronics in existence. They’re also surprisingly tough: they can readily survive temperature changes up to 100F and over a dozen orders of magnitude in acid concentrations. Their small size makes it easy for them to get sucked into the narrowest needles without damage, and maintain their structure and function after injection into an amoeba.”

Radical Commentary: Micro-robots roaming the inside of a human body patrolling for diseases and potentially fixing problems sounds like science fiction. One of the biggest challenges to making this a reality is ensuring these tiny robots — each the width of a human hair — could move reliably and effectively inside the body.

This novel design for an actuator, the mechanism that allows a robot to move, is interesting because it is compatible with standard microelectronics manufacturing processes. This means that the cost to manufacture micro-robots will drop significantly, and as such, they could become more widely used. Innovations such as these actuators offer a step-change to cost structures that often lead to the creation of new markets. That would bring an entire field of applications for micro-robotics in medicine closer to reality.

2) AI and Government Policy: Human-centered redistricting automation in the age of AI (Science)

“Redistricting — the constitutionally mandated, decennial redrawing of electoral district boundaries — can distort representative democracy. An adept map drawer can elicit a wide range of election outcomes just by regrouping voters… When there are thousands of precincts, the number of possible partitions is astronomical, giving rise to enormous potential manipulation.

… Automation in redistricting is not a substitute for human intelligence and effort; its role is to augment human capabilities by regulating nefarious intent with increased transparency, and by bolstering productivity by efficiently parsing and synthesizing data to improve the informational basis for human decision-making. Redistricting automation does not replace human labor; it improves it. The critical goal for AI in governance is to design successful processes for human-machine collaboration. This process must inhibit the ill effects from sole reliance on humans as well as over reliance on machines. Human-machine collaboration is key, and transparency is essential.”

Radical Commentary: Redistricting is another example in the ongoing debate around the trade-offs when using machines that can identify patterns better than human experts. Society benefits from these prediction machines in cases where there is high visibility around the process and quick feedback on accuracy, such as in the case of breast cancer screening. Risk for malpractice increases when transparency around inputs, computational results, and their use in decision-making are delayed and feedback on accuracy is slow.

Across sectors there is a movement toward people collaborating with machines to produce results not otherwise possible for machine or human. At this time, human value judgments cannot be replaced as machines do not possess “human rationality”. For example when redistricting, humans must still articulate the inputs to the algorithm (i.e. the initial criteria for the construction of a fair electoral map such as population equality, compactness measures, constraints on breaking political subdivisions, and representation thresholds). As noted in the article, neglecting the essential human role is to substitute a machine’s lack of rationality for human bias. In this particular case, machines may be most effectively used supportively in decision-making to clarify various trade-offs and their feasibility.

3) Guidelines for AI Health Research: New guidelines can boost transparency of clinical trials evaluating AI health solutions (News Medical Life Sciences)

“As evaluation of health interventions involving machine learning or other AI systems moves into clinical trials, an international group has developed guidelines aiming to improve the quality of these studies and ensure that they are reported transparently.

The use of these international guidelines will enable patients, health care professionals and policy-makers to be more confident on whether an AI intervention is safe and effective. This is a key step towards trustworthy AI in health.”

Radical Commentary: Last week, the first international standards on clinical trial reporting were released as a result of collaboration between researchers from the University of Birmingham, University Hospitals Birmingham NHS Foundation Trust and institutes across the world including the US and Canada. These reporting standards are aimed at providing transparency in the design and delivery of clinical trials.

We have previously written about the massive potential of AI to improve patient outcomes and to augment the practice of medicine. New standards are a step in the right direction to enabling better measurement and benchmarking of the technology. The guidelines will also serve to empower patients and help the overall industry move towards being more patient centric. Detailed information on the guidelines published by the Lancet can be found here.

4) AI Fighting Wildfires: Artificial Intelligence is Helping to Spot California Wildfires (GovTech)

“Now, fire spotting has gone high tech. And the technology to address it is getting exponentially better and faster, trained by a growing body of data about wildfires. It’s making firefighters more nimble and keeping them safer. The only question is whether silicon-powered progress can keep up with the climate change-fuelled flames.”

Radical commentary: Technology, through apps and public alert systems, has been used for the last couple of decades to provide warnings about natural disasters and emergencies like flooding, earthquakes, and wildfires. AI is taking this one step further now in the fight against wildfires by spotting dangerous wildfires sooner, both to help protect people and to limit damage. Similarly, in India and Bangladesh, Google is using AI to draw on historical and contemporary data to predict rainfalls so people can get to safety before a flood hits their neighbourhood. As climate change increases the risk of natural disasters, AI applications that recognize and provide early warnings of potential natural disasters will play a key role in keeping people safe.

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Editor’s Note:
We will continue to use this platform to share without commentary articles focused on data and the use of it to illustrate and illuminate racial injustice. Because you cannot fix problems you cannot see or understand.

5) Decriminalizing Race: The case for investing in community and social support for imprisoned racialized women in Canada (Canadian Centre for Policy Alternatives)

“This report examines the criminalization of racialized women, Canada’s fastest growing prison population. In the last 10 years, the incarceration rate of women sentenced to federal institutions has increased by 32.5% despite the fact that the rate of women accused of a Criminal Code offence decreased by 15% between 2000 and 2017.

Indigenous and Black women are both disproportionately represented in prisons. Despite Indigenous and Black women accounting for 4.3% and 3%, respectively, of Canada’s adult female population, they make up nearly half of all prisoners in federal women’s institutions.

Over-policing, trauma, poverty, and sex work are key factors in explaining why racialized women are disproportionately jailed for their responses to marginalization.”

— R —