haruki zaemon

As leaders, there’s a fairly narrow gap between absenteeism and intervention. I’ll readily admit that I have found it challenging to get that right. We want to encourage empowerment, autonomy, and agency; on the other hand, we want to ensure we are providing the right level of support and guidance.

Our desire to step back and let go can lead us to not being around and only showing up when something goes wrong. As a result, we can lack understanding of the context and the challenges our teams are facing, making it more difficult to know what they need. When we do turn up, our presence signals a lack of trust and confidence in others’ ability to do the job and diminishes our ability to support them when they need it most.

Our desire to nurture a culture of continuous improvement can lead to us being too involved. If our identity is as a problem solver, we might be too quick to jump in and fix things. Perhaps worse, we might so crave the need to solve problems that we don’t realise we’re creating them in the first place. Longer term, we are likely to engender a culture of dependence and learned helplessness where our teams don’t believe in themselves sufficiently to solve their own problems.

In my experience, the best leaders genuinely participate without judgement or agenda in order to enable and empower their teams. They are there when they’re needed and not when they’re not. They are present and engaged without taking over. They are involved and invested without looking for problems to fix. They are supportive and encouraging without building dependence.

Participatory leadership is knowing when to lead, when to be led, and when to get out of the way. It’s connecting people, creating and holding the space for them, helping with jobs to be done, and allowing things to play out. It’s fostering a sense of ownership and accountability by actively involving and guiding others in problem-solving and decision-making. It’s recognising that no individual possesses all the necessary information or expertise, and that we don’t need to have all the knowledge and all the answers. It’s being comfortable being uncomfortable.

The best and most inspiring leaders I’ve seen deliberately learn and practice techniques that enable them to truly participate.

Sam McAfee:

I would put my money on a CTO who casually references Melville or Dickens over one who only builds side project apps or tinkers with the latest LLM tools on the weekends. Having both traits would be ideal, but the senior technical leader with only hacker vibes and no humanities experience will lead ultimately to ruin.

(via Benji)

The “Zenith Space Command” (c. 1956) was one of the first wireless TV remotes. It had an ingenious design that used ultrasonic sound instead of light to change channels. By pressing a button, a spring-loaded hammer would strike an aluminum rod tuned to a specific frequency, with each button emitting a different high-frequency tone. Employing mechanical buttons, the remote required no batteries, and provided tactile feedback by way of a satisfying click, hence “clicker”!

The more I use tools such as ChatGPT, the more I worry about it erasing my voice. To counter that, I tend to take the output of such tools and largely re-write it, using it as another input to my own creative process.

So, I was fascinated to see that someone had a go at getting ChatGPT to generate a detailed style guide by analysing their writing. The next step would be to try and have ChatGPT use the style guide to generate content mimicking their voice.

I’ll stick with my process, for now.

It probably seems obvious in retrospect (as many things often do) but I really liked this structured approach to problem-solving. In particular, I really like starting with what good looks like, then focusing on countermeasures rather than fixes:

1. Describe the problem in terms of the standard condition (i.e. expected norm), current condition, and gap between them from the customer’s perspective.
2. State what needs to change in terms of outcomes and timelines (not solutions) that are specific, measurable, realistic and time-bound.
3. Explore why the standard and current conditions exist, and obstacles preventing the target from being achieved.
4. Outline countermeasures (rather than solutions) to address those obstacles. Countermeasures aim to prevent problems rather than just fix them.
5. Define specific actions, responsibilities, and deadlines to enact countermeasures.
6. Follow-up by verifying the effects of countermeasures and plans for broader rollout or rollback depending on results.

(via Bruce Schneier)

Steganography is the practice of hiding secret messages in other communications. Information theory provides a mathematical framework for analysing steganography, but perfect secrecy was thought impossible without perfectly simulating human language. New research shows that machine-generated text could enable perfectly secure steganography, as it uses well-defined generation processes rather than messy human language. Researchers developed algorithms that satisfy criteria for security by transmitting information through a channel in a way that makes the presence of a hidden message statistically undetectable. This approach could help people circumvent censorship but may also enable covert communication by spies and adversaries seeking to conceal information. The new algorithms represent an interesting intersection of information theory, machine learning, and practical steganography applications.

Machine learning relies on psychological experiments at scale to label data but ignores the inherent difficulty in psychological measurement. The factors that need to be controlled to accurately measure concepts like intelligence are not intuitive. IQ tests in particular have been disproven as a valid measure of general intelligence, but the concept remains popular due to misconceptions about the ease of such measurements. A lack of understanding of psychological measurement leads to overconfidence in what models and tests can reliably capture. This same issue supports continued beliefs in racist ideologies that purport cognitive differences between groups but have long been debunked. Proper measurement requires expertise in controlling countless subtle factors, as shown by the careful development of the highly validated Cambridge Face Memory Test.

Researchers trained a deep learning model to steal data from keyboard keystrokes using audio recordings with up to 95% accuracy. They recorded keyboard presses and generated spectrograms to train an image classifier to identify keys. Testing involved recording sounds from a phone near the keyboard or through Zoom. The model achieved 95% accuracy from phone recordings and 93% from Zoom. While some mitigations were suggested, like altering typing styles, the attack was still effective even against silent keyboards. This raises serious concerns about data security as sensitive information could be leaked through acoustic side-channel attacks.

Sociotechnical theory views organisations as complex adaptive systems that consider both social/human factors and technical/technological factors as interdependent:

• It emerged in response to views that dismissed human factors and saw technology as the main driver of productivity.
• Joint optimisation is a key idea where optimising one subsystem likely degrades overall system performance.
• organisational change will fail if it focuses only on social or technical in isolation.
• Human values are a core part of sociotechnical theory and enhancing psychological safety optimises the social system.
• Albert Cherns outlined nine/ten sociotechnical design principles as a checklist for organisational change.
• The “forth bridge principle” emphasises that organisational transition is ongoing rather than reaching a stable end state.
• Fear-based management that keeps employees on the edge of losing their jobs often backfires and leads to high turnover.
• Data practices need to help shape meaning and reality rather than just describing the world.

Reveal how candidates will lead by asking them to select the top 5 leadership traits (from a list of 20 options) they themselves desire in a leader. Follow up by having candidates define each trait and give an example leader. Be sure to also ask about important traits the candidate omitted.

The hype around artificial intelligence is distracting companies from practical machine learning projects that can create real value. While new AI technologies are impressive, most current ML applications should simply be called ML, not AI, as that term implies capabilities beyond what the technology can actually do. Referring to all ML projects as AI oversells their abilities and contributes to high failure rates. For most projects, AI is an overblown buzzword that does not accurately represent the technology if it does not mean artificial general intelligence. The hype and narratives around emerging AI, exemplified by tools like ChatGPT, have made the buzzword AI a growing problem as it inflates expectations for what ML can actually achieve for businesses.

I feel seen!

Morgan Housel:

Without flooding your brain with inputs you’ll be stuck in the tiny world of what you’ve personally experienced. But without a strong filter you’ll be overwhelmed with choice and paralyzed by inaction.

A good reading filter is more art than science. You’ll have to find one that works for you. The bigger point is that the highest odds of finding the right piece of information comes from inundating yourself with information but very quickly being able to say, “that ain’t it.”

I’d love to give the mapping activities a go.

The Uncertainty Project:

• Organisations are complex adaptive systems made up of autonomous agents that are interconnected and whose behaviours emerge from their independent decisions.
• Competing drivers that influence agents’ behaviours, like productivity and profitability goals, can push agents in different directions and create trade-offs.
• Uncertainty arises from unpredictable events and outcomes due to a lack of understanding of causes and effects.
• Complexity arises from a large number of interconnected elements, making it difficult to understand cause and effect. Greater complexity leads to more uncertainty.
• Complexity in organisations can come from competing drivers, external noise, and internal events.
• External noise from the environment, like increased amount and frequency of outside influences, can shake the system and increase complexity.
• Internal events from agents’ actions can ripple through the system and create emergent behaviours.
• Capturing and sharing beliefs about external noise can build shared understanding to calibrate drivers and set strategic direction.
• Prioritising with even-over statements can clarify the relative importance of competing drivers to reduce misalignment.
• Taking an outside view (e.g. Customer-centricity) and learning from others can reduce complexity and uncertainty.

UPDATE: This was linked to from the original and is definitely worth a read: 3 simple steps to find the causes of complexity and reduce uncertainty.

(Summary via Kagi)

Nvidia researchers have created a new text-to-image AI model called Perfusion that is significantly smaller and faster to train compared to existing tools. The 100KB model only takes 4 minutes to train, yet it can outperform larger models in terms of personalizing concepts. The key innovation is a “Key-Locking” technique that ties new concepts to general categories, allowing the model to flexibly portray personalized concepts while maintaining their core identity. The small size of Perfusion allows it to easily update only the parts that need to change when fine-tuning, whereas larger models have to retrain the entire model. Nvidia’s focus on efficient AI models like Perfusion could give the company an edge over competitors pouring billions into generative AI research.

Transactive memory is critical to high-performing teams. It’s also critical to high-performing organisations. I’m witnessing first-hand the impact of a lot of (perhaps too much) change at once.

🌀🗞 The FLUX Review, Ep. 111:

When organizational incentives too strongly favor novelty and constant reinvention, it can make it harder for an organization to remember. Too much searching for shiny new things can be antithetical to learning. Why is this? The push to continually innovate can inadvertently suppress the incorporation of long-standing individual memory into collective memory. There’s an inherent tension between the drive for innovation, which often involves the creation of something new and untested, and learning from past experiences. For organizations, just as for individuals, memory consolidation takes time but is critical to learning. When there’s a constant drive toward newness, individuals within an organization may fail to take the time to consolidate their previous experiences.

[…]

Organizations that wish to learn and grow must cultivate a culture that balances innovation with incorporating the long memories of individuals into the collective memory. This could involve rewarding knowledge sharing, creating platforms for historical knowledge exchange, ensuring that new strategies are informed by past experiences, and a reward system that extends over an appropriate length of time. Done well, gardening an organization’s collective memory will help it to learn more quickly. In the long run, that’s the pathway to sustainable innovation.

It took me three attempts to understand WTF was going on here. I think I understand it now, but I’m not sure I could explain it to anyone else.

Kottke:

In the 1994 Caribbean Cup qualifying group stage match between Barbados and Grenada, the 90 minutes of normal time ended with an intentional own goal by Barbados and then with Grenada trying to score either a goal or an own goal and Barbados defending both nets. Say what?! How did this happen?

I love a good AI prompt injection attack:

The researchers warned OpenAI, Google, and Anthropic about the exploit before releasing their research. Each company introduced blocks to prevent the exploits described in the research paper from working, but they have not figured out how to block adversarial attacks more generally. Kolter sent WIRED some new strings that worked on both ChatGPT and Bard. “We have thousands of these,” he says.

(Summary via Kagi)

The SEC has set a four-day deadline for public companies to disclose material cybersecurity incidents to investors. Companies may be granted delays if disclosure risks harming national security or public safety. The rule aims to provide more consistent and useful cyberattack information to investors, after companies like Microsoft were criticized for taking weeks to confirm attacks. While the EU’s GDPR has a three-day cyberattack disclosure deadline, technology companies have pushed back on the SEC’s four-day rule, arguing it may not allow enough time to fully understand an attack. The SEC says the disclosure will benefit both companies and investors.

“Age and gender intersect in ways that are particularly detrimental for women,” Patterson says.

“Research has found that as men age, they are viewed as more valuable and competent in the workplace, while women’s competence tends to be evaluated more negatively.”

[…]

It’s much worse for women, the former Commissioner says. A submission in the Commission’s Willing to Work Inquiry from 2016 noted: “Whereas early signs of ageing such as grey hair and wrinkles can be read as marks of maturity and authority on men, this is not the case for women.”

Chatting with Ray the other day, he posed the question (in a specific context): “Are we looking to move fast, or build capacity?”

This afternoon, as I was reflecting on the events of the day, I remembered something my old Sensei once said:

In the Honbu (“Headquarters”) style, you can get good fairly quickly, but only a very few people achieve greatness. Lots of people stick with it because it’s so enjoyable to practice.

In our style, it takes a long time to get good, but if you stick at it, almost anyone can achieve greatness. Very few people stick at it long enough because it’s so rigorous.

This Dojo, the Founder’s Dojo, is for teaching teachers.

I realised that this has influenced my thinking for over 30 years. I’m more interested in teaching teachers than teaching students. I’m more interested in leading leaders than managing reports. I’m more interested in effectiveness over efficiency. I’m more interested in long-term outcomes than quick results.

I don’t think it’s a question of “which is better?”, but rather “what are we trying to achieve?”