INBOX INSIGHTS: Understanding Technologists, Twitter Algorithm (4/19) :: View in browser
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Understanding Your Technologist
This week, I wanted to try an experiment. I’ve been working on content about the challenges non-technical managers will face when managing a technologist. Think about the roles Chris and I play. I need to understand enough of what he’s doing to support him, and he needs to understand enough of my role to allow me to prioritize and advocate for him.
In the spirit of that, I’ve been working on using ChatGPT prompts to emulate the kind of work Chris would do. I decided to let ChatGPT write this week’s newsletter. There was some editing, but otherwise, this is what the system wrote after learning a little about my writing style.
With the rise of AI technology, the business landscape is changing rapidly. As a result, subject matter experts and technologists are becoming more prevalent, and the need for traditional managers is declining. However, this doesn’t mean that managers are no longer needed. In fact, they are still essential to the success of any organization, particularly when it comes to managing technologists.
For managers who lack a technical background, managing technologists can be a daunting task. They may feel overwhelmed by the specialized knowledge and terminology that technologists use, and struggle to understand what is truly important. This can lead to misunderstandings and communication breakdowns, which can ultimately undermine the success of the organization.
To avoid these challenges, it’s important for technologists to help managers understand what’s important. This means explaining technical concepts in simple terms, and focusing on the big picture rather than getting bogged down in details. Technologists should also be proactive in identifying potential issues and risks, and bringing them to the attention of their managers.
At the same time, managers need to advocate for their technologists and prioritize their needs. They need to block distractions and ensure that technologists have the resources and support they need to succeed. This involves spending time asking the right questions, such as “why did you choose this solution and not something else?” or “what will the risks be to our customers with this solution?”
However, it’s important for managers to approach these questions with respect and humility. They should never assume that technologists will automatically understand their perspective or priorities. Instead, they should work to build a collaborative relationship with their technologists, based on mutual respect and understanding.
Technologists, in turn, need to understand the role of the manager and collaborate with them to achieve shared goals. They should be willing to explain technical concepts and help managers navigate the complexities of the technology landscape. By working together, managers and technologists can achieve a successful outcome for their organization.
Ultimately, managers need to focus on relationship power rather than role power. They need to build strong relationships with their technologists, based on trust, respect, and mutual understanding. By doing so, they can create a culture of collaboration and innovation, where everyone’s skills and strengths are valued and leveraged.
In conclusion, the rise of AI technology is changing the business landscape, but managers are still essential to the success of any organization. To manage technologists effectively, managers need to prioritize their needs, advocate for them, and build strong relationships based on trust and mutual understanding. Technologists, in turn, need to help managers understand what’s important and collaborate with them to achieve shared goals. By working together, managers and technologists can build a successful and innovative organization in the rapidly changing world of AI technology.
Overall, not too bad. This exercise, while simple, is helping me understand Chris’ perspective when he talks about prompt engineering and model refinement. The prompt that I used needs some work, and the model needs more data to learn my voice. These are things that Chris focuses on and studies daily. It’s not good enough for someone without his skillset to start doing it and assume they will be just as good. For me, I now have a better understanding of the time it takes to get it right. Knowing that ChatGPT can generate content quickly doesn’t mean that it’s the version that we’ll use. It needs rounds of revision and editing.
If you’re a manager who is working with a technologist, I would strongly advise you to do a similar exercise. You don’t need to get it right, but at least attempting to step into their shoes for a moment can help you to better communicate on their behalf and have more patience with outputs.
What is your experience being a manager or a technologist?
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In this week’s In-Ear Insights, Christopher Penn and Katie Robbert discuss the impact of artificial intelligence (AI) on jobs. They explore whether generative AI will take over jobs, especially for junior members of an organization. They discuss the aspects of jobs that AI can and cannot replace, such as repetitive tasks versus human creativity and accountability. They also examine the potential management and leadership problems arising from the inverted pyramid of employees that AI can replace, and the future of junior-level employees in organizations. Ultimately, they suggest that individuals should identify their interests and skills and explore how they can supplement and collaborate with AI, rather than seeing it as a threat to their job security. Watch the video to learn more about the evolving relationship between humans and AI in the workplace.
Watch/listen to this episode of In-Ear Insights here »
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In this week’s Data Diaries, let’s talk about Twitter. We’ve been remiss in discussing more mundane marketing stuff in all the excitement about artificial intelligence lately, so let’s dig into the big reveal from March 31. Twitter’s engineering team open-sourced the Twitter recommendation algorithm for the world to see. Now, this isn’t the first social network’s code to be opened up in such a way; open-source platforms like Mastodon have their code open-sourced by default. But this is probably the first recommendation algorithm of a major commercial social network that’s been released intentionally, certainly in recent memory.
What’s in the algorithm code? Well, it’s a combination of different programming languages and architectures that help construct the AI models which power Twitter. (I suppose there was no getting away from AI after all) A number of folks have done very deep dives into the core components, written largely in the languages Scala and Java. We’ll avoid getting into the bits and bytes, though you’re welcome to dive straight into the code yourself if you’re so inclined.
The question marketers most often has is, “how can we get better results on Twitter?”. To answer this question, we need to understand the pre-requisites for those results. Success on Twitter – and any social network powered by recommendation engines – is contingent on being seen. That may mean at the account level – like being recommended as an account to follow – or at the individual content level, when individual pieces of content get visibility.
Within Twitter’s architecture, we see three major components which can affect these results. The first is the search module, which despite its name isn’t about the search function on Twitter. Instead, this is the module that scans available tweets and organizes them for display. Within the search module, we see a series of starting weights the model uses to judge whether a tweet or not should be made available for recommendation. Note that this code is from the March 31 repository snapshot; it has changed substantially since then and is considerably more opaque now.
What we see in these parameters is fairly straightforward; the activities Twitter values most for recommending tweets is whether a Tweet has collected likes, and whether it has been retweeted. There are other suppressing parameters, like your overall reputation, whether the language of the tweet is a mismatch to your profile language, whether your content is flagged as offensive, etc.
So what do we make of these data points? They form a fairly coherent strategy for making more of Twitter. Let’s say you have 50 users available to you, maybe your employees or the people in your advocacy/community group. What’s of greater benefit – 50 people posting tweets about the thing you want to promote, or 50 people retweeting your tweet? As always, it depends, but assuming all the accounts are of relatively the same size, you’d be better off having 50 accounts retweet one tweet. Why?
When we look at the overall system architecture, here’s what we see:
Each stage of this is essentially filtering, winnowing down candidate tweets to ultimately come up with your timeline. The first stage is essential; tweets that don’t have positive attributes don’t even make it into the mix for further ranking. The three components of that stage are the Follow Graph – i.e. who you follow – tweet engagement, and user data. Twitter has stated that its general goal is to have your recommendations be approximately 50% in-network (people you follow) and 50% out-of-network. Thus, if you have 50 people of roughly equal network sizes that aren’t necessarily connected, their individual tweets are probably less likely to be ranked higher in the filtering system. That one tweet with a big pile of engagements is more likely to be selected for display.
Engagements – specifically likes and retweets – are so heavily valued by the system that a single engaged tweet is worth 50 unengaged tweets, possibly more.
From this information, we have a very clear protocol about how to make the most of Twitter. Spend your time and effort amplifying one account’s content as much as you can for the best results, and if you have a large influencer community, make sure they’re retweeting and liking each other’s content to create higher visibility weights in the Twitter recommendation engine.
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